an investigation-of-fetal-growth-in-relation-to-pregnancy-characteristics
TRANSCRIPT
AN INVESTIGATION OF FETAL GROWTH IN RELATION TO
PREGNANCY CHARACTERISTICS by
Joe Max Mongelli MB BS, B Sc (Sydney) MRCOG
Thesis submitted to the University of Nottingham for the degree of Doctor of Medicine, November 1994
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CONTENTS Title page 1 Contents 2 Abstract 3 Acknowledgements 4 Abbreviations 5 Part I - Literature Review Chapter 1 The Determinants of Birth Weight 6 Chapter 2 Adjustable Birth Weight Standards 15 Chapter 3 Ultrasonic Methods of Fetal Weight Estimation 22 Chapter 4 Models of Fetal Growth 29 Chapter 5 Screening Strategies for Abnormal Fetal Growth 37 Part II- Development of Research Techniques Chapter 6 Principles of the Customised Growth Chart 49 Chapter 7 Methods of Gestational Age Estimation 57 Chapter 8 Forward Projection of Fetal Weight Estimate 64 Chapter 9 Selection of Ultrasonic Weight Formula 68 Chapter 10 Ultrasonic Study of Fetal Growth: Patients and
Methods. 76
Part III - Clinical Findings Chapter 11 An Ultrasound Standard for Fetal Weight Gain 83 Chapter 12 Symphysis-fundus Height in Relation to
Gestational Age and Fetal Weight 95
Chapter 13 Fetal Growth Kinetics in Relation to Pregnancy Characteristics
102
Chapter 14 Customised Growth Charts in Relation to Neonatal Outcome
115
Chapter 15 The Prediction of Birth Weight 126 Part IV - General Discussion Chapter 16 Comments and Conclusions 143 References 156
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1. THE DETERMINANTS OF BIRTH WEIGHT
1.1 Introduction
Birth weight is one of the most important measures we have of the
health status of a population, being a strong predictor of both mortality
and morbidity, and reflecting nutritional status and growth rates. Yet
the estimation of the normal growth potential -and hence the definition
of growth retardation - for a given individual has remained an elusive
objective.
Neonatal size can be influenced by a large number of variables.
Kramer (1987), in a lengthy review on low birth weight, listed 43
potential causes, subdivided into 7 groups, while admitting that his
literature search may not have been complete. For the purposes of our
discussion, we will attempt to classify them as pathological or
physiological, depending whether or not they are associated with
adverse perinatal outcome. This classification will be arbitrary for
many of these factors, because of our limited knowledge in this field.
1.2 Pathological Factors
A large number of pregnancy complications are associated with
reduced birth weight. Classically, growth retardation has been
classified as either symmetric or asymmetric (Pearce & Campbell,
1985), depending on whether the fetal body dimensions are
proportionately reduced, or whether some degree of ‘head sparing’ has
occurred. In practice, asymmetric IUGR is associated with either pre-
eclamptic toxaemia or recurrent abruption, while most other causes
lead to symmetric IUGR.
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Hypertensive Disease.
Essential, uncomplicated hypertension poses little or no risk to the
fetus. In early onset or severe pre-eclampsia birth weight may be
reduced by 300-500g and birth length by 1-3 cm, whereas late onset or
mild pre-eclampsia has no such association (Fedrick &
Adelstein,1978; Long et al, 1980). Reduced utero-placental blood flow
is considered to be responsible for reduced growth.
Chronic Maternal Illness
Maternal cyanotic heart disease is associated with fetal growth
retardation in up to 52% of pregnancies, as opposed to 9% in the
acyanotic group (Shime et al, 1987).
Diabetes has complex effects on fetal growth, with a tendency towards
larger babies unless associated with vascular disease and advanced
maternal age, when growth retardation is more likely. It is the main
pathological cause of fetal macrosomia.
Severe chronic respiratory diseases such as poorly controlled asthma
(Greenberger and Patterson, 1983), cystic fibrosis (Palmer et al, 1983)
and bronchiectasis (Thaler et al, 1986) may lead to reduced fetal
growth.
In the case of anaemia, it is difficult to separate the effects of
anaemia per se from the underlying nutritional problems. However,
women with sickle cell disease, sickle-thalassaemia and sickle
haemoglobin-C disease have an increased incidence of growth
retarded infants (Powers et al, 1986).This is most likely due to
placental micro-infarcts resulting from episodes of sickling, leading to
placental insufficiency.
Chronic renal disease of moderate severity is associated with IUGR in
up to 24% of cases (Katz et al, 1989), although some of this effect
may be related to hypertension.
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Systemic lupus erythematosus has been implicated in fetal growth
retardation (Carlson,1988), though this may be a result of the
underlying renal disease or drug therapy.
Maternal Addictions
Smoking mothers have babies that are 150 - 200g lighter than those of
non-smokers (Wilcox et al,1993a); this appears to be caused by
smoking in itself rather than other factors associated with the smoker
(Doughterty,1982).
Excessive alcohol intake may result in small babies with shortened
palpebral fissures and a small head - the fetal alcohol syndrome
(Lemoine P et al, 1968). Fetal weights are reduced by 165-200 grams
among mothers who drink the equivalent of more than 50 ml of
absolute alcohol per day (Ouellette et al, 1977).
Heroin addiction has also been associated with reduced fetal growth
(Naeye et al, 1973).
High Altitude
Babies born at high altitude are lighter than those born at ground level
(Lubchenko, 1963); this appears to be a 'dose-response' effect related
to chronic hypoxia, with smaller babies being born at higher altitude
(Yip, 1987). Interestingly, both of these studies showed higher rates of
preterm delivery.
Malnutrition
Maternal malnutrition, when severe, may result in adverse neonatal
outcome. Stein and Susser (1975) analysed the birth statistics in
Holland during the famine of 1944-1945; they found that birth weights
declined by about 10% only when under-nutrition occurred in the third
trimester with caloric intake below 1500 g. This is to be expected,
since the period of greatest absolute growth is in the last 10 weeks of
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gestation, when the average fetus gains about 2000g during this
interval (Hadlock et al, 1991).
Placental Disorders.
Recurrent antepartum haemorrhages in the first and second trimesters
is strongly related to reduced fetal growth, possibly due to impaired
development of the utero-placental circulation (Fedrick & Adelstein,
1978). Other placental anomalies associated with SGA infants include
circumvallate placenta, and large chorioangiomas.
Infection
Viral infections, particularly rubella and cytomegalovirus, may reduce
fetal weight and length to 80-85% of normal values ( Miller, 1981;
Naeye, 1967). In fatal cases of rubella, the growth restriction is
associated with markedly reduced cell numbers in the fetal organs
(Naeye, 1965). Listeriosis is sometimes associated with IUGR.
In global terms, malaria is probably the most important infectious
agent associated with growth restriction on a world-wide basis. This is
related to haemolytic anaemia and placental insufficiency related to
placental infestation. The pathological changes observed in the
placenta include perivillous fibrinoid deposits, syncytiotrophoblast
necrosis and partial loss of microvilli. A brownish discoloration may
be observed (plasmodial placental pigmentation), and these cases are
associated with significantly lower birth weights (Garin et al, 1985;
Walter et al,1982).
Chromosomal Defects
Most chromosomal and genetic disorders are associated with impaired
fetal growth of varying severity. In Downs’ syndrome the birthweight
is 80-90% of normal, while for trisomy 13 and Turner’s syndrome it is
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80% and 84% respectively (Polani, 1974). More severe growth
restriction is observed in foetuses with trisomy 18, with an average
birth weight of only 62% of normal. Some genetic disorders such as
the Seckel and Russell-Silver syndromes are associated with severe
dwarfing apparent at birth.
On the opposite side of the spectrum, we find a group of genetic
disorders that are associated with fetal growth acceleration. These
include the Beckwith-Wiedemann syndrome, in which trisomy for the
IGF-II gene has been implicated, and ‘stood’ conditions associated
with a dramatic increase of fibrous tissue (Elejalde et al, 1977).
In Sotos’ syndrome, characterised by cerebral gigantism , the birth
weight is not significantly increased but the birth length is increased to
a mean of 55.2 cm.
1.3 Physiological Factors
Duration of pregnancy.
The length of gestation is the most important determinant of birth
weight (Wilcox et al,1993b), and also of perinatal mortality and
morbidity in the pre-term period (Allen et al, 1993). The 'terminal
flattening’ seen in birth weight standards based on menstrual data is
artefactual; it is much less marked in standards derived from
ultrasound-dated populations (Wilcox et al, 1993a).
Parental size.
The relationships between birth weight and parental size have been
studied extensively, both in humans and in animals. The classic
studies by Walton and Hammond (1938) on crosses between the Shire
horse and the Shetland pony showed that the birth weights of foal born
to Shetland dams of Shire sires were close to those of pure Shetlands;
conversely, foals of shire dams by Shetland sires were close to those
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of the pure breed. Evidence in humans on the preponderance of the
maternal effects on fetal growth has been presented by Cawley (1954)
and Ounsted (1966). Both maternal height and weight have positive
correlations with birth weight, the latter being the stronger factor.
Low maternal pre-pregnancy weight is significantly correlated with
both preterm delivery and low birth weight (Garn 1990).
Obesity, as measured by the body mass index, is only weakly
correlated (Abrams, 1986; Wilcox et al, 1993b). The parents' own
birthweight is significantly correlated with that of their offspring
(Alberman et al, 1992).
Parity.
The positive effect of parity on birth weight has been documented in
most races and many mammalian species (Ounsted, 1973; Bantje,
1985), suggesting that its mechanism may have an evolutionary
advantage. Garn (1990) has argued, on epidemiological grounds, that
the effect of parity is a result of the increase in the maternal pre-
pregnancy weight seen in developed countries, rather than an
independent factor. This does not agree with multiple regression
analysis of birth weight, which shows parity to be a factor independent
of mid-pregnancy weight (Wilcox et al, 1993b); this may be because
the mid-pregnancy weight is a compound variable, dependent on both
the pre-pregnancy weight and the maternal weight gain.
There is some evidence that the effect is partner-specific, i.e. a change
in partner may be associated with a reduction in the birth weight of the
first born of the new relationship (Warburton & Naylor, 1971).
It may be argued that because of the significant increase in the
perinatal mortality of first-born infants and those of mothers of high
parity, this factor should be classified as a pathological variable. The
odds ratios, however are only slightly elevated (Kirkup &
Welch,1990), and do not justify this re-classification.
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Race.
Meredith (1970) published an extensive description of the variations
in birth weight among different ethnic groups. Of the 78 groups
considered, the largest newborns were found in Anguilla and Nevis,
weighing a mean of 3.88 Kg. The smallest babies were those of the
Lumi tribe in the Toricelli mountains in New Guinea, with a mean
birth weight of 2.4 Kg. These ethnic differences clearly persist in
mixed populations from the same location (Cheng et al, 1972; Wilcox
et al, 1993b). Birth weight variations do not always correlate with
trends in perinatal mortality. In Singapore, Malay babies have a much
higher perinatal mortality than Indian babies even though their
percentage of low-birthweight (<2500) is significantly smaller
(Hughes, 1984), both groups living in similar socio-economic
conditions with total health care coverage. Similarly, Californian black
babies under 3001g have much lower mortality rates than whites, even
though their birthweights are lower (Williams et al, 1982).
Sex
The female newborn weighs on the average 118 g less than the male
(Wilcox et al, 1993b), and this has been observed in most ethnic
groups studied (Meredith, 1970).Animal studies suggest that the XY
embryo has a growth advantage at the earliest stages of organogenesis
(Snow, 1989); hormonal differences are not responsible, since the sex
differences are noted even in anencephalic foetuses. It is of interest
that in spite of this, females born preterm have lower mortality and
morbidity than males (Allen et al, 1993). This may be due to female
infants having relatively more energy stores in adipose tissue than
males (Oakley et al, 1977).
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Others
Women with a history of SGA infants are more likely to give birth to
small babies. It is not clear whether this is due to genetic factors, or
recurrence of genetic/pathological factors. Work performed by
Ounsted (1965,1966) has shown that mothers who have borne SGA
infants had themselves lower than average birth weights, although
their adult height did not differ significantly from that of women who
had given birth to babies of normal weight.
Consanguinity in parents has been shown to cause a significant
reduction in birth weight in Pakistan (Shami et al, 1991), Japan
(Morton, 1958) and Norway ( Magnus et al, 1985).
A seasonal trend has been observed in birth weights, with significantly
lower values in summer and a peak in winter/early spring (Matsuda,
1992). These fluctuations are rather minor, occurring within a 100g
band.
1.4 Discussion
The distinction between 'pathological ' and 'physiological' factors is
to some extent arbitrary, with a significant 'grey zone' of uncertainty.
In terms of defining normal growth potential, the genetic components
of the physiological factors are probably more important, and this was
stressed by Lazar and colleagues (1975). This distinction is, however,
an important exercise in order to develop valid adjustable growth
standards. It is likely that fetal development is under the control of
inhibitory and stimulatory growth factors, and that some physiological
and pathological factors may well act through common pathways.
Animal studies have shed some light on the relative importance of
fetal genome and maternal effect; these have been reviewed by Snow
(1989). There is good evidence to suggest that maternal effects operate
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late in gestation, whereas early fetal growth is controlled by fetal
genetic mechanisms. Winick (1971) studied the cellular basis of fetal
growth using animal models. Three phases of growth were identified:
cellular hyperplasia, followed by both hyperplasia and hypertrophy,
and then predominantly hypertrophy. Depending on the timing and
duration of experimental insults, different forms of growth restriction
were observed. Disturbances in early pregnancy restricted the total
cell number, so that no 'catch-up' growth was possible, whereas later
in pregnancy cell size was predominantly affected with minimal
reduction in cell numbers , and post-natal recovery was possible with
adequate nutrition. This points to the heterogeneous nature of growth
disturbances and to the need for using appropriate standards.
It is difficult to determine how much of the differences observed
among different ethnic groups are due to genetic factors, as opposed to
environmental factors such as nutrition and socio-economic
conditions. Hence customising for ethnicity can only be justified when
the adjustment factors are derived from sub-populations in the same
location and ideally living under similar socio-economic conditions.
The observed differences in neonatal morbidity between different
sub-populations do not always agree with the birth weight differences.
This lends weight to the argument that, for optimal performance, fetal
weight for gestation as an index of morbidity needs to be evaluated in
relation to other pregnancy characteristics.
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2. ADJUSTABLE BIRTH WEIGHT STANDARDS
2.1 Introduction
Birth weight on its own is only a crude indicator of neonatal welfare,
being a retrospective measure from which it is difficult to make
accurate inferences about prenatal growth kinetics. The definition of
'low birth weight infant' as being a newborn weighing below 2500g
was used as an index of prematurity until the 60's, when it was
realised that a considerable proportion of these cases were in fact
growth restricted (Ounsted, 1970). It has been nevertheless a
convenient tool for epidemiologists, since reliable data on the
duration of gestation is often difficult to obtain, particularly in
developing countries. This, however, fails to make the important
distinction between infants who are small because they were born
preterm and those term babies who are small because of constitutional
or pathological factors.
2.2 Pathological Implication of Abnormal Birth Weight
This has prompted the search for birth weight for gestation standards,
so that given the appropriate variables this distinction can be made.
When birth weight is analysed as a function of gestation, some
important relationships with poor perinatal outcome emerge. In a large
study by Patterson et al (1986) of a database of 44 811 cases, a
U-shaped relationship was found between birth weight centile and the
incidence of morbidity , with minimal morbidity near the middle
ranks of birth weight ; the percentage of the total poor perinatal
outcome occurring below the tenth or above the 90th centiles
increased linearly from 16% at 28-29 weeks to 57% at 40-41 weeks.
There is mounting evidence that being small for gestational age has
pathological implications extending into childhood and adulthood.
Hill et al (1984) , in a small study, related the outcome of term infants
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with nutritional parameters. Malnutrition in the newborn was defined
in terms of subcutaneous tissue thickness. About 45% of the 33
malnourished infants had birth weights below the 10th centiles; in this
group, poor outcome included reduced educational achievement up to
the age of 14. Rantakallio(1985) studied a cohort of 12,000 children
in Finland followed up to 14 years; it was found that the incidence of
neuro-behavioural disturbances was significantly higher in weight
percentile classes below the median. Most of these and similar studies
are flawed by the use of menstrual dates in determining gestation . As
the error tends to be towards overestimation (Gardosi & Mongelli,
1993), more babies would be classified incorrectly as below the 10th
centile than those assigned above the 90th centile.
2.3 Assignment of Gestational Age
With very few exceptions, gestation is usually estimated from
menstrual dates, rounded down to 'completed weeks'. Typically, when
birth weight is plotted against gestation, the resulting standard curves
show considerable flattening near term, and this has been attributed
either to placental ageing/insufficiency, or to physical restriction of
growth. More recent standards in which gestational age has been
calculated on the basis of early ultrasound measurements show a
much more linear relationship between duration of pregnancy and
birth weight (Lindgren, 1988; Wilcox et al,1993a). This inaccuracy in
the estimation of gestational age is also likely to lead to a greater
apparent variance in the birthweight distribution for any given week of
gestation.
2.4 Preterm Delivery and Birth Weight Standards
Birth weight standards have in the past been referred to as 'fetal
growth curves'. Apart from the fact that these are cross-sectional
studies, values derived from preterm deliveries cannot be regarded as
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representative of normal growth. Furthermore, unless the population
sample is very large, the number of babies born preterm is relatively
small, leading to a greater incidence of sampling errors. Preterm
delivery may be associated with growth restriction , and birth weight
norms at these gestations may be well below those derived from serial
ultrasound weight estimations (Ott, 1993). An experimental model of
growth retardation supporting this epidemiological data was described
by Alexander (1964). He, and subsequent workers, found that the
excision of endometrial caruncles in the sheep (before pregnancy)
resulted not only in an increased rate of growth retardation, but also in
increased preterm labour rates and intrauterine death.
Another indicator of pathology in the preterm period is the statistical
distribution of birth weights. Whereas the distribution of birth weights
at term shows a significant positive skewness, in the preterm period
this becomes negative (Wilcox et al, 1993a), most likely because of
the greater number of growth-retarded babies born at these gestations.
It has also been shown that preterm babies delivered electively are
significantly lighter than those born spontaneously, yet even when the
former are excluded from the analysis the negative skewness is
reduced but not entirely eliminated (Yudkin, 1987).
2.5 Effect of Environment
Reference standards may also be strongly affected by the environment
and population characteristics. For example, Lubchenko's (1962) birth
weight chart has been used widely in the United States and elsewhere.
This was derived from a population in Denver, Colorado, at an altitude
of about 10000 ft. Not only did this high altitude result in lower birth
weights for all gestations than any other published standard, but also
the percentage of births occurring preterm was much higher than
expected. There is also some evidence that birth weight standards may
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change historically as living standards and social characteristics vary
(Alberman 1991; Ulizzi & Terrenato, 1992).
2.6 Adjustable Standards
The realisation that these genetic or physiological effects are in
operation led to attempts to introduce adjustment factors to allow for
maternal characteristics. Thomson and colleagues (1968) published
birth weight for gestation tables allowing for sex, parity and inclusive
of correction factors for maternal weight . Altman and Coles (1980)
produced nomograms for the calculation of birth weight centiles based
on this data that included correction factors for parity, maternal height
and weight, and fetal sex.
Lazar and colleagues (1975) used multiple regression analysis to
derive correction factors for both maternal and paternal weight and
height, claiming that paternal weight is as important as maternal
weight; they believed that the effect of these variables is largely of
genetic origin, and in order to improve their predictive power they
estimated what the parental values of height and weight would be at
the age of 20 before entering them in their regression model. Parity
and ethnic group were not considered in their analysis, and their model
was not tested prospectively.
Voigt (1989) and Mamelle(1989) published elaborate tables to allow
adjustment for these variables, but the fact that they are not in general
use attests to their complexity.
Some interesting similarities among different birth weight standards
were described by Dunn (1989). When the centile cut-off points were
expressed as percentages above or below the population median and
plotted against gestation, virtually identical values were obtained for
all the standards. This remarkable correspondence led to the
construction of the Bristol Perinatal Growth Chart, a method that
would allow the production of antenatal and post-natal growth
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standard for population sub-groups. The method assumes that the
latter are normally distributed.
2.7 The Birth Weight Ratio
An alternative variable to describe size for gestation , the 'birth weight
ratio' , was described by Brooke and colleagues (1989) in order to
analyse factors affecting birth weight. This is simply the observed
weight divided by the weight expected for a given gestation. Morley
and colleagues (1990) described a relationship between birthweight
ratio and the need for mechanical ventilation and post-neonatal
mortality in preterm infants; this, however, was not observed in the
study of Brownlee et al (1991). More recently, Wilcox and colleagues
(1993b) analysed a large database of 31 561 computerised records of
term deliveries in order to develop a multiple regression model to
predict birth weight. The variables included gestation, sex, maternal
height, weight, parity and ethnic group. The ratio of the observed
birth weight to predicted weight ('individualised birth weight ratio',
IBR) can then be calculated by a computer program and expressed as a
centile value. This method has been reported to identify a higher
proportion of truly growth retarded infants, as defined by neonatal
ponderal index and skinfold thickness measurements (Sanderson,
1994). The drawback of Wilcox's program is that in its present form it
is only applicable to babies born at term, and cannot be used for
screening purposes in the antenatal period.
It can be shown that when birth weight ratios are transformed into
centile values, these are very similar to the corresponding birth weight
centiles, provided the reference standards used to obtain the mean and
standard deviation are similar (chapter 16).
2.8 Discussion
The large number of birth weight standards in existence is a reflection
of the importance given to this parameter, as well as the need to relate
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birth weight to local conditions. In the English literature alone,
Goldenberg and colleagues (1989) were able to review 13 such
standards published since 1963. Large differences were noted in the
10th centile cut-off point, greater than 500g for some gestational ages.
These discrepancies are partly due to inconsistencies in methodology.
For example, McKeown and Gibson (1951) included both live and
still births in their analysis of the Birmingham data, whereas most
investigators have restricted their samples to live born infants. The
treatment of outliers in the data can vary between studies. A variety of
corrections for bimodal or skew birthweight distributions have also
been adopted (Gruenwald, 1966; Milner & Richards, 1974). A number
of studies are also limited by small sample sizes, making it difficult to
estimate centile distributions of birthweight with any degree of
accuracy.
Another major source of error is gestational age assignment.
Assessment of fetal well-being, by whatever means, requires an
accurate estimate of gestational age. The introduction of routine early
ultrasound scanning in the United Kingdom has eliminated large
errors, but the use of '10-day' or '7-day' rules whereby menstrual dates
are used in preference to ultrasound determined dates if in agreement,
may lead to some loss of accuracy (see Chapter 7). Those women who
book late tend to have poorer outcomes, and ultrasonography may be
of special benefit in this group. Although algorithms have been
developed for the accurate determination of gestational age up until 32
week's gestation (Sabbagha et al, 1978), these have not gained
widespread acceptance.
All of the adjustable standards of fetal growth are limited by their use
of cross-sectional birth weight data. While they may be valid for the
assessment of relative size, they are not suitable for assessing serial
weight estimates, i.e. growth (Altman, 1994).
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With the exception of the IBR, the correction factors are usually
presented in tabular form, and do not take into account gestation-
dependent variations. Thomson and colleagues (1968) stated that these
adjustments should not be made for gestations under 37 weeks, since
their numbers was limited in that range. Nevertheless, their parity
differences were statistically different even at 32 weeks. Even if there
were sufficient numbers, it is doubtful that these adjustment factors
would be applicable to intrauterine fetal weight estimates. In practice
most clinicians do not adjust beyond sex and parity, probably because
of the inconvenience in using complex tables or graphs. In the
standard published by Yudkin and colleagues (1987) - widely used in
paediatric units in the UK-, no adjustment is made apart from fetal
sex.
Although the importance of accurate and valid fetal growth standards
has long been acknowledged, the validity of specific growth standards
when applied to a particular population or study sample is seldom
tested. As a result, the assessment of growth retardation and evaluation
of screening procedures may be inaccurate and biased.
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3. ULTRASONIC METHODS OF FETAL WEIGHT
ESTIMATION
3.1 Introduction
Fetal weight estimation plays an important part in clinical obstetrics
decision-making, often used in screening for IUGR, the management
of diabetes in pregnancy and pregnancy complicated by breech
presentation. In current practice, ultrasonography remains the most
accurate method for determining the estimated fetal weight (EFW).
Although newer imaging techniques such as computerised tomography
and nuclear magnetic resonance are likely to be much more accurate
(Baker et al, 1994), their cost will prevent widespread use; their main
role in the immediate future will remain as research tools. Three-
dimensional ultrasound equipment, on the other hand, is now
affordable, and the better, more reliable definition of anatomical
planes (Kuo, 1991) should lead to reduced operator error and
hopefully to better performance of the existing formulae.
3.2 Fetal Weight Estimation Formulae
The equations for fetal weight estimation in terms of given ultrasound
parameters are usually derived by applying a model of fetal weight
composition to a source population examined shortly before delivery.
The measured ultrasound parameters and the birth weights, are entered
and the relevant coefficients are estimated by multiple regression
analysis. The performance of the formula in term of its prediction
errors is then tested on a separate sample, and 'target' population. One
of the first such formulae to be developed was that of Campbell &
Wilkin (1975 ); this was based on the fetal abdominal circumference
(AC), and it is still in common use in the UK A considerable number
of other formulae that usually employ more than one ultrasound
parameter have since been published. A sample of these are listed in
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table 3.1. They can be broadly classified as exponential or non-
exponential, depending on the type of mathematical expression.
Exponential formulae take the form of:
EFW = exp[F(P1,..Pn)]
where F(p1,..pn) is a polynomial function of the ultrasound parameters
P1 to Pn.
Other approaches to weight prediction have been explored. A
computer neural network program has been developed specifically to
estimate weights in foetuses at risk of macrosomia (Farmer et al,
1992); ultrasound parameters were combined with clinical
measurements such as fundal height, with a reported accuracy of
around 5%. Birnholz (1986) published an algorithmic method for
weight estimation, whereby one of two formulae are chosen by a
computer program depending on the body proportions of the
individual. About 90% of cases had an error less than 80 g/Kg . This
method requires regression analysis of the fetal ultrasound parameters
in the population under study.
3.3 Clinical Performance of Weight Estimation Formulae
This is usually assessed by the statistical analysis of the errors. They
may be expressed as signed or absolute percentage errors, absolute
error in grams, errors in grams per Kg of fetal weight, and percent of
errors beyond a given threshold. A typical weight formula employing
more than one parameter will estimate 75% of cases within 15% of the
actual weight (Thompson et al, 1990).The most common practice is to
report the mean error and its standard deviation (SD); the former gives
a measure of the tendency to under- or over-estimate (the systematic
error), whereas the latter indicates the spread of the errors. It has
recently been suggested that the standard deviation should be replaced
24
by the 95% confidence limit of the errors (Bland & Altman, 1986);
this is certainly preferable in situations when the distribution is not
Gaussian. There is general agreement that equations employing two or
more ultrasound parameters are more reliable than those using only
one (Hadlock et al, 1985; Guidetti et al, 1990). Formulae developed
within an institution tend to perform better than those from other
centres (Thompson et al, 1990), probably because of significant inter-
observer variability (Chang et al, 1993) and differences in equipment
and populations. For example, formulae derived from Chinese
populations perform better on Chinese patients than those developed
from European populations (Chang et al, 1991). It has been reported
that continual review of the results obtained by the methods used by an
obstetric ultrasound department may further enhance its performance
(Thompson et al, 1990). In Hadlocks' studies (1985), the prediction
errors of equations employing three or four ultrasound parameters
(BPD, HC, FL and AC) had a SD of around 8%. Slightly better values
were reported by Issel and colleagues (1991), a SD of about 7% by
measuring up to 7 ultrasound parameters. In clinical practice these
errors tend to be somewhat higher (Miller et al, 1988). The problem
of systematic over- or under-estimation of fetal weight is frequently
reported when such formulae are used by centres other than the one
where the formula originated (Robson et al, 1993). Some of this error
may be due to the variations in the lag times between ultrasound
examination and delivery, which is not usually allowed for by the
authors of the formulae; this means that a fetus examined some days
before delivery will be slightly lighter than at birth, and when the birth
weight is entered into the regression analysis without due
modification, a small but appreciable over-estimation will take place.
This problem was appreciated by Spinnato and colleagues (1993), who
introduced a time component into the established formulae, valid up to
35 days before delivery. A more serious and common problem is the
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existence of trends in the errors. There may be significant and
negative correlation with the size of the fetus (Robson et al, 1993;
Miller et al, 1988); hence small babies tend to be overestimated while
large ones are under-estimated. This can lead to serious data distortion
when producing normal values in growth curves for fetal weight, and
may affect the performance of screening programs for small- and
large- for gestational age infants.
3.4 Discussion
Early retrospective studies on the detection of growth retarded
foetuses by measuring the biparietal diameter suggested that this
parameter could be helpful in defining groups of cases with higher
perinatal mortality and preterm delivery rates (Persson et al, 1978).
The technique, however, was subsequently found by Campbell &
Dewhurst (1971) to have a false positive rate for SGA of 25%. This is
not surprising, since the correlation coefficient of BPD with birth
weight is not as high as other ultrasound parameters such as the
abdominal circumference and femur length (Favre et al, 1993).
Several studies have been published on the performance of different
ultrasound parameters in the detection of the SGA fetus. Neilson and
colleagues (1984) measured a series of fetal parameters in the third
trimester; they found that the product of trunk area and crown-rump
length (as an index of fetal weight) was superior to the trunk diameter
alone. Dudley and colleagues (1990) showed that EFW was the best of
four ultrasound parameters in identifying the small-for-dates infant.
Similarly, Chang and colleagues (1993) reported that a single EFW
estimate based on multiple ultrasound parameters was superior to
abdominal circumference in predicting 2 out of 3 indices of neonatal
nutritional deprivation. The efficacy of growth screening programs
continues to be limited by the error of ultrasonic EFW and by the lack
of a uniform standard for fetal growth and growth velocity. The
26
normal standards of EFW published to date show considerable
disagreement, and at least some of the differences may be attributable
to the choice of weight estimation formula. This is further discussed in
chapter 9.
Volumetric formulae for fetal weight estimation have been proposed
by several workers, including Combs (1993 ) and Birnholz (1986), on
the grounds that fetal volume is proportional to weight when the
specific gravity is constant. There are at least two theoretical
objections to this argument. Firstly, the gestational age-dependent
changes in specific gravity have not been described, and the magnitude
of error from this factor is unknown. Secondly, growth retarded babies
would have considerably less fat stores, increasing their specific
gravity and thus leading to underestimation of weight. That this may
be the case is suggested by the fact that Birnholz noted systematic
underestimation of fetal weight in the under-1000g group, for whom
he had to apply a recursive correction formula. In any case, the
claimed improvements in accuracy of their methods have not been
confirmed by independent workers.
Birnholz (1986) has suggested that , on the grounds of information
theory, averaging serial fetal weight estimates would improve the final
estimate, with the expected improvement being related to the square
root of the number of measurements. This makes the assumption that,
for a given individual, the error in fetal weight estimation on each
occasion is random, i.e. the signed error values are not correlated with
each other. This particular issue has not been reported on in the
literature.
All of the commonly used formulae place an emphasis on bony
landmarks and do not use any other soft tissue measurements apart
from the AC. While bony landmarks are accurate for the purposes of
estimating gestational age, the emphasis on these parameters could
explain their relative inaccuracy in estimating weight.
27
New weight estimation formulae should be explored that include
additional measures of soft tissue parameters. These were explored by
Favre and colleagues (1993), who reported better performance in the
small for dates group using the thigh circumference and femur length.
The standard deviation of the error was nevertheless fairly high at
15.9%, and they did not compare their formulae with older established
equations.
Other measures that should improve not only accuracy of fetal weight
estimation but also performance of other tasks include continual audit
and quality control, to ensure consistent techniques and peak
performance of equipment. Plastic ‘phantoms’ have been designed in
order check the technique and accuracy of the measurements
performed by ultrasonographers, but these are not in common use in
the UK.
The scope for making major errors in estimating growth velocity from
ultrasound fetal weight estimation has been pointed out in
correspondence by Gardosi (1994b). Substantial gains in accuracy will
be needed before abnormalities in growth velocity can be reliably
detected.
28
Table 3.1 Ultrasound fetal weight estimation formulae.
Authors Exponential formulae
Campbell & Wilkin
(1975)
wt=1000*exp(-4.564 +0.282*fac -0.00331*fac*fac)
Hadlock et al (1985) wt=exp(2.695 +0.253*fac -0.00275*fac*fac)
Hadlock et al (1985) log10(wt)=1.3598 +0.051*fac +0.1844*fl -0.0037*fac*fl
Hadlock et al (1985) log10(wt)= 1.4787 -0.003343*fac*fl +0.001837*bpd*bpd
+0.0458*fac +0.158*fac
Hadlock et al (1985) log10(wt)=1.3596 -0.00386*fac*fl +0.0064*hc
+0.00061*bpd*fac +0.0424*fac +0.174*fl
Shepard et al (1982) wt=1000*exp(-1.7492 +0.166*bpd +0.046*fac -
0.002646*fac*bpd)*(ln(10))
Warsof et al (1977) wt=1000*exp(2.302585*(-1.599 +0.144*bpd +0.032*fac
-0.000111*bpd*bpd*fac))
Persson et al (1986) wt=exp(ln(10)*(0.972*ln(bpd)/ln(10) +1.743*ln(ad)/ln(10)
+0.367*ln(fl)/ln(10) -2.646))
Balouet et al (1992) wt=0.1135exp(0.739*ln(fac) +1.179*ln(ethc) -0.041*ln(ithc))
Non-exponential formulae
Combs et al (1993) wt=0.23718*fac*fac*fl +0.03312*hc*hc*hc
Dudley et al (1990) wt=4.1*fl*apa +0.86*fl*hpa
Shinozuka (1987) wt=0.23966*fac*fac*fl +1.6230*bpd*bpd*bpd
Birnholz (1986) wt=(3.42928*bpd*ad*ad/1000) +41.218)
Birnholz (1986) wt=1.0206*{1.88496*[0.01*fl*ad+0.01667*bpd*ad +
0.01*bpd*bpd]*[(((-0.0069558*fl) +1.7394)*fl/10)
-3.3626]} -61.537
29
4. MODELS OF FETAL GROWTH
4.1 Introduction
Awareness of the limitations of birth weight standards as indicators of
fetal growth have led to the pursuit of ultrasound-defined intrauterine
growth standards. But while it is relatively easy to obtain birthweight
data from large populations, to derive ultrasound-defined standards
requires considerable effort in manpower, logistics and equipment.
Usually this data originates from ultrasound departments, and often
does not contain the clinical details of individual cases. As a result, all
of the fetal weight standards published to date have been derived from
relatively small samples.
The norms for commonly measured ultrasound parameters such as the
AC, FL and BPD are well established, yet relatively few studies
specifically address the issue of intrauterine weight gain. This is
partly because for the purposes of growth monitoring, most
ultrasound departments plot the individual measurements rather than
weight estimates. This in spite of several studies suggesting that the
EFW is at least as good as the AC for the detection of IUGR (Chang et
al, 1993; Hedriana & Moore,1994). Here we review the literature on
intrauterine weight curves, and describe an 'average' growth curve,
based on published data.
4.2 Comparative Analysis of Ultrasound-Derived Growth Curves
A total of seven studies describing intra-uterine weight gain were
retrieved .Studies on the growth of linear ultrasound parameters
without weight estimations were excluded, since derived fetal weight
curves can differ markedly depending on the weight equation being
used (see chapter 8 ). The characteristics of these studies are
30
summarised in table 4.1; these include population samples, weight
estimation formulae, mean birth weight and the methods of data
analysis. The fetal growth kinetics described by each study are
displayed in table 4.2. In order to describe the shape of the growth
curves independently of the predicted term weight, growth can be
expressed as a percentage of the predicted 280-day fetal weight, and
plotted as fractional growth curves. This allows comparisons in terms
of arbitrary descriptive landmarks such as gestation at which 50% of
the term weight is reached (G50), and the percentage of term weight
that is expected at 28, 37 and 42 weeks (P28, P37, P42). Figure 4.1
shows the medians of the ultrasound EFW curves plotted to 42 weeks
and also the corresponding birth weight data derived from the East
Midlands Obstetric Database (Wilcox et al, 1993a). Figure 4.2 shows
the derived fractional growth curves, as a percentage of term weight.
The equation for average fractional curve was obtained by taking the
arithmetic average of the coefficients of the derived growth functions
listed in table 4.1. This is plotted in figure 4.3; only a minimal degree
of deceleration is noted at term.
4.3. Alternative models of fetal growth
Rossavik and Deter (1986) proposed a sigmoid function to describe
fetal growth of any parameter, including weight. This function is of
the form:
P= c(t) k+st
where P is the ultrasound parameter, t is the duration of growth, k a
fixed coefficient determined by the anatomical characteristics, c and s
constants related to growth regulatory processes. This function allows
the prediction of individual 'normal' growth channels based on two
31
separate ultrasonic examinations before 27 weeks. This model was
applied prospectively by Simon et al (1989) to a number of parameters
including fetal weight. They found a small but significant systematic
error of overestimation for most of the parameters and fetal weight;
the standard deviation of the errors for fetal weight ranged from 6.7 %
to 9.4% , depending on gestation. This is well within the range of the
published errors of weight estimation formulae. The advantage of this
model is that reference charts are no longer needed; instead, growth
disturbances may be detected as deviations from the individually
projected standard. The main drawbacks are the need for two
ultrasound examinations before 27 weeks' gestation, spaced at least 5
weeks apart, and the need for appropriate computer equipment and
software to carry out complex calculations.
4.4 Discussion
Most of the differences between the published ultrasound growth
curves become apparent during the term period. They agree within a
100g band up to about 36 weeks gestation (figure 4.1). Beyond this
point, Jeanty's curve shows marked deceleration, whereas Deter's and
Otts display moderate acceleration. The remaining four curves
continue a linear trend evident from about 28 weeks. Jeanty's
abdominal circumference values are markedly below other standards
in late pregnancy, and this probably accounts for the deceleration in
his weight curve. The overall fractional average curve (figure 4.3)
shows only minimal deceleration at term, and this is in contrast to
birth weight standards based on menstrual data. Another approach to
deriving this curve would have been to use a weighted average; the
problem here is that two of the studies are cross-sectional. In any case,
the largest studies are included in the middle five curves, and thus it is
32
doubtful that a weighing procedure would change the shape of the
curve significantly.
On the basis of their published birth weight data, Ott's and Deter's
weight formulae overestimate fetal weight at term by 255g and 471 g
respectively, and this may account for the steeper slopes of their
curves. Larsen's study is the only one to use birth weight in order to
select the optimal growth model; yet even here there is a systematic
overestimation at term of about 170g. This contrasts with Hadlock's
study, of similar (cross-sectional) design, where an overestimation of
only about 20g was observed, probably because of better performance
of the weight estimation formula.
Of the five studies whose weight formula was not developed locally,
none compares more than 3 different weight formulas in order to
select the best. As will be discussed in chapter 8, the type of weight
equation selected may result in differences of more than 300g at term.
Hence, when producing standards of intrauterine weight gain,
measures should be taken to correct any systematic error due to the
weight estimation formula, since other centres will not necessarily
employ the same formula.
It is unlikely that the method of gestational dating makes a significant
contribution to the observed differences among these studies. This is
because, with the exception of Ott's study, menstrual dates were used
only if in close agreement with early ultrasound measurements; if they
did not agree, gestation age was estimated from the early ultrasound
measurements of the biparietal diameter .
The study by Larsen and colleagues is the only one to produce separate
standards for males and females; they describe a mean weight
difference between the sexes of 3.8%, but do not elaborate on whether
this holds true for all gestations or only for part of pregnancy.
It is apparent from figure 4.1 that all of the ultrasound derived medians
are higher than the birth weight data, by an average of about 100g.
33
Systematic weight estimation errors may account for some of this
difference, but other factors may be at play , since the differences
persist in the studies where this error was minimal, such as Hadlock's
or even negative, as in Jeanty's. The most likely explanation is that
infants born preterm are more likely to be growth retarded, and
preterm delivery in these cases is an escape mechanism from an
adverse intra-uterine environment.
Complex mathematical models such as Rossavik's, irrespective of
their validity, are unlikely to improve birth weight prediction in view
of the magnitude of ultrasound error. A recent study by Shields and
colleagues (1993) has shown that serial plotting of fetal measurements
on normal curves is as accurate in this respect as complicated
mathematical modelling. This would also be expected on the basis of
information theory (Birnholz, 1986).
34
Figure 4.1. Ultrasound-derived fetal growth standards compared with
Nottingham’s birth weight standard. The continuous curves represent the ultrasound-derived
standards published by Hadlock et al, Gallivan et al, Ott, Persson & Weldner, Deter et al, Larsen et al and
Jeanty (using Shepard’s weight formula). The values for the birth weight standard by Wilcox et al are
displayed as triangles. The middle four curves (Hadlock, Gallivan, Larsen and Persson) are closely
related.
35
Figure 4.2. Proportional growth curves for ultrasound-derived fetal growth
standards.
The ultrasound-derived standards published by Hadlock et al, Gallivan et al, Ott, Persson
& Weldner, Deter et al, Larsen et al and Jeanty (using Shepard’s weight formula) have
been transformed into ‘proportional’ growth curves, whereby the values for each gestation
represent the percentage of the predicted 280 day fetal weight.
36
Figure 4.3. Average proportional fetal growth curve.
The transformed proportional fetal growth curves of the ultrasound-derived standards published by Hadlock et al, Gallivan et al, Ott, Persson & Weldner, Deter et al, Larsen et al and Jeanty (using Shepard’s weight formula) have been averaged arithmetically to yield an average curve
.
37
5. SCREENING STRATEGIES FOR ABNORMAL FETAL
GROWTH
5.1 Introduction
In spite of the widespread introduction of obstetric ultrasound, our
clinical ability to detect the small-for-dates fetus remains poor, with
only about 30% -50% of cases detected in the ante-natal period (Jones,
1986; Hepburn & Rosenberg, 1986) . We are also rather ineffective in
detecting fetal macrosomia, with sensitivities of about 50% (Sandmire,
1993; Pollack et al, 1992).
The question has been raised on several occasions of whether
antenatal detection of growth disturbances is going to significantly
affect neonatal prognosis . While there is no long-term follow-up data
on this issue, there is some evidence that those SGA infants that are
detected tend to have a better short-term outcome than the undetected
cases (DeCourcy-Wheeler, personal communication). Hence we
should persist in our efforts to improve the antenatal detection of the
potentially compromised fetus.
There are two basic methods in practice for the detection of fetal
growth anomalies: obstetric ultrasound and assessment of the fundal
height.
5.2 Fetal Ultrasonography
Obstetric ultrasound has been investigated as a screening technique
since the early 70's (Campbell, 1971).While it has proved successful in
the detection of congenital abnormalities (Chitty et al, 1991) and in
establishing gestational age, the detection of growth restriction and
growth acceleration have remained much more elusive goals. Fetal
macrosomia is a common cause of concern for obstetricians, and it is
38
common practice to refer cases to the ultrasound department for fetal
weight estimation. There is, however, mounting evidence that so far
the performance of ultrasound in this weight group is poor compared
with other categories (Sandmire,1993). Two ultrasonic methods are in
common use for the detection and assessment of fetal growth
abnormalities: serial and static measurements of fetal anatomy .
At least five randomised trials have been performed since 1984 to
assess the efficacy of antenatal ultrasound as a screening tool for
growth restriction or developmental anomalies. These were published
by the following authors:
1. Bakketeig LS and collegues (1984)
2. Waldenstrom U and collegues (1988)
3. Ewigman B and collegues (1990)
4. Ewigman B and collegues (1993)
5. Newnham JP and collegues (1993)
The results have been somewhat contradictory and inconclusive. A
significant reduction in the number of SGA infants, a modest increase
in the mean birth weight and a significant reduction in the induction
rate was demonstrated by Waldenstrom and colleagues, following
routine scanning at 12 weeks. This was attributed to a reduction in
smoking due to visualisation the baby. On the other hand, in a group
undergoing both Doppler and ultrasound imaging on up to 5
occasions, Newnham and colleagues noted a slight but significant
increase in the SGA frequency in the screened group. In the largest
randomised study to date (4) involving 15151 low-risk pregnancies ,
no significant differences in outcome were noted between the screened
and the routine management group. The latter, however, did undergo
ultrasound examination when clinically indicated, thus limiting the
scope of the inferences that can be made from this study.
39
Optimal strategy for the detection of growth restricted fetus continues
to be a controversial issue (Daniellan and colleagues,1994). The study
by Chang and colleagues (1993) suggests that the use of either growth
velocity or single fetal weight estimates is rather limited at detecting
the truly growth restricted fetus as defined by neonatal morphometric
indices; at a false positive rate of 10% only 20-40% of the growth
retarded fetuses were detected. Similar figures were reported by
Daniellan and colleagues (1993). Both of these studies used
morphometric indices as the definitive criteria for IUGR; the
limitations of these indices are discussed in chapter 9. The data
presented by Hedriana and Moore (1994) suggests that a single
ultrasound examination is nearly as good as multiple examinations in
predicting the birth weight, but they did not test the hypothesis that
growth velocity as assessed by multiple measurements is a better
predictor of poor outcome than fetal weight estimated from a single
measurement.
5.3 Fundal height assessment
5.3.1 Introduction
The earliest report on measuring the symphysial-fundal height (SFH)
was published in the German literature by Spiegelberg in 1891.
Rumboltz and McGoogan (1953) were the first to describe a
relationship between reduced growth of the uterine fundus and
'placental insufficiency'. Since then, many conflicting reports have
been published on screening for growth disturbances by the clinical
measurement of the symphysial-fundal height (SFH).
5.3.2 Precision and accuracy of SFH measurements.
The estimation of symphysis-fundus distance is subject to
considerable error. Bagger and colleagues (1985) reported an average
40
intra-observer variation of 1.5-2 cm and an inter-observer variation of
4 cm; these were not correlated with the actual SFH measurements.
Some observers were found to consistently overestimate or
underestimate .The accuracy of SFH measurements was checked by
comparing clinical measurements with those obtained by ultrasound
guided measurement; the differences between the former and the latter
ranged from -0.2 to +2.7 cm. Calvert (1982) found intra-observer and
inter-observer coefficients of variation of 4.6% and 6.4%, slightly
lower values than those published by Bagger's group. The limits of
agreement of the inter-observer variation were estimated in a study by
Bailey and colleagues (1989) to be -5.0 to +1.6 cm, corresponding to a
coefficient of variation of 4%. This study highlights what is probably
the major shortcoming of SFH measurents: that the error due to inter-
observer variation, even between experienced practitioners, is too
wide in relation to the standard deviations in the published reference
charts.
5.3.3 Fetal weight estimation by fundal height measurement.
Estimation of fetal weight by unaided clinical palpation was reported
by Loeffler (1967) to be accurate within 450g of the birth weight in
80% of cases; it is of interest that in this study the accuracy of the
individual observers improved with experience.
The first attempt to estimate fetal weight by measuring the fundal
height was reported by Johnson and colleagues (1954). This method
included correction factors for engagement of the fetal head and
obesity.The standard deviation of the reported errors was 353g, which
is slightly greater than ultrasound estimation using Campbell's formula
for abdominal circumference(Campbell & Wilkin,1975). More
recently, a Belgian study of an African population showed that SFH
was more closely related to fetal weight than gestation (De Muylder
and colleagues,1988).
41
5.3.4 Derivation of SFH standards.
In all the studies, women without 'sure' dates or an early dating
ultrasound scan were excluded. In six of the nine papers reviewed the
data was filtered by removing those cases whose birth weights were
outside arbitrary limits; depending on the degree of restriction, the
standard deviation of the SFH values thus obtained would be
narrower. The inclusion criteria are summarised in table 3. In all of the
above studies there appears to be some flattening of the SFH curve
near term. As pointed out by Westin, miscalculation of gestational age
may lead to serious error. In all the standards so far published,
gestation was reckoned on basis of menstrual dates, ultrasound dating
being used routinely to 'confirm' dates (Pearce),or reserved for those
cases whose last menstrual period was unknown (Calvert, Quaranta)
or otherwise excluding those cases without a known LMP (others).
Geirsson (1991) has convincingly argued that even when certain, LMP
dates are less reliable than those derived from ultrasound, with an
overall tendency to overestimate gestation. He pointed out that birth
weight standards in populations whose gestations are derived from
ultrasound dating show a much less marked 'terminal flattening' of the
reference curves at term. This has also been our experience (Wilcox et
al, 1993a). It is likely that SFH reference standards are also subject to
the same effect.
5.3.5 Ethnic variations in SFH standards.
Table 5.1 shows the differences in SFH standards by ethnic group.
For comparative purposes, the 40-week median value is given for each
group; the SD deviation is omitted because of the widely different
methodologies used. It thus appears that for European populations
there are only small differences among the published standards. Indian
42
populations, however, have lower term values of around 33 cm,
compared with 36 cm for Europeans. Grover and colleagues (1991)
published a reference standard derived from 200 low-risk Indian
women with birth weights within +/- 1 SD of the local standard.
Compared with European curves, their fundal height increments were
similar from 20 weeks until 32 weeks (1 cm/week); some slowing was
noted thereafter, resulting in term values that were 3 -4 cm lower.
Similar values were published by Mathai and colleagues (1987) in
South India. However, Depares and colleagues (1989),on comparing
European and Pakistani SFH values in Bradford (UK), could not
detect significant differences.This may be because of the small
samples in their study.Oguranti studied SFH in 581 unselected
Nigerian women; their values were also lower than European
standards pre-term, but reached similar values at term. These
differences in SFH standards among ethnic groups may arise from the
well-known differences in birth weights, but could also be due to other
factors such as maternal body build and prevalence of fetal pathology.
5.3.6 Clinical performance of SFH measurements.
The definitions of 'positive for SGA' by SFH measurements differ in
the literature. The populations tested also differ, some being high-risk,
hence artificially increasing the detection rate. In most cases at least
two or three consecutive readings have to be below the 10th centile.
Theoretically, increasing the number of abnormal measurements in
order to diagnose SGA should reduce the false positive rate. In a large
uncontrolled study of low risk, uncomplicated pregnancies Westin
(1977) in Sweden showed that SFH measurements were superior to
maternal weight gain, maternal girth measurements, and biochemical
analytes (uE3, HPL) for the detection of the SGA infant.The routine
introduction of reference SFH charts in the case notes of all their
patients was associated with a significantly steeper fall in the local
43
perinatal mortality rate compared with the overall Swedish statistics.
Pearce and Campbell (1987) compared serial SFH mesurements with a
single fetal abdominal circumference (FAC) obtained by ultrasound as
screening tests for SGA. No significant differences were noted
between the two, when specificities were set equal at 79%.
Interestingly, they found a peak sensitivity at 34 weeks, similar to
Quaranta's peak at 32 weeks. The only randomized controlled trial on
the clinical performance of SFH measurement versus clinical
palpation was reported by Lindhard and colleagues (1990), in a
population of 1639 women. No significant differences were found
between the two methods in terms of the detection rate of SGA,
number of interventions, additional diagnostic procedures or the
condition of the newborn.
Table 5.3 summarises the clinical performance of SFH measurements
in detecting SGA infants. Because fundal height standards and the
definitions of an abnormal SFH test vary, it is not possible to pool
results in order to arrive at average values.Persson and colleagues
(1986) summarise positive predictive values for various studies
including their own, which is the largest. They range from 13% to
79%; there is a tendency for larger studies to show lower PPV's. This
is consistent with the hypothesis that the larger the number of
observers, the greater is the effect of inter-observer variability and
hence the poorer the tests' performance.
5.4 Discussion
If , as one would expect, antenatal detection of IUGR improves
neonatal outcome, then an effective screening strategy for growth
disturbances is a major target in perinatal medicine. That randomised
studies have not been able to document a definite improvement in
outcome following routine ultrasound examinations may be due to a
number of factors. To some extent this is likely to reflect the limited
44
accuracy of ultrasound in estimating fetal size; in none of the studies
was fetal weight rather than the individual biometric parameters
plotted. Another factor is the threshold for clinical intervention. In
Newnham's study, the induction rates of the screened women did not
differ significantly from the regular group even though the former
were significantly more likely to be given the diagnosis of IUGR. This
suggests some reluctance on the part of the clinicians to act on the
basis of the ultrasound findings. Not to be discounted is the lack of an
appropriate standard for detecting deviations in growth velocity.
The concept that fetal growth could be monitored by such a simple
and inexpensive tool as a tape measure has generated wide
interest.The lack of agreement in the literature on the efficacy of SFH
measurements is not surprising, given the wide differences in
definitions and population sampling. The fact that the median 40-week
values for different ethnic groups reflect their differences in mean
birth weights provides additional support to the notion that SFH
measurements are an indicator of fetal size.
At least three studies compared traditional clinical palpation with SFH
measurements for the detection of SGA fetuses. Secher and colleagues
(1990) found no significant differences betweeen these two methods.
Similar results were obtained by Pschera and colleagues (1984), and
by Lindhard and colleagues (1990). This may be due to the clinicians’
longer experience with clinical palpation as opposed to the newer SFH
measurement, and hence the results may have been biased by this
factor.
There is no good evidence that introduction of routine SFH
measurements leads to a reduction in perinatal mortality rates. The
improved figures reported by Westin may well have been a chance
result, since this study was not properly controlled.
In view of the magnitude of the error due to inter-observer variability,
it is likely that SFH measurements are clinically more useful when
45
performed serially and frequently by the same observer using a
consistent technique.
There is some evidence to suggest that test performance for SFH may
be optimal around 32-34 weeks, and it is of interest that this coincides
with the period of peak performance for ultrasonic fetal weight
estimation of 32 to 36 weeks (Hedriana & Moore, 1994). If ultrasound
growth screening is to be performed as a one-stage routine, then this
gestational age interval offers the best hope for success. It could also
be possible to improve the accuracy of ultrasonic fetal weight estimate
by combining it with the SFH; this approach was described by Farmer
and colleagues (1992), who, in addition toultrasound data and the SFH
also included maternal characteristics such as height and parity. They
developed a trained neural network which, in the case of suspected
macrosomia, was significantly more accurate in estimating fetal
weight than either Hadlock’s or Shepard’s formula; its mean
percentage error was 4.7% with a standard deviation of 3.9%.
Accurate fetal weight estimation is the key to an effective screening
program for growth disturbances. This is an area that continues to
evolve, and improvements may be brought about by advanced
information processing techniques, using current clinical
measurements.
46
Table 5.1 Criteria for Population Selection of SFH Standards
Study Population selection criteria for
derivation of standard.
Quaranta Birth weight between 25th and 90th
centiles
Belizan Birth weight between 10th and 90th
centiles
Westin Mean birth weight +/- 1 SD
Calvert Birth weight between 10th and 90th
centiles
Pearce Birth weight between 10th and 90th
centiles
Grover Birth weight within mean +/- 1sd
Mathai Term delivery of live infant
Rosenberg Birth weight between 25th and 90th
centiles
Ogunranti All patients sure of their dates.
Persson Infant weight/length ratio between 10th
and 90th centile
47
Table 5.2 Ethnic variation in SFH standards.
Study Ethnic Group Sample size 40-week value
Quaranta European 138 36.5
Belizan Latin American 139 34.5
Westin European 428 36
Calvert European 381 36
Pearce European 699 37
Persson European 1350 36
Ogunranti Afro-caribbean 581 39.4
Grover Indian 200 33
Mathai Indian 250 33.8
48
Table 5.3 Clinical performance of SFH measurements.
Study Definition of abnormal result FP sens spec
Quaranta 2 cons or 3 isolated vals <10th cent 21 73 80
Belizan 1 single val <10th 21 86 90
Westin 1 single val<2cm below median or
3 cons static or decreasing vals
54
75
64
Lindhard As above 41 28 97
Calvert 1 single val<2cm below median or
3 cons static or decreasing vals
80
76
60
Pearce 1 single val below 10th centile 64 76 79
Grover 1 single val < 1sd below mean 16 81 94
Mathai 1 single val < 1sd below mean 23 78 88
Rosenberg 20% of measurements below
10th centile
21
62
85
Cnattingius 'catch-up and low' SFH growth NA 79 92
Persson outside 2sd's NA 27 88
49
6. PRINCIPLES OF THE CUSTOMISED GROWTH CHART.
6.1 Introduction
To produce an adjustable standard that takes into account the
physiological factors influencing fetal weight is a computational task
that is not easily performed by using tables and graphs. The logical
solution to this problem is computer software.
The principles of a computer-generated growth standard which carries
out this task were first published in the Lancet in 1992 by J.Gardosi,
Professor A.Chang and colleagues.Unlike previous attempts that relied
on fixed correction factors from tables applied to birth weight
standards, the calculations were performed by computer software and
growth charts could be displayed on screen and printed. Ultrasound-
derived fetal growth standards rather than birthweight standards were
used for generating growth curves, and corrections factors that
included maternal weight at booking, maternal height, parity, ethnic
group and fetal sex were scaled up or down depending on the
gestational age.
6.2 The prediction of normal growth potential
The initial obstetric database consisted of 4179 pregnancies with
ultrasound-confirmed dates. Multiple regression analysis showed that
in addition to gestation and sex, maternal weight at booking, height ,
ethnic group and parity were factors that significantly affected birth
weight. This was confirmed by analysis of variance.The multiple
regression analysis was repeated by Mr Mark Wilcox on a much larger
sample of 38114 cases, smoking being entered as an independent
variable.Continuous variables such as gestation, height and weight
were centered around their means so as to minimize computational
50
problems. The details of the analysis have been published elsewhere
(Gardosi et al, 1994), and the coefficients are given in table 3.1. This
regression model allows us to estimate the fetal weight at 40 weeks for
any combination of maternal characteristics. In the prediction of
normal birthweight, the confounding effect of smoking is dealt with by
entering the non-smoking coefficient for all cases.The model can only
explain 31% of the variability of birth weight in the database, but this
is likely to be an underestimate, since up to 55% of records in
obstetric databases may have at least one error (Dombrowski, 1994);
the East Midlands Obstetric Database is not subject to rigorous quality
controls.
6.3 The generation of normal fetal growth curves
The method of generating growth curves relies on the working
hypothesis that, for normally grown fetuses, the morphology of the
growth curves is approximately the same irrespective of birth weight.
This means that if the mean curves from population subgroups are
described in terms of a polynomial function of gestation, division of
the polynomial coefficients by the 40-week weight will yield a new
function whose coefficients will not vary appreciably between
subgroups. Some indirect support for this postulate comes from the
work by Dunn (1989) and Thomson (1968).
In chapter 4 we reviewed the literature on ultrasound-derived fetal
weight growth curves and for each mean curve we derived a
'proportional' curve, by dividing the coefficients of the original by its
predicted 40 week weight . An average growth curve was produced by
taking the arithmetic mean of the respective coefficients; this function
will estimate the percentage of term weight for any gestation.
Multiplying this function by the predicted 40-week weight obtained
from the regression model will yield an individual 'ideal' antenatal
growth curve. The 10th and 90th centile reference curves are derived
51
from the standard error of the regression analysis, and are adjusted at
each gestation so that the ratio of the standard error to fetal weight
(coefficient of variation) remains constant at 11%.
Some paired examples of the charts are shown in figures 6.1 to 6.4.
Mrs Average (fig 6.1) is a European woman of average height and
weight (163 cm and 63.4 Kg) who has had a previous delivery of a
male infant weighing 2700 g at 37 weeks. The value within the square
(9) is the centile value of this weight for 37 weeks. The expected birth
weight at 40 weeks is just over 3800 grams. Figure 6.2 shows a
woman of the same parity and size but of Indo-Pakistani origins; the
term birth weight expectation is reduced to 3600 grams, but the
previous delivery is not classified as SGA (centile 19). The chart of a
large European lady with the same obstetric history is shown in figure
6.3; the previous birth weight is given a centile value of 4. In contrast,
a short and light lady with the same history would be given a centile
value of 24 (figure 6.4).
6.4 Other functions
The early versions of the customised growth charts also allowed the
entry of fundal height measurements by including a fundal-height y-
axis on the right side. This was calibrated to approximate the standard
published by Pearce and Campbell (1987).
An axis for the fetal abdominal circumference was also displayed,
based on the standard of Deter and colleagues (1982). Previous
deliveries and their birth weight centiles may be entered and displayed
on the same chart.
The x-axis displays gestation as exact weeks and also the calculated
corresponding dates.
The expected date of delivery, maternal height and weight, parity and
ethnic origin are displayed on the top left hand corner of the chart. In
the latest version of the chart, the maternal body mass index is
52
displayed if this falls below the 10th centile for our pregnant
population at booking, as this indicates the possibility of malnutrition
in the periconceptional period.
6.5 Clinical performance
The initial sample of 4179 deliveries contained 385 cases with a birth
weight below the 10th centile by unadjusted criteria (SGA). Of these,
only 278 were still below the 10th centile following adjustment for
maternal characteristics. Hence 107 (28%) would have been given a
false positive diagnosis of SGA by conventional standard. Adjustment
90 cases that would have been missed by conventional assessment.
Babies that by the conventional standard only were deemed SGA had
significantly fewer instances of low Apgar scores.
6.6 Discussion
It is apparent from the foregoing that this method of producing an
adjustable growth standard relies on many hypotheses on the
physiology of fetal growth. Yet these are necessary if a model is to be
developed. These may be summarised as follows:
1. The physiological variables affecting fetal weight at term are also
effective in the antenatal period in proportion to the fetal weight.
2. The intrinsic shape of the normal fetal growth curve is the same for
all subgroups, differing only by a scalar, or 'magnification' factor
proportional to the predicted term weight. This postulate will be
referred to as the ‘proportionality ‘ principle.
3. The average fetal growth curve entered in the program is close to
the true population average.
53
4. The distribution of fetal weights is approximately normal for all
gestations.
5. The variance of fetal weights is a constant fraction of the gestational
median , ie a constant coefficient of variation of 11% (derived from
term birth weights).
6. The selected variables of maternal weight, height, parity and ethnic
group have mainly a physiological rather than pathological
significance.
A major problem is to separate physiological from pathological effects
e.g. to what extent is a low maternal weight at booking due to
nutritional factors as opposed to constitutional factors.
In view of the known adverse effects of malnutrition in early
pregnancy on fetal growth, measures are needed to prevent the
application of unduly small adjustments for maternal weight in cases
where the low values are due to undernutrition at booking. While this
is an infrequent problem in western populations, this is not the case in
the developing world. To deal with this issue, the current version of
the customised growth chart calculates the body mass index (BMI) at
booking; if this is below the 10th centile the maternal weight at
booking is corrected so that the BMI is at the 10th centile. The
birthweight expectation is thus the one for a normally nourished
individual at the lower end of the normal range. A similar algorithm is
applied at the 90th centile of the BMI.
The rationale for making adjustments on the basis of parity is an issue
open to debate. The primigravid state, although 'natural', would be a
relatively infrequent finding in a female population of reproductive
age unaffected by contraceptive practices, as the statistics from a
54
century ago show. Goldenberg and colleagues (1989) stated that the
genetic potential for fetal growth in primigravidas and multigravidas is
identical and that the differences noted between these two groups are
attributable to growth-restricting factors operating in primigravidas.
Henced they advocated a parity-independent standard derived from a
population of mixed parity. On the other hand, Thomson and
colleagues (1968) argued in favour of adjusting for parity, believing
that the effect of parity is a physiological factor present in the fetal
environment.
The effects of smoking on birthweight are relatively large. An
alternative method to obtaining regression coefficients applicable to
the whole population would be to include non-smokers only. But this
process could theoretically lead to the selection of a genetically
'supra-normal' population, and thus adjustment factors that may not be
universally applicable.
More robust estimates of the coefficients for the different ethnic
groups would have required a much larger sample . We did not have
sufficient numbers of Far Eastern women to separate them from the
heterogenous 'others' grouping, and hence we do not have a reliable
coefficient for these.
In view of the known pathological effects of malnutrition on fetal
growth, measures are needed to prevent the application of unduly
small adjustments for maternal weight in cases where the low values
are due to undernutrition at booking. While this is an infrequent
problem in western populations, this is not the case in the developing
world.
In the current version of the customised growth program, a lower limit
is entered for the weight adjustment. If the maternal booking weight is
below this limit, the adjustment does not decrease; the birthweight
expectation is thus the one for a normally nourished individual at the
lower end of the normal range.
55
Large databases containing maternal characteristics, accurate antenatal
fetal weight estimates and birth weights would be needed to address
these issues.
56
Table 6.1 Multiple regression coefficients for the prediction of normal growth potential . CONSTANT 3409.853 STANDARD ERROR 389.032 ADJUSTED R SQUARE 0.31824 NUMBER OF CASES 38114 Coefficient GESTATION (from 280 days) Gest 1 20.667 Gest 2 -0.21289 Gest 3 -0.000167 SEX Male
- 116.871
Female -233.742 MATERNAL HEIGHT (from 162 cm)
7.764
BOOKING WEIGHT (from 64.3 kg)
Weight 1 8.676 Weight 2 -0.11740 Weight 3 0.000716 ETHNIC GROUP European 31.670 Indian Sub-cont. -154.263 Afro-Caribbean -95.789 Other -33.446 PARITY Para 0 4.898 Para 1 112.904 Para 2 153.458 Para 3 154.767 Para 4 154.690 SMOKING Non-smoker 31.9160 Smokes 1-10 -120.602 11-20 -182.568 > 20 -214.112
57
7. METHODS OF GESTATIONAL AGE ESTIMATION
7.1 Introduction
An accurate assessment of gestation is crucial in the development of
fetal growth standards. It is also of importance in evaluating most of
the variables in current use for fetal monitoring and in the
management of post-term pregnancy.
Gestational age may be estimated ultrasonically by measuring fetal
parameters such as the crown-rump length (Robinson & Fleming,
1975) until 12 weeks and the biparietal diameter (Campbell &
Newman, 1971) until about 20 weeks gestation. Measurement of the
BPD in the mid-trimester has been shown to be 5% more accurate in
predicting the date of delivery than impeccable dates (Pearce and
Campbell,1983).
In practice, most ultrasound departments follow either the '10-day rule'
or the '7-day rule', whereby preference is given to menstrual dates if
these are within 7 or 10 days of the ultrasound estimate by the BPD.
Here we study the reliability of these methods by analysing the East
Midlands Obstetric Database.
7.2 Materials and Methods
The computerised obstetric records of three major maternity units in
the East Midlands (City and University Hospitals in Nottingham and
Derby City Hospital) date from 1986. So far more than 60000 cases
are available for analysis. A significant drawback is that the database
does not allow the reliable exclusion of induced labour. Multiple
pregnancies, stillbirths, congenitally abnormal babies, late bookers
(over 24 weeks), preterm deliveries (<37 weeks) and those with
unknown menstrual dates were excluded. A total of 31747 cases with
both menstrual and ultrasound data were retrieved for analysis.
Gestational age at delivery was calculated in days from 1. The
58
biparietal diameter according to the dating charts by Campbell (1971)
if over 13 weeks at booking, otherwise the crown-rump length was
used (Robinson & Fleming,1975) and 2. The last menstrual period.
The error in predicting the EDD was calculated in days for three
different dating methods: ultrasound data only, the 7-day rule and the
10-day rule as follows:
Error (days) = estimated gestational age at delivery - 280
These were expressed as signed and as absolute values. Statistical
analyses were performed with the 'SPSS for Windows' statistical
package.
7.3 Results
Table 7.1 shows the means, standard deviations and skewness of the
errors for each method. The distribution of the error is weakly but
significantly skewed to the left. Analysis of the signed errors suggests
that ultrasound on its own and the 7-day rule tend to underestimate the
EDD, whereas the 10-day rule overestimates it. Because of the
significant skewness of the data, the differences between the methods
were evaluated non-parametrically using Wilcoxon Matched -Pairs-
Signed-Ranks Test; the results are shown in table 7.2 . The mean and
the standard deviation of the absolute error using ultrasound alone is
slightly smaller than the other methods. Dating by ultrasound only is
significantly more accurate in predicting the EDD than either the 7-
day or 10-day rules.
7.4 Discussion
The length of gestation has traditionally been calculated from the first
day of the last menstrual period using Naegele's rule -i.e., by adding 7
days and 9 months to the date of the last menstrual period. Although
this formula has been attributed to Franz Karl Naegele (1778-1851), it
was first proposed by professor Herman Boerhaave (1709) at the
59
University of Leyden (Speert, 1958). The formula implies a mean
duration of pregnancy of 274 days from the LMP, which is at variance
with the observed value of 280 days (Doring,1962). If the dates were
not 'reliable', gestation was estimated by clinical palpation. There is
mounting evidence that even among women with 'reliable' menstrual
dates, considerable error may arise in the calculation of gestation. This
is because the onset of ovulation within the menstrual cycle is
relatively erratic, particularly in younger women (Geirsson,1991), and
may also vary from cycle to cycle. We have studied the error of
menstrual dates using the BPD-derived gestation as the reference
standard, in a database of 31561 cases (Gardosi & Mongelli, 1993).
For 21.5% of pregnancies ultrasound scan dates were outside plus or
minus 7 days from the dates based on menstrual dates. Furthermore,
the distribution of the error associated with menstrual dates was
significantly skewed, such that the 95% confidence interval for
gestational age derived from menstrual history was -27 to +9 days.
In our unit the ultrasound department calculates the EDD at booking
by using the 10-day rule if reliable menstrual dates are available,
otherwise the BPD or the FL is employed. About 6% of all cases are
induced for post-maturity on this basis, but these cannot be identified
reliably from the computerised records. Therefore one would expect
that this iatrogenic interference on the normal duration of pregnancy
would bias the statistics in favour of the 10-day rule. However, in spite
of this bias, we found that dating by ultrasound alone appears the best
method for predicting the EDD; bearing in mind the direction of the
bias, it is likely that the advantage of using ultrasound only may be
greater than what our figures indicate.
It has been suggested that the use of the BPD alone in estimating
gestational age is flawed, in that the larger babies would be assigned a
longer gestation than the smaller babies (Henriksen & Wilcox, 1994).
Persson and colleagues (1978) did find a relationship between birth
60
weight centiles and the size of the BPD in early pregnancy, but the
difference between the small (<10th centile) and large (>90th centile)
babies amounted to only about 1.5 mm at 18 weeks. This difference is
equivalent to less than one day's variation in gestational age by
Campbell's dating standard. We recently investigated this issue using a
database of 19 singleton pregnancies precisely dated from in-vitro
fertilisation or artificial insemination. No significant correlation was
found between the BPD centiles at the booking ultrasound
examination and the birthweight centiles (Gardosi et al, 1994).
In this study we assumed that the modal length of normal pregnancy of
280 days is equally applicable for all population subgroups. Only a
limited number of studies have been published on this issue, all of
them using menstrual dates. They report trivial differences in duration
of pregnancy between social classes or ethnic groups (Butler &
Bonham, 1963; Henderson, 1967). There is also some evidence that
BPD standards do not vary appreciably between ethnic groups (Vialet
et al, 1988; Simmons et al, 1985), and thus this should not be an
important source of bias.
Our findings do not support the use of the 7-day or 10-day rule in the
assignment of gestational age when valid ultrasound measurements are
available. Although the error associated with their use may not matter
in clinical practice (Mongelli & Gardosi, 1994), these methods are
best avoided in defining normal standards in pregnancy.
61
Table 7.1 Descriptive statistics for the errors resulting from each
dating method.Values expressed in days (N = 31747).
Variable Mean
SD Skew S.E.
Skew
Min Max
Signed Errors
Ultrasound
only
-0.72 8.50 -0.12 0.01 -20 28
7-day rule -0.20 8.65 -0.15 0.01 -27 28
10-day rule 0.18 8.79 -0.16 0.01 -30 30
Absolute Errors
Ultrasound
only
6.91 5.00 0.69 0.01 0 28
7-day rule 7.02 5.06 0.70 0.01 0 28
10-day rule 7.15 5.12 0.73 0.01 0 30
62
Table 7.2 Significance of differences in absolute errors between
dating methods. Wilcoxon Matched -Pairs Signed Ranks Test.
Pairs: A Vs B Mean
Rank
A>B A<B Ties
A=B
Z-value 2-tailed P
value
Ultrasound only
Vs 7-day rule
8443.96
8782.91
8251
8989 14507 -7.1 <0.00005
Ultrasound only
Vs 10-day rule
9493.64
10137.31
9231 10438 12078 -11.4 <0.00005
10-day rule
Vs 7-day rule
1265.5
1140.34
1449 980 29318 -10.4 <0.00005
63
8. FORWARD PROJECTION OF FETAL WEIGHT ESTIMATE
The proportionality principle applied to dynamic fetal weight
estimation.
8.1 Introduction.
Fetal weight estimation from ultrasound parameters or other methods
is almost invariably made from static measurements. In practice,
delivery often does not occur until days or weeks after the ultrasound
examination, and the weight estimation is not corrected for this time
interval. This problem was appreciated by Spinnato and colleagues
(1988) who published a series of dynamic weight estimation formulae
to allow forward projection of the fetal weight estimate to the
expected date of delivery.
Here we present an alternative method that, in addition to forward
weight projection, should also allow for backward fetal weight
interpolation from the birth weight.
8.2 Subjects and Methods.
In order to compare our method with Spinnato’s method, we selected
only those cases who delivered within 35 days of the last ultrasound
examination. Two hundred forty two cases were available for analysis;
these were liveborn infants, inclusive of adverse outcomes and
congenital malformations. The forward projection equation was
derived from the principle that the shape of the fetal growth curve is
similar for all cases irrespective of the birth weight. It is in fact a form
of proportional extrapolation, which we will refer to as the ‘prop-ex’
method. Hadlock’s growth formula was selected because: 1. The
weight estimation formulae considered were also developed by
Hadlock and colleagues and 2. This growth formula is close to the
average of previously published growth formulae (Gardosi et al,
1994). If Hadlock is the growth function, EFW the ultrasound fetal
64
weight estimation, u is the gestational age at ultrasound examination
and d is the gestational age at delivery, the following relationship
applies:
EFWu : EFWd = Hadlock(u) : Hadlock(d)
Therefore:
Projected fetal weight at delivery:
EFWd = EFWu * Hadlock(d)/ Hadlock(u)
Likewise, Spinnato’s equivalent dynamic weight equation for the
Hadlock weight formulae is:
Log (EFWd) = 1.0009 * log (EFWu ) +0.0043(d -u)
Both techniques were applied to our sample. Prediction errors were
calculated both as signed and as absolute percentage errors of the birth
weight (BWT) as follows:
Percentage error = 100* (EFWd -BWT)/BWT
Trends in the error in relationship to birth weight were examined using
Spearman’s rank correlation coefficient. Differences between the two
methods were tested by Wilcoxon’s matched pairs signed-ranks test.
8.3 Results
The signed and absolute percentage errors for the two methods are
shown in Table 8.1. Table 8.2 displays the statistical significance of
the differences between these two methods.
The errors from using Spinnato’s method were not significantly
correlated with true weight ( R = -0.0880, P>0.05), whereas with our
65
method there was a weak but statistically significant inverse
correlation with the true weight (R= -0.23, P<0.0005). This correlation
was not significant if birth weights below 3200g were excluded.
For both techniques, there was no significant correlation between
either the signed or absolute errors and the lag-time interval.
8.4 Discussion
These statistics show that the proportional extrapolation method we
describe for birth weight prediction from remote ultrasonographic
examination is significantly better than Spinnato’s technique in our
population. This may be because the latter uses the lag-time difference
between ultrasound examination and delivery, without considering the
actual values of the gestational ages of these events.
The random errors described in Spinnato’s original paper are slightly
lower than when his method was applied to our population (11 vs 12
%), with a tendency towards underestimation as opposed to
overestimation in our sample.
Two other advantages of the prop-ex technique are that: 1. It is
applicable to any fetal weight estimation technique, whereas
Spinnato’s method requires different equations for different weight
estimating formulae. 2. It can be modified to allow for retrospective
fetal weight estimation from the birth weight by interpolation as
follows:
IFWu = BWT* Hadlock(u)/ Hadlock(d)
where IFWu is the interpolated fetal weight. This formula, however,
cannot be validated without an independent, highly accurate method of
fetal weight estimation such as NMR.
The results of these studies validate the concept of incorporating a
lapse-time factor in equations to predict the birth weight from remote
66
ultrasonographic data. As the errors are not correlated with the lag-
time interval, both methods are accurate throughout the range of time
intervals measured. It is likely that accuracy will be lost in cases
affected by growth disturbances, and this is reflected in the negative
correlation between the signed errors and the birth weight, which is
lost for birth weights over 3200 grams.
These findings also support the ‘proportionality’principle - a key
aspect of the customised growth chart program- which is the
assumption that fetal growth in normal populations is essentially the
same, once growth is expressed in terms independent of the actual
birth weight.
67
Table 8.1.Signed and absolute percentage errors for projected fetal weight estimation. Formula Systematic Error (%) Random Error (%) Prop-ex 6.01 11.0 Spinnato 8.56 12.0 Mean Absolute Error (%) Standard Deviation (%) Prop-ex 9.9 7.7 Spinnato 11.8 8.9 Table 8.2. Non-parametric comparison of the errors generated using the two methods on the same population. Wilcoxon Matched-Pairs Signed-Ranks Test.
Differences in Ranks Mean Rank No Cases
- Ranks (Spinnato < Prop-ex) 172.70 51 + Ranks (Spinnato > Prop-ex) 130.75 225 Ties (Spinnato = Prop-ex) 1
Total = 277
Z = -7.7646 2-Tailed P-value <0 .00005
68
9. SELECTION OF ULTRASONIC W EIGHT FORMULA
9.1 Introduction
The performance of ultrasound fetal weight estimation formulae may
have important implications for growth standards and antenatal
screening. In choosing the best formula, two main selection criteria
need to be satisfied:
(a) Minimal overall error.
(b) Minimal bias in the error in relation to fetal weight.
The first condition is self-evident. The significance of minimising
error trends in relation to fetal size has been somewhat underestimated
in the literature. If a particular formula has a strong tendency to
overestimate the small and underestimate the large fetus, both the
weight standard and screening performance may be adversely affected.
One may argue that fetal growth standards should be weight formula-
specific.If this was the case, then centres where a particular formula is
either not in use or is not suitable will not be able to use the standard.
In this study we examine the effect of different weight estimation
formulae on apparent growth kinetics, and their clinical performance
in our population is evaluated .
9.2 Patients and Methods
Persson and colleagues (1986) have shown that the average fetal
weight curve derived from the means of the ultrasound parameters for
each gestation is very similar to that derived from the means of the
individual weights. Hence, to illustrate the effect of different weight
formulae on growth curves, we studied the means published by Chitty
and Altman (1993) for individual ultrasound parameters. These were
input for the following weight estimation formulae:
- Hadlock (BPD, AC, FL)
69
- Hadlock (AC, FL)
- Warsof (BPD, AC)
- Shepard (BPD, AC)
- Campbell (AC)
- Modified Persson (BPD, AC, FL)
Persson's original formula used the abdominal diameter (AD); this was
modified by converting the AD into AC as follows:
log10 EFW (grams) =
0.972* log10 BPD + log10 (AC/3.1416) +0.367*log10 FL -2.646
The calculations were performed by computer software, and the
different growth curves for each formula displayed graphically using
Cricket Graph software.
In order to determine the best formula for the population in our study,
we selected those cases that delivered within 14 days of an ultrasound
examination. A sample of 129 cases was retrieved, from a total of 171
cases that had been seen one year before the completion of the growth
study. A correction for the interval growth between time of ultrasound
examination and delivery was made by projecting forward the
estimated fetal weight using Hadlock's fetal growth formula as
follows:
Predicted weight =
EFW * H(gestation at delivery)/H(gestation at scan)
where EFW is the ultrasound-estimated fetal weight and H is
Hadlock's fetal growth function as described in chapters 4 and 8.
Using the weight formulae listed above, the signed percentage error
for each case was computed thus:
Percentage error = (Predicted weight - Birth weight)/ Birth weight
70
A positive value indicates overestimation whereas a negative value
indicates underestimation of true fetal weight. The weight formulae
were then corrected for systematic error by multiplying them by a
correction factor as follows:
Correction factor = 1/(1 + systematic error)
where the systematic error is expressed in fractional terms.
Statistical analyses were performed with the SPSS for Windows
statistical package. The means, standard deviations, 95% confidence
limits and the distributions of the errors were computed. The trends
between error and true weight were expressed in terms of Pearson's
moment correlation coefficients.
9.3 Results
Figure 9.1 shows the fetal growth curves obtained for Chitty and
Altman's data using different fetal weight estimation formulae .
For our population, the mean and standard deviations of the errors for
each formula and the correlation of the error with the observed weight
are displayed in table 9.1. Table 9.2 shows the errors at the extremes
of birth weight. All the formulae tested on our population tend to
overestimate true weight, and this applies also to the extremes of fetal
weight (except for Campbell's and Persson's formulae for
weights>4000g). The errors were recalculated after applying the
correction factor for systematic error and the results are shown in
table 9.3.
9.4 Discussion
For any given population, it is apparent from Figure 9.1 that from
about 36 weeks onwards the type of fetal weight formula can have a
marked effect on the apparent fetal growth kinetics, particularly at
term. Hence it is important to select the weight formula that is most
71
suitable for the operator and the population under study. This could
explain some of the variability in the fetal growth kinetics exhibited by
previously published standards, which also appears greatest after 36
weeks.
In our sample all of the formulae studied tended to overestimate the
true fetal weight. The error associated with the use of Hadlock's
formula for BPD, AC and FL was not correlated with the true weight,
whereas all of the other formulae produced errors that were
significantly correlated. Robson and colleagues (1993) also found a
trend to overestimate true fetal weight. They noted a significant
correlation of the error with birth weight for all the formulae they
tested, but these did not include the Hadlock formula included in our
study. This systematic overestimation may arise from differences in
ethnicity between North American and British populations, or in
techniques and equipment. After applying the correction factor for
overestimation, Hadlock's formula for the BPD, AC and FL showed
the smallest SD of the error, and this was the formula selected for our
study. If the BPD could not be measured because of suboptimal
visualisation, the formula used was Hadlock's for the AC and FL. The
modified weight formulae were then applied retrospectively to this
sample and prospectively to the remaining half of the population.
Our approach to selecting weight formulae and correcting for them
makes the assumption that the errors observed at term are similar to
those obtained in the preterm period. To verify this would require a
substantial number of infants born preterm who had had an ultrasound
examination. Another solution is to use echo-planar NMR for accurate
weight estimation in the preterm period and to compare this with the
ultrasonic weight estimates; but this is not as yet an economic
proposition.
72
Table 9.1 Errors of ultrasonic weight formulae in relation to birth
weight for cases delivering within 14 days of examination (N =129).
Formula
Systematic
Error (%)
SD
of Error
Skew
Correlation
(R) with
birth weight
P-value
of R
Persson * 0.43 10.79 0.11 -0.2145 0.015
Campbell 0.89 11.85 0.06 -0.4226 0.000
Hadlock
(AC, FL)
3.23
11.52
0.2
-0.1246
0.159
Warsof
(BPD,AC)
3.78
11.76
0.08
-0.2299
0.009
Hadlock
(BPD,AC,FL)
5.32
1.21
.17
-0.1230
0.165
Shepard
(BPD,AC)
9.24
12.31
0.09
-0.2442
0.005
* original formula using the AD modified to include the AC as follows:
log EFW = 0.972* log BPD + log (AC/3.1416) +0.367*log FL 2.646
73
Table 9.2 Error of ultrasonic weight formulae at the extremes of birth
weight (all cases).
Formula
Birthweight
> 4000g (N = 37)
Mean Error (SD)
Birthweight
< 2500g (N = 17)
Mean Error (SD)
Persson -1.3 (10.36) 7.2 (12.5)
Campbell -3.2 (10.86) 14.0 (16.3)
Hadlock
(AC, FL)
0.9 (10.8)
6.4 (13.8)
Warsof 2.7 (11.8) 11.2 (12.2)
Hadlock
(BPD,AC,FL)
3.4 (10.4)
8.3 (13.2)
Shepard 7.9 (12.3) 17.4 (12.8)
74
Table 9.3 Error of ultrasonic weight formulae for cases delivering
within 14 days of examination after correction for systematic error
(N =129).
Formula Correction
factor
Mean
Error (%)
SD
of Error
Skew
Persson 0.9957 0.00 10.7 0.11
Campbell 0.9911 -0.01 11.7 0.06
Hadlock
(AC, FL)
0.9687
0.00
11.2
0.2
Warsof 0.9636 0.01 11.3 0.08
Hadlock
(BPD,AC,FL)
0.9495
0.00
10.6
0.17
Shepard 0.9154 0.01 11.3 0.09
75
Figure 9.1. Fetal weight curves derived from data by Chitty & Altman.
This is an illustration of how different weight estimation formulae can affect the
kinetics of the resultant fetal growth curve. The curves were obtained from the data
by Chitty and Altman (1994) on the BPD, AC and FL, transformed into fetal weight
according to the weight estimation formulae by Hadlock, Persson, Shepard, Warsof
and Campbell.
76
10. ULTRASONIC STUDY OF FETAL GROWTH: PATIENTS
AND METHODS.
10.1 Introduction
It is apparent from reviewing the literature that considerable
uncertainty exists on fairly basic aspects of fetal growth. Published
ultrasound-based fetal growth standards vary widely, and little is
known on ultrasound-defined fetal growth patterns in human sub-
populations. The analysis of intrauterine fetal weight gain using
ultrasound requires fairly complex techniques, and these are to some
extent arbitrary. Much of our work attempts to address these issues, as
these are at least as important as establishing the clinical validity of
customised growth charts.
10.2 Study Design
Ethics Committee approval was obtained prior to commencing the
study. We aimed to obtain a population sample suitable for the
development of normal standards and to study fetal growth in relation
to maternal characteristics. Inclusion criteria for the study were:
- Singleton pregnancy.
- Maternal age no greater than 35 years.
- Gestational age at booking no greater than 22 weeks.
- 'Low risk' pregnancy at booking.
Smokers and cases who developed pregnancy complications were not
excluded, as long as neonatal outcome was normal on clinical grounds
(see 10.6).
Suitable cases were recruited in the antenatal clinic following their
booking ultrasound examination. Patients were given information
sheets and informed consent was obtained in writing. Women were
examined at intervals of 2-3 weeks commencing at 24 to 32 weeks, for
a maximum of 4 examinations (excluding the booking examination).
77
This schedule allowed us to obtain ultrasound data close to delivery
and in the post-term period. A total of 352 women were recruited. One
patient moved out of the district and delivered elsewhere; delivery
details could not be obtained. Twenty-one cases (6%) did not attend
any planned visits following recruitment; these were excluded from
the analysis. The remaining 325 cases underwent at least one
ultrasound examination. The distribution of the number of
examinations (in addition to the booking ultrasound) is shown in Fig
10.1; 46% attended 4 examinations, 29% attended 3, and the
remainder one or two examinations. In addition to the booking
ultrasound scan, a total of 1021 ultrasound examinations were
performed.
Patients whose fetal growth curves or symphysis-fundus heights were
a cause for concern were referred to their Consultants, but not
excluded unless the neonatal outcome was abnormal.
10.3 Population characteristics
The maternal and neonatal characteristics are summarised in tables
10.1 and 10.2 respectively. As one would expect from the inclusion
criteria, there is a higher proportion of primiparae and a lower
percentage of smokers than in the general population; there are slightly
higher proportions of Europeans and Indo-Pakistanis.
The mean birth weight is about 100g higher than the population
average, and this is explained by the lower preterm delivery rate (5.8%
Vs 7.2%) and lower percentage of smokers.
10.4 Equipment and Methods
Ultrasound examinations were performed using either a Kontron
Sigma 1AC or a Corometrics Aloka 500 with a curvilinear array.
Calculations of gestational length, estimated fetal weight and printing
78
of growth charts were carried out on IBM-compatible personal
computers, using especially designed computer programs.
Measurements of the biparietal diameter, femur length, and abdominal
circumference were taken using standard techniques (Chudleigh &
Pearce, 1986) by one of two experienced operators (JM and AD). The
fundal height was measured in cm with a flexible tape, from the top of
the fundus to the upper border of the symphysis pubis along the
longitudinal axis of the uterus; care was taken to ensure that the
bladder was emptied. Nearly all of these measurements were taken by
one observer (JM).
Maternal height was measured in cm with stadiometers, weights at
booking were measured in Kg using standard scales .Gestation was
calculated in days from the fetal biparietal diameter according to
Campbell's dating chart, using specially-designed computer software.
10.5 Inter-observer and intra-observer variability.
Possible bias in the ultrasound measurements arising from inter-
observer variability was studied in a subset of 12 cases. Measurements
of the BPD and FL were in agreement within +/- 1 mm and thus were
not examined further. The AC is closely related to EFW, and this was
studied in more detail. Twelve randomly selected cases were measured
by both observers. Differences between the paired readings were
tested using the Wilcoxon Matched-Pairs Signed-Ranks Test. No
significant difference between the observers could be detected (Z = -
0.6276, 2-Tailed P = 0.5303). The intra-observer variability was
estimated by measuring the AC of the same baby twice, for 10 cases.
In the case of observer AD, the differences between the two readings
ranged from -1.1 mm to 1.9 mm, the mean being 0.52 (SEM 0.383)
and SD of 1.21, with a normal distribution.
79
10.6 Measures of adverse neonatal outcome
Normal neonatal outcome was defined by the absence of all of the
following:
(a) Congenital abnormalities.
(b) Admission to the Neonatal Intensive Care Unit.
(c) Umbilical cord pH < 7.20.
(d) Umbilical cord base excess < -8.
(e) Apgar score at 5 minutes < 7.
(f) Preterm delivery (<259 days).
Two hundred and eighty-three neonates satisfied these conditions. We
did not use neonatal anthropometric measurements because of their
questionable value, as will be discussed below.
10.7 Calculation of customised and unadjusted fetal and birth weight
centiles.
These were calculated in batches using computer software compiled in
Turbo-Pascal.
Birth weight and fetal weights were entered in grams; gestation was
reckoned in days according to early ultrasound measurement of the
biparietal diameter. For the calculation of unadjusted z-scores and
centiles, the average ‘proportionality curve’ described in chapter 4 was
fitted through the Nottingham birthweight mean of 3443.5g at 280
days and the standard deviation for each gestation was calculated as
11% of the median weight for that gestation. The methods outlined in
chapter 6 were used to calculate the customised centiles and z-scores.
10.7 Discussion
This was essentially an observational study analysed retrospectively.
Its aim was to investigate some basic aspects of fetal growth in the
80
second half of pregnancy and to validate at least some of the principles
on which the customised antenatal growth chart is based.
There is far too much uncertainty on defining normal growth in order
to attempt to draw a valid protocol suitable for a controlled field trial .
This could be one of the reasons why no such trial has to date shown
that ultrasonic screening for fetal growth anomalies improves perinatal
outcome.
We did not exclude cases affected by pregnancy complications such as
pre-eclampsia as long as the condition of the neonate at birth was
normal. Hence mild cases of IUGR and macrosomia are probably
included in the sample, since we believe that there are no reliable and
sensitive techniques to identify them.
Morphometric indices such as the neonatal ponderal index, mid-arm to
head circumference ratio and skin-fold thickness have not been used in
this study.Although they have been employed extensively in
diagnosing IUGR, there is little evidence that they are superior to birth
weight (Chard et al 1992, 1993). On the contrary, the work of Roemer
and colleagues (1991) on over 5000 neonates showed that birth weight
centiles are more closely correlated with acid-base status at birth than
either the ponderal index of Rohrer or the birthweight to length ratio.
Likewise, Wolfe and colleagues (1990) found that the ponderal index
or the weight/length ratio can explain only 52% of the variance in
estimated neonatal body fat; multiple regression analysis of their
sample of 119 neonates showed that birth weight centile and
weight/length ratio were equally good predictors of skin-fold
thickness.
Another weakness of such indices is that they have been derived
from populations whose gestation has been estimated from menstrual
data (Oakley et al ,1977; Georgieff et al, 1988 ). Furthermore, the
measurement of neonatal length is subject to considerable error; the
inter-observer variability being greater than 1 cm in 40% of cases, and
81
the intra-observer variability greater than 1 cm in 15% of cases
(Rosenberg et al, 1992). This error will be cubed in the calculation of
the ponderal index.
Changes in growth velocity as assessed by serial ultrasound have also
been used as indicators of growth disturbances (Chang et al,1993), but
these are poorly related to outcome and in any case have not been
standardised. Deter and colleagues (1990) proposed a neonatal growth
assessment score derived from multiple neonatal measurements
including weight, crown-heel length, head, chest , abdominal and thigh
circumferences, and related to the ultrasonic Rossavik growth
coefficients for these parameters. However, their sample was small (37
infants), and the method has not found wide acceptance.
A case could be made for excluding smokers, as Ott (1988 ) did, but
this may introduce further bias in term of the socio-economic
composition of the population; about 37% already belonged to Class I
or Class II, and by excluding smokers this proportion would increase
significantly, and also reduce the total sample size.
Although a special effort was made to recruit women from non-
European ethnic groups, the final numbers were fairly close to the
overall population norms. These women were less likely to agree to
participate in the study, often because they had large families or could
not afford the extra time for the required additional clinic visits.
82
Table 10.1 Maternal characteristics. Study Group
N = 325 General Population*
Age (years), mean ( SD) 26.7 ( 4.8) 26.5 (5.3) Height (cm), mean ( SD) 163.6 (6.4) 162.3 (6.4) Weight (Kg), mean ( SD) 66.5 (11.8) 65.7 (12.5) Ethnic group (%): European Indo-Pakistani Afro-Caribbean Other
93.8 4.3 1.2 0.6
92.8 3.8 2.5 0.8
Socio-economic groups (%): Class I Class II Class III (M +N) Class IV Class V Unclassified
22 (6.8) 100 (30.8) 119 (36.6) 64 (19.7) 16 ( 4.9) 4 (1.2)
N/A
Parity: Percent of primiparae
49.5
43.8
Smoking: Percent of smokers at booking
16.6
27.0
* Data from Wilcox M, et al (1993b). Table 10.2. Newborn characteristics (N =325). Birth weight (g) mean (SD) range
3406 (553) 1000 - 4900
Sex: percent of males 50.2 Preterm delivery (%) 5.8 Developmental abnormalities (%) 1.5 Admission to neonatal ICU (%) 3.4 Acidosis at birth 3.4
83
11. AN ULTRASOUND STANDARD FOR FETAL WEIGHT
GAIN
11.1 Introduction
The considerable variation in the published literature on normal
intrauterine fetal weight gain as assessed by ultrasound examinations
has prompted us to derive a standard applicable to our local
population. Only one such standard has been published so far for the
United Kingdom (Gallivan et al, 1993), and this was derived from a
sample of 67 cases. Our collection of ultrasound data is larger than
any of the previous studies, enabling us to exclude cases with
abnormal outcome. The purpose of this study is to derive a standard
for fetal growth based on serial ultrasound observations of a normal
population, and to compare this with other norms.
11.2 Materials and Methods
Subjects
The analysis included women who were smokers and
pregnancies which developed complications at a later stage, but
excluded those pregnancies which had an abnormal neonatal outcome,
as defined in chapter 9. A total of 283 of the 352 pregnancies had a
normal outcome by these criteria. After excluding cases who delivered
elsewhere and those who did not attend for at least two ultrasound
scans (in addition to the booking scan) , 267 pregnancies were suitable
for analysis.
Estimation of fetal weight
Ultrasound equipment consisted of either a Kontron Sigma 1AC or a
Corometrics Aloka 500 with curvilinear array transducers. Ultrasound
fetal weight estimation was based on a modified Hadlock's formula
84
for fetal abdominal circumference, femur length and the biparietal
diameter as described in chapter 8.
Modelling of fetal growth
A minimum of 4 points were used to calculate the fetal growth
curve for each individual pregnancy. This included an 18 week fetal
weight value of 223g (Hadlock et al, 1991) which we used as an
invariant point for all cases; at least two EFWs during the third
trimester; and the birth weight. Fetal weight curves were individually
fitted using the least-squares method and a computer program was
written to allow graphical display.
Three growth models were considered:
(a) Simple 2nd or 3rd degree polynomial of gestation.
(b) Rossavik growth model.
(c) Logarithmic transformation of fetal weight expressed as a 2nd or
3rd degree polynomial of gestation.
Although the Rossavik model was slightly better at predicting
the birth weight than the others, the log polynomial model produced a
visually more accurate interpolation. By either method , in nearly all
cases the R-square value was greater than 0.99, as one would expect
by fitting a relatively small number of points. The log-polynomial
model was then applied to all cases thus:
ln(EFW) = a0 +a1 * GA + a2 * GA2 + a3 * GA3
where GA is the gestational age in days according to the booking
ultrasound.
The average curve was obtained by determining the median
weight for each day of gestation, excluding those cases that had
delivered, i.e. there was no extrapolation beyond the observed data.
The standard deviation and the skewness were also calculated for each
day. Functions describing the median curve and the SD were obtained
85
by weighted multiple regression, the number of undelivered cases for
each day acting as the weight.
The growth velocities for our standard and for older standards
were derived by taking the first derivative of the original growth
functions describing the mean curves.
11.3 Results
The medians, standard deviation and skewness for each week are
displayed in table 11.1. Figure 11.1 shows the average growth curve
and the 10th and 90th centiles.
The function describing this curve is as follows:
FWT = 3411.9469-337.82996*GA+9.44545*GA2-0.000000369939*GA6
where FWT is the weight in grams and GA is the gestational age in
exact weeks (e.g. 30.35 weeks).
The distribution of fetal weights was checked for each week of
gestation; although a trend towards positive skewness was noted, it
was not significantly different from normal as assessed by the
Kolgorov-Smirnov test ( p>0.2). The mean birth weight for those
cases delivering between day 273 and day 287 was 3462 g, which is in
close agreement with the predicted 40-week value of the standard
curve (3496g). The Nottingham mean curve is compared with
previously published standards in figure 11.2. The growth velocity
curve is shown in figure11.3, and compared with velocities of
published ultrasound standards in figure11.4.
The kinetics in terms of fractional growth may be derived from figure
11.5, and they are as follows:
G50 = 31 weeks 2 days;
P28 = 34%; P37 = 83%; P42=110%;
86
11.4 Discussion
In deriving this standard we, like all previous workers, made the tacit
assumption that fetal growth is monotonic. Growth spurts have been
documented in preterm infants treated in neonatal intensive care units
(Gairdner & Pearson, 1971), and it is quite possible that they may be
occurring in the intrauterine environment. To be able to detect these
would require a much greater number of observations, and also
considerably more accurate means of estimating both fetal weight and
gestational age than what is available from current technology.
Ours is the only study to our knowledge that combines birth weights
and ultrasound-estimated fetal weights in the derivation of a fetal
growth standard. This is a valid procedure, provided that any
systematic error in the weight estimation formula is corrected in order
to avoid artefactual accelerations or decelerations in the growth curve.
The main advantage of this method is to significantly increase the
range and accuracy of data points into the term and post-term period.
Many of the previously published standards do not give values beyond
40 weeks' gestation, because it is uncommon to perform ultrasound
examinations close to the day of delivery and also because of the small
number of cases recruited. Since the measurements at the booking
ultrasound examination did not allow fetal weight estimation and are
used for precise dating of the pregnancy, we added an invariant 18-
week fetal weight, based on the study by Hadlock and collegues
(1991). This is justified by the fact that the individual variation in size
at such early gestations is consistently small in absolute terms, with
standard deviations ranging from 18.5g (Ott,1988) to 33.5g (Persson
& Weldner,1986 ). The true variation may be even less if imprecision
due to gestational dating is also considered. Early differences in fetal
size, due to physiological or pathological reasons, will exist but unless
extreme, they are not likely to be detected by current techniques of
ultrasound assessment. The inclusion of a fixed 18 week weight point
87
stabilises the growth curve and also allows for accurate interpolation
of fetal weight at the earlier gestations.
There has been some discussion in the recent literature on what should
be the best method of deriving standards of fetal size. Altman (1994)
believes that the method should reflect the purpose of the chart;
longitudinal data should be used for assessment of growth, whereas
cross-sectional data is most suitable for assessment of size. He argues
that individual curve-fitting applied to longitudinal studies would lead
to unduly narrow variances, by 'smoothing ' fluctuations due to
measurement error or growth processes. As Piwoz and colleagues
(1992) pointed out, the resulting centile reference grid would thus be
narrower, and could have potential for misclassification of cases.
Analysis of the published ultrasound-derived fetal weight curves
suggests that this is not likely to lead to major differences. The two
cross-sectional standards of Ott and Hadlock had coefficients of
variation at term of 6.2% and 12.7% respectively, whereas the
longitudinal standards ranged from 9.3% to 19.4% (table 4.1). The
degree of observed variation in the published standards resulting from
population and methodological differences is likely to exceed the
differences that may result from choice of sampling method.
Another methodological issue is whether abnormal cases should be
included. Altman (1994), in describing a cross-sectional study,
believed they should be. We did not include them because their
possibly anomalous growth patterns may distort our standard which is
based on serial data.
On inspection of the graphs, the morphology of our fetal growth curve
appears similar to that of Hadlock and Gallivan, and also to the
average of previously published ultrasound growth curves, in that
there is only minimal deceleration ('flattening') at term. This is
supported by the indices of fractional growth (P50, G28, G37, G42),
which are very similar to these two standards. Our growth model is
88
identical to that used by Gallivan's study, but differs considerably from
Hadlock's cross-sectional study. The coefficient of variation
(SD/EFW) at 40 weeks is 11.6%, very close to Gallivan's 11.5%, but
slightly lower than Hadlock's 12.7% (see table 4.1) . The median fetal
weight values tend to be lower than other standards, and this may be
due to our use of a correction factor to allow for ultrasound
overestimation of fetal weight. It differs considerably from birth
weight standards based on menstrual dates, where the apparent
deceleration at term is more marked. In our local birth weight standard
based on ultrasound-dated pregnancies (Wilcox et al, 1993a), there is
a reversal in the direction of skewness from positive at term to
negative in the preterm period. In contrast, our ultrasound derived
standard shows positive skewness at all gestations except 42 weeks;
the negative value here is probably spurious, since the sample consists
of only 7 cases. The skewness is minimal at around 40 weeks, possibly
because of the stabilizing effect of the birth weight data; some of the
skewness may be due to ultrasound error. These differences between
the ultrasound-derived and the cross-sectional birthweight standards
lend further weight to the theory that a substantial number of preterm
deliveries are associated with intrauterine growth retardation.
89
Table 11.1 Nottingham ultrasound growth standard.
Week *
Sample
size
Median
(grams)
SD 10th
centile
90th
centile
Skewness
24 267 674 109 534 813 0.656
25 267 779 109 639 919 0.645
26 267 899 115 752 1046 0.614
27 267 1033 124 874 1192 0.575
28 267 1180 138 1003 1356 0.539
29 267 1338 155 1140 1537 0.520
30 267 1508 176 1283 1733 0.525
31 267 1688 199 1434 1942 0.547
32 267 1876 224 1590 2163 0.572
33 267 2072 250 1752 2392 0.590
34 267 2273 277 1918 2628 0.592
35 267 2479 304 2089 2868 0.574
36 265 2686 330 2263 3109 0.525
37 262 2894 355 2440 3348 0.444
38 245 3100 376 2618 3582 0.355
39 208 3301 394 2797 3806 0.253
40 135 3496 407 2975 4017 0.159
41 54 3681 414 3152 4211 0.308
42 7 3854 413 3326 4382 -0.692
* Gestation in exact weeks dated by ultrasound.
90
Figure 11.1 Nottingham ultrasound-derived fetal growth standard.
The median, 10th and 90th centile curves for fetal weight are shown from 24 weeks,
derived from 267 women who underwent serial ultrasound examination. The original
individual curves have been forced through a fixed 18-week point and the birth
weight. Gestational age on the x-axis is in exact weeks, calculated on the basis of the
BPD measurement at booking.
91
Figure 11.2 Nottingham fetal growth standard compared with others.
The Nottingham standard is shown as a thick line. Note the similarity with the four
middle standards (Hadlock, Persson, Gallivan and Larsen). Our values tend to be
systematically lower than these, possibly because of the use of a corrected weight
estimation formula.
92
Figure 11.3 Nottingham fetal growth velocity.
The growth velocity in grams per week was obtained by plotting the first derivative of the median for the growth standard. Peak velocity is reached at 36 weeks (210g/week), followed by a rapid decline thereafter
93
Figure 11.4 Nottingham fetal growth velocity compared with others.
The Nottingham growth velocity in grams per week is compared with the
previously published standards of Hadlock et al, Persson & Weldner, Ott,
Larsen et al, Jeanty et al (using Shepard’s formula), Deter et al and Gallivan et
al
94
Figure 11.5 Nottingham proportional fetal growth curve.
The Nottingham median fetal growth curve has been transformed into a
‘proportional’ function, whereby for each week of gestational age the weight is
given as a percentage of the predicted 280 day value. This allows comparison
of growth dynamics independent of the absolute
weight.
95
12. SYMPHYSIS-FUNDUS HEIGHT IN RELATION TO
GESTATION AND FETAL WEIGHT.
12.1 Introduction
Clinical estimation of fetal size, and sometimes gestation, is often
performed by measuring the symphysis-fundus height. This may be
plotted against gestation on a reference chart or, more commonly,
McDonald's rule of equating one centimetre of fundal height for each
week of gestation is used. An alternative clinical method is to estimate
fetal weight by simple palpation.
In the early version of the customised growth chart, a fundal height
axis was included on the right side.This was based on the standard
published by Pearce & Campbell (1987), to allow the evaluation of
fetal size by this parameter.
The aim of this study was to evaluate the relationships between SFH,
gestation and EFW, and also to derive a new scale for the fundal
height axis of the growth chart based on local data. We also examined
the relationships between fundal height growth velocity and
birthweight centiles.
12.2 Patients and methods
For the purpose of obtaining a fetal weight estimation formula based
on SFH, two populations were studied: the derivation set and the
validation set.
The derivation set comprised 284 prospectively recruited low-risk
singleton pregnancies , examined on 3 to 5 occasions prior to
delivery.The exclusion criteria for abnormal neonatal outcome
described in Chapter 9 were applied. The final study group included
267 singleton pregnancies. In order to study the correlations between
SFH and fetal size and SFH with gestation, only one set of
measurements was selected randomly from each patient.Ultrasound
96
and fundal height measurements were performed as described in
Chapter 9.
The validation set consisted of a separate population of 130 unselected
patients who were examined just prior to elective caesarian section or
induction of labour. This was done in order to eliminate error arising
from the lag time between measurement and delivery. The fundal
height, engagement of the presenting part, booking height and weight,
and parity were recorded. Birth weight was entered in grams. Weight
estimation errors were expressed as percentages of the true weight as
follows:
Percentage Error =
100*(Predicted weight - observed weight)/(observed birth weight)
To examine the relationship between mean SFH velocity and
birthweight-for-gestation z-scores, the whole study group was
examined, including cases with abnormal outcome.
Customised and uncustomised birth weight centiles and z-scores were
calculated using the software described in chapter 9. The mean SFH
growth velocity for cases with at least three SFH measurements was
calculated by computer software, fitting a straight line using the least
squares method.
Relationships between pairs were expressed in terms of Pearson's
moment correlation coefficients. Interrelated variables were examined
by stepwise multiple regression analysis, with P<0.05 as the inclusion
criterion.
Statistical analyses were performed using SPSS for Windows (SPSS
Inc.,Chicago]. Graphs were plotted using either the SPSS graphics
facility or Cricket Graph for Windows (Computer Associates, San
Diego, California).
97
12.3 Results
Figures 12.1 and 12.2 are scatterplots of SFH versus EFW and SFH
versus gestation respectively. The correlation between SFH and EFW
is stronger than that between gestation and SFH ( R-square values of
0.74 versus 0.69 ).We found that there was no significant
improvement in weight prediction by allowing for engagement or
including girth (results not shown). A very slight improvement could
be attained by allowing for maternal body mass index .
The regression line for EFW in terms of SFH is :
EFW (grams) = 225.99 * SFH - 5012.29
When this formula was applied to the validation set, the mean error
was found to be -3.7%, with a standard deviation of 18.1%. The
distribution of the errors was not significantly different from normal.
In the 244 cases with more than 2 SFH measurements, there was a
statistically significant positive correlation between SFH velocity and
birthweight z-scores. For customised birthweight z-scores, the Pearson
correlation coefficient was 0.3201 (P<0.0001), which was better than
that between SFH velocity and unadjusted birthweight centiles (R =
0.2921, P<0.0001).
12.4 Discussion
In his review of the study by Lindhard and colleagues (1990)
published in the Cochrane Database, Neilson (1993) cautiously
concluded that ‘it would seem unwise to abandon the use of SFH
measurements unless a much larger trial likewise suggests that it is
unhelpful’. This is a fair representation of the uncertainty in the
literature on the value of this obstetric parameter.
98
Some of the variability in the results obtained by previous workers is
likely to be due to the different approaches in deriving local reference
charts. Unlike most of the previous standards for SFH that show some
deceleration of the curve at term, our study suggested a linear
relationship between gestation and SFH throughout pregnancy. This is
most likely due to the increased precision in gestational age estimation
resulting from the use of early ultrasound measurements, without
using menstrual dates.
If we assume that SFH gradients reflect fetal growth velocity, then the
finding of a better correlation of SFH velocity with customised birth
weight z-scores than with unadjusted z-scores supports the view that
adjusting for pregnancy characteristics gives a better indication of
fetal growth dynamics than otherwise. However, the low values of the
correlation coefficients for these observations implies that mean SFH
velocity on its own is likely to be of limited clinical utility in the
detection of the SGA infant.
Before the advent of obstetric ultrasound, SFH measurement or
clinical palpation were in common use as a way of estimating fetal
size. There is considerable evidence to show that, for fetal weight
estimation, SFH measurement is at least as good as clinical palpation (
Secher,1990; Pschera et al, 1984; Lindhard et al, 1990), and the latter
is accurate to within 450g of the birth weight in 80% of cases
(Loeffler, 1967). However, it is quite possible that clinicians making
fetal weight estimates on the basis of ‘clinical palpation’ may
subconsciously also be using other information to them available, such
as gestational age and maternal size; in Loeffler’s study the patients
were in labour, and the clinicians had full access to all the clinical
data. If this is confirmed in a well designed study, it would mean that
as an objective measure clinical palpation may be inferior to SFH
measurement.
99
Our finding of a better correlation of fundal height with fetal weight
than with gestation is in agreement with the work of De Muylder and
colleagues (1988), and lends some support to using this technique for
growth screening. Furthermore, by using ultrasound fetal weight
estimates, we were able to show that SFH measurements can be used
to estimate fetal weight in the pre-term period. No data has been
published on the accuracy of simple clinical palpation for fetuses
delivering at these gestations. Since clinicians have ‘calibrated’ their
fetal weight estimation skill by clinical palpation on infants delivering
mostly at term, it may well be that in the pre-term period clinical
palpation is not as accurate. However, our figures suggest that the
accuracy of isolated measurements is , not clinically useful unless the
values are extreme. This is not surprising , since fundal height is a
combined measure of maternal and fetal tissues, and is in any case
more likely to reflect fetal length than weight. It may have greater
power if performed serially and frequently , thus allowing trend
analysis, and by the same observer, to reduce error from inter-observer
variability.
100
101
102
13. FETAL GROWTH KINETICS IN RELATION TO
PREGNANCY CHARACTERISTICS .
13.1 Introduction
Although the relationships betwen pregnancy characteristics and birth
weight have been extensively documented, little is known on the
relationships between these characteristics and ultrasound-estimated
fetal weight in the antenatal period.
One of the assumptions of the customised growth chart is that the
factors influencing birth weight are also operative earlier in the third
trimester. In this study, we attempt to explore this issue using three
techniques: (1) graphic display of mean growth curves derived from
different subgroups, (2) non-parametric assessment of differences
between groups, and (3) multiple regression analysis. We also
examine fetal growth among babies born pre-term.
13.2 Patients and methods
Two hundred and sixty seven cases had clinically normal outcome as
described in chapter 5 and also sufficient data points to generate
individual growth curves. Ultrasound-estimated fetal weights were
calculated from the individual growth curves at 26, 28, 30, 32, 34 and
36 to 40 exact weeks, without extrapolation. Hence the samples
became progressively smaller from 37 weeks.
For the purpose of comparing mean growth curves, the population was
subdivided into the following groups: primiparas and multiparas, male
and female fetuses, European and Indo-Pakistani, smokers and non-
smokers, tall vs medium vs short stature, heavy vs medium vs light
maternal weight (at booking). For height and weight, the population
was divided in three equal groups, corresponding approximately to the
33rd and 67th centiles of these variables. Mean growth curves for each
group were derived using the methods described in chapter 10.
103
Descriptive statistics for the individual growth coefficients were
calculated. Differences in birthweight and EFW's at each gestation
were evaluated using non-parametric tests. For two independent
samples, the Mann-Whitney U test was used, whereas for continuous
variables such as maternal height and weight Spearman's rank
correlation coefficient was employed. Stepwise multiple regression
analysis (selection criteria : prob. in = 0.05; prob. out =0.10) was used
to identify factors that were significantly related to fetal weight and
also to birth weight. Categorical variables were recoded as dummy
variables ( 1 or 0), while continuous variables such as height and
weight were entered without transformation. Gestation was not
entered, since the EFW’s were analysed at fixed gestational points (26
to 40 weeks).
In order to compare the growth patterns of the cases born preterm with
the normal cases born at term a different statistical procedure was
adopted, because of the 17 preterm cases only 11 had sufficient
datapoints to generate individual growth curves. An average growth
curve for these cases was plotted by the method described above.
All the ultrasound-estimated fetal weights (up to 37 weeks) for the
normal cases born at term (the control group) were transformed into z-
scores according to the longitudinal fetal growth standard described in
chapter 10, and pooled together to yield a dataset of 736 points. For
the preterm deliveries, both the birth weights and the EFW’s were
transformed into standard deviates by the same growth standard,
leading to a dataset of 55 points. The differences between the means of
these z-scores could then be analysed using Student’s T-test.
Statistical analyses were carried out using SPSS for Windows (ver
6.0). Graphs were plotted using Cricket-Graph graphic software.
104
13.3 Results
A description of the population characteristics is shown in table 13.1;
these are similar to the overall population figures shown in tables 9.1
and 9.2. The distribution of fetal weights at each week did not differ
significantly from normal, as assessed by the Kolgorov-Smirnov test at
the 0.05 level (table 10.1). The fetal growth curves in different
subgroups are shown in Figures 13.1 to 13.7. Ethnic groups other than
Indo-Pakistani were not plotted because of the small numbers in these
categories.
The basic statistics for the individual growth coefficients of the log
polynomial equations are shown in table 13.2. These are significantly
skewed and with unequal variances; furthermore they are closely
correlated to each other (R>0.99 for all pair-wise combinations). The
results of the non-parametric tests are shown in tables 13.3 and 13.4.
The multiple regression constants, coefficients and R-square values
are shown for each week of gestation in table 13.5; the coefficients are
entered as zero if they are not statistically significant.
The differences between the z-scores of EFW’s of pre-term and term
deliveries are shown in table 13.6. Infants delivering pre-term had fetal
and birthweights that were on the average 0.30 standard deviates (or
10 centiles ) below those of babies delivering at term, a difference that
is statistically significant. This analysis was repeated by excluding
birthweights from the preterm group; the latter’s values were still
significantly lower by the Wald-Wolfowitz Runs Test (z =-1.6537, P=
0.0491).The mean growth curve for the preterm group is displayed
graphically in figure 13.7.
13.4 Discussion
Non-parametric and multiple regression analysis of EFW at different
gestational ages confirms that at least some of the pregnancy
characteristics that are affecting birth weight are also operative in the
105
antenatal period. The differences in growth patterns evident in the
graphs for different subgroups are interesting, but are rather refractory
to statistical analysis. Multivariate tests such as Hotelling T2 in
relation to the individual growth coefficients could be applied, but this
would not be a valid exercise because the coefficients have
significantly unequal variances and also a markedly skewed
distribution (table 13.2). These variables cannot be transformed
without changing the interpretation of the results.
The most marked differences in the growth curves are seen in
comparing groups of maternal size .Maternal weight is highly
correlated with EFW from 26 weeks onwards, whereas the effect of
height does not become significant until 32 weeks. This is supported
by the multiple regression analysis, which identifies maternal weight
and its powers as significant throughout the gestational interval
examined.
The growth kinetics for primiparae and multiparae appear similar up
until 36 weeks; thereafter primiparae show some relative slowing in
fetal growth, whereas in multiparae it continues almost linearly. The
differences do not become statistically significant by the non-
parametric test until 39 weeks, although by multiple regression
analysis parity is an independent significant variable except from 32 to
36 weeks. This suggests that most of the known variation in
birthweight due to parity develops late in the third trimester, and may
be related to differences in the intrauterine environment rather than
fetal genetic factors.
Differences in growth between Europeans and Indo-Pakistanis become
significant by the Mann-Whitney U-test in our study from 34 weeks. It
is of interest that although the kinetics of their growth curves are
dissimilar, these differences are not borne out by the multiple
regression analysis. This may be due to differences in maternal size
being responsible for most of the differences, and also the small
106
sample size. The very few cases belonging to the Afro-Caribbean
group and ‘Others’ are nevertheless identified as significant
independent variables intermittently from 26 weeks. Chang and
colleagues (1992) reported a longitudinal ultrasound study on fetal
growth in a sample of 20 Bangladeshi and 67 European women;
statistically significant differences between the mean estimated fetal
weights of the two groups were noted from 28 weeks onwards, but
their analysis did not allow for the confounding effects of maternal
size and parity. Two articles have been published on ultrasonic fetal
growth parameters in different ethnic groups. Vialet and colleagues
(1988) studied two groups from the same location by ultrasound: 201
African women and 201 European women. These were matched by
socioeconomic status and parity. The BPD, FL and AD growth curves
for the two populations were obtained using the Rossavik model. They
found that while the AD growth curves were similar, African fetuses
tended to have significantly smaller BPD's and longer FL's in the
second half of pregnancy. Their birth weights were on average 200g
lighter than Europeans, with no significant differences in the duration
of pregnancy. Although weight gain curves were not produced, these
observed differences suggest that the weight estimation formulae in
current use may yield biased results in this population, since they were
derived from largely European populations.Simmons and colleagues
(1985) were able to study a group of Bengali patients longitudinally;
they measured the BPD and the abdominal area from 14 weeks
gestation, and plotted their results against Campbell and Newman's
standard for Europeans.The mean BPD measurements were below the
median from about 18 weeks, and the abdominal areas were also lower
from about 30 weeks, in both cases never below the 5th centiles. That
their mean birthweight was 300g lower than the European mean is to
be expected, because of the strong correlation of abdominal area with
fetal weight. Both of these studies suggest that fetal growth does not
107
vary significantly within ethnic sub-groups up to about 20 weeks,
maternal influences becoming effective thereafter. This is in
agreement with the animal work discussed in chapter 1 (Snow, 1989),
showing that maternal effects tend to operate late in pregnancy.
Male fetuses were heavier than females from 26 weeks, but these
differences did not reach statistical significance until 36 weeks in our
study. Sex-related differences in individual ultrasound parameters
(BPD, HC and AC) have been reported from as early as 24 weeks
(Parker et al, 1984) .
Cigarette smoking, as reported in mid-trimester, results in lower fetal
weights in the third trimester which in our sample shows statistical
significance from 36 weeks. This is consistent with the effect of
cigarette smoking on birthweight which appears independent of other
characteristics such as maternal height and booking weight (Wilcox et
al, 1993a) .
The lower R-square values in the multiple regression analysis for the
earlier gestations imply that less of the variability in EFW can be
explained by pregnancy characteristics , about 10% as opposed to 22%
at 40 weeks. It is likely that this is due to ultrasound error and
interpolation error in estimating fetal weight for the required
gestations from the growth curves.
Apart from the study by Persson and colleagues (1978) on growth of
the biparietal diameter, previous reports of increased prevalence of
retarded growth among cases born pre-term were based on comparison
of birthweight datasets with ultrasound derived standards (Ott,1993;
Secher et al,1987; Persson, 1989). We were able to show that the z-
scores of EFWs and birthweights of babies born preterm were
significantly lower than the EFWs measured before 37 weeks by the
same standard in babies delivering at term. Since only 30.9% (17/55)
of the data points from the preterm group were birthweights, it is
unlikely that the inclusion of this data could be a source of bias; the
108
Hadlock ultrasound weight estimation formula had been corrected for
systematic error, and also the error from this formula is not correlated
with fetal weight. Exclusion of preterm birthweights from the analysis
still shows lower values for this group, but the statistical significance
is weakened due to the smaller sample. Since our pooled values are
gestation-independent, and also because of the small numbers, we
cannot determine at what gestation these differences become
significant. Persson and colleagues (1978) were able to detect
significantly smaller biparietal diameters in the babies destined to be
born prematurely from 26 weeks onwards, and since this parameter
tends to be relatively ‘spared’ in IUGR, it is possible that fetal weight
differences may exist even before this gestation. It also suggests that
the growth retardation pattern in this group of babies is of the
symmetric type.
Our data show that factors which are known in the first half of
pregnancy - such as maternal height and booking weight, parity and
ethnic group - and which have an effect on birth weight, are also
associated with variation in fetal weight in the third trimester of
pregnancy. These findings suggest that no single standard can
accomodate for the variation of fetal growth, which needs to be
assessed in the context of individual pregnancy characteristics.
109
Table 13.1 Demographic characteristics of
normal population (N = 267).
Ethnic Group: No. (%)
European 251 (94)
Indo-Pakistani 13 (4.9)
Afro-Caribbean 2 (0.7)
Other 1 (0.4)
Smokers: 40 (15.0)
Fetal sex:
Males 135 (50.6)
Parity:
Primiparas 127 (47.6)
110
Figure 13.1 Maternal weight at booking and fetal growth.
Fetal growth is plotted for two groups: those whose mothers had booking weights above the 67th percentile and those below the 33rd percentile. Fetuses of the heavier mothers display accelerated growth from the 26th week of gestational age.
111
Figure 13.2 Maternal height and fetal growth.
Fetal growth is plotted for two groups: those whose mothers had heightsabove the
67th percentile and those below the 33rd percentile. Fetuses of the taller mothers
display accelerated growth from the 32nd week of
gestation.
112
Figure 13.3. Sex and fetal growth.
Fetal growth is plotted for male and female fetuses. Both sexes have a similar growth
pattern, but males are significantly heavier from the 36th week of gestational
age.
113
Figure 13.4 Parity and fetal growth.
Fetal growth is plotted for primiparae and multiparae. Growth follows a similar
pattern until 36 weeks, therafter the primiparae show a slightly decelerative course.
Statistically significant differences are noted from 39
weeks.
114
Figure 13.5 Ethnicity and fetal growth. European vs Indo-Pakistani.
Fetal growth is plotted for Europeans and Indo Pakistani. Growth is slower in the
Indo-Pakistani from about 30 weeks. Statistically significant differences are noted
from 34 weeks, but these could not be shown to be independent of maternal size.
115
Figure 13.6 Effect of smoking on fetal growth.
Fetal growth is plotted for smokers and non-smokers. Fetuses of smoking mothers
are lighter throughout the gestational interval studied. Statistically significant
differences are noted from 36 weeks.
116
Figure 13.7 Preterm delivery and fetal growth.
Fetal growth is plotted for fetuses delivering preterm (<259 days) and those
delivering at term. Fetuses delivering preterm are significantly lighter than
those proceeding to deliver at term.
117
14. CUSTOMISED GROWTH CHARTS IN RELATION TO
NEONATAL OUTCOME.
14.1 Introduction
In order to assess two different methods for the detection of a
condition a third independent method, or 'gold standard', would be
required for an unbiased comparison. Regrettably, in the case of IUGR
there is no generally accepted neonatal test that is accurate and
reproducible. To overcome this difficulty, this study was limited to
comparing the performance of adjusted and unadjusted growth charts
in a population with clinically normal neonatal outcome. This would
allow us to determine which method is best at defining normality, by
giving us estimates of the true negative and false positive rates.
14.2 Materials and methods
Only those cases with normal neonatal outcome, as defined by the
criteria listed in chapter 9, were selected. A total of 267 pregnancies
with a number of ultrasound examinations sufficient to plot individual
growth curves were included in this analysis.
In comparing the customised and the uncustomised growth charts, care
was taken that the standard deviations entered for the two methods
had the same coefficients of variation.
Curve-fitting was carried out according to the method described in
chapter 7. A computer program was developed in order to check
whether the growth curve for each case was wholly within the 10th
and 90th centile boundaries or crossed either from 27 weeks until
delivery. Both customised and uncustomised reference grids were
used. The uncustomised boundaries were defined by using the
'average' proportionality growth curve described in chapter 6, forced
through the Nottingham population mean for 40 exact weeks of
118
3447g. Thus each case could be classified by both customised and
uncustomised criteria in one of four groups as follows:
1. Crosses the 10th centile.
2. Within the 10th and 90th centiles.
3. Crosses the 90th centile.
4. Crosses the 10th and 90th centiles.
Differences between the two methods could then be tested using non-
parametric tests for related samples. Group 4 cases were excluded
from analysis, since these are more likely to contain large errors in
ultrasound fetal weight estimation .
We also investigated the relationship between the standard deviation
entered in the customised growth chart program and the percentage of
cases that would cross the 10th centile boundary. The coefficient of
variation (SD/median) in the computer program described above was
increased in steps of 1 per cent, and the resulting proportion of cases
crossing the 10th centile was plotted.
14.3 Results
Table 14.1 shows the percentage of cases within each group according
to the type of reference boundary. The differences due the
classification method are statistically significant according to the
Wilcoxon Matched-Pairs Signed-Ranks test (table 14.2);
customisation of the reference range results in significantly fewer
normal cases crossing below the 10th centile, but more cases cross the
90th centile. If the population is re-grouped in two categories, as either
crossing or not crossing the 10th centile, the differences due to the
classification method remain highly significant (table 14.3), with
fewer of the normal cases crossing below the adjusted 10th centile
boundary. Fewer cases cross both the 90th and the 10th centile
119
boundaries using the customised compared with the uncustomised
chart (6 Vs 9), but this is not significant. A more detailed breakdown
of the categorical shifts in terms of cases crossing the 10th centile is
given in table 14.4. Many more of the cases labelled as SGA by the
unadjusted method are reclassified as not SGA by the customised
method than the reverse.
In order to study the systematic trends of the two methods, the EFW's
were calculated for all cases from their growth curves at 28 weeks, and
then transformed into customised and uncustomised centiles. The
mean customised centile was 33, whereas the mean uncustomised
centile was 43 (P<0.00001, Wilcoxon Matched-Pairs Signed-Ranks
Test).
Figure 14.1 shows the relationship between the coefficient of variation
of the standard deviation entered in the customised chart program and
the percentage of cases crossing the 10th centile reference line; it is
likely that most of these would be false positives for IUGR.
14.4 Discussion
Statistical analysis of these results suggest that when serial ultrasound
examinations are performed, fewer of the cases with normal outcome
will be labelled as SGA using customised growth charts than using a
fixed reference standard. McNemar's Test is the best method to
analyse this data, because it is a non-parametric test that can be used to
test whether dichotomous variables generated by one method differ
significantly from dichotomous variables generated by another method
applied to the same sample.
A 2 x 2 table is constructed, and the significance level is determined
by either the Chi-square test or by the binomial distribution if fewer
than 25 cases are re-categorised by the second variable.
Our selection criteria for this study - being independent of birthweight
and morphometry - will not exclude the milder, asymptomatic cases of
120
IUGR. Conversely, not all of the neonates with abnormal outcome
have been affected by growth disturbances. These considerations will
prevent us from making an objective assessment of true negative and
false positive rates for IUGR, but should not affect our conclusions
when we are comparing the two methods applied to the same
essentially normal population.
The rather high percentage of cases crossing the 10th and 90th centile
boundaries is likely to be due to fluctuations in the growth trajectory
resulting from ultrasound error, and also the relatively small number
of ultrasound examinations carried out per patient.
As a result the, the 'SGA' rates of 33.2% by uncustomised and 25.5%
by customised growth charts are too high by both methods. This
suggests that the standard deviation generated by the program is too
narrow in view of the ultrasound error. As figure 14.1 shows, the
coefficient of variation should be at least 16% in order to reduce the
percentage of cases labelled as SGA in this normal population to well
under 10%. This approaches the figure of 19.4% in Jeanty's reference
standard (1984). If this growth chart were to be used on its own as is,
the action thresholds would need to be reduced to a degree
commensurate with the local ultrasound error rates in order to avoid
excessive referrals and unnecessary parental anxiety.
Allowing for maternal characteristics in defining the 10th centile cut
off is likely to result in a significant reduction in the false positive rate
for IUGR. Of the cases classified as SGA by the uncustomised
method, 27.5% (25/91) are reclassified as normal by the customised
chart. Conversely, some 2.3% (4/174) of the cases classified as normal
by the unadjusted chart are reclassified as abnormal by the customised
chart. Although a higher proportion of cases may be crossing the 90th
centile using the customised method, this boundary is less related to
neonatal morbidity than the 10th centile (Patterson,1986 ), and hence
less clinically important. Work in progress in our unit on a sample of
121
more than 1000 neonates shows a progressive reduction in neonatal
morbidity with increasing birthweight rank (Dr T. Muls, personal
communication), rather than the U-shaped distribution described by
Patterson between 38 and 41 weeks' gestation .
Our finding of fewer cases crossing the 10th centile boundary is
against an overall trend for customised centiles to be lower than
uncustomised centiles (chapter 14), which is evident for both birth
weights (chapter 14) and EFW. This suggests that the customisation
method is operating more selectively than uncustomised charts.
Further support is the fact that fewer cases cross both centile
boundaries using the customised charts than with the uncustomised
charts.
The design of this study does not allow us to make any statements
about the positive predictive value of customised charts, since as
discussed above we do not have clear criteria to define IUGR. This is
a problem shared with other tests used in perinatal medicine, such as
the biophysical profile, in that they have high negative predictive
values but limited positive predictive values. It is likely that to some
degree this may be due to our limited diagnostic arsenal in identifying
and classifying perinatal pathology.
122
Table 14.1 Percentage of fetal growth curves crossing 10th and 90th
uncustomised and customised centiles for 274 cases with neonatal
normal outcome.
Group Uncustomised Customised
Category No. Number
of cases
% Number
of cases
%
Cross the 10th centile 1 91 33.2 70 25.5
Within 10th and 90th
centiles
2 133 48.5 136 49.6
Cross the 90th centile 3 41 15.0 62 22.6
Cross both 10th and 90th
centiles
4 9 3.3 6 2.2
Total 274 100.
0
274 100.0
123
Table 14.2 Customised versus uncustomised centile boundaries.
Differences in group ranks according to the classification
method.(Group 4 excluded).
Rank 1: crosses 10th centile.
Rank 2: wholly within 10th and 90th centile boundaries.
Rank 3: crosses 90th centile .
Wilcoxon Matched-Pairs Signed-Ranks Test:
Mean rank Cases Direction
24.03 38 - Ranks (UNCUST < CUST)
21.00 8 + Ranks (UNCUST > CUST)
218 Ties (UNCUST = CUST)
Total 264 Z = -4.0697
2-Tailed P <0 .00005
124
Table 14.3 Customised versus uncustomised centile boundaries.
Population reclassified as either Group (1): crosses the 10th centile;
or Group (2): does not cross the 10th centile
( valid N= 265). McNemar Test.
Cust. Gp 2 Cust. Gp 1 Totals
Uncustomised Gp1 25 66 91
Uncustomised Gp2 170 4 174
Totals 195 70
Cases = 265; Chi-square = 13.8; P = 0.0002;
Table 14.4 Shifts in categories according to classification method.
(Group 4 excluded.)
From: To: Frequency Percent
SGA by
customised
not SGA by
uncustomised
4 1.5
SGA by
uncustomised
not SGA by
customised
25 9.4
unchanged unchanged 236 89.1
Total: 265 100.0
125
Figure 14.1 Relationship between coefficient of variation and false positive rate
for SGA.
The graph shows the percentage of individual growth curves crossing the 10th
percentile as a function of the coefficient of variation (SD/EFW), as the latter is
artificially widened or narrowed. The standard deviation needs to be approximately
15.5% of the median weight in order to allow 10% of the population to cross the
10th percentile cut-off.
126
15. THE PREDICTION OF BIRTH WEIGHT
15.1 Introduction
As demonstrated in the preceding chapters, the combination of
ultrasound weight estimation error and error in gestational age can
result in substantial fluctuations in the apparent fetal growth curves, to
the extent that some 25% of cases will cross the 10th centile reference
line using customised growth charts. Birth weight, although it is not an
accurate indicator of individual growth kinetics, remains one of the
most reliable measurements in this study. Both the customised growth
chart and the IBR programs base their calculations on a predicted birth
weight at a given gestation. It is important therefore to compare the
predictive ability of the adjusted versus the unadjusted standards.We
also analysed the birthweights in our normal sample using three
standardisation methods: the customised growth chart, the unadjusted
standard and the IBR .
15.2 Patients and Methods
We analysed both the population with normal outcome (N = 282) and
also those with abnormal outcome ( N = 42), to see the extent the
weight prediction error would be increased by including pathological
cases. The unadjusted growth standard was that described in chapter
13, whereas the customised program was the same as described in
chapter 6. Software for the calculation of the IBR and IBR centiles
was compiled with the assistance of Mr Mark Wilcox.
The predictive ability of the customised and uncustomised standards
was assessed by calculating the signed and the absolute prediction
errors for each case, both in grams and as a percentage of the true
weight.
The percentage error was calculated thus:
127
Percent Error = 100 * (Predicted - Observed)/Observed
This was done by modifying the computer programs described in
chapter 13 ; the predicted weight by the customised program was
calculated using the multiple regression model, while by the
unadjusted standard the corresponding value was the median for
gestation.
The cumulative frequency distribution of the errors by both methods
was then plotted.
The significance level of the differences between the two methods was
calculated by applying Wilcoxon matched-pairs signed-ranks test.
The frequency distribution of the number of cases in each decile
category according to the different birthweight standards were plotted
as histograms, for the overall population and also for non-smokers,
since smoking is probably the most common cause of mild IUGR.
In order to examine time trends in relation to customised and IBR
centiles, the centile difference between the two was calculated for each
case and plotted as a function of gestation. The strength of this
relationship was quantified by calculating Spearman's rank correlation
coefficient.
The relationships between customised, uncustomised and IBR centiles
were also studied using Spearman's rank correlation coefficients, and
displayed graphically.
The percentile values obtained by the three methods were categorised
in ten groups, 1 to 10, according to the corresponding decile values.
Differences in categorisation between two standards were tested using
the Wilcoxon matched pairs signed-ranks test, for the three possible
combinations: customised vs uncustomised, customised vs IBR and
uncustomised vs IBR .
128
15.3 Results
The descriptive statistics (including the Kolgomorov-Smirnov test for
normality) for the term birthweights in the population with normal
outcome are shown in table 15.1. The distribution did not differ
significantly from normal, with only a very slight degree of positive
skewness.
The customised growth chart program was significantly better at
predicting birthweights than the unadjusted chart, with a mean
absolute error of 303.5 g versus 342.6 g respectively (z = -4.11,P <
0.00001). The cumulative frequency distribution of the absolute error
for the two methods is shown in figure 15.1. The error was less than
250g in 50% of the population using multiple regression, whereas
using the unadjusted, gestation-specific standard this figure was
reduced to 46% The Spearman correlation coefficient between the
predicted weight by multiple regression and the birth weight was
0.9642.
The standard deviation of the signed prediction errors for the normal
population was 5.35% of the true weights for the customised program
and 5.63% using the unadjusted method. The corresponding figures in
the neonates with adverse outcomes were 7.65% and 8.02% (table
15.2 ).
The frequency distributions of deciles according to the three different
methods tested are shown as histograms in figures 15.2 to 15.4.
These are significantly different from each other, as detailed in table
15.2. Frequency distributions were also drawn for the non-smokers
and these are shown in figures 15.5 to 15.7. For the unadjusted
standard, the exclusion of smokers results in a reduction of cases
below the 10th centile by 1.7% while the corresponding figures for the
IBR and customised chart are 1.1% and 2.8%. Spearman's correlation
coefficient for customised and IBR centiles was 0.9939; the scatterplot
for these values is shown in figure 15.8. A highly significant negative
129
correlation was noted between gestation and the difference between
customised and IBR centiles (R = -0.607, P< 0.0001 ).This is shown in
figure 15.9.
15.4 Discussion
The multiple regression model in use by the adjustable standards
performs significantly better than the unadjusted standard in predicting
birth weights. What is more remarkable is that the overall predictive
performance of the models using gestation alone or gestation in
combination with maternal characteristics is superior to that of the
ultrasound weight-estimation formula used in our study (SD of
ultrasound error: 10.2%, table 9.3). Even in the abnormal population,
where one would expect an increased prevalence of growth disorders,
the SD of the error using multiple regression (7.65%) matches the best
reported figures in the ultrasound literature (Hadlock et al, 1985). In
the multiple regression analysis of the East Midlands Obstetric
Database, the standard error of the regression model, which was
centred on 40 weeks, was 389 grams , which is about 11% of the
overall mean birthweight at 40 weeks (3443g). The reason for the
improved performance of the model in our population is probably due
to the presence of frequent inaccurate values in the obstetric database,
as opposed to our carefully checked entries in our study group.
That multiple regression using pregnancy characteristics only may be
as good as ultrasound was also suggested by Rogers and colleagues
(1993) in Hong Kong. They developed a multiple regression model for
the prediction of birthweight (inclusive of the same variables as our
model) from half of a Chinese database of 23750 singleton deliveries,
and analysed its performance by testing it against the other half. The
standard deviation of their absolute errors was 130g/Kg, i.e.13% of
actual birthweight. The corresponding value in our sample was at
worst 4.32% (table 15.2).
130
Likewise the correlation coefficient between predicted and
birthweights in their sample was 0.53, as opposed to ours of 0.96.
Their poorer performance is almost certainly due to the fact that their
population was not routinely dated by ultrasound, and not selected on
the basis of pregnancy outcome. These findings suggest that accurate
knowledge of only two factors is sufficient in order to make
reasonable fetal weight estimates: a population-specific average
growth curve and gestational age. It also emphasises the effect of
gestational age on fetal weight.
The use of the two adjusted standards results in a much smoother
distribution of deciles than the unadjusted standard. This means that
the random fluctuations in the centile values of the unadjusted
standard due to the small sample size tend to even out when processed
by the customised standard together with the variation in pregnancy
characteristics. It may well be that this feature of the adjustable
standards makes them more suitable for analysing small data sets.
There is a relatively high number of cases below the 20th centile
using the unadjusted and the customised standards (26.5% and 22.6%
respectively, versus the expected 20%) , while the corresponding
figure for the IBR is 19.7%.
The customised charts results in values that are usually lower than the
IBR centiles; this is probably because the customised growth chart is
predicting birthweights for a non-smoking population, whereas in the
IBR program the smoking factor is ignored.
This difference in the way the two programs handle the influence of
smoking is also reflected in the changes in the percentage of cases
below the 10th centile when the smokers are excluded from the term
population. This change is greatest for the customised growth chart,
which resulted in 2.8% fewer cases being labelled as ‘SGA’ when the
smokers were excluded.
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The highly significant negative correlation between gestational age
and the difference between customised and IBR centiles is likely to be
due to the different growth functions; the customised growth chart
uses an average function which is almost a linear relationship between
fetal weight and gestation, whereas the IBR uses the birthweight curve
derived from the East Midlands Obstetric Database, which shows
some degree of 'flattening' at term. Hence with increasing gestation,
the difference in birthweight expectation between the customised and
the IBR programs widens, resulting in an increasing difference
between the centiles calculated by the two methods. This implies that,
of the infants born post-term, more would be labelled as ‘SGA’ by the
customised standard than using the IBR program, suggesting greater
sensitivity of the customised standard at this critical gestational age.
The very high correlation between the IBR and customised centiles is
indirect evidence in favour of the clinical efficacy of the customised
chart program, since the IBR program has been shown by Sanderson
and colleagues (1993) to be better able at detecting IUGR than the
unadjusted birthweight standard.
132
Table 15.1 Descriptive statistics for term birth weights for infants with normal outcome. Mean (SD) 3474.8 (490.8) g S.E. of mean 29.2 Skewness (SE) 0.086 (0.145) Kolmogorov-Smirnov test for normal distribution
z = 0.6248 P = 0.8298
Range 2890 - 4900 g Valid No. 283 Table 15.2 Analysis of birthweight prediction errors in the populations with normal and abnormal neonatal outcomes.
Sample Method Systematic Error (%)
Standard Deviation (%)
Mean Absolute Error (%)
Standard Deviation (%)
Normal (N=283)
Unadjusted (Gestation only)
0.67 5.63 4.93 2.79
Multiple Regression Model
0.18 5.39 4.57 2.81
Abnormal (N= 42 )
Unadjusted (Gestation only)
0.65 8.02 6.71 4.32
Multiple Regression Model
-0.94 7.65 6.39 4.19
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Table 15.3 Differences in classification by standardisation method. Wilcoxon matched-pairs signed-ranks test for the population of infants born at term. Pair comparison: A with B
A<B A>B A = B (ties)
Number
Mean rank
P-value
Customised with IBR
Customised < IBR Customised > IBR Customised = IBR
161 0 121
81.0 0.0
< 0.00001
Uncustomised with customised
Uncust < cust Uncust > cust Uncust = cust
113 54 115
91.32 68.68
< 0.00001
Uncustomised with IBR
Uncust < IBR Uncust > IBR Uncust = IBR
171 18 94
99.47 52.50
< 0.00001
134
Figure 15.1 Birth weight prediction errors: customised vs uncustomised charts.
The graph shows the cumulative percentage of absolute birth weight prediction
errors using either the pregnancy characteristics-adjusted (‘customised’) or
unadjusted (‘uncustomised’) charts when applied to the population with normal
neonatal outcome. Customised charts give significantly better predictions than the
unadjusted charts.
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136
137
138
139
140
141
142
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16. COMMENTS AND CONCLUSIONS
Adjusting for pregnancy characteristics.
The concept that growth standards should be adjustable according to
pregnancy characteristics has been rather controversial. It was
postulated by Thomson and colleagues (1968), and was based on the
known relationships between these characteristics and birth weight.
Their birth weight standard was parity and sex-specific; the adjustment
coefficients for maternal height and weight were fixed, because not
enough data was available to analyse gestational-age dependent
changes. They did not provide any clinical evidence, however, that
adjusting for maternal characteristics leads to superior performance
than unadjusted charts, and their arguments were based on rather
intuitive grounds. These issues were debated in a discussion chaired
by Professor Whittle (1989), and attended by Nicolaides, Alberman,
Wigglesworth, Steer, Campbell and others. It was pointed out that
although differences in mean birthweights between subgroups may
appear small, shifts of such magnitude will affect the tails of the
distributions significantly and these can actually be associated with
important changes in perinatal mortality.We also have found that
small shifts in the median values will lead to major changes in the
number of cases that are reclassified as SGA (Gardosi et al, 1994). It
was agreed at this discussion that it is legitimate to correct for sex and
plurality, because in these instances the mortality of the smaller group
is lower; but no agreement could be reached on parity and maternal
weight. It was felt that because maternal malnutrition was a common
cause of IUGR in the developing world it is not legitimate to adjust
for maternal weight. Steer believed the effect of parity to be mediated
through maternal pre-conceptional weight, and therefore should not be
considered as an independent adjustment factor. He referred to work
by Van Der Spuy and collegues (1983), purporting to show that
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women who were underweight at the time of conception (as defined
by the body-mass index) had double the risk of preterm delivery and a
three-fold increase in the incidence of SGA.This was even greater if
ovulation was induced. While this may be true for malnourished
women, it disregards the relationship between maternal weight and
birthweight for women within the normal range of body mass index.
Another limitation of this study is that maternal weights were recorded
at booking (<16 weeks), rather than pre-pregnancy. This introduces
the confounding factor of weight gain, which may be considerable by
16 weeks, and is significantly correlated with birth weight. In our
multiple regression analysis of the East Midlands Obstetric Database
we found parity to be a significant and independent factor influencing
birth weight (Wilcox et al, 1993a). This has also been the experience
of other workers (Thomson et al, 1968; Voigt et al, 1989).
Our data shows that in terms of predicting birth weight, adjusting for
pregnancy characteristics is significantly more accurate than using
gestational age alone.
We were able to show that in our sample the factors influencing birth
weight are also operative in the antenatal period from as early as 26
weeks. There seems a rough inverse relationship between the
importance of the maternal factor and the apparent gestational age of
onset of the factor, with the stronger factors being operative from an
earlier stage than the weaker factors. Parity, maternal weight, and
ethnic group appear to be major independent significant variables,
although less of the variability in EFW can be explained by these
factors , probably because of ultrasound error. This supports the
principle of adjusting the standard for pregnancy characteristics
throughout the third trimester.
The origin of the observed differences in fetal size within maternal
subgroups remain obscure. The studies using animal models suggest
that genetic influences do not become important until the second half
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of pregnancy, and this is supported by the human studies showing
minimal inter-ethnic differences in the ultrasound dating parameters. It
is not possible at the moment to determine whether the differences in
birth weights between two groups are due to a preponderance of
growth stimulatory effects in one or growth retarding effects in the
other. It may be that effects of physiological origin are mediated by
growth stimulatory factors, whereas differences due to pathological
factors are mediated by growth retarding factors.
Design of the customised growth chart.
We found the prediction of birth weight using the multiple regression
model to be unexpectedly good. Given gestation and pregnancy
characteristics, the accuracy of the model was in fact better than the
ultrasound fetal weight estimation formula we used, and matches the
published figures for such formulae. This degree of accuracy is most
likely to be due to the use of early ultrasound in the estimation of
gestational age, and it supports the validity of the ‘proportional’ fetal
growth curve. This method is unlikely to be accurate in cases where
some degree of growth disturbances is suspected, since the multiple
regression model is designed to predict median values. Although it is
effective in predicting weight, we do not know whether the assigned
percentile values are indeed better related to perinatal morbidity and
mortality.
Some of the other principles on which the customised growth chart is
based upon remain open to discussion. The method of deriving the
adjustment coefficients for pregnancy characteristics by multiple
regression analysis was a compromise between selecting a 'supra-
normal', non-smoking population and an unselected population
without making any allowance for smoking. Other methods of
obtaining these coefficients should be explored, as they could possibly
lead to improved accuracy.As maternal weight is one of the most
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important adjustment parameters, the customised growth chart
program has a 'range-checking' mechanism to prevent making
inappropriate adjustments for weight when there is evidence of
malnutrition or gross obesity. This is based on the normal values of
body mass index in mid-pregnancy.
An alternative technique to using multiple regression for the
prediction of term birth weight is a computer neural network. This is a
method used in artificial intelligence whereby observational data
related to a particular outcome is fed repeatedly to a multi-layered
network of inter-related 'neurones', which will then form weighted
connections. The network will then be able to make predictions on
outcome when faced with a new set of data. This has been applied to
the East Midlands Obstetric Database, and interestingly the predictive
power of the network was not superior to the multiple regression
model (Mr Mark Wilcox, personal communication).
The use of a single fractional fetal growth curve derived from an
average of previous studies may be questioned. Prior to our work,
there was no ultrasound data on fetal weight to suggest major
morphological differences between subgroups, and hence there was no
alternative to using a single type of growth curve.
Apart from the evidence favouring EFW over the individual
ultrasound measurements as a screening tool for growth disturbances
(Chang et al,1993; Hedriana & Moore,1994), the main reason we
chose this parameter is that the adjustment coefficients used in the
customised growth chart have been derived from birthweight data.
Another reason is that very little has been published on the
relationship between pregnancy characteristics and individual
ultrasound parameters.
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Fetal Size Versus Growth Velocity
There has been considerable debate in the recent literature over the
relative merits of fetal size and growth velocity in the prediction of
fetal compromise (Gardosi, 1994; Chang, 1993; Danielian, 1993).
While it is indisputable that fetal size at any one time is the result of
average growth velocity since conception, it is also likely that
disturbances in growth velocity immediately before that time may be
of pathological significance. For instance, at a given gestation a fetus
may be of above average size because of a high average velocity, but
growth retardation may have been occurring for the previous few
weeks. Some evidence to support growth velocity as a predictor of
poor neonatal outcome was provided by the work of Chang and
colleagues (1993).
In this study, changes in the standard deviation scores of EFW and AC
between the first and the last ultrasound examination were compared
with the final values of the AC, EFW and Doppler indices. Poor
neonatal outcome was defined in terms of morphometric indices of
IUGR. It was found that serial assessment of EFW and AC was an
overall better predictor of IUGR than the other parameters. One of the
difficulties of this study was that the changes in the standard deviate
scores were not expressed per unit time, and hence mathematically
these are not accurate measures of velocity. Another issue is the
validity of the morphometric indices; if the analysis is restricted to
subscapular skinfold thickness - which is perhaps the most logical
index of IUGR- there appear to be only minor differences in the areas
under the ROC curves for all the parameters measured.
One of the practical problems with using growth velocity is the
additive effect of the ultrasound errors of two or more measurements;
in order to compensate for this error, frequent serial measurements
would have to be taken to observe significant trends. Another problem
is the timing interval. The importance of this factor has been discussed
148
in correspondence by Gardosi (1994). The shorter the timing interval,
the earlier the detection of anomalies but greater the relative error in
estimating velocity; on the other hand, lengthening the interval will
make timely intervention difficult. An additional difficulty is the lack
of any practical published standard for growth velocity.
It is probable that both fetal size and growth velocity need to evaluated
in the optimal assessment of fetal well-being. The often-made
statement that 'size does not matter as long as the baby is growing
well' is based on very little empirical evidence, and may well be
misleading in a clinical context.
Defining IUGR in the neonate.
The ideal method to compare the performance of different tests on the
same sample is by analysing receiver-operating characteristic curves
(Zweig & Campbell, 1993) . This requires a clear division between
affected and unaffected populations. In our sample this could not be
made because of the lack of a 'gold standard' in defining either IUGR
or macrosomia. Hence our finding of significantly lower rate of SGA
in a normal population using the adjustable standard does not imply a
better positive predictive value or detection rate for IUGR, since this
could not be tested against a sample of 'truly' growth retarded fetuses.
Nevertheless, indirect evidence that this may be the case comes from
the work of Sanderson and colleagues (1994 ), who found that the
individualised birth weight ratio is more closely related to
morphometric indices of IUGR, including skinfold thickness. We have
found a very high correlation (r = 0.99) between customised centiles
and IBR centiles ; this is because the method of adjusting for maternal
characteristics is the same, differences being due to the growth curve
selected and in determining the standard deviation for a given
gestational age.
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Our choice of the 10th centile as the definition of SGA was arbitrary,
based on conventional clinical practice, and we are certainly not
advocating this as a definition of IUGR.
Chard and colleagues (1993) have argued that fetal size for gestation
at birth, as opposed to maturity, is irrelevant to outcome and have
questioned the existence of fetal growth retardation at term. They
believe that most small term infants are not at risk, and that a
considerable number of babies thought to be small for dates are only
so because of inaccurate gestational age estimation. This may well be
so because of the imprecision in estimating weight -for-gestation rank
resulting from error in gestational age assignment and the use of
inappropriate, unadjusted birthweight standards (eg Lubchenko's
standard applied to a sea-level population ). No epidemiological
studies have yet been published on abnormal neonatal outcome or
neurodevelopmental disability in pregnancies dated by
ultrasonography. The study on neurodevelopmental handicap by
Taylor and Howie (1989) showed that affected infants were
significantly lighter at birth than controls, with lower birthweight
centiles, but only when complications of pregnancy were present. In
their sample only 16% of the population was pre-term, but at the time
these children were born, gestational dating by ultrasound was still in
its infancy, and hence this figure is suspect. Work in progress in our
unit on ultrasound-dated populations has shown a clear, inverse
relationship between birthweight centiles and neonatal morbidity,
which is even more marked for customised birthweight centiles (Dr
Muls, personal communications). Hence we believe that there is a role
for estimating fetal size in antenatal risk assessment.
Defining normal fetal growth.
Our work was essentially a longitudinal, observational study designed
to investigate fetal growth in the third trimester and to explore the
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clinical viability of customised growth charts. For this purpose we
recruited a 'low-risk' population in order to focus on normal fetal
development. The choice of ultrasound weight-estimating formula was
an important aspect of the project. We studied this problem (chapter
9) in a subset of the population that delivered near term. We had to
make the assumption that the findings applicable to this group and the
correction factors for the modified weight-estimation formulas would
also be valid in the pre-term period, as we did not have a sufficiently
large group of infants born below 37 weeks’ gestation. In choosing an
appropriate formula, accuracy is a major consideration, but another
important issue which is often disregarded is the correlation of the
error with the size of the fetus. Such a correlation, if significant , will
lead to data distortion at the extremes of the measured range. We
found that the Hadlock formula for BPD, AC and FL was not only the
most accurate, but also was free of any significant trend in the error.
We decided to develop a longitudinally-derived growth standard , as
opposed to a cross-sectional one, because in clinical practice the
EFW's of high-risk fetuses are usually plotted serially. As pointed out
by Altmann (1994), longitudinal data is most suitable for defining
growth process. The variance would thus be somewhat smaller than
that derived from a cross-sectional standard , depending on the degree
of ultrasound error, but the median curve should remain unchanged.
As a consequence, the 10th centile may be higher than if the standard
was obtained by cross-sectional analysis. In clinical terms, this would
lead to an increased test sensitivity but a reduction in specificity,
which can be easily corrected by shifting the 'action threshold'
downwards, say the 5th centile instead of 10th. Using the standard
error of the multiple regression analysis in order to derive the standard
deviation for the growth charts may be criticised in that it leads to
reference ranges that are too narrow in view of the ultrasound error.
We feel that it is not practical to widen the standard deviation to
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accomodate ultrasound error in EFW because the latter varies
considerably according to formula and the local conditions. The
decision of which action threshold to use thus needs to be dealt with at
a local level in view the performance in estimating fetal weight.
Our finding of a virtually linear relationship between fetal weight and
gestation using serial ultrasound data is at variance with older
standards derived from samples whose gestational age is estimated
from menstrual data. These are characterised by marked deceleration
of growth at term. To a large extent our linear relationship is due to
the improved accuracy in gestational age estimation using early BPD
measurements; another factor is our population selection criteria
which excludes neonates with abnormal outcome and likely
anomalous growth patterns. It is unlikely to be an artefact due to the
fetal weight estimation formula, since the error associated with the
Hadlock formula we used was not correlated with fetal size and also
because of the stabilising effect of including birthweights in the
individual growth curves.
Preterm Delivery and Growth Retardation.
In a substantial proportion of preterm deliveries the cause is unknown,
or undetected.
We were able to confirm previous reports (Ott,1993; Secher,1987;
Persson,1989) of impaired fetal growth in infants born pre-term, using
an ultrasound-derived fetal growth standard applied to both the EFWs
and birth weights of infants born preterm. In clinical practice these
cases are usually missed, either because of the use of cross -sectional,
birthweight-derived reference standards or from the inaccuracy of
ultrasound measurements.
We also found that the distribution of our ultrasonic fetal weight
estimates was positively skewed, but not to a statistically significant
degree. This is in contrast to the significant negative skewness of the
152
preterm birthweight data (Wilcox, 1993) and corroborates the concept
that a significant proportion of the infants born preterm may be growth
retarded.
In view of the poor detection rate of growth retardation using current
methods, the practice of long-term tocolysis in pregnancies with
threatened preterm labour should be viewed with caution. Preterm
delivery may well be an escape mechanism for those babies whose
nutritional needs are not being met by the utero-placental unit, and its
pharmacological suppression could lead to deterioration in fetal
condition.
Recommendations for further research.
It will be difficult to improve the predictive value of any method for
the detection of growth disturbances without first developing an
accurate, quantitative standard for the diagnosis of IUGR in the
newborn. The problems arising from the lack of a gold standard in the
definition of deficient growth were discussed by Keirse (1984), and it
is of interest that little progress has been made since then. Ideally this
standard should give an indication of the energy stores of the neonate,
since these are depleted in conditions of sub-acute or chronic hypoxia.
This is a field of research which is being actively pursued in our
department. An alternative to using indices of IUGR is to use actual
clinical outcomes, such as acid-base status, admission to neonatal
intensive care, etc..This allows the population to be divided two or
more groups, according to neonatal outcome. The problem with this
approach is that the pathological group will be heterogeneous, with
only some of the morbidity being due to growth retardation. Hence the
positive predictive value of the test cannot be clearly defined, unless it
is restricted to clear-cut clinical conditions within a sufficiently large
sample.
153
It may be possible that a single method of adjusting for maternal
characteristics may not work optimally for all conditions, so that for
instance a system that works optimally for IUGR may not be as
effective in screening for macrosomia. Alternative methods of
adjusting for maternal smoking need to be evaluated. In order to
develop optimal customisation methods, large databases of cases with
a variety of abnormal outcomes would be needed.
The current program uses a single fractional growth curve for all
maternal subgroups. Our work suggests that maternal subgroups may
differ in the shape of their fractional curves, as is the case for
nulliparous and multiparous populations. An issue to explore is
whether better performance from the customised growth chart may be
obtained by using more than one type of fractional growth curve. In
other words, the customisation process could perhaps be optimal if it
includes not only the prediction of birth weight at term, but also the
likely shape of the growth curve.
The distribution of our ultrasound fetal weight estimates in the normal
sample showed a non-significant trend towards positive skewness at
all gestations, which agrees with the observed significant positive
skewness in term birthweights extracted from the East Midlands
Obstetric Database. The customised growth chart does not incorporate
this skewness, making the assumption of normality at all gestations. It
would be a relatively simple procedure to reproduce this skewness in
the computer program, and it would be interesting to compare the
efficacy of this version of the program.
The multiple regression coefficients allowing the prediction of birth
weight at term were derived from a database that has been
accumulating over many years. It is possible that, if the population
characteristics change significantly with time, the coefficients may
also change. The significance of this issue needs to be investigated by
extracting the coefficients from the East Midlands Obstetric Database
154
for a series of time intervals. If this is the case, then the performance
of the customised growth chart could improved by using 'up-dated'
coefficients.
Irrespective of whether adjusted or unadjusted charts are used, the
error in fetal size estimation is a major limiting factor to the test
performance. It is a possible explanation for the relative lack of
success of screening programs using ultrasonic fetal growth
parameters. Two approaches are possible in order to tackle this
problem. Firstly, better formulas may be developed that, in addition to
the AC, include other measures of soft tissue, such as limb
circumferences (Balouet et al, 1992). Secondly, more advanced
equipment is likely to lead to better results. Three-dimensional
ultrasound machines, by improving the definition of tissue planes,
should reduce the error in measuring ultrasound parameters, and will
also allow more reproducible measuring of limb circumferences. The
use of echo-planar magnetic resonance imaging (Baker et al, 1994) is a
very promising technique for weight estimation, but will not be in
common use until its prohibitive cost is reduced drastically. This
should be case once low-cost high temperature super-conductors are
available. Another significant source of error is the inter-observer
variability, which for ultrasonic weight estimation by two observers
ranges from -187g to 140g (Chang et al, 1993). Increasing the number
of observers will result in greater variability. The ideal of having only
one observer performing all the serial examinations for the same
patient is probably unattainable in a busy obstetric ultrasound
department, but should be within reach of community midwifery care,
using symphysis-fundus height measurements. A community-based
project is now underway in order to assess the value of serial SFH
measurements performed by the same observer and plotted on the
customised charts in terms of detecting growth disturbances. Cases
that are screen-positive on the basis of abnormal serial or single SFH
155
measurements are referred for ultrasound examination and biophysical
profiles including Doppler when indicated.
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REFERENCES Alberman E. Are our babies becoming bigger? J Roy Soc Med (1991), 84:257-260. Alberman E, Emanuel I, Filakti H et al. The contrasting effects of parental birthweight and gestational age on the birthweight of offspring.Pediatric and Perinatal Epidemiology (1992), 6:134-144. Alexander G. Studies on the placenta of the sheep (Ovis aries L.).J Reprod Fertil (1964), 7:307-322. Allen MC, Donohue PK, Dusman AE. The limit of viability-neonatal outcome of infants born at 22 to 25 weeks' gestation. NEJM (1993), 329(22):1597-1601. Altman DG, Chitty LS. Charts of fetal size: 1. Methodology.Br J Obstet Gynaecol (1994), 101(1): 29-34. Altman DG, Coles EC. Assessing birth weight-for-dates on a continuous scale. Annals of Human Biology (1980),7(1):35-44. Altman DG, Coles EC. Nomograms for the precise determination of birth weight for dates. Br J Obstet Gynaecol (1980), 87(2):81-86. Bagger PV, Eriksen PS, Secher NJ, Thisted J, Westgaard L..The precision and accuracy of symphysis-fundus distance measurements during pregnancy. Acta Obstet Gynecol Scand (1985), 64:371-374. Bailey SM, Sarmandal P, Grant JM. A comparison of three methods of assessing interobserver variation applied to measurement of the symphysis-fundal height. Br J Obst Gynaecol (1989), 96:1266-1271. Baker PN, Johnson IR, Gowland PA et al. Fetal weight estimation by echo-planar magnetic resonance imaging. Lancet (1994), 343:644-645. Bakketeig LS, Jacobsen G, Brodtkorb CJ, Eriksen BC, Eik-Nes SH, Ulstein MK, Balstad P, Jorgensen NP.Randomised controlled trial of ultrasonographic screening in pregnancy. Lancet (1984), ii: 207-211. Balouet P, Speckel D, Herlicoviez M. Estimation echographique du poids foetal. Interet de la mesure de la graisse des membres. J Gynecol Obstet Biol Reprod (1992), 21: 795-802. Bantje H. A multiple regression analysis of variables influencing birth weight. Tropical and Geographical Medicine (1986), 38:123-130. Barth JA. Birthweight dependency on parental length and weight with special reference to gestational age.Arztliche Jugendkunde(1990), Band 81:3. Batra A, Chellani HK, Mahajan J et al.Ultrasonic variables in the diagnosis of intrauterine growth retardation.Indian J Med Res (1990), 92B:399-403. Beazley JM, Underhill R. Fallacy of the fundal height.BMJ (1970), 4:404-406. Belizan JM, Villar J, Nardin JC, Malamud J, Sainz de Vicuna L. Diagnosis of intrauterine growth retardation by a simple clinical method: measurement of uterine height. Am J Obstet Gynecol (1978), 131:643-646.
157
Benacerraf BR, Gelman R, Frigoletto FD. Sonographically estimated fetal weights:accuracy and limitations. Am J Obstet Gynecol (1988),159(5):1118-1121. Bergsjo P, Denman III DW, Hoffmann HJ et al. Duration of human singleton pregnancy. Acta Obstet Gynecol Scand (1990),69:197-207. Bhargava S.K., Sachdev HPS,Iyer PU et al. Current status of infant growth measurements in the perinatal period in India.Acta Paediatr Scand (1985), Suppl.319:103-110. Birnholz JC. An algorithmic approach to accurate ultrasonic fetal weight estimation. Investigative Radiology (1986), 21(7):571-576. Bower S, Campbell S. Doppler in obstetrics.In:Ultrasound in obstetrics and gynaecology. Eds. Dewbury K,Meire H,Cosgrove D. London: Churchill Livingstone (1993), pp251-270. Boyce A, Mayaux MJ, Schwartz D. Classical and true gestational postmaturity. Am J Obstet Gynecol (1976),125(7):911-914. Brandt, I. Growth dynamics of low-birth-weight infants.Acta Paediatr Scand (1985), Suppl.319:38-47. Brooke OG. Energy expenditure in the fetus and neonate: sources of variability. Acta Paed Scand (1985), Suppl 319:128-134. Brooke OG, Anderson HR, Bland JM. Birth weight,stress and behaviour; principal results from a prospective population study. BMJ (1989), 298:795-801. Butler NR, Bonham DG. Perinatal mortality: the first report of the 1958 British Perinatal Mortality Survey under the auspices of the National Birthday Trust Fund. E. and S. Livingstone, Edinburgh 1963. Calvert JP, Crean EE, Newcombe RG et al. Antenatal screening by measurement of symphysis-fundus height. BMJ (1982),285:846-849. Campbell S, Dewhurst CJ. Diagnosis of the small for dates fetus by ultrasound cephalometry. Lancet (1971),2:1002. Campbell S, Newman GB. Growth of the fetal biparietal diameter during normal pregnancy.J Obstet Gynaecol Brit C'wlth ( 1971),78: 513-519 Campbell S, Wilkin D.Ultrasonic measurement of fetal abdomen circumference in the estimation of fetal weight.Br J Obstet Gynaecol (1975), 82(9):689-697. Carlson DE. Maternal diseases associated with intrauterine growth retardation. Semin Perinatol (1988), 12:17-22. Cawley RH, McKeown T, Record RG. Parental stature and birthweight. Am J Human Genet (1954),6:448-456. Chamberlain P. Composite sonographic assessment of fetal health. Current Opinion Obstet Gynaecol (1992), 4:256-263. Chang TC. Is obstetric and neonatal outcome worse in fetuses who fail to reach their own growth potential? (Correspondence).
158
Br J Obstet Gynaecol (1993). Chang TC, Robson SC, Spencer JAD, Gallivan S. Fetal growth: A comparison between Bangladeshi and Caucasian women. Br J Obstet Gynaecol (1992), 99(12):1027-1028. Chang TC, Robson SC, Spencer JAD, Gallivan S. Ultrasonic fetal weight estimation: analysis of inter- and intra-observer variability. J Clin Ultrasound (1993), 21:515 -519. Chang TC, Robson SC, Spencer JAD, Gallivan S. Identification of fetal growth retardation: comparison of Doppler waveform indices and serial ultrasound measurements of abdominal circumference and fetal weight. Obstet Gynecol (1993), 82(2):230-236. Chard T, Costleloe K, Leaf A. Evidence of growth retardation in neonates of apparently normal weight. Eur J Obstet Gynaecol Reprod Biology (1992), 45:59-62. Chard T, Yoong A, Macintosh M. The myth of fetal growth retardation at term. Br J Obstet Gynaecol (1993),100:1076-1081. Chauhan SP, Lutton PM, Bailey KJ, Guerrieri JP, Morrison JC. Intrapartum clinical, sonographic and parous pateints' estimates of newborn birth weight. Obstet Gynecol (1992), 79(6):956-958. Chudleigh P, Pearce JM. Obstetric Ultrasound. London: Churchill Livingstone 1986. pp 51-58, 135-137. Chitty LS, Altman DG.Charts of fetal size.In: Ultrasound in Obstetrics and Gynaecology. Eds. Dewbury K, Meire H, Cosgrove D .London:Churchill Livingstone 1993; 513-595. Chitty LS, Hunt GH, Moore J, Lobb MO. Effectiveness of routine ultrasonography in detecting fetal structural abnormalities in a low risk population. BMJ(1991); 303:1165-9. Cheng MCE,Chew PCT,Ratnam SS. Birthweight distribution of Singapore Chinese, Malay and Indian infants from 34 weeks to 42 weeks gestation. J Obst Gyn Br Cmwth (1972), 79:149-153. Cnattingius S, Axelsson O, Lindmark G.. Symphysis-fundus measurements and intrauterine growth retardation. Acta Obstet Gynecol Scand (1984), 63:335-340. Combs CA, Jaekle RK, Rosenn B, Pope M, Miodovnik M, Siddiqi T. Sonographic estimation of fetal weight based on a model of fetal volume. Obstet Gynaecol (1993), 82(3):365-370. Colley NV, Tremble JM, Henson GL. Head circumference/abdominal circumference ratio, ponderal index and fetal malnutrition.Should head circumference/abdominal circumference ratio be abandoned? Br J Obstet Gynaecol (1991), 98:524-527. Danielian PJ, Allman ACJ, Steer PJ. Is obstetric and neonatal outcome worse in fetuses who fail to reach their own growth potential? (Correspondence).Authors' reply.
159
Daniellan PJ, Allman ACJ, Steer PJ. Is obstetric and neonatal outcome worse in fetuses who fail to reach their growth potential? Br J Obstet Gynaecol (1993), 99:452-454. Danielian PJ, Allman ACJ, Steer PJ. Is obstetric and neonatal outcome worse in fetuses who fail to reach their growth potential? (Letter) Br J Obstet Gynaecol (1994),100:87. Davies PSW, Lucas A. Quetelet's index as a measure of body fatness in young infants. Early Human Development (1989), 20:135-141. De Muylder X, De Somer ML, Bouckaert A. Early labour symphysis-fundus height as a predictor of birthweight. Ann Soc Belge Med Trop (1988), 68:61-65. Depares JC, Thornton JG, Clayden AD. Symphysis-fundus measurements in Asian and Caucasian women in Bradford. Eur J Obstet Gynecol Reprod Biol (1989),31:201-206. Deter RL, Harrist RB,Hadlock FP, Poindexter AN. Longitudinal studies of fetal growth with the use of dynamic image ultrasonography. Am J Obst Gyn (1982), 143(5):545-554. Deter RL, Harrist RB,Hill RM. Neonatal growth assessment score: A new approach to the detection of intrauterine growth retardation in the newborn. Am J Obstet Gynecol (1990), 162:1030-1036. Deter RL, Hill RB, Tennyson RN: Predicting the birth characteristics of normal fetuses 14 weeks before delivery. J Clin Ultrasound (1989),17:89-93. Deter RL, Rossavik IK, Carpenter RJ. Development of individual growth standards for estimated fetal weight: II.Weight prediction during the third trimester and at birth. J Clin Ultrasound (1989),17:83-88. Deter RL, Rossavik IK, Harrist RB, Hadlock FP. Mathematic modelling of fetal growth: Development of individual growth curve standards. Obstet Gynecol (1986), 68(2):156-161. Deter RL, Rossavik IK, Harrist RB. Development of individual growth curve standards for estimated fetal weight:I. Weight estimation procedure. J Clin Ultrasound (1988),16:215-225 Deter RL, Rossavik IK. A simplified method for determining individual growth curve standards. Obstet Gynecol (1987), 70(5):801-805. Dombrowski MP, Tomlinson MW, Bottoms SF, Sokol RJ. Computerised admission forms: a better idea. Abstracts: Fourth World Symposium 'Computers in Obstetrics, Neonatology and Gynaecology' Munich 1994. Doring GK. Uber die Tragzeit post ovulationem. Geburtshilfe Frauenheilkunde (1962),22:1191-1195.
160
Doubilet PM, Benson CB. Fetal growth disturbances. Seminars in Roentgenology (1990), 25(4):309-316. Dougherty CRS, Jones AD. The determinants of birth weight. Am J Obstet Gynaecol (1982), 144:190. Dubowitz LMS, Goldberg C. Assessment of gestation by ultrasound in various stages of pregnancy in infants differing in size and ethnic origin. Br J Obstet Gynaecol (1981),88:255-259. Dudley JN, Lamb MP, Hatfield JA et al. Estimated fetal weight in the detection of the small-for-menstrual-age fetus. J Clin Ultrasound (1990),18:387-393. Dunn PM. The creation of a perinatal growth chart for international reference. In :Fetal growth retardation: Diagnosis and treatment. Eds Beazley JM, Kurjak A. CRC Press, Boca Raton, Florida,1989. Elejalde BR, Giraldo C, Jimenez R, Gilbert EF. Acrocephalopolydactylous dysplasia. Birth Defects, Orig Art Series (1977),13(3B):53-67. Evans T, Farrant P, Gowland M et al. Report of the Fetal Measurements Working Party of the BMUS (1990). Ewigman BG, Crane JP, Frigoletto FD, LeFevre ML, Bain RP, McNellis D. Effect of prenatal ultrasound screening on perinatal outcome. NEJM (1993), 329 (12):821-827. Ewigman B, LeFevre M, Hesser J. A randomised trial of routine prenatal ultrasound. Obstetrics Gynecol (1990),76(2):189-194. Falkner F.Some introductory concepts of human growth: an overview. Acta Paediatr Scand (1985), Suppl. 319:17-20. Farmer RM, Medearis AL, Hirata GI, Platt LD. The use of a neural network for the ultrasonographic estimation of fetal weight in the macrosomic fetus. Am J Obstet Gynecol (1992), 166(5):1467-1472. Favre R, Nisand G, Bettahar K, Grange G, Nisand I.Measurement of limb circumferences with three-dimensional ultrasound for fetal weight estimation. Ultrasound Obstet Gynecol (1993), 3:176-179. Fay RA, Dey PL, Saadie CMJ et al. Ponderal index: A better definition of the 'at risk' group with intrauterine growth problem than birth-weight for gestational age in term infants. Aust NZ J Obstet Gynaecol (1991),31(1):17-19. Fedrick J, Adelstein P. Factors associated with low birth weight of infants delivered at term. Br J Obstet Gynaecol (1978), 85:1-7. Fescina RH, Martell M, Martinez G, Lastra L, Schwarcz R.Small for dates: evaluation of different diagnostic methods. Acta Obstet Gynecol Scand (1987),66:221-226.
161
Forbes JF, Smalls MJ. A comparative analysis of birthweight for gestational age standards. Br J Obstet Gynaecol (1983), 99: 297-303. Gairdner D, Pearson J. A growth chart for premature and other infants. Arch Dis Child (1971), 46:783-787. Gallivan S, Robson SC, Chang TC, Vaughan J, Spencer JAD.An investigation of fetal growth using serial ultrasound data. Ultrasound Obstet Gynecol (1993), 3:109-114. Gann P, Nghiem L, Warner S. Pregnancy characteristics and outcomes of Cambodian refugees. Am J Public Health (1989),79:1251-1257. Gardosi J. Is obstetric and neonatal outcome worse in fetuses who fail to reach their own growth potential? (Correspondence) Br J Obstet Gynaecol (1993), 100:295. Gardosi J. Is obstetric and neonatal outcome worse in fetuses who fail to reach their own growth potential? (Correspondence) Br J Obstet Gynaecol (1994), 100:295. Gardosi J, Chang A, Kalyan B et al. Customised antenatal growth charts. Lancet (1992), 339:283-287. Gardosi J, Mongelli M. Risk assessment adjusted for gestational age in maternal serum screening for Down's syndrome. BMJ (1993), 306:1509. Gardosi J, Mongelli M, Chang A. Screening and assessment of fetal growth. in : European Community Concerted Action Project: 'New methods for perinatal surveillance', Eds. HP Van Geijn & Copray. Elsevier 1994. Chapter 11,437-450. Gardosi J, Mul T, Mongelli M, Wilcox M. Ultrasound dating and birth weight at term. BMJ (1994), 308:1635. Gardosi J, Mul T, Mongelli M. Ultrasound dating and birth weight at term. BMJ (1994), 308:1635. Garin YJ, Blot P, Walter P, Pinon JM, Vernes A. Malarial infection of the placenta. Parasitologic, clinical and immunological aspects. Arch Fr Pediatr (1985), 42 (Suppl 2):917-920. Garn S. Pre-pregnancy weight. In: Maternal Nutrition and Pregnancy Outcomes. Eds: Krasovec K, Anderson MA. Pan American Health Organization, Scientific Publication No. 529 (1990), 69-85. Geirsson RT. Ultrasound instead of last menstrual period as the basis of gestational age assignment.Ultrasound Obstet Gynecol (1991),1:212-219. Geirsson RT, Persson P. Diagnosis of intrauterine growth retardation using ultrasound. Clinics in Obstetrics and Gynaecology (1984), 11(2):457-479.
162
Georgieff MK, Sasanow SR, Chockalingam UM et al. A comparison of the mid-arm circumference/head circumference for the evaluation of newborn infants after abnormal intrauterine growth. Acta Paediatr Scand (1988),77: 214-219. Gerhard I, Vollmar B, Runnebaum et al: Weight percentile at birth. II Prediction by endocrinological and sonographic measurements. Eur J Obstet Gynecol Reprod Biol (1987) , 26:313-328. Goldenberg RL, Cutter GR, Hoffman HJ et al. Intrauterine growth retardation: standards for diagnosis. Am J Obstet Gynecol (1989),161:271-277. Goldenberg RL, Davis RO, Cutter GR, Hoffman HJ, Brumfield CG, Foster JM. Prematurity, postdates and growth retardation: The influence of use of ultrasonography on reported gestational age. Am J Obstet Gynecol (1989), 160(2):462-470. Goldkrand JW, Fox E, Foggo BM. Coronal biparietal diameter. A reliable alternative to the traditional transverse biparietal diameter. J Ultrasound in Med (1990), 9(10): 555-557. Greenberger PA, Patterson R. Beclomethasone dipropionate for severe asthma during pregnancy. Ann Intern Med (1983), 98:478-480. Grover V, Usha R, Kalra S, et al. Altered fetal growth: antenatal diagnosis by symphysis-fundal height in India and comparison with western charts. Int J Gynecol Obstet (1991), 35:231-234. Gruenwald P. Growth of the human fetus. I. Normal growth and its variations. Am J Obstet Gynecol (1966), 94:1112-1119. Guidetti DA, Divon MY, Braverman JJ et al. Sonographic estimates of fetal weight in the intrauterine growth-retarded population. Am J Perinatol (1990), 7(1):5-7. Hadlock FP, Harrist RB, Carpenter RJ. Sonographic estimation of fetal weight. Radiology (1984),150:535-540. Hadlock FP. Harrist RB, Shah YP et al. Sonographic growth standards - are current data applicable to a racially mixed population? J Ultrasound in Medicine (1990), 9:157-160. Hadlock FP, Harrist RB, Martinez-Poyer J. In-utero analysis of fetal growth: a sonographic weight standard. Radiology (1991),181:129-133. Hadlock FP, Harrist RB, Sharman RS, Deter RL, Park SK.Estimation of fetal weight with the use of head, body and femur measurements - a prospective study. Am J Obstet Gynecol (1985), 151(3):333-337. Harvey D, Prince J, Bunton J, Parkinson C, Campbell S.Abilities of children who were small for gestational age babies. Pediatrics (1982),69: 296-300.
163
Hakala TH, Ylikorkala O. Effective prenatal care decreases the incidence of low birthweight. Am J Perinatol (1989),6(2): 222-225. Hedriana HL, Moore TR. A comparison of single versus multiple growth ultrasonographic examinations in predicting birth weight. Am J Obstet Gynecol (1994), 170(6):1600-1605. Henriksen TB, Wilcox A. Ultrasound dating subject to bias. BMJ (1994), 308:201. Henderson, M. Differences in the duration of pregnancy. Negro and white women of low socioeconomic class. Arch Environ Hlth (1967),14:904. Hendricks CH. Delivery patterns and reproductive efficiency among groups of differing socioeconomic status and ethnic origins. Am J Obstet Gynecol (1967), 97(5):608-624. Hepburn M ,Rosenberg K. An audit of the detection and management of small-for-gestational age babies. Br J Obstet Gynaecol (1986), 93:212-216. Heymsfield SB, Wang J, Lichtman S et al. Body composition in elderly subjects: a critical appraisal of clinical methodology. Am J Clin Nutr (1989), 50:1167-1175. Hill LM, Breckle R, Wolfgram KR, et al. Evaluation of three methods for estimating fetal weight. J Clin Ultrasound (1986),14:171-178. Hill RM, Verniaud WM, Deter RL, Tennyson LM, Rettig GM, Zion TE, Vorderman AL, Helms PG, McCulley LB, Hill LL.The effect of intrauterine malnutrition on the term infant. Acta Paediatr Scand (1984), 73:482-487. Holmes GE, Miller HC, Hassanein K,Lansky SB, Goggin JE.Postnatal somatic growth in infants with atypical fetal growth patterns. Am J Dis Child (1977), 131:1078-1083. Hughes K, Tan NR, Lun KC. Low birthweight of live singletons in Singapore. Int J Epidemiol (1984), 13:465-471. Issel EP. Der einfluss des mathematischen modells und die anzahl der messtrecken auf die genauigkeit der fetalen gewichtsschatzung mittels ultraschall. Ultraschall in Med.(1991), 12:111-118. Jeanty P, Cantraine F, Romero R. A longitudinal study of fetal weight growth. J Ultrasound Med (1984), 3:321-328. Jensen OH, Larsen S. Evaluation of symphysis-fundus measurements and weighing during pregnancy. Acta Obstet Gynaecol Scand (1991),70:13-16. Johnson RW, Toshach C, Saginaw M. Estimation of fetal weight using longitudinal mensuration. Am J Obstet Gynecol (1954), 68(3):891-896.
164
Johnson JWC, Longmate JA, Frentzen B. Excessive maternal weight and pregnancy outcome. Am J Obstet Gynecol (1992),167:353-72. Jones RAK, Robertson NRC. Small for dates babies:are they really a problem? Arch Dis Child (1986), 61:877-880. Karlberg P,Niklasson A, Ericson A et al. A methodology for evaluating size at birth. Acta Paediatr Scand (1985), Suppl.319:26-37. Katz AI, Davison JM, Hayslett JP et al. Pregnancy in women with kidney disease. Kidney Int (1980),18:192-206. Keen DV, Pearse RG. Intrauterine growth curves:problems and limitations. Acta Paediatr Scand (1985), Suppl.319:52-54. Keirse M. Epidemiology and aetiology of the growth retarded baby. Clinics in Obstet & Gynaecol (1984), 11(2):415-433. Keirse M. Frequent prenatal ultrasound: time to think again. Lancet (1993), 342:878-9. Kessel E, Sastrawinata S, Mumford SD. Correlates of fetal growth and survival. Acta Paediatr Scand (1985), Suppl.319:120-127. Kirkup B, Welch G. 'Normal but dead':perinatal mortality in non-malformed babies of birth weight 2.5 Kg and over in the Northern Region in 1983. Br J Obstet Gynaecol (1990),97:381-392. Kuo H, Chang F, Wu C, Yao B, Liu C. The primary application of three-dimensional ultrasonography in obstetrics. Am J Obst Gynecol (1991),166:880-886. Kurtz AB, Wapner RJ, Kurtz RJ. Analysis of biparietal diameter as an accurate indicator of gestational age. J Clin Ultrasound (1980), 8:319-326. Kramer MS. Determinants of low birth weight: methodological assessment and meta-analysis. Bull WHO (1987), 65(5): 663-737. Kramer MS, Olivier M, McLean FH, Willis DM, Usher RH.Impact of intrauterine growth retardation and body proportionality on fetal and neonatal outcome. Pediatrics (1990),86:707-713. Kramer MS, McLean FH, Olivier M et al. Body proportionality and head and length 'sparing' in growth retarded neonates: a critical reappraisal. Pediatrics (1989), 4:717-723. Larsen T, Petersen S, Greisen G et al . Normal fetal growth evaluated by longitudinal ultrasound examinations. Early Human Development (1990), 24: 37-45. Lazar P, Dreyfus J, Papiernik-Berkhauer E. Individual correction of birth weight for parental stature with special reference to small-for-date and large-for-date infants.J Perinat Med (1975),3:242-247.
165
Lechtig A. Predicting risk of delivering low birthweight babies:which indicator is better? J Trop Ped (1988), 34:34-41. Leff M, Orleans M, Haverkamp AD, Baron AE, et al.The association of maternal low birthweight and infant low birthweight in a racially mixed population. Pediatric and Perinatal Epidemiology (1992), 6:51 -61. Lee JN, Chard T. Determination of biparietal diameter in the second trimester as a predictor of intrauterine growth retardation. Int J Gynaecol Obstet (1983),21:213-215. Lemoine P, Harousseau H, Borteyru J. Children of alcoholic parents. Observed anomalies (127 cases). Quest Med (1968), 21:476-482. Lessoway VA, Schulzer M, Wittmann BK. Sonographic measurement of the fetal femur: factors affecting accuracy. J Clin Ultrasound (1990),18:471-476. Lindgren R, Selbing A, Leander E. Which fetal growth charts should be used? Acta Obstet Gynecol Scand (1988),67:683-687. Lindhard A, Nielsen PV, Mouritsen LA, et al. The implications of introducing the symphyseal-fundal-height measurement . A prospective randomised controlled trial. Br J Obstet Gynaecol (1990), 97:675-680. Loeffler FE. Clinical foetal weight estimation. J Obstet Gynaecol Brit Cwlth (1967), 74:675-677. Long PA, Abell DA, Beischer NA. Fetal growth retardation and pre-eclampsia. Br J Obstet Gynaecol (1980), 87:13-18. Lubchenko LO, Hansman C, Dressler M et al. Intrauterine growth as estimated from liveborn birth-weight data at 24 to 42 weeks of gestation. Pediatrics (1963), 32:793-800. Lucas A, Morley R. Birthweight ratio. Arch Dis Child (1991), 66:1099 -1104. Lucas A, Cole J, Candy. Birthweight centiles in preterm infants reappraised. Early Human Development (1986), 13:313-322. Magnus P, Berg K, Bjerkedal T. Association of parental consanguinity with decreased birth weight and increased rate of early death and congenital malformation. Clinical Genetics (1985), 28:335-342. Mamelle N, Laumon B. Nouvelles limites de poids de naissance pour le diagnostic de retard de croissance intra-uterin. Rev Fr Gynecol Obstet (1989), 84(7-9):589-591. Mayhew T. Scaling placental oxygen diffusion to birthweight: studies on placentae from low- and high- altitude pregnancies. J Anat (1991), 175:187-194.
166
Mathai M, Jairaj P, Muthurathnam S. Screening for light-for-gestational-age infants: a comparison of three simple measurements. Br J Obstet Gynaecol (1987), 94:217-221. Matsuda S, Sone T, Doi T et al. Seasonal variation of mean birth weight in Okinawa. Jap J Hygiene (1992),46(6):1063-1070. McDonald E. Mensuration of the child in the uterus with new methods. JAMA (1906),47:1979. McGregor JA, Leff M, Orleans M, Baron A. Fetal gender differences in preterm birth: findings in a North American cohort. Am J Perinatol (1992), 9(1):43-48. McKeown T, Gibson JR. Observations on all births (23970) in Birmingham. Br J Soc Med (1951), 5:98-112. McKeown T, Record RG. Influence of pre-natal environment on correlation between birth weight and parental height. Am J Human Genet (1954), 6:457-463. McLean FH, Boyd ME, Usher RH. Post-term infants: Too big or too small? Am J Obstet Gynecol (1991),164:619-624. Meadows NJ, Till J, Leaf A, Hughes E, Jani B, Larcher V.Screening for intrauterine growth retardation using ratio of mid-arm circumference to occipitofrontal circumference. BMJ (1986), 292:1039-1040. Meredith HV. Body weight at birth of viable human infants:a worldwide comparative treatise. Human Biology (1970),42:217-264. Merz E, Grussner A, Kern F. Mathematical modelling of fetal limb growth. J Clin Ultrasound (1989),17:179-185. Merz E, Lieser H, Schicketanz KH et al. Predicting fetal weight by ultrasound. Ultraschall (1988), 9:15-24. Miller HC, Hassanein K. Diagnosis of impaired fetal growth in newborn infants. Pediatrics (1971), 48(4):511-522 . Miller JM, Kissling GA, Brown HL, Gabert HA. Estimated fetal weight:applicability to small- and large-for-gestational -age fetus. J Clin Ultrasound (1988),16:95-97. Milner RDG, Richards B. An analysis of birthweight by gestational age of infants born in England and Wales 1967 to 1971. J Obstet Gynaecol Br Commw (1974), 81:956-967. Miller HC. Intrauterine infection. An unmet challenge. Am J Dis Child (1981), 135:944-948. Mongelli M, Gardosi J. Accuracy of determining 'post-term' pregnancy. Br J Obstet Gynaecol (1994),101(8):733-734.
167
Morley R, Brooke OG, Cole TJ, Powell R, Lucas A. Birth weight ratio and outcome in preterm infants. Archives of Diseases in Childhood (1990), 65:30-34. Morton NE. Empirical risks in consanguineous marriages: birth weight, gestation time, and measurement of infants. Am J Hum Genet (1958), 10:344-349. Morrison J, Williams GM, Najman JM, Andersen MJ. The influence of paternal height and weight on birthweight. Aust NZ J Obstet Gynaecol (1991), 31(2):12. Murao, F. Measurements of the fetal liver size, hormonal level and pregnancy outcome. Gynaecol Obstet Invest (1991), 32:153-156. Naeye RL. Cytomegalic inclusion disease: the fetal disorder. Am J Clin Pathol (1967), 47:738-744. Naeye RL. Malnutrition. Probable cause of fetal growth retardation. Arch Pathol (1965), 79:284-291. Naeye RL, Blanc W, Leblanc W, Khatamee MA. Fetal complications of maternal heroin addiction: abnormal growth, infections, and episodes of stress. J Pediatr (1973), 83:1055-61. Naeye RL, Tafari N. Biologic bases for international fetal growth curves. Acta Paediat Scand (1985), Suppl.319:164-169. Neilson JP. Routine serial symphysis-fundal height measurement. In: Pregnancy and Chilbirth Module (eds. Enkin MW, Keirse MJNC, Renfrew MJ, Neilson JP), ‘Cochrane Database of Systematic Reviews’: Review No. 06889, 24 March 1993. Published through ‘Cochrane Updates on Disk’, Oxford: Update Software, 1993, Disk Issue 2. Neilson JP, Munjanja SP, Whitfield CR. Screening for small for dates fetuses: a controlled trial. BMJ (1984), 289: 1179-1182. Newnham JP, Evans SF, Michael CA, Stanley FJ, Landau LI.Effects of frequent ultrasound during pregnancy: a randomised controlled trial. Lancet (1993), 342:887-891. Niklasson A, Ericson A, Fryer JG et al. An update of the Swedish reference standards for weight, length and head circumference at birth for given gestational age. Acta Paediatr Scand (1991),80:756-762. Nishida H, Sakamoto S, Sakanque M. New fetal growth curves for Japanese. Acta Paediatr Scand (1985), Suppl 319:62-67. Oakley JR, Parsons RJ, Whitelaw AGL. Standards for skinfold thickness in British newborn infants. Arch Dis Child (1977), 52:287-290. Ogunranti JO. Fundal height in normal pregnant Nigerian women: anthropometric gravidogram.
168
Int J Gynecol Obstet (1990),33:299-305. Okonofua FE, Ayangade SO, Ajibulu OA. Ultrasound measurement of fetal abdominal circumference and the ratio of biparietal diameter to transverse abdominal diameter in a mixed Nigerian population. Int J Obst Gyn (1988), 27:1-6. Osinusi BO. Ultrasound femur length as a means of assessing gestational age among Nigerians. West African Journal of Medicine (1990),9(2):116-119. Osinusi BO, Okubanjo DA. Ultrasonic fetal head circumference as a means of assessing gestational age in Nigerians. West African J Med (1990), 9(1):22-25. Ott, WJ. Accurate gestation dating. Obstet Gynecol (1985), 66(3): 311-315. Ott WJ. The diagnosis of altered fetal growth. Obstetrics and Gynecology Clinics of North America (1988),15(2): 237-263. Ott WJ, Doyle S, Flamm S, Wittman J. Accurate ultrasonic estimation of fetal weight.Prospective analysis of new ultrasonic formulas. Am J Perinatol (1986), 3(4):307-310. Ott, W. Intrauterine growth retardation and preterm delivery. Am J Obstet Gynecol (1993),168 (6):1: 1710-1717. Ouellette EM, Rosett HL, Rosman NP, Weiner L. Adverse effects on offspring of maternal alcohol abuse during pregnancy. N Eng J Med (1977), 297:528-530. Ounsted M, Ounsted C. Maternal regulation of intrauterine growth. Nature (1966), 3: 995-997. Ounsted MK. Maternal constraint of fetal growth in man. Dev Med Child Neurol (1965), 7:479-491. Ounsted MK. Unconstrained fetal growth in man. Dev Med Child Neurol (1966), 8:3-8. Palmer J, Dillon-Baker C, Tecklin JS et al. Pregnancy in patients with cystic fibrosis. Ann Intern Med (1983), 99:596-600. Palo P, Erkkola R, Piiroinen O, Ruotsalainen P. Accuracy of ultrasonic fetal weight estimation and detection of small for gestational age fetuses. Am J Perinatol (1989), 6(4): 400-404. Parker AJ, Davies P, Mayho AM, Newton JR. The ultrasound estimation of sex-related variations of intrauterine growth. Am J Obstet Gynecol (1984), 149(6):665-669. Pattinson RC, Norman K, Odendaal HJ. The role of Doppler velocimetry in the management of high risk pregnancies. Br J Obstet Gynaecol (1994),101(2):114-120.
169
Patterson RM, Prihoda TJ, Gibbs CE, Wood RC. Analysis of birthweight percentile as a predictor of perinatal outcome. Obstet Gynecol (1986), 68(4):459-463. Patterson RM, Pouliot MR. Neonatal morphometrics and perinatal outcome: who is growth retarded? Obstet Gynecol (1986), 68(4):691-693 Pearce JM, Campbell S. A comparison of symphysis-fundal height and ultrasound as screening tests for light-for-gestational age infants. Br J Obstet Gynaecol (1987), 94:100-104 Pearce JM, Campbell S.Intra-uterine growth retardation. In: The management of labour. John Studd, editor. Blackwell Scientific Publications, 1985. pp 68-91. Pearce JM, Campbell S. Ultrasonic monitoring of normal and abnormal fetal growth. In: Modern management of high-risk pregnancy. Laursen NH, editor.Plenum Medical Book Co. 1983. pp 57-100. Persson PH. Fetal growth curves. In: Fetal Growth. Sharp F, Milner RDG, Fraser RB, editors. RCOG Publications 1989. pp 13-25. Persson PH, Grennert L, Gennser G. Impact of fetal and maternal factors on the normal growth of the biparietal diameter. Acta Obstet Gynecol Scand (1978), Suppl 78:21-27. Persson B, Stangenberg M,Lunell NO, Brodin U, Holmberg NG, Vaclavinkova V. Prediction of size of infants at birth by measurement of the symphysis-fundus height. Br J Obstet Gynaecol (1986), 93:206-211 Persson PH ,Weldner BM. Reliability of ultrasound fetometry in estimating gestational age in the second trimester. Acta Obstet Gynecol Scand (1986),65:481-483. Persson PH, Weldner BM. Intrauterine weight curves obtained by ultrasound. Acta Obstet Gynecol Scand (1986), 65:169-173 Petersen S, Gotfredsen A, Knudsen FU. Lean body mass in small for gestational age and appropriate for gestational age infants. J Pediatrics (1988), 113 (5):886-889. Piwoz EG, Peerson JM, Brown KH. Potential for misclassification of infants' growth increments by using existing reference data. Am J Clin Nutr (1992), 56:58-64. Polani PE. Chromosomal and other genetic influences on birth weight variation. In: Size at Birth, Ciba Foundation Symposium No.27 . Elsevier, Amsterdam, 1974,127-59. Powars D, Sandhu M, Niland-Weiss J, Johnson C, Bruce S, Manning PR. Pregnancy in sickle cell disease. Obstet Gynecol (1986), 67:217-228. Pschera H, Soderberg G. Estimation of fetal weight by external abdominal measurements. Acta Obstet Gynecol Scand (1984),63:175-179.
170
Quaranta P, Currell R. Prediction of small-for-dates infants by measurement of symphysial-fundal-height. Br J Obstet Gynaecol (1981),88:115-119. Ransome-Kuti, O. Intrauterine growth, birthweights and maturity of the African newborn. Acta Pediatr Scand (1985), Suppl.319:95-102. Rantakallio P. Fourteen year follow-up of children with normal and abnormal birth weight for their gestational age. Acta Paediatr Scand (1985),74: 62-69. Robinson HP, Fleming JEE. A critical evaluation of sonar 'crown-rump length' measurements. Br J Obstet Gynaecol 1975; 82:702-10. Robinson JS, Falconer J, Owens JA. Intrauterine growth retardation: clinical and experimental. Acta Paediatr Scand (1985), Suppl 319:135-142. Robson SC, Gallivan S, Walkinshaw SA, Vaughan J, Rodeck CH. Ultrasonic estimation of fetal weight: use of targeted formulas in small for gestational age fetuses. Obstet Gynecol (1993), 82(3):359-363. Rochelson B, Bracero LA, Porte J, Farmakides G.Diagnosis of IUGR as a two-step process with morphometric ultrasound and Doppler umbilical artery velocimetry. J Reprod Med (1992), 37(11):925-929. Roemer VM, Kieback DG, Buhler K. Gestationszeit und geburtsgewicht.3.Mitteilung: die plazenta und mutterliche kofaktoren. Z Geburtsh u Perinat (1991),195:195-208. Roemer VM, Kieback DG, Buhler K, Kohling U. Gestationsalter und geburtsgewicht.3.Mitteilung:ponderal-index, fetaler saure-basen-haushalt und hypotrophie. Z Geburtsh u Perinat (1991),195:239-249. Rogers MS, Chung TKH, Chang AMZ. Ultrasound fetal weight estimation: precision or guess-work? Aust NZ J Obstet Gynaecol (1993), 33(2):142-144. Rooth G, Meirik O, Karlberg P. Estimation of the 'normal' growth of Swedish infants at term. Acta Paediatr Scand (1985), Suppl 319:76-79. Rosenberg K, Grant JM, Tweedie I, Aitchison T. Measurement of fundal height as a screening test for fetal growth retardation. Br J Obstet Gynaecol (1982),89:447-450. Rosenberg SN, Verzo B,Engstrom JL et al. Reliability of length measurements for preterm infants. Neonatal Network (1992),11(2):23-27. Rossavik IK, Deter RL. Mathematical modelling of fetal growth:I.Basic principles. J Clin Ultrasound (1984),12:529-533.
171
Rossavik IK, Deter RL, Hadlock FP. Mathematical modelling of fetal growth.IV. Evaluation of trunk growth using the abdominal profile area. J Clin Ultrasound (1987),15:31-35. Rumbolz WL, McGoogan LS. Placental insufficiency and the small undernourished full term infant. Obstet Gynaecol (1953), 1(3):294-301. Sabbagha RE, Barton FB, Barton BA. Sonar biparietal diameter.I. Analysis of percentile growth differences in two normal populations using the same methodology. Am J Obstet Gynecol (1976), 126(4): 479-484. Sabbagha RE, Barton BA, Barton FB et al. Sonar biparietal diameter.II. Predictive of three fetal growth patterns leading to a closer assessment of gestational age and neonatal weight. Am J Obstet Gynecol (1976), 126(4): 485-490 Sabbagha RE, Hughey M, Dopp R. The assignment of growth-adjusted sonographic age (GASA): a simplified method. Obstet Gynecol (1978), 51:383-386. Sanderson DA, Wilcox MA, Johnson IR. The individualized birth weight ratio: a new method of identifying intrauterine growth retardation. Br J Obstet Gynaecol (1994),101:310-314. Sanderson DA, Wilcox MA, Johnson IR. Relative macrosomia identified by the individualized birth weight ratio. Acta Obstet Gynecol Scand (1994), 73:246-249. Sandmire HF. Whither ultrasonic prediction of fetal macrosomia? Obstet Gynecol (1993) 82:5, 860- 862. Sarmandal P, Bailey SM, Grant JM. A comparison of three methods of assessing inter-observer variation applied to ultrasonic fetal measurement in the third trimester. Br J Obstet Gynaecol (1989), 96:1261-1265. Sarmandal P, Grant JM. Effectiveness of ultrasound determination of fetal abdominal circumference and fetal ponderal index in the diagnosis of asymmetrical growth retardation. Br J Obstet Gynaecol (1990), 97:118-123. Sasanow SR, Georgieff MK, Pereira GR. Mid-arm circumference and mid-arm/head circumference ratios: Standard curves for anthropometric assessment of neonatal nutritional status. J Pediatrics (1986),109(2):311-315. Secher NJ, Hansen PK. A randomized study of fetal abdominal circumference and fetal weight estimation for detection of LGA infants in low risk pregnancies. Br J Obstet Gynaecol (1987), 94:105-109. Secher NJ, Hansen PK, Thomsen BL, Keiding N. Growth retardation in preterm infants. Br J Obstet Gynaecol (1987), 94:115-120. Secher NJ, Lundbye-Christensen S,Qvist I. An evaluation of clinical estimation of fetal weight and symphysis fundal distance for the detection of SGA infants.
172
Eur J Obstet Gynecol Reprod Biol (1990), 38:91-96. Seidman DS, Ever-Hadani P, Gale R. The effect of maternal weight gain in pregnancy on birth weight. Obstet Gynaecol (1989), 74(2): 240-246. Shalev E, Romano S, Weiner E et al.Predictive value of the femur length to abdominal circumference ratio in the diagnosis of intrauterine growth retardation. Isr J Med Sci (1991),27:131-133. Shami SA, Qadeer T, Schmitt LH et al. Consanguinity, gestational period and anthropometric measurements at birth in Pakistan. Ann Hum Biology (1991),18 (6):523-527. Shepard MJ, Richards VA, Berkowitz RL, et al. An evaluation of two equations for predicting fetal weight by ultrasound. Am J Obstet Gynecol (1982), 142:47-54. Shime J, Mocarski EJM, Hastings D, Webb GD, McLaughlin PR. Congenital heart disease in pregnancy: short and long-term implications. Am J Obstet Gynecol (1987), 156:313-322. Shinozuka N, Okai T, Kohzuma S et al. Formulas for fetal weight estimation by ultrasound measurements based on neonatal specific gravities and volumes. Am J Obstet Gynecol (1987), 157:1140-1145. Simmons K, Savage W, Nicholls B, Rao U. Fetal growth measured by ultrasound in Bengali women. J Obstet Gynaecol (1985), 5:233-236. Simon NV, Deter RL, Shearer DM. Prediction of normal fetal growth by the Rossavik growth model using two scans before 27 weeks, menstrual age. J Clin Ultrasound (1989),17:237-243. Simon NV, Levisky JS, Shearer DM et al. Influence of fetal growth patterns on sonographic estimation of fetal weight. J Clin Ultrasound (1987),15:376-383. Simon NV, O'Connor TJ, Shearer DM. Detection of intrauterine fetal growth retardation with abdominal circumference and estimated fetal weight using cross-sectional growth curves. J Clin Ultrasound (1990),18: 685-690. Snow MHL. Effect of genome on size at birth. In: Fetal Growth. Eds. Sharp F, Fraser RB & Milner RDG. RCOG (1989). pp 3-12. Speert H. Essays in eponymy. New York: Macmillan, 1958. Spiegelberg O. Lehrbuch der geburtshulfe fur arzte und studierende. 3:126, aufl. neu bearbeitet von Max Wiener,Lahr, 1891; M. Schauenburg, 1891 . Spinnato JA, Allen RD, Mendenhall HW. Birth weight prediction from remote ultrasonographic examination. Am J Obstet Gynecol (1989), 161(3): 742-747. Spinnato JA, Allen RD, Mendenhall HW. Birth weight prediction from remote ultrasound examination.
173
Obstet Gynecol (1988), 71(3): 893-898. Stefos T, Deter RL. Individual growth curve standards for fetal head and abdominal circumferences: effect of the type of measurement on growth prediction. J Clin Ultrasound (1989),17:33-36. Stefos T, Deter RL, Simon NV. Effect of timing of initial scan and interval between scans on Rossavik growth model specification. J Clin Ultrasound 17:319-325. Stein Z, Susser M. The Dutch famine, 1944-1945, and the reproductive process. I. Effects on six indices at birth. Pediatr Res (1975), 9:70. Stewart HL, Duration of pregnancy and postmaturity. JAMA (1952), 148(13):1079-1083. Taylor DJ, Howie PW. Fetal growth achievement and neurodevelopmental disability. Br J Obstet Gynaecol (1989), 96:789-794. Thaler I, Bronstein M, Rubin AE. The course of pregnancy associated with bronchiectasis. Br J Obstet Gynaecol (1986), 93:1006-1008. Thompson HO, Casaceli C, Woods JR. Ultrasonic fetal weight estimation by an integrated computer-assisted system: Can each laboratory improve its accuracy? Am J Obstet Gynecol (1990), 163(3):986-994. Thomson AM, Billewicz WZ, Hytten FE. The assessment of fetal growth. J Obstet Gynaec Brit Cwlth (1968), 75:903-916. Ulizzi L,Terrenato L. Natural selection associated with birth weight.VI. Towards the end of the stabilizing component. Ann Hum Genet (1992),56:113-118. Van der Spuy ZM, Steer PJ, McCusker M, Steele SJ, Jacobs HS. Outcome of pregnancy in underweight women after spontaneous and induced ovulation. BMJ (1983), 296:962-964. Vialet R, Mbaye K, de Mouzon J, Spira A. Comparaison par echographie de la croissance foetale des enfants de meres africaines et europeennes. J Gynecol Obstet Biol Reprod (1988), 17:1003-1010. Voigt M, Eggers H, Jahrig K, Grauel .Beziehungen zwischen Alter, Paritat, Korpergewicht und -lange der Mutter und dem geburtsgewicht der neugeborenen. Zentralblatt fur Gynakologie (1989), 11(6):337-349. Waldenstrom U, Nilsson S, Fall O, Axelsson O, Eklund G, Lindberg S, Sjodin Y. Effects of routine one-stage ultrasound screening in pregnancy: a randomised controlled trial. Lancet (1988), ii: 585-588. Wallin A, Gyllensward A, Westin B. Symphysis-fundus measurements in prediction of fetal growth disturbances. Acta Obstet Gynaecol Scand (1981), 60:317-323.
174
Walter PR, Garin Y, Blot P. Placental pathologic changes in malaria. A histologic and ultrastructural study. Am J Pathol (1982), 109(3):330-340. Walton A, Hammond S Maternal effects on growth and conformation in Shire horse-Shetland pony crosses. Proceedings of the Royal Society (1938), 125B:311. Warburton D, Naylor AF. The effect of parity on placental weight and birthweight: An immunological phenomenon? A report of the collaborative study of cerebral palsy. Am J Hum Genetics (1971), 23:41-54. Warsof SL, Gohari P, Berkowitz RL, et al. The estimation of fetal weight by computer-assisted analysis. Am J Obstet Gynecol (1977),128:881-92. Weiner CP, Robinson D. Sonographic diagnosis of intrauterine growth retardation using the postnatal ponderal index and the crown-heel length as standards of diagnosis. Am J Perinatol(1989), 6(4):380-383. Westin B. Gravidogram and fetal growth. Comparison with biochemical supervision. Acta Obstet Gynecol Scand (1977), 56:273-282. Wharton BA. Sorrento studies of birthweight. Acta Paediatr Scand (1985), Suppl 319:170-179. Whittle MJ et al. Discussion. in :Fetal Growth. Sharp F, Fraser RB and Milner RDG, Editors.RCOG 1989. pp196-202. Wilcox AJ. Birth weight, gestation, and the fetal growth curve. Am J Obstet Gynecol (1981), 139 (8):863-867. Wilcox M, Gardosi J, Mongelli M, Ray C, Johnson I. Birth weight from pregnancies dated by ultrasonography in a multicultural British population. BMJ (1993), 307:588-91. Wilcox M, IR Johnson, Maynard P. Individualized birth weight ratio: a more logical outcome measure than birthweight alone. Br J Obstet Gynaecol (1993), 100:342-347. Williams RL, Creasy RK, Cunningham GC, et al. Fetal growth and perinatal viability in California. Obstet Gynecol (1982), 59(5):624 - 632. Winick M. Cellular changes during placental and fetal growth. Am J Obstet Gynecol (1971), 109:166-176. Witter FR, Luke B. The effect of maternal height on birth weight and length. Early Human Development (1991), 25 :181 - 186. Wolfe HM, Gross TL, Sokol RJ. Recurrent small for gestational age at birth: perinatal risks and outcomes. Am J Obstet Gynecol (1987), 157:288-293. Wolfe HM, Brans YW, Gross TL. Correlation of commonly used measures of intrauterine growth with estimated neonatal body fat.
175
Biol Neonate (1990), 57:167-171. Yip R. Altitude and birth weight. J Pediatrics (1987), 111(6):1:869-876. Yudkin PL, Aboualfa M, Eyre JA, Redman CWG,Wilkinson AR. Influence of elective preterm delivery on birthweight and head circumference standards. Arch Dis Child(1987), 62:24-29. Yudkin PL, Harlap S. High birthweight in an ethnic group of low socio-economic status. Br J Obstet Gynaecol (1983), 90:291-296. Yudkin PL, Aboualfa M, Eyre JA et al. Influence of elective preterm delivery on birthweight and head circumference standards.Archives of Disease in Childhood (1987), 62:24-29. Yudkin PL, Aboualfa M, Eyre JA , Redman CWG, Wilkinson AR. New birthweight and head circumference centiles for gestational ages 24 to 42 weeks.Early Human Development (1987), 15:45-52. Zador IE, Sokol RJ, Chik L. Interobserver variability: a source of error in obstetric ultrasound. J Ultrasound Med (1988), 7:245 - 249. Zweig MH, Campbell G. Receiver-operator characteristic plots: A fundamental evaluation tool in clinical medicine. Clin Chem (1993), 39(4): 561-577.