faktor risiko dan hasil luaran early dan late
TRANSCRIPT
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OBSTETRICS
Incidence of preeclampsia: risk factors and outcomesassociated with early- versus late-onset diseaseSarka Lisonkova, MD, PhD; K. S. Joseph, MD, PhD
OBJECTIVE: The population-based incidence of early-onset (
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therefore carried out a population-basedstudy to describe the gestational age-
specic incidence of preeclampsia onset
among women with singleton pregnan-cies and to examine risk factors and birth
outcomes associated with early-onset
and late-onset disease.
MA T E R I A L S A N D METHODS
We included all singleton deliveries in
Washington State during the period from
2003 to 2008, utilizing information from2 large population databases: (1) the
Comprehensive Discharge Abstract Da-
tabase (CHARS) which included allhospitalizations in Washington State, and
(2) the Birth Events Record Database(BERD), which included birth records of
all live born infants and fetal deaths.Women with a diagnosis of preeclampsiaor eclampsia (henceforth referred to as
preeclampsia), including preeclampsia
superimposed on chronic hypertensionwere identied from the CHARS data-
base (International Classication of Dis-eases, ninth revision [ICD-9] diagnostic
codes 642.4, 642.5, 642.6, and 642.7).
Hospitalization records with a diag-nosis of preeclampsia were linkedto birth
records to obtain information about
gestational age at delivery, maternalcharacteristics, clinical risk factors, and
birth outcomes. The number of weeksbetween the hospitalization when the
preeclampsia diagnosis was made and
birthhospitalizationwas calculated basedon theCHARS and BERD record linkage.
Preeclampsia occurring at less than
34 weeks of gestation was identied asearly-onset disease, whereas preeclamp-
sia that occurred at 34 weeks or later waslabeled late-onset disease, irrespective of
the gestational week at delivery. Infantbirth records were also linked to CHARS(infant) hospitalization records to iden-
tify cases of severe neonatal morbidity
(see the following text).There were 484,111 women who were
residents of Washington State and whodelivered a singleton stillbirth or live
birth in a Washington State hospital be-
tween 2003 and 2008. Women with amissing estimate of gestation or gesta-
tional age at delivery less than 20 weeks
and women without a linkage betweenthe birth/fetal death certicate (BERD
database) and maternal hospitalizationdata (CHARS database) were excluded
(5.7%, n 27,443).
Maternal characteristics and clinicalrisk factors examined for potential as-
sociation with preeclampsia included
maternal age (younger than 20 and35 years old or older vs 20-34 years);
parity (number of previous live births,none vs 1 or more); marital status (single/
widowed/separated vs married/common
law); education (less than high schoolvs high school education or greater);
race (non-Hispanic white vs Hispanic,
African-American, Native-American, andother); smoking during pregnancy (yes/
no); infertility treatment (yes/no); dia-betes mellitus (yes/no); chronic hyper-
tension prior to pregnancy (yes/no);infants sex (male/female); and congenitalanomalies (yes/no).
Fetal death was dened as in utero or
intrapartum death of a fetus delivered at20 weeks gestation or later, neonatal
death was dened as a death of an infantwithin 28 days after birth, and perinatal
death included fetal or neonatal death.
Using birth hospitalization data for in-fants (obtained from the linked infants
birth and hospitalization records), the
following adverse birth outcomes wereidentied based on ICD-9 codes: bron-
chopulmonary dysplasia (BPD; code770.7), intraventricular hemorrhage
(IVH) grade III and IV (codes 772.13
and 772.14), periventricular leukomala-cia (PVL; code 779.7), retinopathy of
prematurity (ROP; code 362.2), necro-
tizing enterocolitis (NEC; code 777.5),and neonatal sepsis (code 771.81). Other
neonatal outcomes were identied frombirth records, namely, neonatal seizures,
Apgar score at 5 minutes, and neonatalintensive care unit (NICU) admission.Severe neonatal morbidity included
any of the following: a 5-minute Apgar
score of 3 or less, neonatal seizures, BPD,IVH grade III or IV, PVL, ROP, NEC, and
neonatal sepsis. The composite out-come of neonatal mortality/morbidity
included both neonatal death and severe
neonatal morbidity, whereas perinatalmortality/morbidity included perinatal
death and severe neonatal morbidity.
Small-for-gestational-age (SGA) infantswere dened as those weighing less than
the 10th percentile of the sex- and gesta-tional ageespecic birthweight reference
for the United States,26 whereas large-for-
gestational age infants were those weigh-ing over the 90th percentile. We used the
clinical estimate of gestation provided in
the data source because this is more ac-curate than gestational age estimated by
the last menstrual period.27,28
Gestational ageespecic rates of pre-
eclampsia were calculated using ongoing
pregnancies as the denominator. c2 tests
were used to assess the differences be-
tween rates of early-onset and late-onset
preeclampsia across maternal and clin-ical characteristics. The Cox regressionmodel, with preeclampsia onset as the
outcome and gestational age as the time
axis, was usedto estimate adjusted hazardratios (AHRs) and 95% condence in-tervals (CIs). This enabled us to create the
appropriate risk sets, with censoring of
subjects who developed preeclampsia orwho delivered at any particular gestation.
When the proportional hazards as-
sumption was not satised, we examined
the interaction term between the risk
factor and gestational age at diagnosiscategorized as less than 34 weeks and 34
weeks or longer and obtained AHRs for
both early-onset and late-onset pre-eclampsia separately. The Wald statisticwas used to assess statistical signicance
of the interaction terms. Shoenfeld re-
siduals were used to evaluate the pro-
portional hazards assumption of thenal model.29
Birth outcomes including fetal death,
perinatal death, and severe neonatal
morbidity were analyzed using thefetuses-at-risk approach. Under this
formulation, all fetuses at a specic
gestation were considered at risk foradverse outcomes at that gestation.
Thus, for example, all fetuses at 28 weekswith early-onset preeclampsia were
considered to be at risk of live birth,
stillbirth, neonatal death, or severeneonatal morbidity at 28 weeks, irre-
spective of whether they actually deliv-ered at 28 weeks or at a subsequent
gestational week.30,31 Two fetuses-at-riskebased logistic regression models
were used to estimate causal associations
between early-onset and late-onset pre-eclampsia and birth outcomes.
www.AJOG.org Obstetrics Research
DECEMBER 2013 American Journal of Obstetrics &Gynecology 544.e2
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TABLE 1
Maternal characteristics and clinical factors associated with early- and late-onset preeclampsia, singletondeliveries, Washington State, 2003-2008
Maternal
characteristics/clinicalfactors
Ongoingpregnanciesat 20 weeks Early-onset preeclampsia
Ongoingpregnanciesat 34 weeksa Late-onset preeclampsia
(n[ 456,668) (n[ 1752) Rate per 1000 (n[ 447,822) (n[ 12,449) Rate per 1000
Age, y
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These models were constructed using
ongoing pregnancies(ie,fetuses at risk)as the denominator32,33; all ongoing
pregnancies at 20 weeks gestation were
included in models examining birthoutcomes following early-onset pre-
eclampsia, whereas all ongoing preg-
nancies at 340 weeksgestation (amongwomen without early-onset preeclamp-
sia) were included in the denominatorfor birth outcomes following late-onset
preeclampsia.
In addition, we compared neonatal
outcomes between infants born tomothers with and without early-onset or
late-onset preeclampsia, adjusting forgestational age at delivery (traditional
analysis). This analysis used live birthsat a particular gestational age as the
denominator and provided a predictive
(noncausal) model comparing the oddsof adverse neonatal outcomes amongmothers with and without preeclampsia,
conditional on delivery of a live-born
infant at a specic gestational age.32,33
We further compared birth outcomes
between mothers with early-onset pre-eclampsia who delivered at 34 weeks or
longer, and mothers with late-onsetpreeclampsia (who, by denition, all
delivered at 34 weeks).
We performed sensitivity analyses
examining theeffect of obesity on theriskof early-onset and late-onset pre-
eclampsia and its association with birthoutcomes. Obesity was dened as a body
mass index (BMI) greater than 30 kg/m2.
Missing values for BMI (27.7%, 14.6%,
and 13.9% in the early-onset, late-onset,
and no preeclampsia groups, respec-tively) were imputed using multipleimputation procedures (proc MI, SAS
software, version 9.2; SAS Institute Inc,
Cary, NC). In addition, we adjusted fortime period (year of delivery) to address
the potential effects of changes in ob-stetric and neonatal practices.
All analyses were performed on pub-
licly accessible de-identied data. Anexemption from ethics approval was
granted by the Department of Social and
Health Services, State of Washington.Analyses were carried out using SAS
software, version 9.2 (SAS Institute Inc.,Cary, NC). A 2-tailed P < .05 was
considered signicant.
RESULTS
The study included 456,668 women who
delivered a singleton live birth or still-
birth between 2003 and 2008. The rate ofpreeclampsia was 3.11 per 100 singleton
deliveries (14,201 of 456,668), and the
rate of eclampsia was 4.12 per 10,000singleton deliveries (188 of 456,668).
The frequency of early-onset pre-eclampsia was 0.38 per 100 deliveries,
and the frequency of late-onset pre-
eclampsia was 2.72 per 100 deliveries(Table 1). The gestational ageespecic
incidence of preeclampsia increased withpregnancy duration, from 0.01 per 1000
ongoing pregnancies at 20 weeksgesta-
tion to 9.62 per 1000 ongoing pregnan-cies at 40 weeksgestation (Figure).
Women who were at the extremes ofmaternal age(younger than 20 or 35 years
TABLE 1
Maternal characteristics and clinical factors associated with early- and late-onset preeclampsia, singletondeliveries, Washington State, 2003-2008 (continued)
Maternal
characteristics/clinicalfactors
Ongoingpregnanciesat 20 weeks Early-onset preeclampsia
Ongoingpregnanciesat 34 weeksa Late-onset preeclampsia
(n[ 456,668) (n[ 1752) Rate per 1000 (n[ 447,822) (n[ 12,449) Rate per 1000
Congenital anomalies
Yes 2249 23 10.2 1949 66 33.9
No 454,419 1729 3.8 445,873 12,383 27.8
The number of pregnancies does not add up to the total in some categories because of missing values (missing values exceeding 3% were 4.1% for education, 3.6% for smoking, and 6.6% fornumber of prior live births in the early-onset preeclampsia group).
a Ongoing pregnancies without early-onset preeclampsia.
Lisonkova. Early- vs late-onset preeclampsia. Am J Obstet Gynecol 2013.
FIGURE
Gestational ageespecific incidence of preeclampsia, singletondeliveries, Washington State, 2003-2008
Lisonkova. Early- vs late-onset preeclampsia. Am J Obstet Gynecol 2013 .
www.AJOG.org Obstetrics Research
DECEMBER 2013 American Journal of Obstetrics &Gynecology 544.e4
http://www.ajog.org/http://www.ajog.org/ -
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old or older), African-American, un-married, and nulliparous had higher rates
of early-onset preeclampsia (Table 1).
Similarly, women who had diabetesmellitus or chronic hypertension, used
infertility treatment to conceive, and hadan infant with a congenital anomaly also
had higher rates of early-onset pre-
eclampsia. Women who were very young(younger than 20 years of age), unmar-
ried, nulliparous, had diabetes mellitusor chronic hypertension, used infertility
treatment to conceive, and were pregnant
with a male fetus also had higher rates oflate-onset preeclampsia. On the other
hand women who smoked or belongedto the other race category (ie, other
than non-Hispanic white, Hispanic,African-American and Native-American;
Table 2) had lower rates of late-onset
disease.Several risk factors were associated with
preeclampsia, without a signicant dif-
ference in adjusted hazard ratios for early-and late-onset disease (Table 2). These
included smoking during pregnancy(AHR, 0.87; 95% CI, 0.82e0.93 for both
early- and late-onset disease), unmarried
status (AHR, 1.14; 95% CI, 1.10e1.19),older maternal age (AHR, 1.15; 95% CI,
1.10e1.21), and infants sex (AHR for
male sex, 1.10; 95% CI, 1.06e1.14).
In contrast, several risk factors dif-fered signicantly in their association
with early- vs late-onset preeclampsia.
African-American race, chronic hyper-tension, and congenital anomalies were
more stronglyassociated with early-onsetdisease, whereas young maternal age(younger than 20 vs 20-35 years), other
race (not including African-American,
Hispanic or Native-American vs non-Hispanic white), nulliparity, and dia-
betes mellitus were more stronglyassociated with late-onset disease. Other
race had a protective effect on late-onset
disease compared with non-Hispanicwhite race (AHR, 0.68; 95% CI,
0.63e0.73).
Women with chronic hypertensionhad the highest risk for preeclampsia,
TABLE 2
Crude and adjusted hazard ratios for early- and late-onset preeclampsia, singleton deliveries, Washington State,2003-2008
Demographic/clinical factors
Preeclampsia, unadjusted analysis Preeclampsia, adjusted analysis
Early onset Late onset Early onset Late onset
HR 95% CI HR 95% CI AHR 95% CI AHR 95% CI
Age, y
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with more than a 10-fold higher rateof early-onset disease (AHR, 11.7; 95%
CI, 10.1e13.6) and an approximately
5-fold higher rate of late-onset disease(AHR, 5.8; 95% CI, 5.4e6.3) as
compared with women without chronichypertension.
The rates of all adverse birth
outcomes, except for large for gesta-tional age (LGA), were signicantly
higher among women with early-onsetpreeclampsia compared with women
without early-onset disease (Table 3).Among women with early-onset pre-
eclampsia, approximately 12%, deliv-
ered at 34 weeksgestation or later, andalmost one half of births (49.5%) were
very low birthweight (
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The adjusted rates of adverse birth
outcomes were also higher amongmothers with late-onset disease as
compared with mothers without pre-
eclampsia, although the differences inrates were substantially less and some
were not statistically signicant (Table 4).
The rates of fetal, neonatal, and perinatal
death, for example, were not signi
-cantly higher among women with late-onset preeclampsia (AOR, 1.29; 95%
CI, 0.81e1.96; AOR, 1.09; 95% CI,
0.61e1.96, and AOR, 1.19; 95% CI,0.83e1.69, respectively). Rates of SGA,
in contrast, were signicantly elevated
among mothers with late-onset disease(AOR, 2.68; 95% CI, 2.54e2.82).
From the prognostic perspective, live-born infants of mothers with early-onset
preeclampsia were less likely to die in the
neonatal period (AOR, 0.51; 95% CI,0.36e0.74) compared with those born at
the same gestation to mothers without
preeclampsia (Appendix;SupplementaryTable 1). However, these infants had
higher odds of severe neonatal morbidity(AOR, 1.35; 95% CI, 1.16e1.57), NICU
admission (AOR, 2.44; 95% CI,
2.13e2.80), and SGA (AOR, 2.78; 95%CI, 2.46e3.13). The unadjusted odds of
LGA were lower among infants of
mothers with late-onset disease (odds
ratio, 0.78; 95%CI, 0.73e
0.83), althoughadjustment for gestational age and other
covariates increased the relative odds
(AOR, 1.09; 95% CI, 1.02e1.16).Birth outcomes among women with
early-onset preeclampsia who deliveredat a gestation of 34 weeks or longer and
women with late-onset preeclampsia
were similar in terms of fetal andneonatal death (Table 5). However, the
rates of the most common outcomes,such as NICU admission and SGA, weresignicantly elevated among the early-
onset group (AOR, 2.22; 95% CI,1.60e3.07; and AOR, 1.66; 95% CI,
1.24e2.23, respectively).
Sensitivity analyses showed that highBMI was a stronger risk factor for early-
onset than for late-onset disease (AHR,
2.10; 95% CI, 1.91e2.32; and AHR, 1.71;95% CI, 1.65e1.77, respectively), similar
to the association between chronic hy-
pertension and the preeclampsia sub-types. The AHR for chronic hypertension
decreased after additional adjustment forobesity (AHR, 9.4; 95% CI, 8.2e10.9; and
AHR, 2.24; 95% CI, 2.11e2.38, for early-
and late-onset preeclampsia, respec-tively). The associations between early-
and late-onset preeclampsia and birthoutcomes were not appreciably affected
by additional adjustment for BMI except
for LGA, which was no longer signi-cantly elevated among live-born infants
of mothers with late-onset preeclampsia(AOR, 1.03; 95% CI, 0.97e1.10).
Additional adjustment for time period(year of birth) did not change the results.
Women excluded from the study weredifferent from the study population
with regard to several risk factors for
preeclampsia. Some risk factors (suchas African-American race, no prior
live births, chronic hypertension, and
congenital anomalies) were overrep-resented among the excluded women,
whereas other risk factors (such as oldermaternal age, Hispanic and Native-
American race, single parent status, and
diabetes mellitus) were less frequent thanexpected (Appendix; Supplementary
Table 2).
COMMENT
Our population-based study showed
that the gestational ageespecic inci-
dence of preeclampsia among womenwith singleton pregnancies increased
sharply with gestational age. The rate ofearly-onset disease (
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maternal age were associated with ahigher risk of late-onset preeclampsia.
Early-onset preeclampsia conferred a
substantially higher risk for adverse birthoutcomes than late-onset preeclampsia.
In contrast, the prognosis for neonataldeath among infants born to women
with early-onset preeclampsia was better
than the prognosis for infants born at thesame gestation because of other causes.
The lower risk of neonatal death amonginfants born to mothers with pre-
eclampsia at preterm gestation has been
reported previously.2,16,17 This differ-
ence in prognosis, however, was condi-
tional on a live birth and not evidentin the causal fetuses-at-risk analysis
(Table 4). Furthermore, the rates ofNICU admission and SGA were signi-
cantly higher among infants born to
mothers with early-onset preeclampsia.Approximately 12% of the women with
early-onset preeclampsia delivered at agestation of 34 weeks or longer.
Our population-based study exam-
ined gestational ageespecic rates ofpreeclampsia in a large cohort of sin-
gletons. Overall rates of preeclampsiawere consistent with previous estimates
from industrialized countries, which
have reported preeclampsia rates be-tween 3 and 5 per 100 deliveries2,3 and
eclampsia rates between 2.7 and 8.2 per10,000 deliveries.2,7
Although the causes of preeclampsiaarenot known, placental dysfunction has
been implicated in its origins,18,20,34 and
placental morphology studies suggestthat preeclampsia is a heterogeneous
entity.34-36 Our results show that effect of
risk factors such as race/ethnicity, nulli-parity, chronic hypertension, and dia-
betes vary according to the subtype ofpreeclampsia. For example, congenital
anomalies were more strongly associated
with early-onset disease, suggesting thepresence of associated placental abnor-
malities that affect perfusion and result
in early-onset disease. In contrast, astronger positive association between
diabetes mellitus and late-onset pre-eclampsia suggests that relative placental
insuf
ciency is more likely to occur indiabetic pregnancies with a larger fetus.We also observed a stronger association
between early-onset disease and SGA (as
compared with late-onset disease andSGA), likely because of the profound
effects of poor placental perfusion earlyin gestation and differences in disease
severity.20,37
Our study has a few limitations.
Gestational age at the onset of pre-
eclampsia was estimated based on the
time between hospital admission forpreeclampsia and hospital admission fordelivery. We were not able to capture
women with preeclampsia who were not
hospitalized andthose who did notdeliverin the hospital. However, such missed
cases of preeclampsia were likely milder
cases that did not result in serious com-plications requiring hospitalization. In
some cases, theonset of preeclampsia mayhave occurred a few days before the
admission to the hospital. The delay be-
tween the onset of preeclampsia andadmission to the hospital may have
resulted in some misclassication of early-
onset disease as late-onset preeclampsia.As with any administrative database,
the accuracy of diagnoses was contingenton documentation and abstraction from
medical records. However, it has been
shown that linkage between hospitali-zation data and birth/infant death cer-
ticates increases data accuracy and that
the accuracy of major obstetric diagnoses
and procedures is relatively high inWashington States linked data le.38
TABLE 5
Crude and adjusted odd ratios for birth outcomes contrastingearly-onset vs late-onset preeclampsia, singleton deliveriesat gestation of 34 weeks, Washington State, 2003-2008
Birth outcomes
Early-onset preeclampsiawith delivery at gestation34 weeks (n[ 213)
Late-onsetpreeclampsia(n[ 12,449) Pvaluea
Gestational age at delivery, wks
34-36 128 (60.1) 2911 (23.4) < .01
37-43 85 (39.9) 9538 (76.6)
Birthweight, g
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Other potential weaknesses of thestudy include limited information about
risk factors, such as BMI. Sensitivity
analyses showed that high BMI had astronger association with early- vs late-
onset preeclampsia (similar to chronic
hypertension), suggesting that metabolicsyndrome (associated with high BMI)
may play a stronger role in early-onset vslate-onset disease.
We did not have detailed information
about antenatal screening, and obstetricand neonatal care practices in the
different hospitals in Washington state
including use of aspirin among womenat high risk for preeclampsia. Potential
inaccuracies in diagnosis and differencesin intervention between physicians and
hospitals may have resulted in somenondifferential misclassication of riskfactors and outcomes and led to some
dilution in observed associations.
Finally, subjects excluded from thestudy because of missing information or
unlinked records were signicantlydifferent from those included in the
study, with respect to some maternal
characteristics, although the fractionexcluded was small (5.7%).
The strengths of our study include a
large study population and gestationalageespecic incidence rates dened us-
ing the onset of disease. The populationperspective obtained by using statewide
information minimized potential selec-
tion bias that may be present in hospital-based studies, especially those that
include selected hospital patients.30-33
The large study size offered statistical
power for analysis of rare adverse birthoutcomes. Finally, the use of the fetuses-
at-risk approach in addition to tradi-
tional perinatal modeling provides thecausal and prognostic perspectives on
the relation between preeclampsia andbirth outcomes.
In summary, population-based data
on singleton deliveries in WashingtonState between 2003 and 2008 showed
that the incidence of preeclampsiaincreased sharply with gestational age.
Even though some risk factors were
common to both early- and late-onsetdisease, several risk factors had quanti-
tatively different associations with the 2subtypes of preeclampsia. Early-onset
preeclampsia had far greater adverse ef-
fects on the fetus and infant comparedwith late-onset disease. Our study thus
conrms the heterogeneity of pre-
eclampsia and shows that the timing ofdisease onset is one important indicator
of disease severity and possibly of diseaseetiology. Research studies should treatthe 2 preeclampsia subtypes as distinct
entities from an etiological and prog-nostic standpoint. -
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AP P E N D I X
SUPPLEMENTARY TABLE 1
Crude and AORs for neonatal outcomes among livenewborns, following early- and late-onset preeclampsia,singleton deliveries, Washington State, 2003-2008a
Neonatal outcomes
Early-onset preeclampsia Late-onset preeclampsia
OR 95% CI AORa 95% CI OR 95% CI AORa 95% CI
SGA (90th percentile) 0.27 0.14e0.52 0.46 0.29e0.74 0.78 0.73e0.83 1.09 1.02e1.16
Apgar score at 5 min 3 0.46 0.36e0.59 0.84 0.64e1.10 2.62 2.14e3.22 1.98 1.59e2.46
NICU admission 1.90 1.66e2.17 2.44 2.13e2.80 3.82 3.62e4.03 1.65 1.55e1.76
Severe neonatal morbidityb 0.82 0.72e0.94 1.35 1.16e1.57 2.59 2.29e2.93 1.57 1.37e1.79
Neonatal death 0.29 0.22e0.39 0.51 0.36e0.74 1.31 0.78e2.19 0.71 0.39e1.28
Neonatal death/severe morbidity 0.75 0.66e0.85 1.18 1.01e1.37 2.50 2.21e2.82 1.51 1.32e1.72
Regression models adjusted for gestational age, race, parity, maternal age, maternal education, infants sex, marital status, infertility treatment, chronic hypertension, diabetes, and congenital
anomalies.
AOR, adjusted odds ratio; CI, confidence interval; LGA, large for gestational age; NICU, neonatal intensive care unit; OR, odds ratio; SGA, small for gestational age.
a Prognostic model comparing neonatal outcomes conditional on live birth, adjusted for gestational age at delivery. Fetal death was not included in the prognostic model because such deaths occurbefore birth; b Includes any of the following: a 5 minute Apgar score of 3 or less, bronchopulmonary dysplasia, necrotizing enterocolitis, neonatal seizures, neonatal sepsis, intraventricularhemorrhage grades 3 and 4, periventricular leukomalacia, and retinopathy of prematurity.
Lisonkova. Early- vs late-onset preeclampsia. Am J Obstet Gynecol 2013.
Research Obstetrics www.AJOG.org
544.e11 American Journal of Obstetrics &Gynecology DECEMBER 2013
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SUPPLEMENTARY TABLE 2
Demographic and clinical characteristics, singleton deliveries with andwithout inclusion criteria, Washington State, 2003-2008a
Demographic/clinical risk factors
Included Excluded
Pvaluebn[ 456,668 (%) n[27,443 (%)a
Age, y