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AAFCP Outstanding Research Paper Award
I have no financial interests or disclosures in the subject matter or with authors of the discussed publications.
List of Finalist articles are available at AAFCP.net.
Dr. Joseph Stanford will review two articles for which he was a co-author, a finalist paper and the award winning study.
Presentation of the Award
Call for papers announced: AAFCP website and newsletter IIRRM website NFP Professionals list serve
Journals Reviewed American Journal of Obstetrics and Gynecology Fertility and Sterility Human Reproduction Journal of Women’s Health Linacre Quarterly Obstetrics and Gynecology
Current Medical Research, United States Conference of Catholic Bishops (USCCB)
▪ Compilation of recently published studies relating to fertility awareness▪ Edited by Richard Fehring
Members of the Scientific Committee
▪ Julia Cataudella, MD, CCFP, NFPMC, FCP
▪ Robert Chasuk MD, CFCMC (Chair)
▪ Thomas Hilgers, MD, CFCMC, CFCE
▪ Amy Hogan, MD, CFCMC
▪ Elizabeth Tham MD, CFCMC
▪ Maria Wolfs, MD, M.H.Sc, FRCPC
AAFCP Board Liaison▪ Paul Kortz, RN
Quality of Research
Impact on Fertility Awareness and NaProTECHNOLOGY
Adherence to Ethical Standards of the AAFCP
Outstanding Research Paper Award Winner
Pilot Test and Validation of the Peak Day Method of Prospective Determination of Ovulation Against a Handheld Urine Hormone Monitor. Christina Porucznik, Kyley Cox, Karen Schliep, and Joseph StanfordBMC Women's Health, 2014, 14:4 (http://www.biomedcentral.com/1472-6874/14/4)
Honorable Mention:
Impact of Instruction in the Creighton Model FertilityCare Systemon Time to Pregnancy in Couples of Proven Fecundity: Results of a Randomised Trial. Joseph Stanford, Ken Smith, Michael Varner, Paediatric and Perinatal Epidemiology, 2014, 28, 391–399
Letrozole versus Clomiphene for Infertility in the Polycystic Ovary Syndrome Richard Legro, Robert Brzyski, Michael Diamond, Christos Coutifaris , William Schlaff, Peter Casson, Gregory Christman, Hao Huang, QingshangYan, Ruben Alvero, Daniel Haisenleder, Kurt Barnhart, G. Wright Bates, Rebecca Usadi, Scott Lucidi, Valerie Baker, J.C. Trussell, Stephen Krawetz, Peter Snyder, Dana Ohl, Nanette Santoro, Esther Eisenberg, HepingZhang for the NICHD Reproductive Medicine Network. N Engl J Med. 2014 Jul 10; 371(2):119-29
Luteal Phase Deficiency in Regularly Menstruating Women: Prevalence and Overlap in Identification Based on Clinical and Biochemical Diagnostic Criteria. Karen Schliep, Sunni Mumford, Ahmad Hammoud, Joseph Stanford, Kerri Kissell, Lindsey Sjaarda, Neil Perkins, Katherine Ahrens, Jean Wactawski-Wende, Pauline Mendola, Enrique Schisterman. J Clin Endocrinol Metab. 2014 Jun; 99(6):E1007-14
Pregnancy Success After Hysteroscopic Sterilization ReversalCharles Monteith, Gary Berger, Matthew Zerden. Obstetrics and Gynecology. 2014 Dec; 124(6):1183-9.
Outstanding Research Paper Award2014
Presented To
Christina Porucznik, Kyley Cox, Karen Schliep, and Joseph Stanford
In recognition of
“Pilot Test and Validation of the Peak Day Method of Prospective Determination of Ovulation Against a Handheld Urine Hormone Monitor”
Presented by the Science and Research Committee of the American Academy of FertilityCare Professionals
July 17, 2015
Impact of instruction in the Creighton Model
FertilityCare System on Time to Pregnancy
in Couples of Proven Fecundity: results of a
randomised trial
2015 July 17
Paediatric and Perinatal Epidemiology 2014;28:391-399
Joseph B. Stanford, MD, CFCMC
Ken R. Smith, PhD
Michael R. Varner, MD
Hypothesis
CrM instruction will result in higher
cumulative pregnancy probabilities early in
follow-up (shorter time to pregnancy) in
couples with proven fertility
Methods
Randomized trial
Intervention group taught the Creighton
Model FertilityCare System
Comparison group advised to have
intercourse 2-3 times per week and given
preconception health counseling
Methods
Single center study (University of Utah in
Salt Lake City)
Blinded hormonal monitoring of cycles for
both groups of women
Participants
Women between 18 and 35
Prior pregnancy within 8 years with same husband
No history to suggest subfecundity since last pregnancy
At least 3 normal cycles after discontinuing hormonal contraception
No prior knowledge of CrM or other formal fertility awareness methods
Recruitment
Asked not to start trying to conceive until one full cycle in the study.
Asked to identify intent at the beginning of each cycle:
to conceive
to avoid pregnancy
unsure
Study compensation
Initial compensation scheme
$25 per cycle participating in the study
New lump sum compensation scheme
$140 whenever get pregnant or complete 7 cycles
$20 only if get pregnant in the first cycle
Results
Total Screened=663
Not Eligible=337 Eligible=216
Did not want to wait=104
Randomized=143 Declined=73
Control=71 (71) CrM=72 (69)
Pregnant=56
49 births
Pregnant=54
49 births
Characteristics
Control CrMp
n=71 n=69
Age (Mean ± Std.) 28.1 ± 3.2 28.3±3.1 0.57
No. previous
pregnancies ≥2
40
56%
38
55%0.93
College graduate34
48%
40
58%0.20
Received lump-
sum
compensation
50
70%
50
73%0.75
Characteristics
Control CrMp
n=71 n=69
Family Income
≥$40,000/yr.
46
65%
39
58%0.45
Prior oral
contraceptive
use
69
97%
65
94%0.72
Alcohol use12
17%
7
10%0.23
Smoking12
17%
9
13%0.50
Control
N=229
CrM
N=245
To conceive 137
60%
132
54%
To avoid 78
34%
94
38%
Unsure 14
6%
19
8%
Cycle intent
Control
N=166
CrM
N=184
To conceive 133
80%
131
17%
To avoid 20
12%
34
18%
Unsure 13
8%
19
10%
Cycle intent, excluding first cycle
Control
N=229
CrM
N=245
To conceive 31% 36%
To avoid 9% 2%
Unsure 21% 5%
Crude fecundability by cycle intent
Control
N=71
CrM
N=69
9
13%
0
0%
Pregnant in first cycle
Cumulative proportion pregnant
all cycles, all intentions
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7
control LT
CrM LT
Cumulative proportion pregnant
exclude first cycle, intent to conceive
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2 3 4 5 6 7
control LT
CrM LT
Pregnancy rate and reimbursement
Per cycle reimbursement scheme
68% pregnant at one year (crude rate)
“Lump sum” reimbursement scheme
88% pregnant at one year (crude rate)
Did not vary between CrM and control
groups
Time to pregnancy: Cox model without
time-dependent variables
PredictorHazard
Ratio
95% Confidence
Interval
CrM group 0.69 0.44 1.08
Woman’s age 0.88 0.81 0.96
No. of previous
pregnancies ≥ 21.82 1.14 2.91
College graduate 2.05 1.22 3.46
Received lump sum
compensation1.57 0.93 2.64
Time to pregnancy: Cox model with time-
dependent variables
PredictorHazard
Ratio
95% Confidence
Interval
CrM Group 0.86 0.53 1.38
Age 0.89 0.81 0.98
No. of previous
pregnancies ≥ 2
1.590.97 2.60
College graduate 1.86 1.08 3.21
Received lump sum
compensation
1.500.86 2.60
Intent 10.7 4.7 24.4
Strengths
Randomization
Good follow-up
Standardized education with Creighton
Model FertilityCare System
Assessment of intention to conceive at
beginning of each cycle
Weaknesses
Instruction to avoid pregnancy in first cycle was not completely followed by control group.
Couples with proven fertility less likely to benefit from identifying fecund window.
Compensation distorted behavior and outcome (for both study groups).
Discussion
There was no statistical difference in time
to conception between couples assigned
to the CrMS and couples assigned to the
control group in couples of proven fertility.
Cumulative pregnancy probability in these
couples was
About 40% at first cycle
About 90% at sixth cycle
Discussion
Modest impacts of education and women’s
age in expected directions.
Payment schemes may affect time to
pregnancy.
Conclusions
Even couples wanting to become pregnant
may intend to avoid pregnancy in a
significant proportion of cycles.
Cycle intention has a very strong impact
on fecundability.
Conclusions
The intervention was confounded by the
control group being less willing to avoid in
the first cycle.
It is hard to get couples to wait if they want
to be pregnant!
Pilot test and validation of the Peak
Day Method of prospective
determination of ovulation against a
handheld urine hormone monitor
Christina A. Porucznik, PhD MSPH
Kyley J. Cox, MPH
Karen C. Schliep, PhD, MSPH
Joseph B. Stanford, MD, MSPH, CFCMC
2015 July 17BMC Women's Health 2014, 14:4
Motivation
Environmental monitoring at the time
of conception in population-based
studies.
The Peak Day Method
Not a method of natural family planning.
Not designed for avoiding pregnancy.
Not yet tested for achieving pregnancy.
Designed to teach women to identify
ovulation quickly, efficiently, cheaply.
Developed with consultation with several
experts.
The Peak Day Method
Streamlined observation of cervical fluid
Optional supplement of basal body temperature
Observe for cervical fluid each day and record:
Is cervical fluid is present? yes/no
Does it feel slippery?
Is it clear or mostly clear in color? yes/no
Is it stretchy? yes/no
Information conveyed in a brochure
3 pages of text & 3 sample charts
Peak Day
Not the same as CrM peak (or Billings
peak or ST peak)
Estimated day of ovulation
Two algorithms for Peak Day
“Last Day” algorithmPeak Day is the last day when the woman
observes any fluid with at least one of the
three qualities
“Best Quality” algorithm
Peak Day is the last day when the woman
observes the highest number of the three
qualities
Methods
98 women learned the Peak Day Method
from brochure (self-taught)
26 concurrently used blinded daily urine
hormone monitoring (estrone glucuronide and
luteinizing hormone)
Exposure questionnaire completed
immediately after identifying peak day
(medication use, hours of sleep, water
consumption)
The adapted Fertility Monitor
ClearBlue® Fertility Monitor
Blinded research version
Does not display fertility status
Measures LH and E2 urinary metabolites
Takes readings from day 2 to day 31 no
matter what happens to the hormone
measurements
“Gold standard” for occurrence and timing
of ovulation
Sample Chart (Last Day Algorithm)
Sample Chart (Best Day Algorithm)
Results
147 cycles
Woman selected a Peak Day in 130 (88%)
cycles
Completed periovulatory exposure survey in
122 (94%) of cycles with a Peak Day
Expert review of charts
Identify Peak Days to compare to woman-
chosen and to exclude fluid that occurs in the
last seven days of the cycle
Results
Woman-chosen Peak compared to expert
review chosen- last day algorithm
0
10
20
30
40
50
60
70
80
90
<-5 -5 -4 -3 -2 -1 0 1 2 3 4 5 >5
Fre
qu
en
cy
Day
Last Day Algorithm Agreement (n=110)
76%
Results
Woman-chosen Peak compared to expert
review chosen- best quality algorithm
0
2
4
6
8
10
12
14
16
18
20
<-5 -5 -4 -3 -2 -1 0 1 2 3 4 5 >5
Fre
qu
en
cy
Day
Best Quality Algorithm Agreement (n=20)
90%
Results
26 cycles compared with blinded urine LH
0
1
2
3
4
5
6
7
8
9
10
<-5 -5 -4 -3 -2 -1 0 1 2 3 4 5 >5
Fre
qu
en
cy
Day
Last Day Algorithm Agreement
±3 days in 77%
Results
26 cycles compared with blinded urine LH
0
1
2
3
4
5
6
7
8
9
10
<-5 -5 -4 -3 -2 -1 0 1 2 3 4 5 >5
Fre
qu
en
cy
Day
Best Quality Algorithm Agreement
±3 days in 92%
Discussion
Women are able to identify their Peak Day
(EDO) following self-instruction
Compared to expert reviewer
Compared to blind urine LH
Women completed exposure assessment
Strong agreement between expert
reviewers and women
Better agreement for “best quality” algorithm
Discussion
“Best Quality” algorithm had higher
sensitivity to detect the EDO compared to
“Last Day” algorithm
Likely day of ovulation (based on urine LH)
±3 days can be reliably determined using
the Peak Day “Best Quality” algorithm
Moving forward: The HOPE Study
Home Observation of Periconception
Exposures
Impact of environmental chemicals or other
exposures on fertility, pregnancy, newborn
health, and long-term growth and development
Funded by National Institute of Environmental Health Sciences
PI, Porucznik
Methods overview
Recruit couples as early as possible in their
attempts to conceive
Teach them the Peak Day Method (Best Quality)
for prospective identification of ovulation
Charting for ~10 months or until pregnancy
Have them collect biospecimens based upon
biological triggers (urine, saliva, semen)
Questionnaires for diet and lifestyle exposures
Impact
If conception
occurred that
cycle, we
have biomarkers
of exposure
at time of
conception and
implantation!
Current Status, 9 July 2015
Opened general recruiting in January 2012
and ended in March 2015
711 eligible individuals (1054 inquiries)
183 couples enrolled (51.5% of eligible)
124 pregnancies
87 live births to date
15 early pregnancy losses
Pregnancies by Cycle
Pregnant, Cycle 1
Pregnant, Cycle 2
Pregnant, Cycle 3
Study complete, never pregnant
18%
LFU/WD 9%
Still under observation Total Couples, n=183
Total Pregnant, n=124
Pregnant, Cycles 4-
10
Biospecimens
Urine (first-morning via polypropylene cups)Bisphenol-A (BPA)
TCAA (disinfection by-products in water)
Hormones (E1G, PdG, hCG)
Semen Total Count, morphology, and DNA damage
Saliva analytic method for BPA in development by CHT
HairEnrollment and 3 months after enrollment
Other Components
Daily Fertility Tracking
Expert review
585 cycles (81.4% of expected charts) returned to
study from 171 women
Mean 3.5 ± 3.1 charts per woman (Min 0, Max 12)
Compare a portion of cycles to urinary
biomarkers
Pregnancy Outcomes and medical records
Pregnancy complications, birth outcomes,
breastfeeding
Comparison of woman’s choice of peak with
reviewer’s choice (best quality algorithm)
0
50
100
150
200
250
300
350
400
450
500
<-5 -5 -4 -3 -2 -1 0 1 2 3 4 5 >5
Fre
quency
Day
No comparison:
12 cycles
Woman yes
Reviewer no
36 cycles
Woman no
Reviewer yes
20 cycles
Woman no
Reviewer no
Future Plans
PdG and E1G testing in urine to identify
time of ovulation
Further validation of Peak Day method for
estimating time of ovulation
Future Plans
hCG testing in cycles with urine, confirmed
ovulation, and eligible intercourse
Live birth v. pregnancy loss
Preterm birth
Impact of endocrine disruptors (BPA)
Oxidative stress and fecundity
