Identification and replication of prediction models for ovulation, pregnancy and live birth in infertile women with polycystic ovary syndrome

Hongying Kuang, Susan Jin, Karl R. Hansen, Michael Peter Diamond, Christos Coutifaris, Peter Casson, Gregory Christman, Ruben Alvero, Hao Huang, G. Wright Bates, Rebecca Usadi, Scott Lucidi, Valerie Baker, Nanette Santoro, Esther Eisenberg, Richard S. Legro, Heping Zhang

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

STUDY QUESTION Can we build and validate predictive models for ovulation and pregnancy outcomes in infertile women with polycystic ovary syndrome (PCOS)? SUMMARY ANSWER We were able to develop and validate a predictive model for pregnancy outcomes in women with PCOS using simple clinical and biochemical criteria particularly duration of attempting conception, which was the most consistent predictor among all considered factors for pregnancy outcomes. WHAT IS KNOWN ALREADY Predictive models for ovulation and pregnancy outcomes in infertile women with polycystic ovary syndrome have been reported, but such models require validation. STUDY DESIGN, SIZE, AND DURATION This is a secondary analysis of the data from the Pregnancy in Polycystic Ovary Syndrome I and II (PPCOS-I and -II) trials. Both trials were double-blind, randomized clinical trials that included 626 and 750 infertile women with PCOS, respectively. PPCOS-I participants were randomized to either clomiphene citrate (CC), metformin, or their combination, and PPCOS-II participants to either letrozole or CC for up to five treatment cycles. PARTICIPANTS/MATERIALS, SETTING, AND METHODS Linear logistic regression models were fitted using treatment, BMI, and other published variables as predictors of ovulation, conception, clinical pregnancy, and live birth as the outcome one at a time. We first evaluated previously reported significant predictors, and then constructed new prediction models. Receiver operating characteristic (ROC) curves were constructed and the area under the curves (AUCs) was calculated to compare performance using different models and data. Chi-square tests were used to examine the goodness-of-fit and prediction power of logistic regression model. MAIN RESULTS AND THE ROLE OF CHANCE Predictive factors were similar between PPCOS-I and II, but the two participant samples differed statistically significantly but the differences were clinically minor on key baseline characteristics and hormone levels. Women in PPCOS-II had an overall more severe PCOS phenotype than women in PPCOS-I. The clinically minor but statistically significant differences may be due to the large sample sizes. Younger age, lower baseline free androgen index and insulin, shorter duration of attempting conception, and higher baseline sex hormone-binding globulin significantly predicted at least one pregnancy outcome. The ROC curves (with AUCs of 0.66-0.76) and calibration plots and chi-square tests indicated stable predictive power of the identified variables (P-values ≥0.07 for all goodness-of-fit and validation tests). LIMITATIONS, REASONS FOR CAUTION This is a secondary analysis. Although our primary objective was to confirm previously reported results and identify new predictors of ovulation and pregnancy outcomes among PPCOS-II participants, our approach is exploratory and warrants further replication. WIDER IMPLICATIONS OF THE FINDINGS We have largely confirmed the predictors that were identified in the PPCOS-I trial. However, we have also revealed new predictors, particularly the role of smoking. While a history of ever smoking was not a significant predictor for live birth, a closer look at current, quit, and never smoking revealed that current smoking was a significant risk factor.

Original languageEnglish (US)
Pages (from-to)2222-2233
Number of pages12
JournalHuman Reproduction
Volume30
Issue number9
DOIs
StatePublished - Apr 9 2015

Fingerprint

Ovulation Prediction
Polycystic Ovary Syndrome
Live Birth
Pregnancy Outcome
Ovulation
Pregnancy
Logistic Models
Smoking
Clomiphene
letrozole
Chi-Square Distribution
ROC Curve
Area Under Curve
Sex Hormone-Binding Globulin
Metformin
Sample Size
Calibration
Androgens
Linear Models
Randomized Controlled Trials

Keywords

  • calibration
  • conception
  • live birth
  • mathematical modeling
  • polycystic ovaries
  • prediction
  • pregnancy
  • receiver operating characteristic

ASJC Scopus subject areas

  • Reproductive Medicine
  • Obstetrics and Gynecology

Cite this

Identification and replication of prediction models for ovulation, pregnancy and live birth in infertile women with polycystic ovary syndrome. / Kuang, Hongying; Jin, Susan; Hansen, Karl R.; Diamond, Michael Peter; Coutifaris, Christos; Casson, Peter; Christman, Gregory; Alvero, Ruben; Huang, Hao; Bates, G. Wright; Usadi, Rebecca; Lucidi, Scott; Baker, Valerie; Santoro, Nanette; Eisenberg, Esther; Legro, Richard S.; Zhang, Heping.

In: Human Reproduction, Vol. 30, No. 9, 09.04.2015, p. 2222-2233.

Research output: Contribution to journalArticle

Kuang, H, Jin, S, Hansen, KR, Diamond, MP, Coutifaris, C, Casson, P, Christman, G, Alvero, R, Huang, H, Bates, GW, Usadi, R, Lucidi, S, Baker, V, Santoro, N, Eisenberg, E, Legro, RS & Zhang, H 2015, 'Identification and replication of prediction models for ovulation, pregnancy and live birth in infertile women with polycystic ovary syndrome', Human Reproduction, vol. 30, no. 9, pp. 2222-2233. https://doi.org/10.1093/humrep/dev182
Kuang, Hongying ; Jin, Susan ; Hansen, Karl R. ; Diamond, Michael Peter ; Coutifaris, Christos ; Casson, Peter ; Christman, Gregory ; Alvero, Ruben ; Huang, Hao ; Bates, G. Wright ; Usadi, Rebecca ; Lucidi, Scott ; Baker, Valerie ; Santoro, Nanette ; Eisenberg, Esther ; Legro, Richard S. ; Zhang, Heping. / Identification and replication of prediction models for ovulation, pregnancy and live birth in infertile women with polycystic ovary syndrome. In: Human Reproduction. 2015 ; Vol. 30, No. 9. pp. 2222-2233.
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T1 - Identification and replication of prediction models for ovulation, pregnancy and live birth in infertile women with polycystic ovary syndrome

AU - Kuang, Hongying

AU - Jin, Susan

AU - Hansen, Karl R.

AU - Diamond, Michael Peter

AU - Coutifaris, Christos

AU - Casson, Peter

AU - Christman, Gregory

AU - Alvero, Ruben

AU - Huang, Hao

AU - Bates, G. Wright

AU - Usadi, Rebecca

AU - Lucidi, Scott

AU - Baker, Valerie

AU - Santoro, Nanette

AU - Eisenberg, Esther

AU - Legro, Richard S.

AU - Zhang, Heping

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N2 - STUDY QUESTION Can we build and validate predictive models for ovulation and pregnancy outcomes in infertile women with polycystic ovary syndrome (PCOS)? SUMMARY ANSWER We were able to develop and validate a predictive model for pregnancy outcomes in women with PCOS using simple clinical and biochemical criteria particularly duration of attempting conception, which was the most consistent predictor among all considered factors for pregnancy outcomes. WHAT IS KNOWN ALREADY Predictive models for ovulation and pregnancy outcomes in infertile women with polycystic ovary syndrome have been reported, but such models require validation. STUDY DESIGN, SIZE, AND DURATION This is a secondary analysis of the data from the Pregnancy in Polycystic Ovary Syndrome I and II (PPCOS-I and -II) trials. Both trials were double-blind, randomized clinical trials that included 626 and 750 infertile women with PCOS, respectively. PPCOS-I participants were randomized to either clomiphene citrate (CC), metformin, or their combination, and PPCOS-II participants to either letrozole or CC for up to five treatment cycles. PARTICIPANTS/MATERIALS, SETTING, AND METHODS Linear logistic regression models were fitted using treatment, BMI, and other published variables as predictors of ovulation, conception, clinical pregnancy, and live birth as the outcome one at a time. We first evaluated previously reported significant predictors, and then constructed new prediction models. Receiver operating characteristic (ROC) curves were constructed and the area under the curves (AUCs) was calculated to compare performance using different models and data. Chi-square tests were used to examine the goodness-of-fit and prediction power of logistic regression model. MAIN RESULTS AND THE ROLE OF CHANCE Predictive factors were similar between PPCOS-I and II, but the two participant samples differed statistically significantly but the differences were clinically minor on key baseline characteristics and hormone levels. Women in PPCOS-II had an overall more severe PCOS phenotype than women in PPCOS-I. The clinically minor but statistically significant differences may be due to the large sample sizes. Younger age, lower baseline free androgen index and insulin, shorter duration of attempting conception, and higher baseline sex hormone-binding globulin significantly predicted at least one pregnancy outcome. The ROC curves (with AUCs of 0.66-0.76) and calibration plots and chi-square tests indicated stable predictive power of the identified variables (P-values ≥0.07 for all goodness-of-fit and validation tests). LIMITATIONS, REASONS FOR CAUTION This is a secondary analysis. Although our primary objective was to confirm previously reported results and identify new predictors of ovulation and pregnancy outcomes among PPCOS-II participants, our approach is exploratory and warrants further replication. WIDER IMPLICATIONS OF THE FINDINGS We have largely confirmed the predictors that were identified in the PPCOS-I trial. However, we have also revealed new predictors, particularly the role of smoking. While a history of ever smoking was not a significant predictor for live birth, a closer look at current, quit, and never smoking revealed that current smoking was a significant risk factor.

AB - STUDY QUESTION Can we build and validate predictive models for ovulation and pregnancy outcomes in infertile women with polycystic ovary syndrome (PCOS)? SUMMARY ANSWER We were able to develop and validate a predictive model for pregnancy outcomes in women with PCOS using simple clinical and biochemical criteria particularly duration of attempting conception, which was the most consistent predictor among all considered factors for pregnancy outcomes. WHAT IS KNOWN ALREADY Predictive models for ovulation and pregnancy outcomes in infertile women with polycystic ovary syndrome have been reported, but such models require validation. STUDY DESIGN, SIZE, AND DURATION This is a secondary analysis of the data from the Pregnancy in Polycystic Ovary Syndrome I and II (PPCOS-I and -II) trials. Both trials were double-blind, randomized clinical trials that included 626 and 750 infertile women with PCOS, respectively. PPCOS-I participants were randomized to either clomiphene citrate (CC), metformin, or their combination, and PPCOS-II participants to either letrozole or CC for up to five treatment cycles. PARTICIPANTS/MATERIALS, SETTING, AND METHODS Linear logistic regression models were fitted using treatment, BMI, and other published variables as predictors of ovulation, conception, clinical pregnancy, and live birth as the outcome one at a time. We first evaluated previously reported significant predictors, and then constructed new prediction models. Receiver operating characteristic (ROC) curves were constructed and the area under the curves (AUCs) was calculated to compare performance using different models and data. Chi-square tests were used to examine the goodness-of-fit and prediction power of logistic regression model. MAIN RESULTS AND THE ROLE OF CHANCE Predictive factors were similar between PPCOS-I and II, but the two participant samples differed statistically significantly but the differences were clinically minor on key baseline characteristics and hormone levels. Women in PPCOS-II had an overall more severe PCOS phenotype than women in PPCOS-I. The clinically minor but statistically significant differences may be due to the large sample sizes. Younger age, lower baseline free androgen index and insulin, shorter duration of attempting conception, and higher baseline sex hormone-binding globulin significantly predicted at least one pregnancy outcome. The ROC curves (with AUCs of 0.66-0.76) and calibration plots and chi-square tests indicated stable predictive power of the identified variables (P-values ≥0.07 for all goodness-of-fit and validation tests). LIMITATIONS, REASONS FOR CAUTION This is a secondary analysis. Although our primary objective was to confirm previously reported results and identify new predictors of ovulation and pregnancy outcomes among PPCOS-II participants, our approach is exploratory and warrants further replication. WIDER IMPLICATIONS OF THE FINDINGS We have largely confirmed the predictors that were identified in the PPCOS-I trial. However, we have also revealed new predictors, particularly the role of smoking. While a history of ever smoking was not a significant predictor for live birth, a closer look at current, quit, and never smoking revealed that current smoking was a significant risk factor.

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KW - live birth

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KW - polycystic ovaries

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KW - receiver operating characteristic

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