The details matter: personalized prediction of live birth after in vitro fertilization in women with polycystic ovary syndrome

Abstract

ObjectiveTo derive and internally validate a clinical prediction model for live birth in women with polycystic ovary syndrome (PCOS) undergoing in-vitro fertilization (IVF) DesignRetrospective cohort study SettingFour academic reproductive endocrinology clinics Patients207 women with PCOS confirmed by Rotterdam criteria undergoing their first fresh IVF cycle InterventionsNone Main Outcome MeasureThe primary outcome was cumulative live birth (LB) per IVF cycle start. This included any LB that resulted from either fresh embryo transfer or any subsequent frozen embryo transfer from embryos obtained at the index oocyte retrieval. A prediction model was derived using multivariable logistic regression. Covariates considered for inclusion in the prediction model included demographic characteristics, medical history, and prior fertility treatment. Predicted probabilities for live birth were calculated using the prediction model which included the 90% shrinkage factor for each adjusted OR. ResultsThe final model, based on maximization of the area under the receiver operating characteristic curve (AUROC), included age <35 years, White race, presence of polycystic ovaries on ultrasound (PCOM), normal BMI (<25 kg/m2), being metabolically healthy (no metabolic risk factors), and being a non-responder to ovulation induction (OI) agents including letrozole and clomiphene citrate. The AUROC for the model was 0.68 (95% CI: 0.60, 0.77). Predicted probabilities of live birth ranged from 8.1% (95% CI 2.8, 21.5) for a woman who had no favorable predictors to 74.2% (95% CI: 59.5, 84.9) for a woman who had all favorable predictors. ConclusionOur study demonstrates that in addition to anovulation, the underlying pathophysiology and associated co-morbidities alter the likelihood of a successful pregnancy in women with PCOS undergoing IVF. Further validation of this model is needed before it, can serve as a tool to personalize prediction estimates for probability of live birth in women with PCOS.