ArticleEndocrine connections2026
Development and external validation of a parsimonious endocrine-metabolic model for 12-month pregnancy prediction in women with polycystic ovary syndrome (PCOS).
Article in Endocrine connections, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Background: Women with polycystic ovary syndrome (PCOS) exhibit diverse reproductive and metabolic phenotypes, complicating short-term pregnancy counseling. We developed and externally validated a pragmatic model to predict clinical pregnancy within 12 months. Methods: This retrospective cohort study utilized data from Center A (development) and Center B (validation). Predictors included routinely available anthropometric, reproductive endocrine, metabolic, and treatment markers. We developed a multivariable logistic regression model, evaluating discrimination (AUC and precision-recall), calibration (decile-based plots), and clinical utility via decision curve analysis (DCA). Clinical pregnancy, confirmed by ultrasonography, served as the reference standard. A web-based calculator was created for bedside risk estimation. Results: In the development cohort (n = 315; 167 pregnancies), the final model retained age, anti-Müllerian hormone (AMH), LH/FSH ratio, assisted reproductive treatment, HOMA-IR, and homocysteine. The model demonstrated good discrimination (AUC = 0.813) and remained robust after internal validation (AUC = 0.800; AUPRC = 0.823). Calibration showed strong agreement between predicted and observed probabilities, despite minor deviations at higher risk levels. DCA indicated higher net benefit compared to 'treat-all' or 'treat-none' strategies across relevant thresholds. External validation confirmed acceptable discrimination (AUC = 0.759), with modest calibration attenuation (slope = 0.681; intercept = -0.019). Conclusion: This endocrine-metabolic model provides individualized 12-month pregnancy predictions for women with PCOS, demonstrating validated discrimination and decision-analytic utility. While the web calculator facilitates clinical implementation, local recalibration is recommended to ensure accuracy across different populations.
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