• Hang Ge, Hui Chang, Yu Wang, Jing Cong, Yang Liu, Bei Zhang, and Xiaoke Wu.
• Heilongjiang University of Chinese Medicine, Harbin Heilongjiang, China.
• Medicine (Baltimore). 2024 Apr 5; 103 (14): e37733e37733.

BackgroundThe mechanisms underlying ovulatory dysfunction in PCOS remain debatable. This study aimed to identify the factors affecting ovulation among PCOS patients based on a large sample-sized randomized control trial.MethodsData were obtained from a multi-centered randomized clinical trial, the PCOSAct, which was conducted between 2011 and 2015. Univariate and multivariate analysis using binary logistic regression were used to construct a prediction model and nomogram. The accuracy of the model was assessed using receiver operating characteristic (ROC) curves and calibration curves.ResultsThe predictive variables included in the training dataset model were luteinizing hormone (LH), free testosterone, body mass index (BMI), period times per year, and clomiphene treatment. The ROC curve for the model in the training dataset was 0.81 (95% CI [0.77, 0.85]), while in the validation dataset, it was 0.7801 (95% CI [0.72, 0.84]). The model showed good discrimination in both the training and validation datasets. Decision curve analysis demonstrated that the nomogram designed for ovulation had clinical utility and superior discriminative ability for predicting ovulation.ConclusionsThe nomogram composed of LH, free testosterone, BMI, period times per year and the application of clomiphene may predict the ovulation among PCOS patients.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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