Prediction model of persistent ovulatory dysfunction in Korean women with polycystic ovary syndrome

Abstract

Aim There is no validated tool to predict persistent ovulatory dysfunction after medication with oral contraceptives in women with polycystic ovary syndrome (PCOS), which is the most severe subtype of PCOS. We aimed to build a model to predict persistent ovulatory dysfunction after medication of oral contraceptives in women with PCOS. Methods A total of 286 patients with PCOS were treated with and without oral contraceptives at a tertiary academic medical center. Data were obtained from the electronic medical record system between January 2016 and March 2019. A risk prediction model was developed using multivariable logistic regression. Model 1 was based on age and chief complaints and Model 2 further included predictors evaluated during a clinic visit. Model 3 additionally included laboratory findings. Results Of the study population, ovulatory dysfunction was persistent in 117 patients (40.9%). Compared with the simple model (Models 1 and 2), the full prediction model (Model 3) had better Akaike's information criterion (286, 244 vs. 225) and the area under the curve (AUC) increased from 0.74 and 0.79 to 0.84. The full model included 7 covariates measured during the evaluation of PCOS, and two covariates were significant predictors of persistent ovulatory dysfunction in PCOS: age (OR 0.91; 95% CI 0.84–0.97), and anti‐Müllerian hormone (OR 1.17; 95% CI 1.09–1.26). This model demonstrated good discrimination (AUC, 0.84) and calibration (Hosmer–Lemeshow goodness of fit test, p = 0.74). Conclusions This prediction model was shown to be a useful method for predicting persistent ovulatory dysfunction after oral contraceptive medication in patients with PCOS.