Development of a novel risk prediction and risk stratification score for polycystic ovary syndrome

Summary Objective The aim of this study was to develop a simple phenotypic algorithm that can capture the underlying clinical and hormonal abnormalities to help in the diagnosis and risk stratification of polycystic ovary syndrome (PCOS). Methods The study consisted of 111 women with PCOS fulfilling the Rotterdam diagnostic criteria and 67 women without PCOS. A Firth's penalized logistic regression model was used for independent variable section. Model optimism, discrimination and calibration were assessed using bootstrapping, area under the curve (AUC) and Hosmer‐Lemeshow statistics, respectively. The prognostic index (PI) and risk score for developing PCOS were calculated using independent variables from the regression model. Results Firth penalized logistic regression model with backward selection identified four independent predictors of PCOS namely free androgen index [β 0.30 (0.12), P = 0.008], 17‐OHP [β = 0.20 (0.01), P = 0.026], anti‐mullerian hormone [AMH; β = 0.04 (0.01) P < 0.0001] and waist circumference [β = 0.08 (0.02), P < 0.0001]. The model estimates indicated high internal validity (minimal optimism on 1000‐fold bootstrapping), good discrimination ability (bias corrected c ‐statistic = 0.90) and good calibration (Hosmer‐Lemeshow χ 2 = 3.7865). PCOS women with a high‐risk score (q1 + q2 + q3 vs q4) presented with a worse metabolic profile characterized by a higher 2‐hour glucose ( P = 0.01), insulin ( P = 0.0003), triglycerides ( P = 0.0005), C‐reactive protein ( P < 0.0001) and low HDL‐cholesterol ( P = 0.02) as compared to those with lower risk score for PCOS. Conclusions We propose a simple four‐variable model, which captures the underlying clinical and hormonal abnormalities in PCOS and can be used for diagnosis and metabolic risk stratification in women with PCOS.