Characterizing the Clinical and Genetic Spectrum of Polycystic Ovary Syndrome in Electronic Health Records

Context Polycystic ovary syndrome (PCOS) is one of the leading causes of infertility, yet current diagnostic criteria are ineffective at identifying patients whose symptoms reside outside strict diagnostic criteria. As a result, PCOS is under diagnosed and its etiology is poorly understood. Objective: We aim to characterize the phenotypic spectrum of PCOS clinical features within and across racial and ethnic groups. Methods: We developed a strictly defined PCOS algorithm (PCOS regex-strict ) using International Classification of Diseases, 9th and 10th edition (ICD9/10) and regular expressions mined from clinical notes in electronic health records (EHRs) data. We then systematically relaxed the inclusion criteria to evaluate the change in epidemiological and genetic associations resulting in three subsequent algorithms (PCOS coded-broad , PCOS coded-strict , PCOS regex-broad ). We evaluated the performance of each phenotyping approach and characterized prominent clinical features observed in racially and ethnically diverse PCOS patients. Results: The best performing algorithm was our PCOS coded-strict algorithm with a positive predictive value (PPV) of 98%. Individuals classified as cases by this algorithm had significantly higher body mass index (BMI), insulin levels, free testosterone values, and genetic risk scores for PCOS, compared to controls. Median BMI was higher in African American women with PCOS compared to White and Hispanic women with PCOS. Conclusions: PCOS symptoms are observed across a severity spectrum that parallels genetic burden. Racial and ethnic group differences exist in PCOS symptomology and metabolic health across different phenotyping strategies.