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

Ky'Era V Actkins,Kritika Singh,Jeeyeon Cha,Melissa Wellons,Digna R Velez Edwards,Lea K Davis,Donald Hucks,Melinda Aldrich

doi:10.1210/clinem/dgaa675

Abstract

ContextPolycystic 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 underdiagnosed and its etiology is poorly understood.ObjectiveWe aim to characterize the phenotypic spectrum of PCOS clinical features within and across racial and ethnic groups.MethodsWe developed a strictly defined PCOS algorithm (PCOSkeyword-strict) using the International Classification of Diseases, ninth and tenth revisions and keywords 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 3 subsequent algorithms (PCOScoded-broad, PCOScoded-strict, and PCOSkeyword-broad). We evaluated the performance of each phenotyping approach and characterized prominent clinical features observed in racially and ethnically diverse PCOS patients.ResultsThe best performance came from the PCOScoded-strict algorithm, with a positive predictive value 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 females with PCOS compared to White and Hispanic females with PCOS.ConclusionsPCOS symptoms are observed across a severity spectrum that parallels the continuous genetic liability to PCOS in the general population. Racial and ethnic group differences exist in PCOS symptomology and metabolic health across different phenotyping strategies.

Highlights

Polycystic ovary syndrome (PCOS) is an endocrine disorder that is the leading cause of infertility in women
We developed a simple but stringent automated phenotyping algorithm that could be applied to electronic health records (EHRs) to identify women with confirmed
We identified women with PCOS within the Synthetic Derivative (SD) using a strict automated phenotyping algorithm that yielded a positive predictive value (PPV) of at least 90%

Summary

Introduction

Polycystic ovary syndrome (PCOS) is an endocrine disorder that is the leading cause of infertility in women. The genetic, environmental, and metabolic variables that contribute to its complex architecture influence clinical heterogeneity among individuals with PCOS, resulting in a broad spectrum of symptoms. The heterogenous clinical presentation of PCOS makes it difficult to diagnose and an estimated 75% of women with PCOS remain undiagnosed [1,2]. There are three commonly used diagnostic criteria for PCOS which each have specific symptom requirements for diagnosis. The first diagnostic definition was put forth by the National Institute of Health (NIH) who required an ovulatory phenotype (i.e., oligomenorrhoea) and hyperandrogenism for diagnosis [3,4]. The second diagnostic definition for PCOS was created by the European Society for Human

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