A Longitudinal Study Based on Secondary Usage of Electronic Health Record for Identification of Erectile Dysfunction (ED) Risk Factors and Identification of Patients

Abstract

ED affects one in five men in the United States, and its prevalence increases with age. Recognizing ED’s chronic nature and the need for comprehensive, long-term healthcare documentation with multi-factor analysis, our study utilized integrated and enriched electronic health records (EHR) from the Electronic MEdical Records and Genomics (eMERGE) cohort 3 at the Kaiser Permanente/Washington University site. We developed a novel method for identifying ED cases from multiple sources to classify individuals with ED. We then conducted inferential analysis using logistic regression and Cox proportional hazard regression models, with longitudinal trajectory analysis for ED. Our study provides new insights into disease pathogenesis, enables better clinical management, and ultimately aims to improve the quality of life and healthcare outcomes for affected individuals. The utilization of integrated EHR-based informatics holds great promise for accelerating the understanding and management of complex chronic conditions like ED.