Identifying Patients with Rare Disease Using Electronic Health Record Data: The Kaiser Permanente Southern California Membranous Nephropathy Cohort

• DOI: 10.7812/tpp/19.126
• Publication Date: 2020-02-07T17:13:20Z
• Authors: Amy Z Sun; Yu-Hsiang Shu; Teresa N Harrison; Aviv Hever; Steven J Jacobsen; Michelle M O’Shau...; John J Sim
• Keywords: Male; Sensitivity and Specificity; 3. Good health; 03 medical and health sciences; Rare Diseases; 0302 clinical medicine; International Classification of Diseases; Electronic Health Records; Humans; Female; Algorithms; Retrospective Studies

Developing a reliable means to identify and study real-world populations of patients with membranous nephropathy (MN) using electronic health records (EHRs) would help advance glomerular disease research. Identifying MN cases using EHRs is limited by the need for manual reviews of biopsy reports.To evaluate the accuracy of identifying patients with biopsy-proven MN using the EHR in a large, diverse population of an integrated health system.A retrospective cohort study was performed between June 28, 1999, and June 25, 2015, among patients with kidney biopsy results (N = 4723), which were manually reviewed and designated as MN or non-MN. The sensitivity, specificity, and positive predictive value (PPV) of International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes were determined using 2 approaches: 1) clinical (MN-specific codes 581.1, 582.1, or 583.1) and 2) agnostic/data-derived (codes selected from supervised learning at the highest predictive performance).One year after biopsy, the sensitivity and specificity of an MN diagnosis were 86% and 76%, respectively, but the PPV was 26%. The data-driven approach detected that using only 2 codes (581.1 or 583.1) improved specificity to 94% and PPV to 58%, with a small decrease in sensitivity to 83%. When any code was reported at least 3 times, specificity was 98%; PPV, 78%; and sensitivity, 64%.Our findings suggest that ICD-9 diagnosis codes might be a convenient tool to identify patients with MN using EHR and/or administrative claims information. Codes selected from supervised learning achieved better overall performance, suggesting the potential of developing data-driven methods.