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.