ArticleThe Permanente journal2020
Identifying Patients with Rare Disease Using Electronic Health Record Data: The Kaiser Permanente Southern California Membranous Nephropathy Cohort.
Article in The Permanente journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Accuracy of ICD-10 coding for identifying immunoglobulin A nephropathy (IgAN) prior to 2023.Clinical kidney journal · 2025Article
- Advancements in Electronic Medical Records for Clinical Trials: Enhancing Data Management and Research Efficiency.Cancers · 2025Review
- Identifying alcoholic liver disease patients using electronic health records within an integrated health system.Translational gastroenterology and hepatology · 2024Article
- Data driven approach to characterize rapid decline in autosomal dominant polycystic kidney disease.PloS one · 2024Article
- A case study in applying artificial intelligence-based named entity recognition to develop an automated ophthalmic disease registry.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Epidemiology of cardiac amyloidosis in Germany: a retrospective analysis from 2009 to 2018.Clinical research in cardiology : official journal of the German Cardiac Society · 2023Article
- OARD: Open annotations for rare diseases and their phenotypes based on real-world data.American journal of human genetics · 2022Article
- Enhanced rare disease mapping for phenome-wide genetic association in the UK Biobank.Genome medicine · 2022Article
- Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Different approaches to improve cohort identification using electronic health records: X-linked hypophosphatemia as an example.Intractable & rare diseases research · 2021Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDeveloping 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.
objectiveTo evaluate the accuracy of identifying patients with biopsy-proven MN using the EHR in a large, diverse population of an integrated health system.
methodsA 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).
resultsOne 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%. DISCUSSION: 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.
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