{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T22:09:33Z","timestamp":1793398173254,"version":"4.5.0"},"reference-count":19,"publisher":"BMJ","issue":"2","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"unspecified","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006942","name":"Medical University of South Carolina","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006942","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Core Center for Clinical Research, Improving Minority Health"},{"name":"NIH","award":["P30 AR072582"],"award-info":[{"award-number":["P30 AR072582"]}]},{"name":"NIH","award":["P30 AR072583"],"award-info":[{"award-number":["P30 AR072583"]}]},{"name":"NIH","award":["UL1 TR000062"],"award-info":[{"award-number":["UL1 TR000062"]}]},{"name":"BMS, EMD Serono, GSK, Janssen, Lupus Research Alliance"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Lupus Sci Med"],"accepted":{"date-parts":[[2024,9,18]]},"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to validate claims-based algorithms for identifying SLE and lupus nephritis (LN) in Medicare data, enhancing the use of the Lupus Index for geospatial research on SLE prevalence and outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively evaluated the performance of rule-based algorithms using the International Classification of Diseases, 10th Revision (ICD-10) codes to identify SLE and LN in a well-defined prospective longitudinal cohort of patients with and without SLE from a South Carolina registry and rheumatology outpatient clinics. The analysis included comparison of algorithms based on Medicare fee-for-service claims data to these rigorously phenotyped populations. The primary classification for SLE cases was based on the American College of Rheumatology and Systemic Lupus Erythematosus International Collaborating Clinics criteria for SLE and LN. Algorithms were based on the number of ICD-10 codes with and without a 30-day separation in the observation period, including all of 2016\u20132018.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The algorithm using two ICD-10 codes for SLE, with or without a 30-day separation, showed the best overall performance. For LN, specific ICD-10 codes outperformed combinations of SLE and renal\/proteinuria codes that were found in ICD-9.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The findings of this study highlight the performance of specific ICD-10 code algorithms in identifying SLE and LN cases within Medicare data, providing a valuable tool for informing use of the Lupus Index. This index allows for improved geographical targeting of clinical resources, health disparity studies and clinical trial site selection. The study underscores the importance of algorithm selection based on research objectives, recommending more specific algorithms for precise tasks like clinical trial site identification and less specific ones for broader applications such as health disparities research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/lupus-2024-001329","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T22:45:13Z","timestamp":1728945913000},"page":"e001329","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":11,"title":["Validating claims-based algorithms for a systemic lupus erythematosus diagnosis in Medicare data for informed use of the Lupus Index: a tool for geospatial research"],"prefix":"10.1136","volume":"11","author":[{"given":"Candace","family":"Feldman","sequence":"first","affiliation":[{"name":"Division of Rheumatology, Brigham and Women's Hospital, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey R","family":"Curtis","sequence":"additional","affiliation":[{"name":"Division of Clinical Immunology and Rheumatology, The University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3508-4094","authenticated-orcid":false,"given":"Jinoos","family":"Yazdany","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, University of California San Francisco School of Medicine, San Francisco, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5445-2182","authenticated-orcid":false,"given":"Peter","family":"Izmirly","sequence":"additional","affiliation":[{"name":"Medicine-Rheumatology, New York University Grossman School of Medicine, New York City, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5426-1359","authenticated-orcid":false,"given":"Jim C","family":"Oates","sequence":"additional","affiliation":[{"name":"Medicine, Medical University of South Carolina College of Medicine, Charleston, South Carolina, USA"},{"name":"Medical Service, Ralph H Johnson VA Medical Center, Charleston, South Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"2025091513475722000_11.2.e001329.1","first-page":"1296","article-title":"Exploration of machine learning methods to predict systemic lupus erythematosus hospitalizations","volume":"31","author":"Jorge","year":"2022","journal-title":"Lupus (Los 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the Connective Tissue Disease Screening Questionnaire among urban African-American women","volume":"14","author":"Karlson","year":"2005","journal-title":"Lupus (Los Angel)"},{"key":"2025091513475722000_11.2.e001329.9","doi-asserted-by":"publisher","DOI":"10.1097\/01.ASN.0000108969.21691.5D"},{"key":"2025091513475722000_11.2.e001329.10","doi-asserted-by":"publisher","DOI":"10.1002\/art.1780251101"},{"key":"2025091513475722000_11.2.e001329.11","doi-asserted-by":"crossref","DOI":"10.1186\/s12911-017-0518-1","article-title":"Word2Vec inversion and traditional text classifiers for phenotyping lupus","volume":"17","author":"Turner","year":"2017","journal-title":"BMC Med Inform Decis Mak"},{"key":"2025091513475722000_11.2.e001329.12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103258"},{"key":"2025091513475722000_11.2.e001329.13","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0281929","article-title":"Using a data-driven approach for the development and evaluation of 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Disability evaluation under social security: Ssa publication 64-039. 2019. 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