ArticleArthritis care & research2017
Developing Electronic Health Record Algorithms That Accurately Identify Patients With Systemic Lupus Erythematosus.
Article in Arthritis care & research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it, 75 citations in OpenAlex.
- Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus.Nature communications · 2023Pooled it
- Validating ICD code case definitions for condition case ascertainment in multimorbidity measurement: a retrospective chart review.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Epidemiology of Traumatic Brain Injury in Medicare Beneficiaries over the Last Decade: A Retrospective Observational Study.Health science reports · 2026Article
- Development and validation of a multi-institutional electronic juvenile idiopathic arthritis phenotype.Seminars in arthritis and rheumatism · 2026Article
- Depression, anxiety, and post-traumatic stress disorder in association with cardiovascular disease among patients with systemic lupus erythematosus and rheumatoid arthritis in the All of Us Research Program.Clinical rheumatology · 2026Article
- Severe Maternal Morbidity During and After Pregnancy Among Veterans With Systemic Lupus Erythematosus or Rheumatoid Arthritis.ACR open rheumatology · 2025Article
- Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages.Nature communications · 2025Article
- Treatment trends of systemic lupus erythematosus from 2007 to 2023 in the USA.Lupus science & medicine · 2024Article
- 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.Lupus science & medicine · 2024Article
- Article
- A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model.Frontiers in immunology · 2024Article
- Article
- Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record.Arthritis & rheumatology (Hoboken, N.J.) · 2023Article
- Article
- Developing electronic health record algorithms that accurately identify patients with juvenile idiopathic arthritis.Seminars in arthritis and rheumatism · 2023Article
- Renin-Angiotensin System-Modifying Antihypertensive Drugs Can Reduce the Risk of Cardiovascular Complications in Lupus: A Retrospective Cohort Study.The American journal of medicine · 2023Article
- Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.PloS one · 2023Article
- Rates of Pneumocystis jirovecii pneumonia and prophylaxis prescribing patterns in a large electronic health record cohort of patients with systemic lupus erythematosus.Seminars in arthritis and rheumatism · 2022Article
- Ancestry,Lupus science & medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo study systemic lupus erythematosus (SLE) in the electronic health record (EHR), we must accurately identify patients with SLE. Our objective was to develop and validate novel EHR algorithms that use International Classification of Diseases, Ninth Revision (ICD-9), Clinical Modification codes, laboratory testing, and medications to identify SLE patients.
methodsWe used Vanderbilt's Synthetic Derivative, a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least 1 SLE ICD-9 code (710.0), yielding 5,959 individuals. To create a training set, 200 subjects were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive antinuclear antibody (ANA), ever use of medications, and a keyword of "lupus" in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5,759 subjects.
resultsThe algorithm with the highest PPV at 95% in the training set and 91% in the validation set was 3 or more counts of the SLE ICD-9 code, ANA positive (≥1:40), and ever use of both disease-modifying antirheumatic drugs and steroids, while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes.
conclusionWe developed and validated the first EHR algorithm that incorporates laboratory values and medications with the SLE ICD-9 code to identify patients with SLE accurately.
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