ArticleDiabetes care2013
Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data.
Article in Diabetes care, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 148 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.
A Randomized Trial of Care Navigator to Support Younger Latinx Adults Newly Diagnosed With Type 2 Diabetes (AURORA)
Testing a New Population Management Model for Hypoglycemia Prevention in High-Risk KPNC Members
Who cites it
148 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Pharmacist Intervention for Safer Prescribing in Patients With Type 2 Diabetes at High Risk: A Randomized Clinical Trial.JAMA network open · 2026Trial
- Diabetes-related acute care utilization following initiation of automated insulin delivery (AID): A comparison of a disposable tubeless AID system and durable tubed systems.Journal of managed care & specialty pharmacy · 2026Article
- Novel type 1 diabetes polygenic scores improve identification of type 1 diabetes in diverse populations.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Primary Care-Initiated Continuous Glucose Monitoring in Adults With Insulin-Treated Diabetes.JAMA network open · 2026Article
- Glucagon-like peptide-1 receptor agonists for major cardiovascular and kidney outcomes in type 1 diabetes.Nature medicine · 2026Article
- The Development and Validation of Multivariable Electronic Health Record-Based Models to Predict Diabetic Ketoacidosis-Related Hospitalizations for Adults with Type 1 Diabetes.Diabetes technology & therapeutics · 2026Article
- Cardio-Kidney-Metabolic Therapy Use Among Adults With Type 1 Diabetes and Chronic Kidney Disease.Diabetes, obesity & metabolism · 2026Article
- Hospital-Treated Severe Hypoglycemia in Type 1 Diabetes in the US: A Retrospective Claims-Based Study of Diabetes-Related Complications and Direct Medical Costs.Advances in therapy · 2026Article
- New-onset prediabetes/diabetes worsens overall survival in patients with cancer: A real-world retrospective cohort study.Diabetes, obesity & metabolism · 2026Article
- Immune cell-based transcriptomic Mendelian randomization and colocalization study on type 1 diabetes.BMC medicine · 2025Article
- Type 2 diabetes mellitus, antidiabetics, and the risk of amyotrophic lateral sclerosis.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2025Article
- Equivalence of Type 2 Diabetes Prevalence Estimates: Comparative Study of Similar Phenotyping Algorithms Using Electronic Health Record Data.JMIR public health and surveillance · 2025Article
- Clinical and Demographic Characteristics Associated With Diabetes Remission in Six Integrated Health Care Systems: A Retrospective Cohort Study.Diabetes care · 2025 · on this mapArticle
- Initiation of Continuous Glucose Monitoring and Mortality in Type 2 Diabetes.Diabetes technology & therapeutics · 2025Article
- Developing and Validating Models to Predict Suboptimal Early Glycemic Control Among Individuals With Younger Onset Type 2 Diabetes.Medical care · 2025Article
- Trends and Disparities in Technology Use and Glycemic Control in Type 1 Diabetes.JAMA network open · 2025Article
- Trends in obesity and glucagon-like peptide-1 receptor agonist prescriptions in type 1 diabetes in the United States.Diabetes, obesity & metabolism · 2025Article
- Developing a Computable Phenotype for Identifying Children, Adolescents, and Young Adults With Diabetes Using Electronic Health Records in the DiCAYA Network.Diabetes care · 2025Article
88 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveTo create surveillance algorithms to detect diabetes and classify type 1 versus type 2 diabetes using structured electronic health record (EHR) data. RESEARCH DESIGN AND
methodsWe extracted 4 years of data from the EHR of a large, multisite, multispecialty ambulatory practice serving ∼700,000 patients. We flagged possible cases of diabetes using laboratory test results, diagnosis codes, and prescriptions. We assessed the sensitivity and positive predictive value of novel combinations of these data to classify type 1 versus type 2 diabetes among 210 individuals. We applied an optimized algorithm to a live, prospective, EHR-based surveillance system and reviewed 100 additional cases for validation.
resultsThe diabetes algorithm flagged 43,177 patients. All criteria contributed unique cases: 78% had diabetes diagnosis codes, 66% fulfilled laboratory criteria, and 46% had suggestive prescriptions. The sensitivity and positive predictive value of ICD-9 codes for type 1 diabetes were 26% (95% CI 12-49) and 94% (83-100) for type 1 codes alone; 90% (81-95) and 57% (33-86) for two or more type 1 codes plus any number of type 2 codes. An optimized algorithm incorporating the ratio of type 1 versus type 2 codes, plasma C-peptide and autoantibody levels, and suggestive prescriptions flagged 66 of 66 (100% [96-100]) patients with type 1 diabetes. On validation, the optimized algorithm correctly classified 35 of 36 patients with type 1 diabetes (raw sensitivity, 97% [87-100], population-weighted sensitivity, 65% [36-100], and positive predictive value, 88% [78-98]).
conclusionsAlgorithms applied to EHR data detect more cases of diabetes than claims codes and reasonably discriminate between type 1 and type 2 diabetes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.