Automated Detection and Classification of Type 1 Versus Type 2 Diabetes Using Electronic Health Record Data

Klompas, Michael; Eggleston, Emma; McVetta, Jason; Lazarus, Ross; Li, Lingling; Platt, Richard

doi:10.2337/dc12-0964

Cited by 220Machine Learning in HealthcareMedical Coding and Health InformationDiabetes Management and Research

Automated Detection and Classification of Type 1 Versus Type 2 Diabetes Using Electronic Health Record Data

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Klompas, Michael, Eggleston, Emma, McVetta, Jason, Lazarus, Ross, Li, Lingling, Platt, Richard·2013·Diabetes Care

DOI 10.2337/dc12-0964PMC PMC3609529

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Abstract

OBJECTIVE: To create surveillance algorithms to detect diabetes and classify type 1 versus type 2 diabetes using structured electronic health record (EHR) data. RESEARCH DESIGN AND METHODS: We 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. RESULTS: The 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]). CONCLUSIONS: Algorithms 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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The study used a validated algorithm to discriminate type 2 diabetes from type 1 diabetes using a ratio of ICD-9 and ICD-10 codes.
“using a validated algorithm to discriminate from type 1 diabetes using a ratio of International Classification of Diseases (ICD)-9 and ICD-10 codes12”
From Race and ethnicity and pharmacy dispensing of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes (2024) · cites this paper as [12]
The Results (Candidate algorithms; page unavailable) describe a ratio of type 1 to type 2 ICD-9 codes, and the Validation section reports testing the optimized classification algorithm. The cited paper does not support the claim that the ratio used ICD-10 codes; it also says the optimized algorithm incorporated laboratory results and prescriptions, not just diagnosis-code ratios. (LLM, conf=0.90)
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Electronic Health Record Data
Klompas, M., Eggleston, E., McVetta, J., Lazarus, R. M., Phd, L. L., & Platt, R. G. (2015)
Self-citation
Development and validation of algorithms to classify type 1 and 2 diabetes according to age at diagnosis using electronic health records
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An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study
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Development and Validation of a Novel Algorithm for Classifying Diabetes Type in Adolescents and Young Adults Using Electronic Health Record Data
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2117-LB: Comparing Computable Phenotypes for Identification of Diabetes in an Integrated Health Care System
Zhou, H., ZHOU, M. M., McCarthy, D., Harrison, T. N., Mefford, M. T., CHANG, J., MENSAH, N., Chang, J. J., & REYNOLDS, K. (2025) · Diabetes · doi:10.2337/db25-2117-lb

Authors: Klompas, M., Eggleston, E., McVetta, J., Lazarus, R., Li, L., & Platt, R.
Affiliations: Harvard University (US)
Heliotropic Inc., Los Angeles, California
Corresponding author: Klompas, Michael
ORCID: Klompas, Michael
Lazarus, Ross
Li, Lingling
Platt, Richard
Venue: Diabetes Care
Publisher: American Diabetes Association
Published: 2013-03-14
Volume: 36
Issue: 4
Pages: 914-921
ISSN: 0149-5992 (ISSN-L)
1935-5548
DOI: 10.2337/dc12-0964
PMC: PMC3609529
OpenAlex: W2101071243
Cited by: 220
References: 23
Citations by year: 2026: 17 · 2025: 21 · 2024: 19 · 2023: 18 · 2022: 22 · 2021: 22 · 2020: 23 · 2019: 14 · 2018: 21 · 2017: 13 · 2016: 10 · 2015: 7 · 2014: 11 · 2013: 2
FWCI: 6.30
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/3.0/ (vor, 3093 d)
Locations: Diabetes Care (publishedVersion, cc-by-nc-nd)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Funders: Centers for Disease Control and Prevention (1P01HK00088, P01HK000088)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Keywords: type 1 diabetes, type 2 diabetes, diabetes detection, diabetes classification, electronic health records, surveillance algorithms, ICD-9 codes, C-peptide, autoantibodies, laboratory findings, prescription data
MeSH terms: Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Female, Humans, Infant, Infant, Newborn, Male, Middle Aged, Young Adult, Electronic Health Records

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Automated Detection and Classification of Type 1 Versus Type 2 Diabetes Using Electronic Health Record Data

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