Does the encounter type matter when defining diabetes complications in electronic health records?
Open accessAbstract
Background: Electronic health records (EHRs) and claims records are widely used in defining type 2 diabetes mellitus (T2DM) complications across different types of health care encounters. Objective: This study investigates whether using different EHR encounter types to define diabetes complications may lead to different results when examining associations between diabetes complications and their risk factors in patients with T2DM. Research Design: The study cohort of 64,855 adult patients with T2DM was created from EHR data from the Research Action for Health Network (REACHnet), using the Surveillance Prevention, and Management of Diabetes Mellitus (SUPREME-DM) definitions. Incidence of coronary heart disease (CHD) and stroke events were identified using International Classification of Diseases (ICD)-9/10 codes and grouped by encounter types: (1) inpatient (IP) or emergency department (ED) type, or (2) any health care encounter type. Cox proportional hazards regression was used to estimate associations between diabetes complications (ie, CHD and stroke) and risk factors (ie, low-density lipoprotein cholesterol and hemoglobin A1c). Results: The incidence rates of CHD and stroke in all health care settings were more than twice the incidence rates of CHD and stroke in IP/ED settings. The age-adjusted and multivariable-adjusted hazard ratios for incident CHD and stroke across different levels of low-density lipoprotein cholesterol and hemoglobin A1c were similar between IP/ED and all settings. Conclusion: While there are large variations in incidence rates of CHD and stroke as absolute risks, the associations between both CHD and stroke and their respective risk factors measured by hazard ratios as relative risks are similar, regardless of alternative definitions.
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Models constructed with clinical data such as electronic health records, laboratory tests, and imaging are the most widely used due to their high accessibility.
“At the feature level, models constructed with clinical data such as EHR [148, 149], laboratory tests [150, 151], and imaging [152, 153] are the most widely used due to their high accessibility.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [148]
The Research Design passage describes a model using EHR data, and the passage labeled Abstract > Conclusion says EHRs are widely used to define type 2 diabetes complications in observational studies. The paper does not establish that clinical-data models are the most widely used or that high accessibility explains their use. This source supports the EHR example, but not those broader claims. (LLM, conf=0.84)
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Filling the Public Health Science Gaps for Diabetes With Natural Experiments
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Self-citation
- Authors
- Hong, D., Shen, Y., Monnette, A., Liu, S., Shao, H., Nauman, E., Price-Haywood, E., Hu, G., & Shi, L.
- Affiliations
- Tulane University (US)
- Pennington Biomedical Research Center (US)
- University of Florida (US)
- Louisiana Public Health Institute (US)
- Center for Outcomes and Health Services Research, Ochsner Health System, New Orleans, LA
- Corresponding author
- Hong, Dongzhe
- Monnette, Alisha
- Liu, Shuqian
- Shi, Lizheng
- Venue
- Medical Care
- Published
- 2020-05-14
- Volume
- 58
- Pages
- S53-S59
- ISSN
- 0025-7079 (ISSN-L)
- PMC
- PMC8424908
- OpenAlex
- W3005325749
- Cited by
- 4
- References
- 46
- Citations by year
- 2026: 1 · 2024: 1 · 2021: 1 · 2020: 1
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Care (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Civil War Book Review (submittedVersion)
- Funders
- National Institute of General Medical Sciences (U54GM104940)
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- type 2 diabetes, diabetes complications, coronary artery disease, stroke, electronic health records, inpatient encounters, emergency department visits, LDL cholesterol, HbA1c, risk factor association, incidence rates
- MeSH terms
- Aged, Coronary Disease, Diabetes Mellitus, Type 2, Emergency Service, Hospital, Female, Humans, Inpatients, Male, Patient Acceptance of Health Care, Risk Factors, Proportional Hazards Models, Stroke, Diabetes Complications, Electronic Health Records
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