ArticleBMJ open diabetes research & care2024
Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study.
Article in BMJ open diabetes research & care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- Development and Validation of Nomogram-Based Predictive Models for Severe Hypoglycemia in Adults With Type 1 Diabetes Treated With Multiple Daily Injections: The SEHYPAN Study.Diabetes/metabolism research and reviews · 2026Observational
- Association of impaired awareness of hypoglycemia and real-time continuous glucose monitoring with the occurrence and frequency of severe hypoglycemia in adults with type 1 diabetes mellitus: a real-world panel data analysis from the PR-IAH study.Diabetology international · 2025Article
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5 authors at 3 institutions in 1 country.
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Abstract
introductionSevere hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures. RESEARCH DESIGN AND
methodsData from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics.
resultsData from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score.
conclusionsMachine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.
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