ArticleJournal of clinical epidemiology2025
Machine learning for predicting cardiovascular events in older adults with type 2 diabetes using Medicare claims and electronic health records.
Article in Journal of clinical epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectivesTo address the limitations of existing models for research and population health applications in older adults with type 2 diabetes, we developed and validated cardiovascular disease (CVD) and heart failure risk models using linked Medicare claims and electronic health records (EHRs; 2013-2020). STUDY DESIGN AND
settingThe study included adults aged >65 years with type 2 diabetes and ≥1 HbA
resultsThere were 14,776 patients with baseline CVD (mean [SD] age: 77 [8] years) and 10,679 without baseline CVD (mean [SD] age: 74 [7] years). Claims-only models achieved a c-statistic of 0.75 and a Brier score of 0.09 in patients with baseline CVD, while in those without baseline CVD, the c-statistic was 0.73, and the Brier score was 0.01. For both subgroups, calibration intercepts were ∼0, with slopes ∼1. Claims-EHR models provided similar performance.
conclusionIn older adults with diabetes, our models predicted 1-year cardiovascular outcomes with good discrimination and accuracy, independently of CVD history. PLAIN LANGUAGE SUMMARY: Older adults with type 2 diabetes have a high risk of heart disease, heart failure, and death, yet it is difficult to predict who is most at risk. Most existing prediction tools are designed for use during a single clinic visit, not for large health care databases that researchers use to study treatment safety and effectiveness. In this study, we developed computer-based models using Medicare claims data and, for some models, additional information from EHRs. These models predicted the chance of having a major heart event or dying within 1 year. We created separate models for people with and without existing heart disease because their risk factors differ. Our models accurately predicted risk in both groups. Adding EHR data did not improve performance compared to using claims data alone. This means that claims-only models can still be useful for researchers studying treatments in large health care databases. These models can help identify people at higher risk, guide research on diabetes medications, and support better planning for health care resources.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.