ArticleJournal of the American Heart Association2025
Claims-Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke.
Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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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.
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Who cites it
3 citing papers in PubMed.
- The association of a common HFE gene variant with stroke disability utilizing predictive machine learning and electronic health records.Scientific reports · 2026Article
- Choice of antiseizure medications and associated outcomes in Medicare beneficiaries after acute ischemic stroke.Epilepsia · 2025Article
- Choice of Epilepsy Anti-Seizure Medications and Associated Outcomes in Medicare Beneficiaries.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundWe developed a classifier to infer acute ischemic stroke severity from Medicare claims using the modified Rankin Scale at discharge. The classifier can be used to improve stroke outcomes research and support the development of national surveillance tools.
methodsThis multistate study included all participating centers in the Paul Coverdell National Acute Stroke Program database from 9 US states. This database was linked to Medicare data sets for patients hospitalized with acute ischemic stroke, employing demographics, admission details, and diagnosis codes to create unique patient matches. We included Medicare beneficiaries aged 65 and older who were hospitalized for an initial acute ischemic stroke from January 2018 to December 2020. Using Lasso-penalized logistic regression, we developed and validated a binary classifier for modified Rankin Scale outcomes and as a secondary analysis we used ordinal regression to model the full modified Rankin Scale. Performance was evaluated on held-out test data using area under the receiver operator characteristic curve, receiver operator characteristic precision-recall, sensitivity, and specificity.
resultsWe analyzed data from 68 636 eligible patients. The mean age was 79.5 years old. Seventy-seven and a half percent of beneficiaries were White, 14% were Black, 2.6% were Asian, and 2% were Hispanic. The classifier achieved an area under the receiver operator characteristic curve score of 0.86 (95% CI, 0.85-0.86), sensitivity of 0.81 (95% CI, 0.80-0.81), specificity of 0.73 (95% CI, 0.72-0.74), and precision-recall area under the curve of 0.90 (95% CI, 0.90-0.91) on the test set.
conclusionsAmong Medicare beneficiaries hospitalized for acute ischemic stroke, the claims-based classifier demonstrated excellent performance in area under the receiver operator characteristic curve, precision-recall area under the curve, sensitivity, and acceptable specificity for modified Rankin Scale classification.
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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.