Claims-Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke

We 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.

This 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.

We 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.

Among 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.