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

We developed a classifier to infer acute ischemic stroke (AIS) severity from Medicare claims using the Modified Rankin Scale (mRS) at discharge. The classifier can be utilized to improve stroke outcomes research and support the development of national surveillance tools.

This was a multistate study included all participating centers in the Paul Coverdell National Acute Stroke Program (PCNASP) database from nine U.S. states. PCNASP was linked to Medicare data sets for patients hospitalized with AIS, 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 AIS from January 2018 to December 2020. Using Lasso-penalized logistic regression, we developed and validated a binary classifier for mRS outcomes and as a secondary analysis we used ordinal regression to model the full mRS scale. Performance was evaluated on held-out test data using ROC AUC, ROC Precision-Recall, sensitivity, and specificity.

We analyzed data from 68,636 eligible patients. The mean age was 79.5 years old. 77.5% of beneficiaries were White, 14% were Black, 2.6% were Asian, and 2% were Hispanic. The classifier achieved an ROC AUC score of 0.85 (95%CI: 0.85-0.86), sensitivity of 0.81 (95%CI: 0.80-0.81), specificity of 0.73 (0.72 - 0.74), and Precision-Recall AUC of 0.90 (95%CI: 0.90-0.91) on the test set.

Among Medicare beneficiaries hospitalized for AIS, the claims-based classifier demonstrated excellent performance in ROC AUC, Precision-Recall AUC, sensitivity, and acceptable specificity for mRS classification.