A Claims-Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
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.
Methods
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.
Results
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.
Conclusion
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.
Clinical Perspective
What Is New?
Developed a novel claims-based classifier to infer acute ischemic stroke (AIS) severity using the Modified Rankin Scale (mRS) at discharge.
Integrated Medicare claims with clinical data from the stroke registry, utilizing penalized logistic regression for both binary and ordinal classification.
What Are the Clinical Implications?
Provides a robust tool for assessing stroke severity, which can enhance stroke outcomes research and quality improvement initiatives.
Supports the development of national surveillance tools, potentially guiding clinical decision-making and resource allocation in stroke care.
Research Perspective
What New Question Does This Study Raise?
How can claims-based severity classifiers be effectively integrated into existing stroke research and clinical practice to enhance outcome measurement?
To what extent is the classifier generalizable to diverse populations beyond Medicare beneficiaries?
What Question Should be Addressed Next?
Future research should evaluate the impact of incorporating such classifiers into risk adjustment processes and their effect on long-term stroke outcomes.
Investigate whether similar modeling approaches can be adapted for other patient groups and healthcare settings to improve surveillance and treatment strategies.
