Development and Validation of a Web-Based Machine Learning Model for Predicting Early Neurological Deterioration Following Stroke Thrombolysis: Multicenter Study

Abstract

ENDRAS integrates END prediction into actionable clinical pathways, potentially improving postthrombolysis care through personalized monitoring strategies and targeted interventions. Its robust performance in merged cohorts, efficient computation time, and structured management framework address key challenges in stroke care while enhancing resource utilization. Further prospective validation across diverse populations is needed to fully establish ENDRAS as a standard clinical decision-support system, but its ability to identify high-risk patients early may significantly improve outcomes in AIS.

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