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

Background: Early neurological deterioration (END) significantly worsens outcomes in patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis, yet clinicians lack reliable tools to identify high-risk patients who need intensified monitoring and preemptive interventions. Objective: This study aimed to develop and validate a high-performance machine learning model for END prediction that enables personalized risk-stratified management of patients with AIS after thrombolysis. Methods: This multicenter study analyzed 1927 patients with AIS who were treated with intravenous thrombolysis in 3 hospitals, comprising a development cohort (n=1361) from Lianyungang Clinical Medical College and an external validation cohort (n=566) from 2 independent hospitals. We systematically evaluated 27 clinical parameters using multiple machine learning algorithms to develop ENDRAS (Early Neurological Deterioration Risk Assessment Score), a prediction model based on 6 readily available clinical variables. Model performance was assessed through comprehensive metrics (area under the receiver operating characteristic curve, accuracy, precision, recall, F1-score) in both internal and external validation cohorts. Results: The XGBoost-based ENDRAS showed promising predictive performance (area under the receiver operating characteristic curve=0.988, 95% CI 0.983-0.993) using 6 readily available parameters: Trial of ORG 10172 in Acute Stroke Treatment classification, intracranial artery stenosis severity, National Institutes of Health Stroke Scale score, systolic blood pressure, neutrophil count, and red blood cell distribution width. We established a dual-pathway management protocol for stratifying patients into low-risk (<29%) and high-risk (≥29%) groups, where high-risk patients receive intensive monitoring with hourly assessments and expedited imaging, while low-risk patients follow a resource-optimized protocol without compromising safety. Implemented as a web-based calculator with a <0.02-second computation time, ENDRAS enables real-time clinical decision support at the point of care. Conclusions: 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.