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

ABSTRACT:

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. W

SUBMITTER: Li J 

PROVIDER: S-EPMC12694949 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature