Risk prediction models for early neurological deterioration after intravenous thrombolysis in acute ischemic stroke patients: a systematic review and meta-analysis
Abstract
Background: An increasing number of risk prediction models have been developed to predict early neurological deterioration (END) in patients with acute ischemic stroke after intravenous thrombolysis. However, the methodological quality and clinical applicability of these models remain unclear.
Objective: To systematically review studies on risk prediction models for END in patients with acute ischemic stroke treated with intravenous thrombolysis (IVT).
Design: Systematic review and meta-analysis of observational studies.
Methods: Seven databases (CNKI, Wanfang Database, VIP database, PubMed, Web of Science, the Cochrane Library, and Embase) were systematically searched from inception to December 20, 2025. Information extracted from the included studies comprised study design, data sources, outcome definitions, sample size, predictors, model development methods, and model performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess evaluate the risk of bias and applicability of the included studies.
Results: Eighteen studies were ultimately included, comprising 18 prediction models. All studies utilized logistic regression to predict END. The incidence of END after IVT among patients with acute ischemic stroke patients ranged from 7.7% to 31.3%. The baseline National Institutes of Health Stroke Scale (NIHSS) score was the most frequently identified predictor. All studies were rated as having a high risk of bias based on the PROBAST assessment,…
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