Scholarlink

Background <p>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.</p> Objective <p>To systematically review studies on risk prediction models for END in patients with acute ischemic stroke treated with intravenous thrombolysis (IVT).</p> Design <p>Systematic review and meta-analysis of observational studies.</p> Methods <p>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.</p> Results <p>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, primarily due to inconsistent outcome definitions, inappropriate data sources, and inadequate reporting within the analysis domain. The pooled area under the curve (AUC) for the five validated models was 0.87 (95% CI: 0.84–0.91), suggesting moderate predictive performance.</p> Conclusion <p>Although the included prediction models showed moderate discriminative performance for END in patients with acute ischemic stroke following intravenous thrombolysis, all studies were assessed as having a high risk of bias based on the PROBAST checklist. These findings emphasize the need for nurse-led risk monitoring, standardized neurological assessments, and early preventive nursing interventions for high-risk patients.</p> Clinical trial registration <p>The protocol for this study is registered with PROSPERO (registration number: CRD42025628890).</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Risk prediction models for early neurological deterioration after intravenous thrombolysis in acute ischemic stroke patients: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

查看全文
  • Yanqin Chen,
  • Caiyue Li,
  • Lijun Fan,
  • Wan Zhang,
  • Yujiao Ding,
  • Hua Zhao

摘要

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, primarily due to inconsistent outcome definitions, inappropriate data sources, and inadequate reporting within the analysis domain. The pooled area under the curve (AUC) for the five validated models was 0.87 (95% CI: 0.84–0.91), suggesting moderate predictive performance.

Conclusion

Although the included prediction models showed moderate discriminative performance for END in patients with acute ischemic stroke following intravenous thrombolysis, all studies were assessed as having a high risk of bias based on the PROBAST checklist. These findings emphasize the need for nurse-led risk monitoring, standardized neurological assessments, and early preventive nursing interventions for high-risk patients.

Clinical trial registration

The protocol for this study is registered with PROSPERO (registration number: CRD42025628890).