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SYSTEMATIC REVIEW article

Front. Neurol.

Sec. Neurocritical and Neurohospitalist Care

Volume 16 - 2025 | doi: 10.3389/fneur.2025.1623645

This article is part of the Research TopicPrecision Medicine in Neurocritical CareView all 8 articles

Risk Prediction Models for Short-Term Mortality in ICU Stroke Patients: A Systematic Review and Meta-Analysis

Provisionally accepted
Zhang  JialiZhang Jiali1,2YiJie  fuYiJie fu2Yan  LiuYan Liu3TianHeng  LiuTianHeng Liu3Yue  DengYue Deng2LiFei  DaiLiFei Dai1,2Tianmin  ZhuTianmin Zhu4*Hui  LiHui Li1,2*
  • 1Chengdu University, Chengdu, China
  • 2School of Basic Medical Sciences & School of Nursing, Chengdu University, chengdu, China
  • 3Clinical Medical college & Affiliated Hospital, Chengdu University, Chengdu, Sichuan, China, chengdu, China
  • 4Health and Rehabilitation College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan,China, chengdu, China

The final, formatted version of the article will be published soon.

This study aims to systematically review and evaluate risk prediction models for short-term mortality in ICU stroke patients, thereby providing scientific evidence to inform future model development and clinical application.We searched the Cochrane Library, EMBASE, PubMed, and Web of Science for studies on prediction models for short-term mortality in ICU stroke patients, covering the period from January 2005 to January 2025. Data extracted included study characteristics and detailed information on the prediction models. The Risk of Bias and applicability of the models were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A meta-analysis was performed using a random-effects model in Stata 18.0, and heterogeneity across studies was assessed using the I²statistic. Subgroup analyses were conducted based on stroke type, geographic region, and modeling approach. a sensitivity analysis performed to evaluate the robustness of the findings.A total of 6,874 studies were retrieved, and 12 studies met the inclusion criteria, yielding 14 prediction models, as two studies included two models each that were extracted separately. Four models were externally validated. The reported area under the curve (AUC) values ranged from 0.761 to 0.977. Meta-analysis yielded a pooled AUC of 0.82 (95% CI: 0.80-0.85), indicating good discriminative ability of the models in predicting short-term mortality in ICU stroke patients.However, heterogeneity was high (I² = 80.1%, P = 0.000). Subgroup analyses by stroke type, modeling approach, and geographical region revealed no statistically significant sources of heterogeneity.The PROBAST assessment shows that all models exhibit high risk of bias and low applicability. The most frequently reported predictors were Glasgow Coma Scale (GCS), white blood cell count (WBC), age, and blood glucose levels.This study shows that prediction models for short-term mortality in ICU stroke patients have good discriminatory performance. However, due to high bias risk and low applicability, their overall quality remains suboptimal. Important predictors such as GCS, WBC, age, and blood glucose levels should be included in future models. Future research should 4 focus on prospective, multicenter, and externally validated studies guided by the PROBAST tool to improve clinical applicability and reliability.

Keywords: Stroke, Mortality, risk, Prediction models, ICU Intruduction

Received: 06 May 2025; Accepted: 01 Jul 2025.

Copyright: © 2025 Jiali, fu, Liu, Liu, Deng, Dai, Zhu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Tianmin Zhu, Health and Rehabilitation College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan,China, chengdu, China
Hui Li, School of Basic Medical Sciences & School of Nursing, Chengdu University, chengdu, China

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