Zhang Jiali, Fu Yijie, Liu Yan, Liu TianHeng, Deng Yue, Dai LiFei, Zhu Tianmin, Li Hui
School of Preclinical Medicine and School of Nursing, Chengdu University, Chengdu, Sichuan, China.
Clinical Medical College and Affiliated Hospital, Chengdu University, Chengdu, Sichuan, China.
Front Neurol. 2025 Jul 16;16:1623645. doi: 10.3389/fneur.2025.1623645. eCollection 2025.
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 I statistic. 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 (I = 80.1%, = 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 focus on prospective, multicenter, and externally validated studies guided by the PROBAST tool to improve clinical applicability and reliability.
本研究旨在系统评价和评估重症监护病房（ICU）中风患者短期死亡率的风险预测模型，从而为未来模型的开发和临床应用提供科学依据。
我们检索了Cochrane图书馆、EMBASE、PubMed和Web of Science，以查找关于ICU中风患者短期死亡率预测模型的研究，涵盖2005年1月至2025年1月期间。提取的数据包括研究特征和预测模型的详细信息。使用预测模型偏倚风险评估工具（PROBAST）评估模型的偏倚风险和适用性。在Stata 18.0中使用随机效应模型进行荟萃分析，并使用I统计量评估研究间的异质性。根据中风类型、地理区域和建模方法进行亚组分析。进行敏感性分析以评估研究结果的稳健性。
共检索到6874项研究，12项研究符合纳入标准，产生了14个预测模型，因为有两项研究各自包含两个分别提取的模型。四个模型进行了外部验证。报告的曲线下面积（AUC）值范围为0.761至0.977。荟萃分析得出合并AUC为0.82（95%CI：0.80 - 0.85），表明这些模型在预测ICU中风患者短期死亡率方面具有良好的判别能力。然而，异质性较高（I = 80.1%，P = 0.000）。按中风类型、建模方法和地理区域进行的亚组分析未发现具有统计学意义的异质性来源。PROBAST评估表明所有模型均表现出高偏倚风险和低适用性。最常报告的预测因素是格拉斯哥昏迷量表（GCS）、白细胞计数（WBC）、年龄和血糖水平。
本研究表明，ICU中风患者短期死亡率的预测模型具有良好的判别性能。然而，由于高偏倚风险和低适用性，其整体质量仍不理想。未来模型应纳入GCS、WBC、年龄和血糖水平等重要预测因素。未来的研究应专注于由PROBAST工具指导的前瞻性、多中心和外部验证研究，以提高临床适用性和可靠性。
