Deng Qi, Yang Yu, Bai Hongyu, Li Fei, Zhang Wenluo, He Rong, Li Yuming
Department of Neurology, Tianjin Kanghui Hospital, Tianjin, China.
Department of Respiratory, Tianjin Kanghui Hospital, Tianjin, China.
Brain Behav. 2025 Jan;15(1):e70198. doi: 10.1002/brb3.70198.
Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effective tools for early identification or prediction of this risk. As machine learning (ML) is increasingly used in clinical practice, its effectiveness in predicting cerebral edema risk in stroke patients has been explored. Nonetheless, the lack of systematic evidence on its predictive value challenges the update of simple and user-friendly risk assessment tools. Therefore, we conducted a systematic review to evaluate the predictive utility of ML for cerebral edema in stroke patients.
We searched PubMed, Embase, Web of Science, and the Cochrane Database up to February 21, 2024. The risk of bias in selected studies was assessed using a bias assessment tool for predictive models. Meta-analysis synthesized results from validation sets.
We included 22 studies with 25,096 stroke patients and 25 models, which were constructed using common and interpretable clinical features. In the validation cohort, the models achieved a concordance index (c-index) of 0.840 (95% CI: 0.810-0.871) for predicting poststroke cerebral edema, with a sensitivity of 0.76 (95% CI: 0.72-0.79) and a specificity of 0.87 (95% CI: 0.83-0.90).
ML models are significant in predicting poststroke cerebral edema, providing clinicians with a powerful prognostic tool. However, radiomics-based research was not included. We anticipate advancements in radiomics research to enhance the predictive power of ML for poststroke cerebral edema.
中风患者发生脑水肿的风险很高，可能会产生严重后果。然而，目前几乎没有有效的工具用于早期识别或预测这种风险。随着机器学习（ML）在临床实践中的应用越来越广泛，其在预测中风患者脑水肿风险方面的有效性已得到探索。尽管如此，缺乏关于其预测价值的系统性证据，这对更新简单且用户友好的风险评估工具构成了挑战。因此，我们进行了一项系统综述，以评估机器学习对中风患者脑水肿的预测效用。
我们检索了截至2024年2月21日的PubMed、Embase、Web of Science和Cochrane数据库。使用预测模型的偏倚评估工具对所选研究中的偏倚风险进行评估。荟萃分析综合了验证集的结果。
我们纳入了22项研究，涉及25,096名中风患者和25个模型，这些模型是使用常见且可解释的临床特征构建的。在验证队列中，这些模型预测中风后脑水肿的一致性指数（c指数）为0.840（95%CI：0.810 - 0.871），敏感性为0.76（95%CI：0.72 - 0.79），特异性为0.87（95%CI：0.83 - 0.90）。
机器学习模型在预测中风后脑水肿方面具有重要意义，为临床医生提供了一个强大的预后工具。然而，基于影像组学的研究未被纳入。我们期待影像组学研究取得进展，以增强机器学习对中风后脑水肿的预测能力。
