Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta‐Analysis - Qi Deng; Yu Yang; Hongyu Bai; Fei Li; Wenluo Zhang; Rong He; Yuming Li (Brain and Behavior)
ABSTRACT Introduction 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. Methods 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. Results 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). Conclusion 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.