Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis (Preprint)

作者：Shengfeng Wei, Xiangjian Guo, Shuaibo He, Chunhua Zhang, Zhizhuan Chen, Jianmei Chen, Yanmei Huang, Fan Zhang, Qiangqiang Liu · 年份：2024 · DOI：10.2196/preprints.67871 · 研究领域：Cardiac Arrest and Resuscitation、Artificial Intelligence in Healthcare and Education、Trauma and Emergency Care Studies

BACKGROUND Currently, there is a lack of effective early assessment tools for predicting the onset and development of cardiac arrest (CA). With the increasing attention of clinical researchers on machine learning (ML), some researchers have developed ML models for predicting the occurrence and prognosis of CA, with certain models appearing to outperform traditional scoring tools. However, these models still lack systematic evidence to substantiate their efficacy. OBJECTIVE This systematic review and meta-analysis was conducted to evaluate the prediction value of ML in CA for occurrence, good neurological prognosis, mortality, and the return of spontaneous circulation (ROSC), thereby providing evidence-based support for the development and refinement of applicable clinical tools. METHODS PubMed, Embase, the Cochrane Library, and Web of Science were systematically searched from their establishment until May 17, 2024. The risk of bias in all prediction models was assessed using the Prediction Model Risk of Bias Assessment Tool. RESULTS In total, 93 studies were selected, encompassing 5,729,721 in-hospital and out-of-hospital patients. The meta-analysis revealed that, for predicting CA, the pooled C-index, sensitivity, and specificity derived from the imbalanced validation dataset were 0.90 (95% CI 0.87-0.93), 0.83 (95% CI 0.79-0.87), and 0.93 (95% CI 0.88-0.96), respectively. On the basis of the balanced validation dataset, the pooled C-index, sensitivity, and...