Author Affiliations: Clinical Assistant Professor (Dr Moffat) and Assistant Professor (Dr Xu), School of Nursing, Purdue University, West Lafayette, Indiana.
Clin Nurse Spec. 2022;36(1):29-44. doi: 10.1097/NUR.0000000000000644.
PURPOSE/AIMS: Despite advances in healthcare, the incidence of in-hospital cardiac arrest (IHCA) has continued to rise for the past decade. Identifying those patients at risk has proven challenging. Our objective was to conduct a systematic review of the literature to compare the IHCA predictive performance of machine learning (ML) models with the Modified Early Warning Score (MEWS).
The systematic review was conducted following the Preferred Reporting Items of Systematic Review and Meta-Analysis guidelines and registered on PROSPERO CRD42020182357.
Data extraction was completed using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies checklist. The risk of bias and applicability were evaluated using the Prediction model Risk of Bias Assessment Tool.
Nine articles were included in this review that developed or validated IHCA prediction models and compared them with the MEWS. The studies by Jang et al and Kim et al showed that their ML models outperformed MEWS to predict IHCA with good to excellent predictive performance.
The ML models presented in this systematic review demonstrate a novel approach to predicting IHCA. All included studies suggest that ML models had similar or better predictive performance compared with MEWS. However, there is substantial variability in performance measures and concerns for risk of bias.
目的/目标：尽管医疗保健取得了进步，但过去十年来，院内心搏骤停（IHCA）的发病率仍持续上升。证明识别那些有风险的患者具有挑战性。我们的目的是对文献进行系统回顾，以比较机器学习（ML）模型与改良早期预警评分（MEWS）对 IHCA 的预测性能。
该系统评价是按照系统评价和荟萃分析的首选报告项目以及 PROSPERO CRD42020182357 进行的。
使用关键评估和系统评价预测模型研究的数据集提取清单完成数据提取。使用预测模型风险偏倚评估工具评估风险和适用性。
本综述纳入了 9 篇开发或验证 IHCA 预测模型并将其与 MEWS 进行比较的文章。Jang 等人和 Kim 等人的研究表明，他们的 ML 模型在预测 IHCA 方面优于 MEWS，具有良好到极好的预测性能。
本系统评价中提出的 ML 模型展示了一种预测 IHCA 的新方法。所有纳入的研究表明，与 MEWS 相比，ML 模型具有相似或更好的预测性能。然而，在性能指标和风险偏倚方面存在很大的变异性。
