Department of Anaesthesiology, Critical Care and Pain Medicine, Policlinico Umberto I,, Sapienza University of Rome, 00185, Rome, Italy.
Department of Mechanical and Aerospace Engineering, Policlinico Umberto I, Sapienza University of Rome, 00185, Rome, Italy.
J Anesth. 2024 Jun;38(3):301-308. doi: 10.1007/s00540-024-03316-6. Epub 2024 Apr 9.
Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for prediction and detection poses a challenge. This study aims to provide a comprehensive literature review on the application of machine learning (ML) algorithms for predicting and detecting new-onset atrial fibrillation (NOAF) in ICU-treated patients. Following the PRISMA recommendations, this systematic review outlines ML models employed in the prediction and detection of NOAF in ICU patients and compares the ML-based approach with clinical-based methods. Inclusion criteria comprised randomized controlled trials (RCTs), observational studies, cohort studies, and case-control studies. A total of five articles published between November 2020 and April 2023 were identified and reviewed to extract the algorithms and performance metrics. Reviewed studies sourced 108,724 ICU admission records form databases, e.g., MIMIC. Eight prediction and detection methods were examined. Notably, CatBoost exhibited superior performance in NOAF prediction, while the support vector machine excelled in NOAF detection. Machine learning algorithms emerge as promising tools for predicting and detecting NOAF in ICU patients. The incorporation of these algorithms in clinical practice has the potential to enhance decision-making and the overall management of NOAF in ICU settings.
心房颤动（AF）是 ICU 患者中主要观察到的心律失常。然而，缺乏快速和精确的预测和检测方法是一个挑战。本研究旨在提供一篇关于机器学习（ML）算法在预测和检测 ICU 治疗患者新发心房颤动（NOAF）中的应用的综合文献综述。根据 PRISMA 建议，本系统综述概述了用于预测和检测 ICU 患者中 NOAF 的 ML 模型，并将基于 ML 的方法与基于临床的方法进行了比较。纳入标准包括随机对照试验（RCT）、观察性研究、队列研究和病例对照研究。共确定并回顾了 2020 年 11 月至 2023 年 4 月期间发表的五篇文章，以提取算法和性能指标。综述研究从数据库（例如 MIMIC）中获取了 108724 例 ICU 入院记录。检查了八种预测和检测方法。值得注意的是，CatBoost 在预测 NOAF 方面表现出色，而支持向量机在检测 NOAF 方面表现出色。机器学习算法是预测和检测 ICU 患者中 NOAF 的有前途的工具。将这些算法纳入临床实践有潜力增强 ICU 环境中 NOAF 的决策制定和整体管理。
