Krzysztof Glaser, Luca Marino, Janos Domonkos Stubnya, Federico Bilotta
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Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review
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.
期刊介绍:
The Journal of Anesthesia is the official journal of the Japanese Society of Anesthesiologists. This journal publishes original articles, review articles, special articles, clinical reports, short communications, letters to the editor, and book and multimedia reviews. The editors welcome the submission of manuscripts devoted to anesthesia and related topics from any country of the world. Membership in the Society is not a prerequisite.
The Journal of Anesthesia (JA) welcomes case reports that show unique cases in perioperative medicine, intensive care, emergency medicine, and pain management.