Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment—implications for clinical practice

Abstract

Background: Artificial intelligence (AI)-based models can augment clinical decision-making, including prediction, diagnosis, and treatment, in all aspects of medicine. Research questions: The current systematic review aims to provide a summary of existing data about the role of machine learning (ML) techniques in predicting in-hospital cardiac arrest, life-threatening ventricular arrhythmias, and respiratory arrest. Methods: The study was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) framework. PubMed, Embase, and Web of Science without any restriction were searched to extract relevant manuscripts until October 20, 2023. Additionally, the reference list of all potential studies was searched to identify further relevant articles. Original publications were regarded as eligible if they only recruited adult patients (≥18 years of age), employed AI/ML algorithms for predicting cardiac arrest, life-threatening ventricular arrhythmias, and respiratory arrest in the setting of critical care, used data gathered from wards with critically ill patients (ICUs, cardiac ICUs, and emergency departments), and were published in English. The following information was extracted: first author, journal, ward, sample size, performance and features of ML and conventional models, and outcomes. Results: ML algorithms have been used for cardiac arrest prediction using easily obtained variables as inputs. ML algorithms showed promising results (AUC 0.73-0.96) in predicting cardiac arrest in different settings, including critically ill ICU patients, patients in the emergency department and patients with sepsis, they demonstrated variable performance (AUC 0.54-0.94) in predicting respiratory arrest in COVID-19 patients, as well as other clinical settings. Conclusion: ML algorithms have shown promising results in predicting in-hospital cardiac and respiratory arrest using readily available clinical data. These algorithms may enhance early identification of high risk patients and support timely interventions, thereby reducing mortality and morbidity rates. However, the prospective validation of these algorithms and their integration into clinical workflows need further exploration.

Ähnliche Arbeiten