Open AccessEvaluation of prognostic prediction models for out‐of‐hospital cardiac arrest
Author(s) -
Lo Yat Hei,
Siu Yuet Chung Axel
Publication year - 2021
Publication title -
hong kong journal of emergency medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.145
H-Index - 12
eISSN - 2309-5407
pISSN - 1024-9079
DOI - 10.1177/1024907920966912
Subject(s) - medicine , return of spontaneous circulation , logistic regression , emergency department , chain of survival , predictive modelling , sudden cardiac arrest , intensive care medicine , emergency medicine , cardiopulmonary resuscitation , medical emergency , resuscitation , basic life support , machine learning , psychiatry , computer science
Accurate prognostic prediction of out‐of‐hospital cardiac arrest is challenging but important for the emergency team and patient's family members. A number of prognostic prediction models specifically designed for out‐of‐hospital cardiac arrest are developed and validated worldwide. Objective: This narrative review provides an overview of the prognostic prediction models out‐of‐hospital cardiac arrest patients for use in the emergency department. Discussion: Out‐of‐hospital cardiac arrest prognostic prediction models are potentially useful in clinical, administrative and research settings. Development and validation of such models require prehospital and hospital predictor and outcome variables which are best in the standardised Utstein Style. Logistic regression analysis is traditionally employed for model development but machine learning is emerging as the new tool. Examples of such models available for use in the emergency department include ROSC After Cardiac Arrest, CaRdiac Arrest Survival Score, Utstein‐Based Return of Spontaneous Circulation, Out‐of‐Hospital Cardiac Arrest, Cardiac Arrest Hospital Prognosis and Cardiac Arrest Survival Score. The usefulness of these models awaits future studies.