Cardiac arrest: prediction models in the early phase of hospitalization.

Abstract

There is a need for an early assessment of outcome in patients with return of spontaneous circulation after cardiac arrest. During the last decade, several models were developed in order to identify predictive factors that may facilitate prognostication and stratification of outcome. In addition to prognostication tools that are used in intensive care, at least five scores were recently developed using large datasets, based on simple and immediately available parameters, such as circumstances of arrest and early in-hospital indicators. Regarding neurological outcome, predictive performance of these models is good and even excellent for some of them. These scores perform very well for identifying patients at high-risk of unfavorable outcome. The most important limitation of these scores remains the lack of replication in different communities. In addition, these scores were not developed for individual decision- making, but they could instead be useful for the description and comparison of different cohorts, and also to design trials targeting specific categories of patients regarding outcome. Finally, the recent development of big data allows extension of research in epidemiology of cardiac arrest, including the identification of new prognostic factors and the improvement of prediction according to the profile of populations. In addition to the development of artificial intelligence, the prediction approach based on adequate scores will further increase the knowledge in prognostication after cardiac arrest. This strategy may help to develop treatment strategies according to the predicted severity of the outcome.