Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes.

Background: Previous studies have used machine leaning to predict clinical deterioration to improve outcome prediction. However, no study has used machine learning to predict cardiac arrest in patients with acute coronary syndrome (ACS). Algorithms are required to generate high-performance models for predicting cardiac arrest in ACS patients with multivariate features.

Decision tree model for predicting in-hospital cardiac arrest among patients admitted with acute coronary syndrome.

Background: In-hospital cardiac arrest (IHCA) may be preventable, with patients often showing signs of physiological deterioration before an event. Our objective was to develop and validate a simple clinical prediction model to identify the IHCA risk among cardiac arrest (CA) patients hospitalized with acute coronary syndrome (ACS).

Hypothesis: A predicting model could help to identify the risk of IHCA among patients admitted with ACS.

Characteristics and outcomes of in-hospital cardiac arrest in adults hospitalized with acute coronary syndrome in China.

Aims: This retrospective study aims to analyze and explore the clinical characteristics, risk factors, and in-hospital outcomes - including return of spontaneous circulation (ROSC) and survival to discharge - of hospitalized patients admitted with acute coronary syndrome (ACS) suffering cardiac arrest.

Methods: ACS patients admitted to three tertiary hospitals in Fujian, China, were evaluated retrospectively from January 1, 2012 to December 30, 2016. Data were collected, based on the Utstein Style, for all cases of attempted resuscitation for IHCA.