Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for patients with out-of-hospital cardiac arrest using machine learning: A retrospective study.

ABSTRACT:

Background

Return of spontaneous circulation (ROSC) before arrival at the emergency department is an early indicator of successful resuscitation in out-of-hospital cardiac arrest (OHCA). Several ROSC prediction scores have been developed with European cohorts, with unclear applicability in Asian settings. We aimed to develop an interpretable prehospital ROSC (P-ROSC) score for ROSC prediction based on patients with OHCA in Asia.

Methods

This retrospective study examined patients who suffered from OHCA between Jan 1, 2009 and Jun 17, 2018 using data recorded in the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. AutoScore, an interpretable machine learning framework, was used to develop P-ROSC. On the same cohort, the P-ROSC was compared with two clinical scores, the

SUBMITTER: Liu N 

PROVIDER: S-EPMC9096672 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature