Machine Learning Identifies Higher Survival Profile In Extracorporeal Cardiopulmonary Resuscitation.

ABSTRACT:

Objectives

Extracorporeal cardiopulmonary resuscitation (ECPR) has been shown to improve neurologically favorable survival in patients with refractory out-of-hospital cardiac arrest (OHCA) caused by shockable rhythms. Further refinement of patient selection is needed to focus this resource-intensive therapy on those patients likely to benefit. This study sought to create a selection model using machine learning (ML) tools for refractory cardiac arrest patients undergoing ECPR.

Design

Retrospective cohort study.

Setting

Cardiac ICU in a Quaternary Care Center.

Patients

Adults 18-75 years old with refractory OHCA caused by a shockable rhythm.

Methods

Three hundred seventy-six consecutive patients with refractory OHCA and a shockable presenting rhythm were

SUBMITTER: Crespo-Diaz R 

PROVIDER: S-EPMC11166735 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature