Prehospital Post-Resuscitation Vital Sign Phenotypes are Associated with Outcomes Following Out-of-Hospital Cardiac Arrest.

ABSTRACT:

Objectives

The use of machine learning to identify patient 'clusters' using post-return of spontaneous circulation (ROSC) vital signs may facilitate the identification of patient subgroups at high risk of rearrest and mortality. Our objective was to use k-means clustering to identify post-ROSC vital sign clusters and determine whether these clusters were associated with rearrest and mortality.

Methods

The ESO Data Collaborative 2018-2022 datasets were used for this study. We included adult, non-traumatic OHCA patients with >2 post-ROSC vital sign sets. Patients were excluded if they had an EMS-witnessed OHCA or were encountered during an interfacility transfer. Unsupervised (k-means) clustering was performed using minimum, maximum, and delta (last minus first) systoli

SUBMITTER: Smida T 

PROVIDER: S-EPMC11790374 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature