Machine learning based clinical prediction model for 1-year mortality in Sepsis patients with atrial fibrillation.

ABSTRACT:

Background

Atrial fibrillation (AF) emerges as a pivotal risk determinant for unfavorable outcomes in septic patients. Despite its recognized role, the enduring impact of AF on sepsis prognosis remains ambiguous. This investigation seeks to elucidate the connection between AF and both short and long-term outcomes in sepsis patients. Additionally, it aims to formulate a prognostic model for 1-year mortality utilizing pertinent clinical variables.

Methods

A retrospective analysis encompassed sepsis patients admitted to Beth Israel Deacon Medical Center's intensive care unit. The evaluation encompassed the prevalence of AF and its influence on hospitalization duration, stays in the Intensive Care Unit (ICU), and mortality rates at distinct intervals. Propensity score matching w

SUBMITTER: Meng H 

PROVIDER: S-EPMC11544070 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature