Evaluate prognostic accuracy of SOFA component score for mortality among adults with sepsis by machine learning method.

ABSTRACT:

Introduction

Sepsis has the characteristics of high incidence, high mortality of ICU patients. Early assessment of disease severity and risk stratification of death in patients with sepsis, and further targeted intervention are very important. The purpose of this study was to develop machine learning models based on sequential organ failure assessment (SOFA) components to early predict in-hospital mortality in ICU patients with sepsis and evaluate model performance.

Methods

Patients admitted to ICU with sepsis diagnosis were extracted from MIMIC-IV database for retrospective analysis, and were randomly divided into training set and test set in accordance with 2:1. Six variables were included in this study, all of which were from the scores of 6 organ systems in SOFA score. T

SUBMITTER: Pan X 

PROVIDER: S-EPMC9903420 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature