Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning study.

ABSTRACT:

Background

Sepsis is a significant health burden on a global scale. Timely identification and treatment of sepsis can greatly improve patient outcomes, including survival rates. However, time-consuming laboratory results are often needed for screening sepsis. We aimed to develop a quick sepsis screening tool (qSepsis) based on patients' non-laboratory clinical data at the emergency department (ED) using machine learning (ML), and compare its performance with established clinical scores.

Methods

This retrospective study included patients admitted to the ED of Zhongnan Hospital of Wuhan University (Wuhan, China) from 1/1/2015 to 5/31/2022. Patients who were under 18 years of age, had cardiopulmonary arrest upon arrival at the ED, or had missing and abnormal medical record data

SUBMITTER: Jiang S 

PROVIDER: S-EPMC11773271 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature