A machine learning model for robust prediction of sepsis-induced coagulopathy in critically ill patients with sepsis.

ABSTRACT:

Introduction

Sepsis-induced coagulopathy (SIC) is a common disease in patients with sepsis. It denotes higher mortality rates and a poorer prognosis in these patients. This study aimed to develop a practical machine learning (ML) model for the prediction of the risk of SIC in critically ill patients with sepsis.

Methods

In this retrospective cohort study, patients were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the Inner Mongolia Autonomous Region People's Hospital database. Sepsis and SIC were defined based on the Sepsis-3 criteria and the criteria developed based on the International Society of Thrombosis and Haemostasis (ISTH), respectively. We compared nine ML models using the Sequential Organ Failure Assessment (SOFA) score

SUBMITTER: Sun J 

PROVIDER: S-EPMC12179180 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature