A machine learning model for early and accurate prediction of overt disseminated intravascular coagulation before its progression to an overt stage.

ABSTRACT:

Background

Recent studies suggested an expected survival benefit associated with anticoagulant therapies for sepsis in patients with disseminated intravascular coagulation (DIC). However, anticoagulant therapies for overt DIC are no longer assumed to regulate pathologic progression as overt DIC is a late-phase coagulation disorder. Therefore, methods for early prediction of sepsis-induced DIC before its progression to an overt stage are strongly required.

Objectives

We aimed to develop a prediction model for overt DIC using machine learning.

Methods

This retrospective, observational study included adult septic patients without overt DIC. The objective variable was binary classification of whether patients developed overt DIC based on International Society on Thrombosi

SUBMITTER: Umemura Y 

PROVIDER: S-EPMC11363840 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature