Anti-factor Xa during unfractionated heparin therapy in critically ill patients: Development of prediction models using machine learning.

ABSTRACT:

Objective

Unfractionated heparin (UFH) is a widely used therapy in intensive care units (ICUs) and is associated with an increased risk of serious adverse events or death if the therapeutic target is not reached quickly. Adjusting UFH dosage is challenging, and no reliable algorithms exist for predicting anti-Xa levels in ICUs. This study aimed to develop and evaluate machine learning algorithms to predict anti-Xa levels during UFH therapy, helping clinicians optimize dosing.

Methods

This single-center retrospective cohort study was conducted using Rennes University Hospital's clinical data warehouse from December 21, 2019 to November 22, 2021. Critically ill patients ≥ 18 years on UFH, without other anticoagulants and complete data, were included. Anti-Xa levels were classi

SUBMITTER: Delange B 

PROVIDER: S-EPMC12035305 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature