Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach.

ABSTRACT:

Background

Unfractionated heparin (UFH) is an anticoagulant drug that is considered a high-risk medication because an excessive dose can cause bleeding, whereas an insufficient dose can lead to a recurrent embolic event. Therapeutic response to the initiation of intravenous UFH is monitored using activated partial thromboplastin time (aPTT) as a measure of blood clotting time. Clinicians iteratively adjust the dose of UFH toward a target, indication-defined therapeutic aPTT range using nomograms, but this process can be imprecise and can take ≥36 hours to achieve the target range. Thus, a more efficient approach is required.

Objective

In this study, we aimed to develop and validate a machine learning (ML) algorithm to predict aPTT within 12 hours after a specified bolus and

SUBMITTER: Abdel-Hafez A 

PROVIDER: S-EPMC9531006 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature