Concentration monitoring and dose optimization for infliximab in Crohn's disease patients: a machine learning-based covariate ensemble model.

ABSTRACT:

Background

Trough concentration of Infliximab (IFX) was associated with its efficacy and toxicity. However, traditional therapeutic drug monitoring often results in suboptimal outcomes because dose adjustments are delayed. We aimed to develop and validate a machine learning (ML) framework to enable real-time trough concentration prediction (pre-infusion point-of-care prediction) and individualized dosing for Crohn's disease (CD) patients.

Methods

Leveraging data from a retrospective cohort of 274 Chinese CD patients (460 samples), we dichotomized outcomes based on an IFX trough concentration threshold (≥3 μg/mL). After a systematic evaluation of nine nonlinear ML algorithms, we identified four optimal predictive models. These were subsequently integrated into a soft-voting e

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12719469 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature