Identifying azithromycin responders with an individual treatment effect model in COPD.

ABSTRACT:

Objective

Long-term azithromycin treatment effectively prevents acute exacerbations of chronic obstructive pulmonary disease (COPD). However, patients would benefit from better identification of responders and non-responders to minimise unnecessary exposure. We aimed to assess treatment effect heterogeneity and estimate individual treatment effects (ITEs) to distinguish patients most likely to benefit from prophylactic treatment.

Methods

We used data from 1025 patients of the MACRO trial to assess the ITE of azithromycin on annual exacerbation rate. A Causal Forest was used as a causal machine learning model. We independently validated our findings using data from 83 patients of the COLUMBUS trial.

Results

The tertile of patients with the best predicted ITE within MAC

SUBMITTER: Verstraete K 

PROVIDER: S-EPMC12703347 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature