Identifying azithromycin responders with an individual treatment effect model in COPD

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.

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.

The tertile of patients with the best predicted ITE within MACRO and within the COLUMBUS independent validation cohort showed significant and substantially greater reductions in annual exacerbation rates (in MACRO -0.50, rate ratio 0.70, p=0.01, in COLUMBUS: -2.28, rate ratio 0.43, p<0.001) compared with the average treatment effect across the entire cohort (MACRO -0.35, rate ratio 0.83, p=0.01 and COLUMBUS -1.28, rate ratio 0.58, p=0.001). Conversely, no significant treatment effect was observed in the remaining two-thirds of patients. Primary determinants of ITE included respiratory symptoms, white blood cell count, haemoglobin, C-reactive protein and forced vital capacity. Smoking status did not emerge as a significant predictor.

Based on five easily obtainable parameters to predict ITE, we identified treatment effect heterogeneity in COPD subjects treated with azithromycin maintenance therapy and found a small subgroup of responders driving the average reduction in exacerbations reported in previous trials.