Monitoring symptoms and performance in rehabilitation: Can we detect COPD exacerbation?

Background In patients with COPD, exacerbations mark disease progression, with significant reduction in quality of life and impact on mortality. Early detection is crucial to mitigate their consequences. The aim of this study was to investigate whether monitoring symptoms and exercise endurance at each pulmonary rehabilitation maintenance session could predict severe exacerbations of COPD.Study design and methods Prospective multicentre study including 128 stable COPD patients in maintenance pulmonary rehabilitation programmes, in 21 French rehabilitation centres (NCT05492149). At each session, symptoms with COPD Assessment Test (CAT), perceived dyspnoea (mBorg scale) and endurance exercise performance (in MET.minutes) were recorded. We evaluated whether these parameters could predict severe exacerbations using baseline and lagged logistic regression models, and explored temporal relationships with cross-lagged correlation analyses.Results Over one year and 4,200 recorded sessions (33 ± 25 per patient), 19 out of 128 participants experienced a severe exacerbation (a prevalence of 14.8%). Prediction models using isolated clinical markers such as CAT, mBorg or performance showed limited predictive accuracy (AUCs 0.45–0.58; all p > 0.1). Cross-lagged analyses revealed weak, short-term associations between symptoms (CAT score), perceived dyspnoea (mBorg scale) and exercise performance.Conclusion This study showed that nearly 15% of patients with COPD included in a maintenance rehabilitation programme experienced severe exacerbations during long-term follow-up. In this real-world maintenance pulmonary rehabilitation setting, routine clinical and isolated exercise data were insufficient to reliably predict severe COPD exacerbations. These findings highlight the complexity of severe exacerbations in COPD and emphasise the importance of developing more integrated research and digital monitoring strategies, using multimodal approaches that combine physiological, biological, behavioural, and environmental data.