Data-driven identification and investigation of comorbidity profiles in patients with chronic obstructive pulmonary disease: a multicohort study
Introduction
Comorbidities are common in chronic obstructive pulmonary disease (COPD), adversely affecting
patients’ quality of life and their disease trajectories. While previous studies have predominantly
examined individual comorbidities, there has been limited exploration of their coexistence.
This study aimed to identify comorbidity clusters among real-world cohorts of COPD
patients using machine learning techniques, and to investigate clinical characteristics and
mortality within these clusters.
Poster
Conclusion
This study confirms distinct comorbidity clusters in two well-characterized
cohorts of patients with COPD which can be linked to different patient
subgroups. In a broad COPD patient population, comorbidity profiles could
hold prognostic relevance. The findings of this study enhance the
understanding of the comorbidity landscape in COPD and highlights the
importance of comorbidity assessment in clinical management.
