COPD profiles and treatable traits using minimal resources: identification, decision tree and longitudinal stability

• Main Author: Marques, A.
• Publication Date: 2021
• Other Authors: Souto-Miranda, S., Machado, A. F., Oliveira, A., Jácome, C., Cruz, J., Enes, V., Afreixo, V., Martins, V., Andrade, L., Valente, C., Ferreira, D., Simão, P., Brooks, D., Tavares, A. H.
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10773/36794
• topic: Clinical phenotypes; Profiles; Clusters; Treatable traits; Decision tree; COPD

Introduction: Chronic obstructive pulmonary disease (COPD) is highly heterogeneous and complex. Hence, personalising assessments and treatments to this population across different settings and available resources imposes challenges and debate. Research efforts have been made to identify clinical phenotypes or profiles for prognostic and therapeutic purposes. Nevertheless, such profiles often do not describe treatable traits, focus on complex physiological/ pulmonary measures which are frequently not available across settings, lack validation and/or their stability over time is unknown. Objectives: To identify profiles and their treatable traits based on simple and meaningful measures; to develop and validate a profile decision tree; and to explore profiles’ stability over time in people with COPD. Methods: An observational, prospective study was conducted with people with COPD. Clinical characteristics, lung function, symptoms, impact of the disease (COPD assessment test–CAT), healthrelated quality of life, physical activity, lower-limb muscle strength and functional status were collected cross-sectionally and a subsample was followed-up monthly over six months. A principal component analysis and a clustering procedure with k-medoids were applied to identify profiles. Pulmonary and extrapulmonary (i.e., physical, symptoms and health status, and behavioural/life-style risk factors) treatable traits were identified in each profile based on the established cut-offs for each measure available in the literature. The decision tree was developed with 70% and validated with 30% of the sample, cross-sectionally. Agreement between the profile predicted by the decision tree and the profile defined by the clustering procedure was determined using Cohen’s Kappa. Stability was explored over time with a stability score defined as the percentage ratio between the number of timepoints that a participant was classified in the same profile (most frequent profile for that participant) and the total number of timepoints (i.e., 6). Results: 352 people with COPD (67.4 ± 9.9 years; 78.1% male; FEV1 = 56.2 ± 20.6% predicted) participated and 90 (67.6 ± 8.9 years; 85.6% male; FEV1 = 52.1 ± 19.9% predicted) were followedup. Four profiles were identified with distinct treatable traits. The decision tree was composed by the CAT, age and FEV1% predicted and had an agreement of 71.7% (Cohen’s Kappa = 0.62, p < 0.001) with the actual profiles. 48.9% of participants remained in the same profile whilst 51.1% moved between two (47.8%) and three (3.3%) profiles over time. The overall stability of profiles was 86.8 ± 15%. Conclusions: Profiles and treatable traits can be identified in people with COPD with simple and meaningful measures possibly available even in minimal-resource settings. Regular assessments are recommended as people with COPD may change profile over time and hence their needs of personalised treatment.