Browsing by Author "Enes, Vera"
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- COPD profiles and treatable traits using minimal resources: Identification, decision tree and longitudinal stabilityPublication . Marques, Alda; Souto-Miranda, Sara; Machado, Ana; Oliveira, Ana; Jácome, Cristina; Cruz, Joana; Enes, Vera; Afreixo, Vera; Martins, Vitória; Andrade, L; Valente, Carla; Ferreira, Diva; Simão, Paula; Brooks, Dina; TavaresBackground: 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. Objective: 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), health-related 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 followed-up. 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%. Conclusion: 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.
- COPD profiles and treatable traits using minimal resources: Identification, decision tree and stability over timePublication . Marques, Alda; Souto-Miranda, Sara; Machado, Ana; Oliveira, Ana; Jácome, Cristina; Cruz, Joana; Enes, Vera; Afreixo, Vera; Martins, Vitória; Andrade, Lília; Valente, Carla; Ferreira, Diva; Simão, Paula; Brooks, Dina; Tavares, Ana HelenaBackground and objective: Profiles of people with chronic obstructive pulmonary disease (COPD) often do not describe treatable traits, lack validation and/or their stability over time is unknown. We aimed to identify COPD profiles and their treatable traits based on simple and meaningful measures; to develop and validate a decision tree and to explore profile stability over time. Methods: An observational, prospective study was conducted. Clinical characteristics, lung function, symptoms, impact of the disease (COPD Assessment Test—CAT), health-related 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. A decision tree was developed and validated cross-sectionally. Stability was explored over time with the ratio between the number of timepoints that a participant was classified in the same profile 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 followed-up. Four profiles were identified with distinct treatable traits. The decision tree included CAT (< 18 or ≥ 18 points); age (< 65 or ≥ 65 years) and FEV1 (< 48 or ≥ 48% 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%) or three (3.3%) profiles over time. Overall stability was 86.8 ± 15%. Conclusion: Four profiles and treatable traits were identified with simple and meaningful measures possibly available in low-resource settings. A decision tree with three commonly used variables in the routine assessment of people with COPD is now available for quick allocation to the identified profiles in clinical practice. Profiles and treatable traits may change over time in people with COPD hence, regular assessments to deliver goal-targeted personalised treatments are needed.