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Contains fulltext : 175761.pdf (Publisher’s version ) (Open Access) PURPOSE: The purposes of the study are to identify clinical phenotypes that reflect the level of adaptation to the disease and to examine whether these clinical phenotypes respond differently to treatment as usual (TAU) and pulmonary rehabilitation (PR), the latter with its strong emphasis on improving adaptation. METHODS: Clusters were identified by a cluster analysis using data on many subdomains of the four domains of health status (HS) (physiological functioning, functional impairment, symptoms and quality of life) in 160 outpatients with chronic obstructive pulmonary disease (COPD) receiving TAU. By discriminant analysis in the TAU sample, all 459 PR patients could be assigned to one of the identified clusters. The effect of TAU and PR on HS was examined with paired t tests. RESULTS: Three distinct phenotypes were identified in the TAU sample. Two types were labelled adapted: phenotype 1 (moderate COPD-low impact on HS, n = 53) and phenotype 3 (severe COPD-moderate impact on HS, n = 73). One type was labelled non-adapted: phenotype 2 (moderate COPD-high impact on HS, n = 34). After 1-year TAU, the integral health status of all patients did not improve in any subdomain. In contrast, at the end of PR, significant improvements in HS were found in all three phenotypes especially the non-adapted. CONCLUSIONS: Different phenotypes exist in COPD that are based on behavioural aspects (i.e. the level of adaptation to the disease). Non-adapted patient responds better to treatments with a strong emphasis on improving adaptation by learning the patient better self-management skills. Knowing to which clinical phenotype a patient belongs helps to optimize patient-tailored treatment.

Contains fulltext : 69722.pdf (Publisher’s version ) (Closed access) BACKGROUND: To date, many health status instruments exist, but the validity of these instruments is questionable. This is caused by the fact that health status is poorly defined. Purpose: To develop a validated framework that improves conceptual insight into health status and its domains. METHODS: Based on theoretical and clinical considerations, we defined the domains of health status into concrete sub-domains by formulating conceptual models. Guided by these conceptual models, for each sub-domain, existing instruments were selected. We validated the conceptual models in the data of 168 COPD patients. Using factor analysis, underlying concepts in the data were identified. RESULTS: The resulting framework included physiological functioning, complaints, functional impairment, and quality of life. These main domains were shown to be subdivided into 15 sub-domains. CONCLUSIONS: The present study shows that health status consists of conceptually distinct sub-domains. Integral assessment of health status thus entails measuring all sub-domains. Existing instruments measure only few sub-domains. Integral assessment of health status thus requires the combination of different instruments. The present framework of health status can help in composing such a battery of instruments. Patient profiles obtained by the framework are essential in individualizing treatment.