Arnaud Bourdin1, Nicolas Molinari2, Isabelle Vachier3, Muriel Varrin4, Grégory Marin5, Anne-Sophie Gamez3, Fabrice Paganin6, Pascal Chanez7. 1. Department of Respiratory Diseases, Montpellier, France; INSERM U1046, Hôpital Arnaud de Villeneuve, Université Montpellier I et 2, Montpellier, France. Electronic address: a-bourdin@chu-montpellier.fr. 2. INSERM U1046, Hôpital Arnaud de Villeneuve, Université Montpellier I et 2, Montpellier, France; Service DIM, MISTEA, CHU Montpellier, Montpellier, France. 3. Department of Respiratory Diseases, Montpellier, France. 4. Service DIM, MISTEA, CHU Montpellier, Montpellier, France. 5. Department of Respiratory Diseases, Montpellier, France; Service DIM, MISTEA, CHU Montpellier, Montpellier, France. 6. Department of Respiratory Diseases, Groupe Hospitalier Sud Reunion, Saint Pierre de La Réunion, France. 7. Département des Maladies Respiratoires, AP-HM, UMR7733, Aix-Marseille Université, Marseille, France; Laboratoire INSERM CNRS U 1067, UMR7733, Aix-Marseille Université, Marseille, France.
Abstract
BACKGROUND: Cross-sectional severe asthma cluster analysis identified different phenotypes. We tested the hypothesis that these clusters will follow different courses. OBJECTIVE: We aimed to identify which asthma outcomes are specific and coherently associated with these different phenotypes in a prospective longitudinal cohort. METHODS: In a longitudinal cohort of 112 patients with severe asthma, the 5 Severe Asthma Research Program (SARP) clusters were identified by means of algorithm application. Because patients of the present cohort all had severe asthma compared with the SARP cohort, homemade clusters were identified and also tested. At the subsequent visit, we investigated several outcomes related to asthma control at 1 year (6-item Asthma Control Questionnaire [ACQ-6], lung function, and medication requirement) and then recorded the 3-year exacerbations rate and time to first exacerbation. RESULTS: The SARP algorithm discriminated the 5 clusters at entry for age, asthma duration, lung function, blood eosinophil measurement, ACQ-6 scores, and diabetes comorbidity. Four homemade clusters were mostly segregated by best ever achieved FEV1 values and discriminated the groups by a few clinical characteristics. Nonetheless, all these clusters shared similar asthma outcomes related to asthma control as follows. The ACQ-6 score did not change in any cluster. Exacerbation rate and time to first exacerbation were similar, as were treatment requirements. CONCLUSION: Severe asthma phenotypes identified by using a previously reported cluster analysis or newly homemade clusters do not behave differently concerning asthma control-related outcomes, which are used to assess the response to innovative therapies. This study demonstrates a potential limitation of the cluster analysis approach in the field of severe asthma.
BACKGROUND: Cross-sectional severe asthma cluster analysis identified different phenotypes. We tested the hypothesis that these clusters will follow different courses. OBJECTIVE: We aimed to identify which asthma outcomes are specific and coherently associated with these different phenotypes in a prospective longitudinal cohort. METHODS: In a longitudinal cohort of 112 patients with severe asthma, the 5 Severe Asthma Research Program (SARP) clusters were identified by means of algorithm application. Because patients of the present cohort all had severe asthma compared with the SARP cohort, homemade clusters were identified and also tested. At the subsequent visit, we investigated several outcomes related to asthma control at 1 year (6-item Asthma Control Questionnaire [ACQ-6], lung function, and medication requirement) and then recorded the 3-year exacerbations rate and time to first exacerbation. RESULTS: The SARP algorithm discriminated the 5 clusters at entry for age, asthma duration, lung function, blood eosinophil measurement, ACQ-6 scores, and diabetes comorbidity. Four homemade clusters were mostly segregated by best ever achieved FEV1 values and discriminated the groups by a few clinical characteristics. Nonetheless, all these clusters shared similar asthma outcomes related to asthma control as follows. The ACQ-6 score did not change in any cluster. Exacerbation rate and time to first exacerbation were similar, as were treatment requirements. CONCLUSION: Severe asthma phenotypes identified by using a previously reported cluster analysis or newly homemade clusters do not behave differently concerning asthma control-related outcomes, which are used to assess the response to innovative therapies. This study demonstrates a potential limitation of the cluster analysis approach in the field of severe asthma.
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