RATIONALE: Detailed characterization of asthma phenotypes is essential for identification of responder populations to allow directed personalized medical intervention. OBJECTIVES: The aim of this study was to identify distinctive patient characteristics within subgroups of a well-characterized severe asthma population at risk for exacerbations and to determine the treatment response within each subgroup. METHODS: A supervised cluster analysis with recursive partitioning approach was applied to data from the Dose Ranging Efficacy And safety with Mepolizumab (DREAM) study to identify characteristics that maximized the differences across subgroups. Exacerbation rate ratios were calculated for each cluster comparing mepolizumab versus placebo. MEASUREMENTS AND MAIN RESULTS: Three predictors were identified in four primary clusters: blood eosinophils, airway reversibility, and body mass index. The reduction in exacerbations was significantly greater in patients who received mepolizumab (clusters 2, 3, and 4) with raised eosinophils (responder population). Cluster 2 with low airway reversibility (mean, 11%) had a 53% reduction in exacerbations. These patients more frequently reported sinusitis and nasal polyposis. Those with higher airway reversibility (mean, 28%) were further split by body mass index. The nonobese versus obese (clusters 3 and 4) had a 35 and 67% reduction in exacerbations, respectively. Cluster 4 also had patients with more comorbidities, including hypertension, weight gain, and anxiety. CONCLUSIONS: Using supervised cluster analysis helped identify specific patient characteristics related to disease and therapeutic response. Patients with eosinophilic inflammation received significant therapeutic benefit with mepolizumab, and responses differed within clusters. Clinical trial registered with www.clinicaltrials.gov (NCT01000506).
RCT Entities:
RATIONALE: Detailed characterization of asthma phenotypes is essential for identification of responder populations to allow directed personalized medical intervention. OBJECTIVES: The aim of this study was to identify distinctive patient characteristics within subgroups of a well-characterized severe asthma population at risk for exacerbations and to determine the treatment response within each subgroup. METHODS: A supervised cluster analysis with recursive partitioning approach was applied to data from the Dose Ranging Efficacy And safety with Mepolizumab (DREAM) study to identify characteristics that maximized the differences across subgroups. Exacerbation rate ratios were calculated for each cluster comparing mepolizumab versus placebo. MEASUREMENTS AND MAIN RESULTS: Three predictors were identified in four primary clusters: blood eosinophils, airway reversibility, and body mass index. The reduction in exacerbations was significantly greater in patients who received mepolizumab (clusters 2, 3, and 4) with raised eosinophils (responder population). Cluster 2 with low airway reversibility (mean, 11%) had a 53% reduction in exacerbations. These patients more frequently reported sinusitis and nasal polyposis. Those with higher airway reversibility (mean, 28%) were further split by body mass index. The nonobese versus obese (clusters 3 and 4) had a 35 and 67% reduction in exacerbations, respectively. Cluster 4 also had patients with more comorbidities, including hypertension, weight gain, and anxiety. CONCLUSIONS: Using supervised cluster analysis helped identify specific patient characteristics related to disease and therapeutic response. Patients with eosinophilic inflammation received significant therapeutic benefit with mepolizumab, and responses differed within clusters. Clinical trial registered with www.clinicaltrials.gov (NCT01000506).
Entities:
Keywords:
asthma exacerbations; cluster analysis; eosinophils; mepolizumab; severe asthma
Authors: Njira Lugogo; Cynthia L Green; Noah Agada; Siyi Zhang; Susanne Meghdadpour; Run Zhou; Siyun Yang; Kevin J Anstrom; Elliot Israel; Richard Martin; Robert F Lemanske; Homer Boushey; Stephen C Lazarus; Stephen I Wasserman; Mario Castro; William Calhoun; Stephen P Peters; Emily DiMango; Vernon Chinchilli; Susan Kunselman; Tonya S King; Nikolina Icitovic; Monica Kraft Journal: J Allergy Clin Immunol Date: 2017-06-15 Impact factor: 10.793
Authors: Bruce D Levy; Patricia J Noel; Michelle M Freemer; Michelle M Cloutier; Steve N Georas; Nizar N Jarjour; Carole Ober; Prescott G Woodruff; Kathleen C Barnes; Bruce G Bender; Carlos A Camargo; Geoff L Chupp; Loren C Denlinger; John V Fahy; Anne M Fitzpatrick; Anne Fuhlbrigge; Ben M Gaston; Tina V Hartert; Jay K Kolls; Susan V Lynch; Wendy C Moore; Wayne J Morgan; Kari C Nadeau; Dennis R Ownby; Julian Solway; Stanley J Szefler; Sally E Wenzel; Rosalind J Wright; Robert A Smith; Serpil C Erzurum Journal: Am J Respir Crit Care Med Date: 2015-12-01 Impact factor: 21.405