Pulmonary MRI and Cluster Analysis Help Identify Novel Asthma Phenotypes

医学 肺活量测定 哮喘 邦费罗尼校正 容积描记器 单变量分析 空气滞留 内科学 肺容积 气道 方差分析 支气管扩张剂 呼气 核医学 放射科 多元分析 麻醉 统计 数学
作者
Rachel L. Eddy,Marrissa J. McIntosh,Alexander M. Matheson,David G. McCormack,Christopher Licskai,Grace Párraga
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:56 (5): 1475-1486 被引量:4
标识
DOI:10.1002/jmri.28152
摘要

Outside eosinophilia, current clinical asthma phenotypes do not show strong relationships with disease pathogenesis or treatment responses. While chest x-ray computed tomography (CT) phenotypes have previously been explored, functional MRI measurements provide complementary phenotypic information.To derive novel data-driven asthma phenotypic clusters using functional MRI airway biomarkers that better describe airway pathologies in patients.Retrospective.A total of 45 patients with asthma who underwent post-bronchodilator 129 Xe MRI, volume-matched CT, spirometry and plethysmography within a 90-minute visit.Three-dimensional gradient-recalled echo 129 Xe ventilation sequence at 3 T.We measured MRI ventilation defect percent (VDP), CT airway wall-area percent (WA%), wall-thickness (WT, WT* [*normalized for age/sex/height]), lumen-area (LA), lumen-diameter (D, D*) and total airway count (TAC). Univariate relationships were utilized to select variables for k-means cluster analysis and phenotypic subgroup generation. Spirometry and plethysmography measurements were compared across imaging-based clusters.Spearman correlation (ρ), one-way analysis of variance (ANOVA) or Kruskal-Wallis tests with post hoc Bonferroni correction for multiple comparisons, significance level 0.05.Based on limited common variance (Kaiser-Meyer-Olkin-measure = 0.44), four unique clusters were generated using MRI VDP, TAC, WT* and D* (52 ± 14 years, 27 female). Imaging measurements were significantly different across clusters as was the forced expiratory volume in 1-second (FEV1 %pred ), residual volume/total lung capacity and airways resistance. Asthma-control (P = 0.9), quality-of-life scores (P = 0.7) and the proportions of severe-asthma (P = 0.4) were not significantly different. Cluster1 (n = 15/8 female) reflected mildly abnormal CT airway measurements and FEV1 with moderately abnormal VDP. Cluster2 (n = 12/12 female) reflected moderately abnormal TAC, WT and FEV1 . In Cluster3 and Cluster4 (n = 14/6 female, n = 4/1 female, respectively), there was severely reduced TAC, D and FEV1 , but Cluster4 also had significantly worse, severely abnormal VDP (7 ± 5% vs. 41 ± 12%).We generated four proof-of-concept MRI-derived clusters of asthma with distinct structure-function pathologies. Cluster analysis of asthma using 129 Xe MRI in combination with CT biomarkers is feasible and may challenge currently used paradigms for asthma phenotyping and treatment decisions.3 TECHNICAL EFFICACY: Stage.
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