医学
DLCO公司
慢性阻塞性肺病
肺功能测试
多元统计
单变量
到期
肺容积
单变量分析
多元分析
扩散能力
肺
内科学
心脏病学
贝叶斯多元线性回归
核医学
放射科
线性回归
呼吸系统
肺功能
统计
数学
作者
Matteo Paoletti,Lucia Cestelli,Francesca Bigazzi,Gianna Camiciottoli,Massimo Miniati
出处
期刊:Radiology
[Radiological Society of North America]
日期:2015-08-01
卷期号:276 (2): 571-578
被引量:20
标识
DOI:10.1148/radiol.2015141769
摘要
To determine whether the relationship between pulmonary function and computed tomographic (CT) lung attenuation in chronic obstructive pulmonary disease (COPD), which is traditionally described with single univariate and multivariate statistical models, could be more accurately described with a multiple model estimation approach.The study was approved by the local ethics committee. All participants provided written informed consent. The prediction of the percentage area with CT attenuation values less than -950 HU at inspiration (%LAA-950insp) and less than -910 HU at expiration (%LAA-910exp) obtained with single univariate and multivariate models was compared with that obtained with a multiple model estimation approach in 132 patients with COPD.At univariate analysis, %LAA-950insp and %LAA-910exp values higher than the mean value of this cohort (19.1% and 22.0%) showed better correlation with percentage of predicted diffusing capacity of lung for carbon monoxide (Dlco%) than with airflow obstruction (forced expiratory volume in 1 second [FEV1]/vital capacity [VC]). Conversely, %LAA-950insp and %LAA-910exp values lower than the mean value were correlated with FEV1/VC but not with Dlco%. Multiple model estimation performed with two multivariate regressions, each selecting the most appropriate functional variables (FEV1/VC for mild parenchymal destruction, Dlco% and functional residual capacity for severe parenchymal destruction), predicted better than single multivariate regression both %LAA-950insp (R(2) = 0.75 vs 0.46) and %LAA-910exp (R(2) = 0.83 vs 0.63).The relationship between pulmonary function data and CT densitometric changes in COPD varies with the level of lung attenuation impairment. The nonlinear profile of this relationship is accurately predicted with a multiple model estimation approach.
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