title: Chronic Obstructive Pulmonary Disease: Pulmonary Function and CT Lung Attenuation Do Not Show Linear Correlation.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: From the Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy.
authors.forename: Matteo
authors.initials: M
authors.lastname: Paoletti
authors.affiliation: From the Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy.
authors.forename: Lucia
authors.initials: L
authors.lastname: Cestelli
authors.affiliation: From the Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy.
authors.forename: Francesca
authors.initials: F
authors.lastname: Bigazzi
authors.affiliation: From the Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy.
authors.forename: Gianna
authors.initials: G
authors.lastname: Camiciottoli
authors.affiliation: From the Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy.
authors.forename: Massimo
authors.initials: M
authors.lastname: Pistolesi
mesh_terms: D000368:Aged; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D029424:Pulmonary Disease, Chronic Obstructive / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D012129:Respiratory Function Tests / Q000706:statistics & numerical data; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data