Patient selection for lung volume reduction surgery - An objective model based on prior clinical decisions and quantitative CT analysis

Objectives: We used whole-lung quantitative CT analysis (QCT)-an objective method of evaluating emphysema severity and distribution based on measureme nt of lung density-to determine whether subjective selection criteria for l ung volume reduction surgery are applied consistently and to model the pati ent selection process, and assessed the relationship of the model to postop erative outcome. Design: Logistic regression analysis using QCT indexes of emphysema and pre operative physiologic test results as the independent variables, and the de cision to operate as the dependent variable. Setting: University hospital. Patients: Seventy patients selected for bilateral lung volume reduction sur gery and 32 otherwise operable patients excluded from surgery based on subj ective assessment of emphysema morphology on chest radiography, CT, and per fusion scintigraphy. Intervention: Bilateral lung volume I eduction surgery in the selected grou p. Measurements and results: Emphysema in patients selected for surgery was mo re severe overall and in the upper lungs by multiple QCT indexes (p < 0.01, unpaired two-tailed t test). Physiologic abnormalities were slightly more severe in selected patients (p < 0.05, unpaired two-tailed t test). The ran ge of many QCT and physiologic values overlapped considerably between the s elected and excluded groups, The percent severe emphysema (<- 960 Hounsfiel d units [MU]), upper/lower lung emphysema ratio (- 900 HU threshold), and r esidual volume were the key variables in the model predicting selection dec isions (model r(2) = 0.48; p < 0.0001), The model correctly predicted. sele ction decisions in 87% of all cases, 91% of the selected group, and 78% of the excluded group. Surgical patients with a higher model-derived probabili ty of selection had greater postoperative improvement: in FEV1 and G-min wa lk distance. Conclusions: Radiologic selection criteria are applied consistently to the majority of patients. QCT features are strongly associated with selection d ecisions, are related to outcome, and may help improve consistency and conf idence in patient selection.