Abstract
INTRODUCTION: Bronchoscopic lung volume reduction with endobronchial valves (EBV) is an effective procedure for patient with severe emphysema to improve lung function, exercise tolerance, dyspnoea and quality of life. Optimisation of patient and treatment lobe selection is essential for successful EBV outcomes. While clinical selection criteria are rigorous, many centres use a multidisciplinary team and rely on previous clinical experience for the selection process. To aid objective clinical decision making, we present a mathematical model to facilitate patient and target lobe selection. METHODS: A multidisciplinary team reviewed quantitative high-resolution computed tomography (HRCT) analysis from 119 patients to select candidates for EBV and to select a treatment lobe. Two logistic regression models, (1) candidacy for EBV placement and (2) target-lobe selection, were developed based on the normalised distributions of the four quantitative HRCT variables (fissure completeness score, per cent of voxel density with HU < -910 and HU < -950, and lobar volumes) across all five lung lobes. An external cohort of 50 patients (25 candidates and 25 non-candidates) was used to validate the prediction model. RESULTS: Performance measures of the training cohort demonstrated an area under the curve (AUC) of 0.91, accuracy of 81%, sensitivity of 93% and specificity of 78% compared with the multidisciplinary teams' target lobe selection. The validation cohort demonstrated an AUC of 0.89, accuracy of 84%, sensitivity of 88% and specificity of 83% compared with the multidisciplinary team decision making. CONCLUSIONS: Endobronchial valve lung volume reduction remains a potent palliative measure to improve quality of life in patients with hyperinflated emphysema. Our model for target lobe selection harnesses the multidisciplinary experience at a tertiary care centre to objectively select candidates and target lobes to assist clinicians' decision making. Future studies investigating prediction of lobar collapse and functional improvement after target lobe selection using our model are needed.