Longitudinal Assessment of Tissue Loss and Air Trapping in Functionally Stable Lung Transplant Recipients Using Quantitative CT measurements

Abstract

PURPOSE To investigate lung parenchyma and airway changes in functionally stable lung transplant recipients using quantitative CT. METHOD AND MATERIALS Annual paired CT scans at total lung capacity (TLC) and functional residual capacity (FRC) were collected from patients with double lung transplantation in stable clinical appearance and pulmonary function tests. Only patients with 5 consecutive follow up were included in our study. For each scan we calculated quantitative lung volume (Vol), mean lung density (MLD), standard deviation of MLD (MLD-SD) as surrogate of heterogeneity, and lung weight. For the airway metrics we evaluated wall thickness, wall percentage and Pi10. This was done using dedicated software (MeVis, Frauenhofer, Germany). For statistical analysis, the longitudinal evolution of the metrics was compared within individual patients and relative changes were compared between individuals. Depending on normal distribution, evaluated by Kolmogorov-Smirnov normality test, comparisons were performed either using analysis of variance for repeated measurements or the Friedman test. Linear regression analysis was used for data modeling. RESULTS A total of 100 inspiratory and expiratory CT scans from 20 patients were included. VolTLC slightly decreased over time (p=0.066), while the VolFRC increased (p=0.042); these trends, as reflected by the differences in slopes of the regression lines, did not reach statistical significance (F=3.58, p=0.107). MLD was stable over time (p=0.550), whereas MLDFRC decreased (p0.05). CONCLUSION On quantitative CT, functionally stable lung transplant recipients show a consistent pattern of longitudinal tissue loss, combined to increasing gas trapping. None of these changes are detected by the lung function test currently used as reference standard for following these patients. CLINICAL RELEVANCE/APPLICATION Quantitative CT parameters can identify preclinical parenchymal changes in stable lung transplant recipients. Therefore, quantitative CT could be used for predicting the long-term transplantation outcome

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