Quantitative Computed Tomography Metrics From the Transplanted Lung can Predict Forced Expiratory Volume in the First Second After Lung Transplantation

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Abstract

Purpose:

Bronchiolitis obliterans syndrome after lung transplantation (LTx) manifests as a sustained decline in forced expiratory volume in the first second (FEV1). Quantitative computed tomography (QCT) metrics may predict FEV1 better than semiquantitative scores (SQSs), and the transplanted lung may provide better information than the native lung in unilateral LTx.

Materials and Methods:

Paired inspiratory-expiratory CT scans and pulmonary function testing of 178 LTx patients were analyzed retrospectively. SQS were graded (absent, mild, moderate, severe) for features including mosaic attenuation and bronchiectasis. QCT included lung volumes and air-trapping volumes, by lobe. Multivariate Pearson correlation and multivariate linear least squares regression analyses were performed.

Results:

Multivariate linear least squares regression models using FEV1 as the outcome variable and SQS or QCT metrics as predictor variables demonstrated SQS to be a weak predictor of FEV1 (adjusted R2, 0.114). QCT metrics were much stronger predictors of FEV1 (adjusted R2, 0.654). QCT metrics demonstrated stronger correlation (r) with FEV1 than SQS. In bilateral LTx, whole lung volume difference (r=0.69), left lung volume difference (r=0.69), and right lung volume difference (r=0.65) were better than the sum of SQS (r=−0.54). Interestingly, in left LTx we obtained r=0.81, 0.86, 0.25, and −0.39, respectively. In right LTx, we obtained r=0.69, 0.49, 0.68, and −0.31, respectively.

Conclusions:

QCT metrics demonstrate stronger correlations with FEV1 and are better predictors of pulmonary function than SQS. SQS performs moderately well in bilateral LTx, but poorly on unilateral LTx. In unilateral LTx, QCT metrics from the transplanted lung are better predictors of FEV1 than QCT metrics from the nontransplanted lung.

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