The addition of computed tomography (CT) findings maintains the predictive performance of an existing radiograph-based donor lung acceptability score.

Abstract

The use of lungs from brain-dead donors is low partly as a result of the lack of reliable donor assessment criteria. The validated Lung Donor (LUNDON) score predicts lung acceptance for transplantation by using 9 clinically relevant variables, including the presence of an abnormality on radiograph of the chest. Because most organ donor evaluations now include routine computed tomography (CT) of the chest, we aimed to assess whether the addition of CT findings impacts the LUNDON model's performance. Data including CT findings were collected for adult brain-dead donors from 3 organ procurement organizations from 2014 to 2020. The primary outcome was lung acceptance for transplantation. We collated all CT findings into a weighted CT composite score, with greater scores representing more CT abnormalities, and calculated the score for each donor. The lung acceptance rate was 40.4% among 2454 donors with CTs of the chest and 22.3% among 1980 donors without CTs of the chest. Emphysema, pulmonary edema, and traumatic lung injury on CT were associated with a lower likelihood lung acceptance. The LUNDON model's performance was comparable between use of the original radiograph of the chest variable, the CT composite score, or both variables together (C-statistics 0.883, 0.887, 0.890, respectively). All 3 iterations of the model were predictive of 1-year graft survival. Undergoing CT was independently associated with donor lung acceptance. The incorporation of highly granular findings from CT of the chest to the previously established LUNDON model maintained, but did not meaningfully improve, its excellent baseline ability to predict lung use and its association with graft survival.