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Background: Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation., Methods: Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures., Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2 years posttransplant., Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications., (Copyright © 2023 International Society for the Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.)

Background: Donation after cardiac death (DCD) to overcome the donor organ shortage is well accepted in the clinical setting, although long-term outcome after DCD lung transplantation (LTx) remains largely unknown., Methods: In this retrospective study, DCD LTx recipients (n = 59) were compared with a cohort of donation after brain death (DBD) LTx recipients (n = 331) transplanted between February 2007 and September 2013; follow-up was until January 1, 2016. Short-term (duration of mechanical ventilation, intensive care unit stay, hospital stay, and highest primary graft dysfunction score within 72 hours) and long-term (chronic lung allograft dysfunction-free and overall survival) follow-up were compared over a median follow-up of 50.5 (±3.7) months for DCD and 66.8 (±1.5) months for DBD., Results: There were no differences between groups with regard to patient characteristics: age (P = 0.78), underlying disease (P = 0.30) and type of type of LTx (P = 0.10), except sex where more males were transplanted with a DCD donor (62.7%) vs (48.3%, P = 0.048). There was no difference in time on mechanical ventilation (P = 0.59), intensive care unit stay (P = 0.74), highest primary graft dysfunction score (P = 0.67) and hospital stay (P = 0.99). Moreover, chronic lung allograft dysfunction-free (P = 0.86) and overall survival (P = 0.15) did not differ between the DBD and DCD groups., Conclusions: In our experience, both short- and long-term outcomes in DCD lung recipients are comparable to that of DBD lung recipients. Therefore, DCD LTx can be considered a safe strategy that significantly increased our transplant activity.

Despite donor organ shortage, a large proportion of possible donor lungs are declined for transplantation. Criteria for accepting/declining lungs remain controversial because of the lack of adequate tools to aid in decision-making. We collected, air-inflated, and froze a large series of declined/unused donor lungs and subjected these lung specimens to CT examination. Affected target regions were scanned by using micro-CT. Lungs from 28 donors were collected. Two lungs were unused, six were declined for non-allograft-related reasons (collectively denominated nonallograft declines, n = 8), and 20 were declined because of allograft-related reasons. CT scanning demonstrated normal lung parenchyma in only four of eight nonallograft declines, while relatively normal parenchyma was found in 12 of 20 allograft-related declines. CT and micro-CT examinations confirmed the reason for decline in most lungs and revealed unexpected (unknown from clinical files or physical inspection) CT abnormalities in other lungs. CT-based measurements showed a higher mass and density in the lungs with CT alterations compared with lungs without CT abnormalities. CT could aid in the decision-making to accept or decline donor lungs which could lead to an increase in the quantity and quality of lung allografts., (© 2017 The American Society of Transplantation and the American Society of Transplant Surgeons.)