Histopathologic and radiologic assessment of nontransplanted donor lungs

Vanstapel, Arno; Dubbeldam, Adriana; Weynand, Birgit; Verbeken, Eric; Vos, Robin; Neyrinck, Arne; Vasilescu, Dragoş M.; Ceulemans, Laurens J.; Frick, Anna Elisabeth; Raemdonck, Dirk Van; Verschakelen, Johny; Vanaudenaerde, Bart M.; Verleden, Geert M.; Verleden, Stijn E.

doi:10.1111/ajt.15790

Cited by 7 publications

(8 citation statements)

References 23 publications

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“…Vanstapel et al demonstrated a histopathologic evaluation of the donor lungs declined for transplantation. They reported that 3 of 23 (13.0%) donor lungs that were not transplanted due to extrapulmonary causes displayed severe histologic abnormalities (pneumonia, emphysema) [10]. Similar to their study, 3 of 23 cases (13.0%) in the BLT control group and in 2 of 26 cases (7.7%) in the SLT control group showed infiltrations in EVL-CT in our study.…”

Section: Plos Onesupporting

confidence: 89%

Ex vivo lung CT findings may predict the outcome of the early phase after lung transplantation

et al. 2020

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We developed an ex vivo lung CT (EVL-CT) technique that allows us to obtain detailed CT images and morphologically assess the retrieved lung from a donor for transplantation. After we recovered the lung graft from a brain-dead donor, we transported it to our hospital and CT images were obtained ex vivo before lung transplant surgery. The objective of this study was to investigate the correlation between the EVL-CT findings and post-transplant outcome in patients who underwent bilateral lung transplantation (BLT) or single lung transplantation (SLT). MethodsWe retrospectively reviewed the records of 70 patients with available EVL-CT data who underwent BLT (34 cases) or SLT (36 cases) in our hospital between October 2007 and September 2017. The recipients were divided into 2 groups (control group, infiltration group) according to the findings of EVL-CT of the lung graft in BLT and SLT, respectively. Recipients in the control group were transplanted lung grafts without any infiltrates (BLT control group, SLT control group). Recipients in the infiltration group received lung grafts with infiltrates (BLT infiltration group, SLT infiltration group). ResultsThe recipients in the BLT infiltration group showed significantly slower recovery from primary graft dysfunction and a longer mechanical ventilation period and ICU stay period than those in the BLT control group. The mechanical ventilation period was significantly longer in the recipients in the SLT infiltration group than those in the SLT control group. ConclusionEVL-CT may predict the outcome of the early phase after lung transplantation.

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Section: Plos Onesupporting

confidence: 89%

Ex vivo lung CT findings may predict the outcome of the early phase after lung transplantation

et al. 2020

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“…In our previous work, we obtained CT scans from declined donor lungs and found that CT examination of these specimens by a trained thoracic radiologist provided detailed information of interstitial changes otherwise obscured during routine donor lung assessment. 12,13 However, manual screening of CT scans is hampered by interobserver variability, as well as delays due to accessibility to radiologists. Importantly, time constraints must be minimized to effectively incorporate our strategy of applying CT scanning to donor lung screening.…”

Section: Discussionmentioning

confidence: 99%

“…The copyright holder for this preprint this version posted March 29, 2023. ; https://doi.org/10.1101/2023.03.28.23287705 doi: medRxiv preprint 13 An important attribute of our ML screening method is its ability to focus exclusively on the features presented in CT scans without requiring additional information such as donor or recipient characteristics or clinical data. Due to the novelty of our method, i.e., using clinical CT scans to screen donor lungs, our data came only from this single center study.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

CT-based Machine Learning for Donor Lung Screening Prior to Transplantation

Ram

Verleden

Kumar

et al. 2023

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Background: Assessment and selection of donor lungs remains 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 CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to 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 prior to 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) prior to 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 ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant. 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 post-transplant complications.

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“…Finally, it is possible that the timing of ALAD may impact outcomes, with early-onset ALAD having a more favorable prognosis. 27 Our series included patients with both early and late (>2-3 mo posttransplant) ALAD, which may in part account for our improved outcomes compared to other cohorts with severe ALAD ("lung white out"). Despite the heterogeneity in severe ALAD onset, we did not appreciate superior outcomes in those with early disease.…”

Section: Discussionmentioning

confidence: 99%