Donor Lung Procurement by Surgical Fellow With an Expectation of High Rate of Lung Utilisation

Application of artificial intelligence and machine learning in lung transplantation: a comprehensive review

Lung transplantation (LTx) is an effective method for treating end-stage lung disease. The management of lung transplant recipients is a complex, multi-stage process that involves preoperative, intraoperative, and postoperative phases, integrating multidimensional data such as demographics, clinical data, pathology, imaging, and omics. Artificial intelligence (AI) and machine learning (ML) excel in handling such complex data and contribute to preoperative assessment and postoperative management of LTx, including the optimization of organ allocation, assessment of donor suitability, prediction of patient and graft survival, evaluation of quality of life, and early identification of complications, thereby enhancing the personalization of clinical decision-making. However, these technologies face numerous challenges in real-world clinical applications, such as the quality and reliability of datasets, model interpretability, physicians' trust in the technology, and legal and ethical issues. These problems require further research and resolution so that AI and ML can more effectively enhance the success rate of LTx and improve patients' quality of life.

The 2024 American Association for Thoracic Surgery expert consensus document: Current standards in donor lung procurement and preservation

BACKGROUND

Donor lung procurement and preservation is critical for lung transplantation success. Unfortunately, the large variability in techniques impacts organ utilization rates and transplantation outcomes. Compounding this variation, recent developments in cold static preservation and new technological advances with machine perfusion have increased the complexity of the procedure. The objective of the American Association for Thoracic Surgery (AATS) Clinical Practice Standards Committee (CPSC) expert panel was to make evidence-based recommendations for best practices in donor lung procurement and preservation based on review of the existing literature.

METHODS

The AATS CPSC assembled an expert panel of 16 lung transplantation surgeons from 14 centers who developed a consensus document of recommendations. The panel was divided into 7 subgroups covering (1) intraoperative donor assessment, (2) surgical techniques, (3) ex situ static lung preservation methods, (4) hypothermic preservation, (5) normothermic ex vivo lung perfusion (EVLP), (6) donation after circulatory death (DCD) and normothermic regional perfusion, and (7) donor management centers, organ assessment centers, and third-party procurement teams. Following a focused literature review, each subgroup formulated recommendation statements for each subtopic, which were reviewed and further refined using a Delphi process until a 75% consensus was achieved on each final statement by the voting group.

RESULTS

The expert panel achieved consensus on 34 recommendations for current best practices in donor lung procurement and preservation both in brain-dead as well as DCD donation. The use of new methods of cold preservation, the role of EVLP, and DCD with and without concomitant heart donation are described in detail.

CONCLUSIONS

Consistent and best practices in donor lung procurement and preservation are critical to improve both lung transplantation numbers as well as recipient outcomes. The recommendations described here provide guidance for professionals involved in the care of patients with end-stage lung disease considered for transplantation.

Computed tomography-based machine learning for donor lung screening before transplantation

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.

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

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.

Do Organizational Characteristics of Lung Procurement Operations Matter: The Association Between Transplant Center Centrality and Volume With Total Ischemic Time

BACKGROUND

To understand the association of 2 organizational characteristics of transplant center (TXC), volume and closeness centrality, with total ischemic time for deceased donor lung transplants in conjunction with the removal of donation service area (DSA) lung allocation policy. The organization of donor procurements has received increased attention since DSA was removed from allocation policy. Consistent with network theories of organization, organizational characteristics of a TXC could affect procurement efficiency, as volume and closeness centrality (measuring how connected a TXC is within the Organ Procurement and Transplantation Network) could be associated with total ischemic time. These associations could have changed because of the removal of DSA from allocation policy.

METHODS

We conducted a retrospective, pooled cross-sectional study of total ischemic time for nonperfused deceased donor lung transplants (n = 9281) between 2015 and 2019, using within-between regression.

RESULTS

Higher volume TXCs exhibited lower total ischemic times after the removal of DSA from lung allocation policy (P = 0.011); however, all TXCs that had increased volumes, after the removal of DSA from lung allocation policy, exhibited higher levels of total ischemic time (P ≤ 0.001). Before the removal of DSA, TXCs that had increased volumes exhibited lower levels of ischemic time (P ≤ 0.001). Both within and between closeness centrality exhibited u-shaped associations with total ischemic time (P = 0.012; P = 0.006) and the effect of closeness centrality on total ischemic time was different after DSA removal (P < 0.001).

CONCLUSIONS

Organizational characteristics were associated with the efficiency of deceased organ procurements. The effects on total ischemic time were dependent on whether DSA was used for lung allocation.

Donor quality assessment and size match in lung transplantation

Careful donor quality assessment and size match can impact long-term survival in lung transplantation. With this article, we review the conceptual and practical aspects of the preoperative donor lung quality assessment and size matching.

Procurement of lungs from brain-dead donors

Lung transplantation is the procedure of choice in many patients with end-stage lung disease and is being performed more frequently around the world. However, there continues to be shortage of donor organs with the ever-expanding number of recipients on the waiting list, leading to liberalization of the lung donor selection criteria with increasing acceptance of marginal donors while striving for excellent results. This has placed an increasing emphasis on the technique of donor lung procurement and preservation from marginal donors. Good judgment and procurement techniques are necessary to obtain high-quality donor lungs for transplantation and optimize long-term results. This is a review of our current technique used for the procurement of the lungs from brain-dead donors.

Histopathologic and radiologic assessment of nontransplanted donor lungs

Donor organ shortage results in significant waiting list mortality. Donor lung assessment is currently based on donors' history, gas exchange, chest X-ray, bronchoscopy findings, and ultimately in situ inspection but remains subjective. We correlated histopathology and radiology in nontransplanted donor lungs with the clinical indications to decline the offered organ. Sixty-two donor lungs, not used for transplantation (2010-2019), were procured, air-inflated, frozen, scanned with computed tomography, systematically sampled, and histologically and radiologically assessed. Thirty-nine (63%) lungs were declined for allograft-related reasons. In 13/39 (33%) lungs, histology could not confirm the reason for decline, in an additional 8/39 (21%) lungs, histologic abnormalities were only considered mild. In 16/39 (41%) lungs, radiology could not confirm the reason for decline. Twenty-three (37%) donor lungs were not transplanted due to extrapulmonary causes, of which three (13%) lungs displayed severe histologic abnormalities (pneumonia, n = 2; emphysema, n = 1), in addition to mild emphysema in 9 (39%) lungs and minor bronchopneumonia in 1 (4%). Radiology revealed ground-glass opacities in 8/23 (35%) and emphysema in 4/23 (17%) lungs. Histopathologic and radiologic assessment of nontransplanted donor lungs revealed substantial discrepancy with the clinical reason for decline. Optimization of donor lung assessment is necessary to improve current organ acceptance rates.