Optimizing the Use of Available Lungs

Post-COVID-19 Lung Transplantation Italian Pivotal Protocol: Some Ethical Considerations

SARS‑CoV‑2 mostly affects the respiratory system with clinical patterns ranging from the common cold to fatal pneumonia. During the first wave of the COVID-19 pandemic, owing to the high number of patients who were infected with SARS‑CoV‑2 and subsequently recovered, it has been shown that some patients with post-COVID-19 terminal respiratory failure need lung transplantation for survival. There is increasing evidence coming from worldwide observations that this procedure can be performed successfully in post-COVID-19 patients. However, owing to the scarcity of organs, there is a need to define the safety and efficacy of lung transplant for post-COVID-19 patients as compared to patients waiting for a lung transplant for other pre-existing conditions, in order to ensure that sound ethical criteria are applied in organ allocation. The Milan's Policlinic Lung Transplant Surgery Unit, with the revision of the National Second Opinion for Infectious Diseases and the contribution of the Italian Lung Transplant Centres and the Italian National Transplant Centre, set up a pivotal observational protocol for the lung transplant of patients infected and successively turned negative for SARS‑CoV‑2, albeit with lung consequences such as acute respiratory distress syndrome or some chronic interstitial lung disease. The protocol was revised and approved by the Italian National Institute of Health Ethics Committee. Description of the protocol and some ethical considerations are reported in this article.

The Surplus Transplant Lung Allocation System in Italy: An Evaluation of the Allocation Process via Stochastic Modeling

Background: Lung transplantation is a specialized procedure used to treat chronic end-stage respiratory diseases. Due to the scarcity of lung donors, constructing fair and equitable lung transplant allocation methods is an issue that has been addressed with different strategies worldwide. This work aims to describe how Italy’s “national protocol for the management of surplus organs in all transplant programs” functions through an online app to allocate lung transplants. We have developed two probability models to describe the allocation process among the various transplant centers. An online app was then created. The first model considers conditional probabilities based on a protocol flowchart to compute the probability for each area and transplant center to receive each n-th organ in the period considered. The second probability model is based on the generalization of the binomial distribution to correlated binary variables, which is based on Bahadur’s representation, to compute the cumulative probability for each transplant center to receive at least nth organs. Our results show that the impact of the allocation of a surplus organ depends mostly on the region where the organ was donated. The discrepancies shown by our model may be explained by a discrepancy between the northern and southern regions in relation to the number of organs donated.

Variability in donor organ offer acceptance and lung transplantation survival

BACKGROUND

Lung transplantation offers a survival benefit for patients with end-stage lung disease. When suitable donors are identified, centers must accept or decline the offer for a matched candidate on their waitlist. The degree to which variability in per-center offer acceptance practices impacts candidate survival is not established. The purpose of this study was to determine the degree of variability in per-center rates of lung transplantation offer acceptance and to ascertain the associated contribution to observed differences in per-center waitlist mortality.

METHODS

We performed a retrospective cohort study of candidates waitlisted for lung transplantation in the US using registry data. Logistic regression was fit to assess the relationship of offer acceptance with donor, candidate, and geographic factors. Listing center was evaluated as a fixed effect to determine the adjusted per-center acceptance rate. Competing risks analysis employing the Fine-Gray model was undertaken to establish the relationship between adjusted per-center acceptance and waitlist mortality.

RESULTS

Of 15,847 unique organ offers, 4,735 (29.9%) were accepted for first-ranked candidates. After adjustment for important covariates, transplant centers varied markedly in acceptance rate (9%-67%). Higher cumulative incidence of 1-year waitlist mortality was associated with lower acceptance rate. For every 10% increase in adjusted center acceptance rate, the risk of waitlist mortality decreased by 36.3% (sub-distribution hazard ratio 0.637; 95% confidence interval 0.592-0.685).

CONCLUSIONS

Variability in center-level behavior represents a modifiable risk factor for waitlist mortality in lung transplantation. Further intervention is needed to standardize center-level offer acceptance practices and minimize waitlist mortality.