A new lung donor score to predict short and long-term survival in lung transplantation

Extended Criteria Donor Use in Lung Transplants From Donation After Controlled Circulatory Death

BACKGROUND

Most institutions apply the criteria for controlled donation after cardiac death (cDCD) lung retrieval identical to the criteria for donation after brain death (DBD). The availability of extended criteria donor (ECD) in lung transplants from cDCD remains unclear.

METHODS

The United Network for Organ Sharing (UNOS) database was queried for adult lung transplants from cDCD, from May 03, 2005, to March 15, 2022. ECDs were defined by one or more items at variance from standard criteria: age 55 years or more, PaO2:FiO2 300 or less, smoking 20 pack-years or more, diabetes, or purulent secretions upon bronchoscopy. Recipients were divided into the standard criteria donor (SCD) group and the ECD group, and assessed for short- and long-term survival and postoperative events.

RESULTS

Among 827 records, the SCD and ECD group showed no differences in 5-year (P = .56) survival. No significant differences were found in 30-day, 90-day, 1-year mortality and postoperative outcomes before discharge, whether in length of hospital stay, rate of ventilator support for >48 hours or reintubation, incidence of grade 3 PGD 72 hours posttransplant, acute rejection, or dialysis. None of the 5 donor factors used as criteria for lung retrieval was independently associated with cDCD recipient survival.

CONCLUSIONS

Using donor lungs that extend the DBD criteria may be a safe strategy in cDCD lung transplantation. However, the current criteria may not be a perfect fit for cDCD lung retrieval. The specific DCD criteria for standard lung retrieval need to be determined.

Thoracic and vertebral deformities in lung transplantation: perioperative complications and long-term prognoses

BACKGROUND

Lung transplantation (LTx) is a crucial therapeutic strategy for patients suffering from end-stage respiratory diseases, necessitating precise donor-recipient size matching to ensure optimal graft function. While standard allocation protocols rely on predicted lung capacity based on factors such as sex, age, and height, a subset of patients with respiratory diseases presents an additional challenge - thoracic or vertebral deformities. These deformities can complicate accurate volume predictions and may impact the success of lung transplantation.

METHODS

In this retrospective cohort study of patients who underwent LTx at Tohoku University Hospital between January 2007 and April 2022, with follow-up until October 2022, the primary objective was to assess the influence of thoracic and vertebral deformities on perioperative complications, emphasizing interventions, such as volume reduction surgery. The secondary objective aimed to identify any noticeable impact on long-term prognoses in recipients with these deformities.

RESULTS

Of 129 LTx recipients analyzed, 17.8% exhibited thoracic deformities, characterized by pectus excavatum, while 16.3% had vertebral deformities. Perioperative complications, requiring delayed chest closure, tracheostomy, and volume reduction surgery, were more prevalent in the deformity group. Thoracic deformities were notably associated with the need for volume reduction surgery. However, long-term prognoses did not differ significantly between patients with deformities and those without. Vertebral deformities did not appear to significantly impact perioperative or long-term outcomes.

CONCLUSIONS

This study highlights the prevalence of thoracic deformities in LTx recipients, correlating with increased perioperative complications, particularly the potential need for volume reduction surgery. Importantly, these deformities do not exert a significant impact on long-term prognoses. Additionally, patients with vertebral deformities, such as scoliosis and kyphosis, appear to be manageable in the context of LTx.

Survival Tree Provides Individualized Estimates of Survival After Lung Transplant

INTRODUCTION

Identifying contributors to lung transplant survival is vital in mitigating mortality. To enhance individualized mortality estimation and determine variable interaction, we employed a survival tree algorithm utilizing recipient and donor data.

METHODS

United Network Organ Sharing data (2000-2021) were queried for single and double lung transplants in adult patients. Graft survival time <7 d was excluded. Sixty preoperative and immediate postoperative factors were evaluated with stepwise logistic regression on mortality; final model variables were included in survival tree modeling. Data were split into training and testing sets and additionally validated with 10-fold cross validation. Survival tree pruning and model selection was based on Akaike information criteria and log-likelihood values. Estimated survival probabilities and log-rank pairwise comparisons between subgroups were calculated.

RESULTS

A total of 27,296 lung transplant patients (8175 single; 19,121 double lung) were included. Stepwise logistic regression yielded 47 significant variables associated with mortality. Survival tree modeling returned six significant factors: recipient age, length of stay from transplant to discharge, recipient ventilator duration post-transplant, double lung transplant, recipient reintubation post-transplant, and donor cytomegalovirus status. Eight subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves.

CONCLUSIONS

Survival trees provide the ability to understand the effects and interactions of covariates on survival after lung transplantation. Individualized survival probability with this technique found that preoperative and postoperative factors influence survival after lung transplantation. Thus, preoperative patient counseling should acknowledge a degree of uncertainty given the influence of postoperative factors.

Organ Utilization Rates from Non-Ideal Donors for Solid Organ Transplant in the United States

Background: Non-ideal donors provide acceptable allografts and may expand the donor pool. This study evaluates donor utilization across solid organs over 15-years in the United States. Methods: We analyzed the OPTN STAR database to identify potential donors across three donor eras: 2005-2009, 2010-2014, and 2015-2019. Donors were analyzed by a composite Donor Utilization Score (DUS), comprised of donor age and comorbidities. Outcomes of interest were overall and organ-specific donor utilization. Descriptive analyses and multivariable logistic regression modeling were performed. p-values < 0.01 considered significant. Results: Of 132,465 donors, 32,710 (24.7%) were identified as non-ideal donors (NID), based on a DUS ≥ 3. Compared to ideal donors (ID), NID were older (median 56 years, IQR 51-64 years vs. 35 years, 22-48 years, p < 0.001) and more frequently female (44.3% vs. 39.1%, p < 0.001), Black (22.1% vs. 14.6%, p < 0.001) and obese (60.7% vs. 19.6%, p < 0.001). The likelihood of overall DBD utilization from NID increased from Era 1 to Era 2 (OR 1.227, 95% CI 1.123-1.341, p < 0.001) and Era 3 (OR 1.504, 1.376-1.643, p < 0.001), while DCD donor utilization in NID was not statistically different across Eras. Compared to Era 1, the likelihood of DBD utilization from NID for kidney transplantation was lower in Era 2 (OR 0.882, 0.822-0.946) and Era 3 (OR 0.938, 0.876-1.004, p = 0.002). The likelihood of NID utilization increased in Era 3 compared to Era 1 for livers (OR 1.511, 1.411-1.618, p < 0.001), hearts (OR 1.623, 1.415-1.862, p < 0.001), and lungs (OR 2.251, 2.011-2.520, p < 0.001). Conclusions: Using a universal definition of NID across organs, NID donor utilization is increasing; however, use of DUS may improve resource utilization in identifying donors at highest likelihood for multi-organ donation.

A First Experience of Distant Lung Preservation for Transplantation in the Russian Federation

Lung transplant is an effective method of treating patients with end-stage respiratory diseases, but problems such as the imbalance between the number of donor organs and the number ofrecipients needing organs still play a leading role. From a transplant point of view, a multiorgan donor is considered of greatest efficiency, so that all organs that can potentially be used should be transplanted. The combination of the vast geographical territory of Russia, the shortage of actual donors, and the relatively small number of transplant centers has led to the need to transport donor lungs by air over long distances. There were already precedents in the world for remote preservation of donor organs for transplant. In this study, we have described the unique experience of remote evaluation of donor lungs with their subsequent air transportation and transplantation, which is the first such description in Russia to our knowledge. The donor lungs for lung transplant were brought from medical institutions of the Samara region to Moscow. During remote evaluation, all information was transmitted to the transplant center by providing access to the automated information system "Organ Donation," which was used at that time by the service and contained all information about a potential donor in real time. The 2 transplant candidates had end-stage cystic fibrosis and severe respiratory failure; both patients underwent organ implantation from donors located outside their regions. In conditions of shortages of donor organs, long-distance transportation is a reasonable, feasible, and safe procedure.

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.

Lung transplantation in children

Lung transplantation is a well-established treatment for children facing advanced lung disease and pulmonary vascular disorders. However, organ shortage remains highest in children. For fitting the small chest of children, transplantation of downsized adult lungs, lobes, or even segments were successfully established. The worldwide median survival after pediatric lung transplantation is currently 5.7 years, while under consideration of age, underlying disease, and peri- and posttransplant center experience, median survival of more than 10 years is reported. Timing of referral for transplantation, ischemia-reperfusion injury, primary graft dysfunction, and acute and chronic rejection after transplantation remain the main challenges.

Development and validation of the lung donor (LUNDON) acceptability score for pulmonary transplantation

There is a chronic shortage of donor lungs for pulmonary transplantation due, in part, to low lung utilization rates in the United States. We performed a retrospective cohort study using data from the Scientific Registry of Transplant Recipients database (2006-2019) and developed the lung donor (LUNDON) acceptability score. A total of 83 219 brain-dead donors were included and were randomly divided into derivation (n = 58 314, 70%) and validation (n = 24 905, 30%) cohorts. The overall lung acceptance was 27.3% (n = 22 767). Donor factors associated with the lung acceptance were age, maximum creatinine, ratio of arterial partial pressure of oxygen to fraction of inspired oxygen, mechanism of death by asphyxiation or drowning, history of cigarette use (≥20 pack-years), history of myocardial infarction, chest x-ray appearance, bloodstream infection, and the occurrence of cardiac arrest after brain death. The prediction model had high discriminatory power (C statistic, 0.891; 95% confidence interval, 0.886-0.895) in the validation cohort. We developed a web-based, user-friendly tool (available at https://sites.wustl.edu/lundon) that provides the predicted probability of donor lung acceptance. LUNDON score was also associated with recipient survival in patients with high lung allocation scores. In conclusion, the multivariable LUNDON score uses readily available donor characteristics to reliably predict lung acceptability. Widespread adoption of this model may standardize lung donor evaluation and improve lung utilization rates.

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.

A comparison of outcomes after lung transplantation between European and North American centers

BACKGROUND

With advancements in basic science and clinical medicine, lung transplantation (LT) has evolved rapidly over the last three decades. However, it is unclear if significant regional variations exist in long-term outcomes after LT.

METHODS

To investigate potential differences, we performed a retrospective, comparative cohort analysis of adult patients undergoing deceased donor single or double LT in North America (NA) or Europe between January 2006 and December 2016. Data up to April 2019 were abstracted from the International Society for Heart and Lung Transplantation (ISHLT) Thoracic Organ Registry. We compared overall survival (OS) between North American and European LT centers in a propensity score matched analysis.

RESULTS

In 3,115 well-matched pairs, though 30-day survival was similar between groups (NA 96.2% vs Europe 95.4%, p = 0.116), 5-year survival was significantly higher in European patients (NA 60.1% vs Europe 70.3%, p < 0.001).

CONCLUSIONS

This survival difference persisted in a sensitivity analysis excluding Canadian patients. Prior observations suggest that these disparities are at least partly related to better access to care via universal healthcare models prevalent in Europe. Future studies are warranted to confirm our findings and explore other causal mechanisms. It is likely that potential solutions will require concerted efforts from healthcare providers and policymakers.

Recipient Comorbidities for Prediction of Primary Graft Dysfunction, Chronic Allograft Dysfunction and Survival After Lung Transplantation

Since candidates with comorbidities are increasingly referred for lung transplantation, knowledge about comorbidities and their cumulative effect on outcomes is scarce. We retrospectively collected pretransplant comorbidities of all 513 adult recipients transplanted at our center between 1992–2019. Multiple logistic- and Cox regression models, adjusted for donor-, pre- and peri-operative variables, were used to detect independent risk factors for primary graft dysfunction grade-3 at 72 h (PGD3-T72), onset of chronic allograft dysfunction grade-3 (CLAD-3) and survival. An increasing comorbidity burden measured by Charleston-Deyo-Index was a multivariable risk for survival and PGD3-T72, but not for CLAD-3. Among comorbidities, congestive right heart failure or a mean pulmonary artery pressure >25 mmHg were independent risk factors for PGD3-T72 and survival, and a borderline risk for CLAD-3. Left heart failure, chronic atrial fibrillation, arterial hypertension, moderate liver disease, peptic ulcer disease, gastroesophageal reflux, diabetes with end organ damage, moderate to severe renal disease, osteoporosis, and diverticulosis were also independent risk factors for survival. For PGD3-T72, a BMI>30 kg/m2 was an additional independent risk. Epilepsy and a smoking history of the recipient of >20packyears are additional independent risk factors for CLAD-3. The comorbidity profile should therefore be closely considered for further clinical decision making in candidate selection.

Extracorporeal life support as a bridge to pulmonary retransplantation: prognostic factors for survival in a multicentre cohort analysis

OBJECTIVES

Pulmonary retransplant (ReTx) is considered a controversial procedure. Despite literature reporting outcomes following ReTx, limited data exist in recipients bridged to their ReTx on extracorporeal life support (ECLS). The goal of this study was to investigate the outcomes of recipients bridged to a first-time ReTx by ECLS.

METHODS

We performed a retrospective multicentre cohort analysis from 10 centres in Europe, Asia and North America. The primary outcome was overall survival. Risk factors were analysed using Cox regression models.

RESULTS

ECLS as a bridge to a first-time ReTx was performed in 50 recipients (ECLS-ReTx). During the study period, 210 recipients underwent a first-time ReTx without bridging on ECLS (regular-ReTx) and 4959 recipients had a primary pulmonary transplant (index-Tx). The overall 1-year (55%) and 5-year (29%) survival was significantly worse for the ECLS-ReTx group.Compared to the index-Tx group, the mortality risk was significantly higher after ECLS-ReTx [hazard ratio 2.76 (95% confidence interval 1.94-3.91); P < 0.001] and regular-ReTx [hazard ratio 1.65 (95% confidence interval 1.36-2); P < 0.001].In multivariable analysis, recipient age ≥35 years, time interval <1 year from index-Tx, primary graft dysfunction as transplant indication, venoarterial-extracorporeal membrane oxygenation and Zurich donor score ≥4 points were significant risk factors for mortality in ECLS-ReTx recipients.

CONCLUSIONS

Recipients for ECLS-ReTx should be carefully selected. Risk factors, such as recipient age, intertransplant interval, primary graft dysfunction as transplant indication and type of ECLS should be kept in mind before bridging these patients on ECLS to ReTx.

The Year in Cardiothoracic Transplant Anesthesia: Selected Highlights From 2020 Part I - Lung Transplantation

This special article focuses on the highlights in cardiothoracic transplantation literature in the year 2020. Part I encompasses the recent literature on lung transplantation, including the advances in preoperative assessment and optimization, donor management, including the use of ex-vivo lung perfusion, recipient management, including those who have been infected with coronavirus disease 2019, updates on the perioperative management, including the use of extracorporeal membrane oxygenation, and long-term outcomes.