Propensity Score-Matched Analysis of Posttransplant Outcomes in Living Donor Liver Transplantation for Older Adult Recipients

Takotsubo Syndrome in Orthotopic Liver Transplant: A Systematic Review and Pooled Analysis of Published Studies and Case Reports

BACKGROUND

Takotsubo syndrome (TTS) has been reported in solid-organ transplant recipients. However, the pooled data regarding TTS after liver transplant remain limited.

METHODS

A systematic review was performed through February 2022 using PubMed, Embase, Scopus, and Google Scholar to review case reports/series and original studies on liver transplant-associated TTS. Descriptive analysis was performed for case reports and pooled analysis for the prevalence using random effects models.

RESULTS

A total of 56 case reports were included from 30 articles (51.8 % male; mean age, 53 years; India 56%, US 27%, and Europe 8.93%) and 10 original studies (US 88.65%, India 10.92%) revealing liver transplant-associated TTS. The pooled prevalence of TTS was 1.1% (95% Cl, 0.6%-1.7%) of all liver transplants with comparable rates in studies from India and the US (P = .92). Indications for liver transplant included end-stage liver disease due to alcohol-related cirrhosis (25%), hepatitis C virus infection (17.9%), hepatocellular carcinoma (10.7%), and non-alcohol-related steatohepatitis (8.9%); the average Model for End-Stage Liver Disease score was 24.75. TTS commonly presented as hypotension (30%), dyspnea (14%), and oliguria, occurring mostly post-transplant (82%), whereas 14% were intraoperative. Common electrocardiogram findings were ST changes, ventricular tachycardia, and atrial fibrillation. Common echocardiogram findings showed left ventricular apical ballooning in 46.5% of cases and reduced ejection fraction < 20% in 41.9% of cases. Common complications were cardiogenic shock (32.1 %), acute kidney injury (12.5%), arrhythmia, stroke, cardiac arrest, and hepatic artery thrombosis. Mechanical circulatory support was required in 30.3%. Recurrence was reported in 15, and mortality in 30.4% of patients.

CONCLUSIONS

Takotsubo syndrome prevalence after liver transplant is significantly higher than TTS prevalence in general US hospitalizations with potentially worse outcomes. Prospective registries reporting TTS in liver transplant recipients are warranted.

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

What is the crux of successful living-donor liver transplantation for recipients aged 70 and beyond?

Aim

There is limited evidence regarding the feasibility of living-donor liver transplantation (LDLT) for patients aged over 70. The aims of this study were to assess postoperative outcomes in elderly recipients and to ascertain the potential feasibility and acceptability of LDLT.

Methods

Data were collected from 762 recipients, including 26 in the elderly group (aged ≥70) and 736 in the younger group (aged <70), and reviewed even by propensity score matching (PSM).

Results

No significant differences were observed in the frequency of postoperative complications between the two groups. Additionally, both groups exhibited a comparable 30-day mortality rate after LDLT (3.9% in both) and similar hospital stays (36 days vs. 40 days). The 1-, 3-, and 5-year graft survival rates in the elderly group were 92.0%, which was comparable to those in the younger group (p = 0.517), as confirmed by PSM. Notably, all donors for elderly patients were the children of the recipients, with an average age of 41.6 years, and grafts from donors aged ≥50 years were not utilized, signifying the use of high-quality grafts. Our inclusion criterion for elderly recipients was strictly defined as an ECOG-PS score of 0-2, which played a pivotal role in achieving favorable postoperative outcomes.

Conclusion

LDLT can be performed safely for elderly patients aged 70 years or older, provided they have a preserved PS and receive high-quality grafts from younger donors, inevitably all children of elderly recipients. This approach yields acceptable long-term outcomes. Consequently, age alone should not serve as an absolute contraindication for LDLT.

Survival after LDLT in recipients ≥70 years old in the United States. An OPTN/UNOS liver transplant registry analysis

BACKGROUND

Living donor liver transplantation (LDLT) in the elderly population is currently not well studied. There are single-center studies indicating that patient age should not be a barrier to LDLT, with similar outcomes compared to younger recipients.

METHODS

Using UNOS/STAR data from 2010 to 2022 we retrospectively analyzed patients ≥70 years old receiving a living donor graft (LDLT ≥70y group) versus a deceased donor graft (DDLT ≥70y group). In addition, we compared recipients ≥70 years old undergoing LDLT versus patients 18-69 years old also undergoing LDLT. Donor and recipient baseline characteristics, as well as postoperative outcomes including graft and patient survival were analyzed and compared between groups.

RESULTS

Recipients in the LDLT ≥70y group showed less disease burden and spent significantly less time on the waitlist when compared to recipients in the DDLT ≥70y group (102 [49-201] days versus 170 [36-336] days) respectively; p = .004. With the exception of a longer length of stay (LOS) in the LDLT ≥70y group (p ≤ .001), postoperative outcomes were comparable with recipients in the DDLT ≥70y group, including similar graft and patient survival rates at 1-, 3-, and 5-years. When compared to younger recipients of a graft from a living donor, patients in the LDLT ≥70y group had similar post-transplant functional status, re-transplant rates and similar causes contributing to graft failure. However, significantly lower graft and patient survival rates were observed.

CONCLUSION

LDLT for recipients aged 70 or greater represents a faster access to transplantation in a safe and feasible manner when compared to similar- aged recipients undergoing DDLT.

Artificial intelligence, machine learning, and deep learning in liver transplantation

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Application of machine learning in liver transplantation: a review

BACKGROUND

Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk.

AIM

In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation.

METHODS

Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials.

CONCLUSION

ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.

The impact of older age on long term survival in living donor liver transplantation: A propensity score matching analysis

BACKGROUND

Prevalence of the end-stage liver disease in the elderly patients indicating a liver transplantation (LT) has been increasing. There is no universally accepted upper age limit for LT candidates but the functional status of older patients is important in pre-LT evaluation. This study aimed to examine the impact of older age on survival after living donor liver transplantation (LDLT).

METHOD

A total of 171 LDLT recipients were assessed in two groups: age ≥65 and < 65. To eliminate selection bias propensity score matching (PSM) was performed, and 56 of 171 recipients were included in this study.

RESULTS

There were 20 recipients in the older group and 36 in the younger. The 1-, 3-, and 5-year survival rates were 65.0%, 60.0%, and 60.0% in group 1; 88.9%, 84.7%, and 71.4% in group 2, respectively. The 1-year survival was significantly lower in the older recipients; however, overall survival rates were similar between the groups. Of the 56 recipients, 15 (27%) deaths were observed in overall, and 11 (20%) in 1-year follow-up. The univariate regression analysis after PSM revealed that MELD score affected 1- year survival and the multivariate analysis revealed that age ≥65 years and MELD score were the predictors of 1-year survival.

CONCLUSION

At first sight, before PSM, survival appeared to be worse for older recipients. However, we have shown that there were confounding effects of clinical variables in the preliminary evaluation. After the elimination of this bias with PSM, This study highlights that older recipients have similar outcomes as youngers in LDLT for long-term survival.