Background Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk of these complications, which would flag them for screening tests and potentially life-saving interventions. In this retrospective analysis, we aimed to assess the ability of deep learning algorithms of longitudinal data from two prospective cohorts to predict complications resulting in death after liver transplantation over multiple timeframes, compared with logistic regression models. MethodsIn this machine learning analysis, model development was done on a set of 42 146 liver transplant recipients (mean age 48•6 years [SD 17•3]; 17 196 [40•8%] women) from the Scientific Registry of Transplant Recipients (SRTR) in the USA. Transferability of the model was further evaluated by fine-tuning on a dataset from the University Health Network (UHN) in Canada (n=3269; mean age 52•5 years [11•1]; 1079 [33•0%] women). The primary outcome was cause of death, as recorded in the databases, due to cardiovascular causes, infection, graft failure, or cancer, within 1 year and 5 years of each follow-up examination after transplantation. We compared the performance of four deep learning models against logistic regression, assessing performance using the area under the receiver operating characteristic curve (AUROC). Findings In both datasets, deep learning models outperformed logistic regression, with the Transformer model achieving the highest AUROCs in both datasets (p<0•0001). The AUROC for the Transformer model across all outcomes in the SRTR dataset was 0•804 (99% CI 0•795-0•854) for 1-year predictions and 0•733 (0•729-0•769) for 5-year predictions. In the UHN dataset, the AUROC for the top-performing deep learning model was 0•807 (0•795-0•842) for 1-year predictions and 0•722 (0•705-0•764) for 5-year predictions. AUROCs ranged from 0•695 (0•680-0•713) for prediction of death from infection within 5 years to 0•859 (0•847-0•871) for prediction of death by graft failure within 1 year.Interpretation Deep learning algorithms can incorporate longitudinal information to continuously predict long-term outcomes after liver transplantation, outperforming logistic regression models. Physicians could use these algorithms at routine follow-up visits to identify liver transplant recipients at risk for adverse outcomes and prevent these complications by modifying management based on ranked features.

Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor–recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.

Background: Diabetes significantly impacts long-term survival after liver transplantation (LT). We aimed to identify survival factors for diabetic LT recipients to inform preventive care, using machine learning analysis. Methods: We analyzed risk factors for mortality in patients from across the U.S. using Scientific Registry of Transplant Recipients (SRTR). Patients had undergone LT from 1987-2019, with a follow-up of 6.47 years (SD: 5.95). Findings were validated on a cohort from

Metabolic complications affect over 50% of solid organ transplant recipients. These include post-transplant diabetes, non-alcoholic fatty liver disease, dyslipidemia, and obesity. Pre-existing metabolic disease is further exacerbated with immunosuppression and post-transplant weight gain. Patients transition from a state of cachexia induced by end-organ disease to a pro-anabolic state after transplant due to weight gain, sedentary lifestyle and suboptimal dietary habits in the setting of immunosuppression. Specific immunosuppressants have different metabolic effects, though all the foundation/maintenance immunosuppressants (CNIs, mTOR inhibitors) increase the risk of metabolic disease. In this comprehensive review, we summarize the emerging knowledge of the molecular pathogenesis of these different metabolic complications, and the potential genetic contribution (recipient +/- donor) to these conditions. These metabolic complications impact both graft and patient survival, particularly increasing the risk of cardiovascular and cancer-associated mortality. The current evidence for prevention and therapeutic management of post-transplant metabolic conditions is provided while highlighting gaps for future avenues in translational research.