Prediction of Perioperative Mortality of Cadaveric Liver Transplant Recipients During Their Evaluations

Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP

OBJECTIVE

Accurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) calculator in predicting LOS following surgery for OCC.

MATERIALS AND METHODS

A retrospective multicenter database study was performed at two major academic head and neck cancer centers. Patients with OCC who underwent major free flap reconstructive surgery between January 2008 and June 2019 surgery were selected. Data were pooled and split into training and validation datasets. Statistical and ML models were developed, and performance was evaluated by comparing predicted and actual LOS using correlation coefficient values and percent accuracy.

RESULTS

Totally 837 patients were selected with mean patient age being 62.5 ± 11.7 [SD] years and 67% being male. The ML model demonstrated the best accuracy (validation correlation 0.48, 4-day accuracy 70%), compared with the statistical models: multivariate analysis (0.45, 67%) and least absolute shrinkage and selection operator (0.42, 70%). All were superior to the ACS-NSQIP calculator's performance (0.23, 59%).

CONCLUSION

We developed statistical and ML models that predicted LOS following major free flap reconstructive surgery for OCC. Our models demonstrated superior predictive performance to the ACS-NSQIP calculator. The ML model identified several novel predictors of LOS. These models must be validated in other institutions before being used in clinical practice.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:3664-3672, 2024.

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.

The landscape of liver transplantation for patients with alcohol-associated liver disease in the United States

Indications for liver transplants have expanded to include patients with alcohol-associated liver disease (ALD) over the last decade. Concurrently, the liver allocation policy was updated in February 2020 replacing the Donor Service Area with Acuity Circles (ACs). The aim is to compare the transplantation rate, waitlist outcomes, and posttransplant survival of candidates with ALD to non-ALD and assess differences in that effect after the implementation of the AC policy. Scientific Registry for Transplant Recipients data for adult candidates for liver transplant were reviewed from the post-AC era (February 4, 2020-March 1, 2022) and compared with an equivalent length of time before ACs were implemented. The adjusted transplant rates were significantly higher for those with ALD before AC, and this difference increased after AC implementation (transplant rate ratio comparing ALD to non-ALD = 1.20, 1.13, 1.61, and 1.32 for the Model for End-Stage Liver Disease categories 37-40, 33-36, 29-32, and 25-28, respectively, in the post-AC era, p < 0.05 for all). The adjusted likelihood of death/removal from the waitlist was lower for patients with ALD across all lower Model for End-Stage Liver Disease categories (adjusted subdistribution hazard ratio = 0.70, 0.81, 0.84, and 0.70 for the Model for End-Stage Liver Disease categories 25-28, 20-24, 15-19, 6-14, respectively, p < 0.05). Adjusted posttransplant survival was better for those with ALD (adjusted hazard ratio = 0.81, p < 0.05). Waiting list and posttransplant mortality tended to improve more for those with ALD since the implementation of AC but not significantly. ALD is a growing indication for liver transplantation. Although patients with ALD continue to have excellent posttransplant outcomes and lower waitlist mortality, candidates with ALD have higher adjusted transplant rates, and these differences have increased after AC implementation.

A narrative review: predicting liver transplant graft survival using artificial intelligence modeling

Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict the short- and long-term survival of the liver graft. Recently, artificial intelligence models have been used to evaluate the short- and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this narrative review, we reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022. We picked out 17 studies using our selection criteria and analyzed them, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In eight studies, models estimating only short-term liver graft survival were created, while in five of the studies, models for the prediction of only long-term liver graft survival were built. In four of the studies, artificial intelligence algorithms evaluating both the short- and long-term liver graft survival were created. Medical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were the recipient's age, recipient's body mass index, creatinine levels in the recipient's serum, recipient's international normalized ratio, diabetes mellitus, and recipient's model of end-stage liver disease score. To conclude, in order to define important liver transplantation characteristics that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND

Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models.

AIM

To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws.

RESULTS

Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI.

CONCLUSION

This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.

Artificial Intelligence in Liver Diseases: Recent Advances

Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.

Quantitative methods for optimizing patient outcomes in liver transplantation

Liver transplantation (LT) is a lifesaving yet complex intervention with considerable challenges impacting graft and patient outcomes. Despite best practices, 5-year graft survival is only 70%. Sophisticated quantitative techniques offer potential solutions by assimilating multifaceted data into insights exceeding human cognition. Optimizing donor-recipient matching and graft allocation presents additional intricacies, involving the integration of clinical and laboratory data to select the ideal donor and recipient pair. Allocation must balance physiological variables with geographical and logistical constraints and timing. Quantitative methods can integrate these complex factors to optimize graft utilization. Such methods can also aid in personalizing treatment regimens, drawing on both pretransplant and posttransplant data, possibly using continuous immunological monitoring to enable early detection of graft injury or infected states. Advanced analytics is thus poised to transform management in LT, maximizing graft and patient survival. In this review, we describe quantitative methods applied to organ transplantation, with a focus on LT. These include quantitative methods for (1) utilizing and allocating donor organs equitably and optimally, (2) improving surgical planning through preoperative imaging, (3) monitoring graft and immune status, (4) determining immunosuppressant doses, and (5) establishing and maintaining the health of graft and patient after LT.

Perceptions of surgical difficulty in liver transplantation: A European survey and development of the Pitié-Salpêtrière classification

BACKGROUND

Significant variations exist regarding the definition of difficult liver transplantation. The study goals were to investigate how liver transplant surgeons evaluate the surgical difficulty of liver transplantation and to use the identified factors to classify liver transplantation difficulty.

METHODS

A Web-based online European survey was presented to liver transplant surgeons. The survey was divided into 3 parts: (1) participant demographics and practices; (2) various situations based on recipient, liver disease, tumor treatment, and technical factors; and (3) 8 real-life clinical vignettes with different levels of complexity. In part 3 of the survey, respondents were asked whether they would perform liver transplantation but were not aware that these patients eventually underwent liver transplantation.

RESULTS

A total of 143 invites were sent out, and 97 (67.8%) participants completed the survey. Most participants considered previous spontaneous bacterial peritonitis, previous supra-mesocolic surgery, hypertrophy of segment I, and obesity to be recipient factors for high-difficulty liver transplantation. Most participants considered liver transplantation to be challenging in patients with Budd-Chiari syndrome, Kasai surgery, polycystic liver disease, diffuse portal vein thrombosis, and a history of open hepatectomy. The proportion of participants indicating that liver transplantation was warranted varied across the 8 cases, from 69% to 100%. Our classification of the surgical difficulty of liver transplantation employed these recipient-related, surgical history-related, and liver disease-related variables and 3 difficulty groups were identified: low, intermediate, and high difficulty groups.

CONCLUSION

This survey provides an overview of the surgical difficulty of various situations in liver transplantation that could be useful for further benchmark and textbook outcome studies.

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.

Validation of the Liver Transplant Risk Score in Europe

The Liver Transplant Risk Score (LTRS) is a simple clinical instrument developed to predict post liver transplant outcomes based on patient characteristics measured at the time of listing. The LTRS was developed using data of adult patients transplanted in the United States. In this study, we validated the performance of the LTRS in a cohort of patients transplanted in Europe.

CAQ Corner: Surgical evaluation for liver transplantation

The evaluation of a liver transplantation candidate is a complex and detailed process that in many cases must be done in an expedited manner because of the critically ill status of some patients with end-stage liver disease. It involves great effort from and the collaboration of multiple disciplines, and during the evaluation several studies and interventions are performed to assess and potentially prepare a patient for liver transplant. Here we review the liver transplantation evaluation from a surgical perspective.

Development of a risk score model for 1-year graft loss after adult deceased donor liver transplantation in Japan based on a 20-year nationwide cohort

Aim

Using nationwide data collected over the past 20 years, we aimed to investigate deceased donor liver transplantation (DDLT) outcomes to develop a unique risk model that can be used to establish a standard for organ acceptance in Japan.

Methods

Data were collected for 449 recipients aged ≥18 years who underwent DDLT between 1999 and 2019. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to develop an original risk score model for 1-year graft loss (termed the Japan Risk Index [JRI]). We developed risk indices according to recipient, donor, and surgery components (termed JRI-R, D, and S, respectively). The JRI was validated via a 5-fold cross-validation. We also compared DDLT outcomes and risk indices among Era1 (-2011), Era2 (-2015), and Era3 (-2019).

Results

The 1-year graft survival rate was 89.5% and improved significantly, reaching 84.7%, 87.6%, and 93.9% in Era1, Era2, and Era3, respectively. The JRI was calculated as JRI-R (re-transplantation, Model for End-Stage Liver Disease score, medical condition in intensive care unit) × JRI-D (age, catecholamine index, maximum sodium, maximum total bilirubin) × JRI-S (total ischemic time) × 0.84. The risk model achieved a mean C-statistic value of 0.81 in the validation analysis. The risk index was significantly lower in Era3 than in Era2.

Conclusion

Changes in the risk index over time indicated that avoiding risks contributed to the improved outcomes in Era3. The JRI is unique to adult DDLT in Japan and may be useful as a reference for organ acceptance in the future.

The promise of machine learning applications in solid organ transplantation

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.

Balancing Outcome vs. Urgency in Modern Liver Transplantation

Background

Current allocation mechanisms for liver transplantation (LT) overemphasize emergency, leading to poorer longtime outcomes. The utility was introduced to recognized outcomes in allocation. Recently, Molinari proposed a predictive outcome model based on recipient data.

Aims

The aims of this study were to validate this model and to combine it with the utility to emphasize outcome in allocation.

Methods

We retrospectively analyzed 734 patients who were transplanted between January 2010 and December 2019. Points were assigned as in Molinari's model and the score sum was correlated with observed 90-day mortality. The utility was calculated as the product of 1-year survival times 3-month mortality on the waiting list. The weighting of different compounds was introduced, and utility curves were calculated. Model for End-Stage Liver Disease (MELD) scores according to maximal utility were determined.

Results

In total, 120 patients (16.3%) had died within 90 days after LT. Higher MELD score, obesity, and hemodialysis prior to LT were confirmed risk factors. Overall survival was 83.8 and 77.4% after 90 days and 12 months, respectively. General utility culminated at MELD scores >35 in the overall population. Emphasizing the outcome shifted the maximal utility to lower MELD scores depending on Molinari scores.

Conclusions

Emphasizing outcome, at least in certain recipient risk categories, might improve the longtime outcomes and might be integrated into allocation models.

Combined Effect of Deceased Donor Macrovesicular and Microvesicular Steatosis on Liver Transplantation Outcomes: Analysis of SRTR Data Between 2010 and 2018

BACKGROUND

Because of the rising prevalence of obesity, the use of steatotic grafts in orthotopic liver transplantation is becoming increasingly obligatory. The purpose of this study was to determine the relative distribution of microvesicular steatosis (MiS) burden across categories of macrovesicular steatosis (MaS) and the effect of biopsy-sourced MaS and MiS on graft failure, recipient death, and retransplantation.

METHODS

We performed a retrospective analysis of 13,889 adults with deceased donor liver transplantations from the Scientific Registry of Transplant Recipients between 2010 and 2018. Multivariable Cox proportional hazards models were run to examine the independent and combined effects of MaS and MiS on major transplantation outcomes.

RESULTS

Recipients had a mean age of 56.5 years and a body mass index (BMI) of 29.2 kg/m²; 70% were men, and 74% were non-Hispanic white. Considering the independent effect of MaS, recipients of livers with 30% to 60% MaS had 97% and 129%, 71% and 81%, 39% and 43%, and 40% and 19% increased risks of graft failure and death at 1 month, 3 months, 1 year, and 3 years post-transplantation, respectively. Considering the combined effects of MaS and MiS, 16% to 60% MaS increased the risk of graft failure and recipient death regardless of MiS burden within the first 3 months post-transplantation. These risks were also increased among recipients of livers with 5% to 15% MaS and the additional burden of 16% to 60% MiS.

CONCLUSIONS

Our findings suggest that risk threshold of adverse transplantation outcomes owing to steatosis appears to be lower than previously recognized and currently practiced. These risks must be weighed and mitigated against the duress of organ shortage and saving lives.

Impact of County Health Rankings on Nationwide Liver Transplant Outcomes

BACKGROUND

There is limited information concerning whether social determinants of health affect postliver transplant (LT) outcomes. This study aims to understand to what extent the health of LT recipients' counties of residence influence long-term LT outcomes.

METHODS

We used the United Network for Organ Sharing data to identify adult LT recipients transplanted between January 2010 and June 2018. Patient-level data were matched to county-level County Health Ranking (CHR) data using transplant recipient zip code, and nationwide CHRs were created. Mixed-effects Cox proportional hazards models were used to examine associations between CHRs and graft and patient survival post-LT.

RESULTS

Health outcomes rank was significantly associated with posttransplant graft and patient survival, with worst tertile counties showing a 13% increased hazard of both graft failure and patient mortality compared to the best tertile counties.

CONCLUSIONS

Although county health is associated with LT outcomes, it also appears that LT recipient selection is effective at mitigating major disparities based on county of residence and helps yield equitable outcomes in this respect.

Machine Learning Applications in Solid Organ Transplantation and Related Complications

The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.

The Future Role of Machine Learning in Clinical Transplantation

The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.

The liver transplant risk score prognosticates the outcomes of liver transplant recipients at listing

BACKGROUND

We assessed if the risk of post-liver transplant mortality within 24 h could be stratified at the time of listing using the liver transplant risk score (LTRS). Secondary aims were to assess if the LTRS could stratify the risk of 30-day, 1-year mortality, and survival beyond the first year.

METHODS

MELD, BMI, age, diabetes, and the need for dialysis were the five variables used to calculate the LTRS during patients' evaluation for liver transplantation. Mortality rates at 24 h, 30 days, and 1-year were compared among groups of patients with different LTRS. Patients with ABO-incompatibility, redo, multivisceral, partial graft and malignancies except for hepatocellular carcinoma were excluded. Data of 48,616 adult liver transplant recipients were extracted from the Scientific Registry of Transplant Recipients between 2002 and 2017.

RESULTS

24-h mortality was 0.9%, 1.0%, 1.1%, 1.7%, 2.3%, 2.0% and 3.5% for patients with LTRS of 0,1,2,3,4, 5 and ≥ 6, respectively (P < 0.001). 30-day mortality was 3.5%, 4.2%, 4.9%, 6.2%, 7.6%, 7.2% and 10.1% respectively (P < 0.001). 1-year mortality was 8.6%, 10.8%, 12.9%, 13.9%, 18.5%, 20.3% and 28.6% respectively (P < 0.001). 10-year survival was 61%, 56%, 57%, 54%, 47%, and 31% for patients with 0, 1, 2, 3, 4, 5 and ≥ 6 points respectively (P < 0.001).

CONCLUSION

Perioperative mortality and long-term survival of patients undergoing LT can be accurately estimated at the time of listing by the LTRS.

In-hospital mortality of liver transplantation and risk factors: a single-center experience

Background

Liver transplantation (LT) is a life-saving treatment for patients with end-stage liver disease and acute liver failure. However, in-hospital death cannot be avoided. We designed this study to analyze patients' in-hospital mortality rate after LT and the factors correlated with in-hospital death.

Methods

The data of patients who received LT in our hospital between January 11, 2015, and November 19, 2019, were obtained from the China Liver Transplant Registry and medical records. The in-hospital mortality rate was calculated, and factors related to mortality, cause of death, and factors related to cause of death were analyzed by reviewing patients' data.

Results

A total of 529 patients who underwent cadaveric LT were enrolled in this study. Modified piggyback orthotopic LT was performed for all patients. Seventy patients died in the hospital after LT, and the in-hospital mortality rate was 13.2%. Factors including model for end-stage liver disease (MELD) score, Child-Pugh grading, intraoperative blood loss, and anhepatic phase were correlated with in-hospital death. MELD score and intraoperative blood loss were determined as the two independent risk factors of in-hospital death. The first two causes of death were infection (34.3%) and primary non-function (15.7%). Pulmonary fungal infection was the main cause of infectious death. MELD score was the independent risk factor for infectious death, and both body mass index of donors and cold ischemic time were independent risk factors of primary non-function.

Conclusions

In-hospital death poses a threat to certain patients undergoing LT. Our study suggests that the main cause of in-hospital death is an infection, followed by primary non-function.

Preoperative Stratification of Liver Transplant Recipients: Validation of the LTRS

BACKGROUND

The liver transplant risk score (LTRS) was developed to stratify 90-day mortality of patients referred for liver transplantation (LT). We aimed to validate the LTRS using a new cohort of patients.

METHODS

The LTRS stratifies the risk of 90-day mortality of LT recipients based on their age, body mass index, diabetes, model for end-stage liver disease (MELD) score, and need for dialysis. We assessed the performance of the LTRS using a new cohort of patients transplanted in the United States between July 2013 and June 2017. Exclusion criteria were age <18 years, ABO incompatibility, redo or multivisceral transplants, partial grafts, malignancies other than hepatocellular carcinoma and fulminant hepatitis.

RESULTS

We found a linear correlation between the number of points of the LTRS and 90-day mortality. Among 18 635 recipients, 90-day mortality was 2.7%, 3.8%, 5.2%, 4.8%, 6.7%, and 9.3% for recipients with 0, 1, 2, 3, 4, and ≥5 points (P < 0.001). The LTRS also stratified 1-year mortality that was 5.5%, 7.7%, 9.9%, 9.3%, 10.8%, and 15.4% for 0, 1, 2, 3, 4, and ≥5 points (P < 0.001). An inverse correlation was found between the LTRS and 4-year survival that was 82%, 79%, 78%, 82%, 78%, and 66% for patients with 0, 1, 2, 3, 4, and ≥5 points (P < 0.001). The LTRS remained an independent predictor after accounting for recipient sex, ethnicity, cause of liver disease, donor age, cold ischemia time, and waiting time.

CONCLUSIONS

The LTRS can stratify the short- and long-term outcomes of LT recipients at the time of their evaluations irrespective of their gender, ethnicity, and primary cause of liver disease.

Peri-transplant management of nonalcoholic fatty liver disease in liver transplant candidates

The incidence of non-alcoholic fatty liver disease (NAFLD) is rapidly growing, affecting 25% of the world population. Non-alcoholic steatohepatitis (NASH) is the most severe form of NAFLD and affects 1.5% to 6.5% of the world population. Its rising incidence will make end-stage liver disease (ESLD) due to NASH the number one indication for liver transplantation (LT) in the next 10 to 20 years, overtaking Hepatitis C. Patients with NASH also have a high prevalence of associated comorbidities such as type 2 diabetes, obesity, metabolic syndrome, cardiovascular disease, and chronic kidney disease (CKD), which must be adequately managed during the peritransplant period for optimal post-transplant outcomes. The focus of this review article is to provide a comprehensive overview of the unique challenges these patients present in the peritransplant period, which comprises the pre-transplant, intraoperative, and immediate postoperative periods.