Potential value and limitations of different clinical scoring systems in the assessment of short- and long-term outcome following orthotopic liver transplantation

Physiology and health assessment, risk balance, and model for end-stage liver disease scores: Postoperative outcome of liver transplantation

BACKGROUND

Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life. The number of organs available for transplantation is lower than the demand. To provide fair organ distribution, predictive mortality scores have been developed.

AIM

To compare the Acute Physiology and Chronic Health Evaluation IV (APACHE IV), balance of risk (BAR), and model for end-stage liver disease (MELD) scores as predictors of mortality.

METHODS

Retrospective cohort study, which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.

RESULTS

The transplant recipients were mainly male, with a mean age of 58.1 years. Donors were mostly male, with a mean age of 41.6 years. The median cold ischemia time was 3.1 hours, and the median intensive care unit stay was 5 days. For APACHE IV, a mean of 59.6 was found, BAR 10.7, and MELD 24.2. The 28-day mortality rate was 9.5%, and at 90 days, it was 3.5%. The 28-day mortality prediction for APACHE IV was very good [area under the curve (AUC): 0.85, P < 0.001, 95%CI: 0.76-0.94], P < 0.001, BAR (AUC: 0.70, P < 0.001, 95%CI: 0.58-0.81), and MELD (AUC: 0.66, P < 0.006, 95%CI: 0.55-0.78), P < 0.008. At 90 days, the data for APACHE IV were very good (AUC: 0.80, P < 0.001, 95%CI: 0.71-0.90) and moderate for BAR and MELD, respectively, (AUC: 0.66, P < 0.004, 95%CI: 0.55-0.77), (AUC: 0.62, P < 0.026, 95%CI: 0.51-0.72). All showed good discrimination between deaths and survivors. As for the best value for liver transplantation, it was significant only for APACHE IV (P < 0.001).

CONCLUSION

The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.

Machine Learning Algorithms in Controlled Donation After Circulatory Death Under Normothermic Regional Perfusion: A Graft Survival Prediction Model

BACKGROUND

Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, their performance is unsatisfactory in the Spanish population, where most cDCD livers are recovered using normothermic regional perfusion (NRP). Consequently, we explored the role of different machine learning-based classifiers as predictive models for graft survival. A risk stratification score integrated with the model of end-stage liver disease score in a donor-recipient (D-R) matching system was developed.

METHODS

This retrospective multicenter cohort study used 539 D-R pairs of cDCD livers recovered with NRP, including 20 donor, recipient, and NRP variables. The following machine learning-based classifiers were evaluated: logistic regression, ridge classifier, support vector classifier, multilayer perceptron, and random forest. The endpoints were the 3- and 12-mo graft survival rates. A 3- and 12-mo risk score was developed using the best model obtained.

RESULTS

Logistic regression yielded the best performance at 3 mo (area under the receiver operating characteristic curve = 0.82) and 12 mo (area under the receiver operating characteristic curve = 0.83). A D-R matching system was proposed on the basis of the current model of end-stage liver disease score and cDCD-NRP risk score.

CONCLUSIONS

The satisfactory performance of the proposed score within the study population suggests a significant potential to support liver allocation in cDCD-NRP grafts. External validation is challenging, but this methodology may be explored in other regions.

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.

The BAR Score Predicts and Stratifies Outcomes Following Liver Retransplantation: Insights From a Retrospective Cohort Study

Liver retransplantation (reLT) yields poorer outcomes than primary liver transplantation, necessitating careful patient selection to avoid futile reLT. We conducted a retrospective analysis to assess reLT outcomes and identify associated risk factors. All adult patients who underwent a first reLT at the Medical University of Innsbruck from 2000 to 2021 (N = 111) were included. Graft- and patient survival were assessed via Kaplan-Meier plots and log-rank tests. Uni- and multivariate analyses were performed to identify independent predictors of graft loss. Five-year graft- and patient survival rates were 64.9% and 67.6%, respectively. The balance of risk (BAR) score was found to correlate with and be predictive of graft loss and patient death. The BAR score also predicted sepsis (AUC 0.676) and major complications (AUC 0.720). Multivariate Cox regression analysis identified sepsis [HR 5.179 (95% CI 2.575-10.417), p < 0.001] as the most significant independent risk factor for graft loss. At a cutoff of 18 points, the 5 year graft survival rate fell below 50%. The BAR score, a simple and easy to use score available at the time of organ acceptance, predicts and stratifies clinically relevant outcomes following reLT and may aid in clinical decision-making.

Albumin–Globulin Score Combined with Skeletal Muscle Index as a Novel Prognostic Marker for Hepatocellular Carcinoma Patients Undergoing Liver Transplantation

Background: Sarcopenia was recently identified as a poor prognostic factor in patients with malignant tumors. The present study investigated the effect of the preoperative albumin–globulin score (AGS), skeletal muscle index (SMI), and combination of AGS and SMI (CAS) on short- and long-term survival outcomes following deceased donor liver transplantation (DDLT) for hepatocellular carcinoma (HCC) and aimed to identify prognostic factors. Methods: A total of 221 consecutive patients who underwent DDLT for HCC were enrolled in this retrospective study between January 2015 and December 2019. The skeletal muscle cross-sectional area was measured by CT (computed tomography). Clinical cutoffs of albumin (ALB), globulin (GLB), and sarcopenia were defined by receiver operating curve (ROC). The effects of the AGS, SMI, and CAS grade on the preoperative characteristics and long-term outcomes of the included patients were analyzed. Results: Patients who had low AGS and high SMI were associated with better overall survival (OS) and recurrence-free survival (RFS), shorter intensive care unit (ICU) stay, and fewer postoperative complications (grade ≥ 3, Clavien–Dindo classification). Stratified by CAS grade, 46 (20.8%) patients in grade 1 were associated with the best postoperative prognosis, whereas 79 (35.7%) patients in grade 3 were linked to the worst OS and RFS. The CAS grade showed promising accuracy in predicting the OS and RFS of HCC patients [areas under the curve (AUCs) were 0.710 and 0.700, respectively]. Male recipient, Child–Pugh C, model for end-stage liver disease (MELD) score > 20, and elevated CAS grade were identified as independent risk factors for OS and RFS of HCC patients after DDLT. Conclusion: CAS grade, a novel prognostic index combining preoperative AGS and SMI, was closely related to postoperative short-term and long-term outcomes for HCC patients who underwent DDLT. Graft allocation and clinical decision making may be referred to CAS grade evaluation.

Liver Transplantation in Recipients With a Positive Crossmatch: A Retrospective Single-Center Match-Pair Analysis

A positive crossmatch (XM+) is considered a contraindication to solid abdominal organ transplantation except liver transplantation (LT). Conflicting reports exist regarding the effects of XM+ on post-transplant outcomes. The goal of this retrospective single-center analysis is to evaluate the influence of XM+ on relevant outcome parameters such as survival, graft rejection, biliary and arterial complications. Forty-nine adult patients undergoing LT with a XM+ between 2002 and 2017 were included. XM+ LT recipients were matched 1:2 with crossmatch negative (XM-) LT recipients based on the balance of risk (BAR) score. Patient and graft survival were compared using Kaplan-Meier survival analysis and the log-rank test. Comparative analysis of clinical outcomes in XM+ and XM- groups were conducted. Patient and graft survival were similar in XM+ and XM- patients. Rejection episodes did not differ either. Recipients with a strong XM+ were more likely to develop a PCR+ CMV infection. A XM+ was not associated with a higher incidence of biliary or arterial complications. Donor age, cold ischemia time, PCR+ CMV infection and a rejection episode were associated with the occurrence of ischemic type biliary lesions. A XM+ has no effects on patient and graft survival or other relevant outcome parameters following LT.

Calcification of the visceral aorta and celiac trunk is associated with renal and allograft outcomes after deceased donor liver transplantation

PURPOSE

Atherosclerosis affects clinical outcomes in the setting of major surgery. Here we aimed to investigate the prognostic role of visceral aortic (VAC), extended visceral aortic (VAC+), and celiac artery calcification (CAC) in the assessment of short- and long-term outcomes following deceased donor orthotopic liver transplantation (OLT) in a western European cohort.

METHODS

We retrospectively analyzed the data of 281 consecutive recipients who underwent OLT at a German university medical center (05/2010-03/2020). The parameters VAC, VAC+, or CAC were evaluated by preoperative computed tomography-based calcium quantification according to the Agatston score.

RESULTS

Significant VAC or CAC were associated with impaired postoperative renal function (p = 0.0016; p = 0.0211). Patients with VAC suffered more frequently from early allograft dysfunction (EAD) (38 vs 26%, p = 0.031), while CAC was associated with higher estimated procedural costs (p = 0.049). In the multivariate logistic regression analysis, VAC was identified as an independent predictor of EAD (2.387 OR, 1.290-4.418 CI, p = 0.006). Concerning long-term graft and patient survival, no significant difference was found, even though patients with calcification showed a tendency towards lower 5-year survival compared to those without (VAC: 65 vs 73%, p = 0.217; CAC: 52 vs 72%, p = 0.105). VAC+ failed to provide an additional prognostic value compared to VAC.

CONCLUSION

This is the first clinical report to show the prognostic role of VAC/CAC in the setting of deceased donor OLT with a particular value in the perioperative phase. Further studies are warranted to validate these findings. CT computed tomography, OLT orthotopic liver transplantation.

Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor-Recipient Matching?

Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor-recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered "unbalanced." In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D-R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.

Osteopenia is associated with inferior survival in patients undergoing partial hepatectomy for hepatocellular carcinoma

Osteopenia is known to be associated with clinical frailty which is linked to inferior outcomes in various clinical scenarios. However, the exact prognostic value of osteopenia in patients undergoing curative intent-surgery for hepatocellular carcinoma (HCC) is not completely understood. This retrospective study was conducted in a cohort of 151 patients who underwent partial hepatectomy for HCC in curative intent at a German university medical center (05/2008-12/2019). Preoperative computed tomography-based segmentation was used to assess osteopenia, and the prognostic impact of pathological changes in bone mineral density (BMD) on perioperative morbidity, mortality, and long-term oncological outcome was analyzed. Five-year overall survival of osteopenic patients was significantly worse compared to those with normal BMD (29% vs. 65%, p = 0.014). In line with this, the probability of disease-free survival at 5 years was significantly worse for patients with osteopenia (21% vs. 64%, p = 0.005). In our multivariable model, osteopenia was confirmed as an independent risk-factor for inferior overall survival (Hazard-ratio 7.743, p = 0.002). Concerning perioperative complications, osteopenic patients performed slightly worse, even though no statistical difference was detected (Clavien-Dindo ≥ 3b; 21% vs. 9%, p = 0.139). The present study confirms osteopenia as an independent risk-factor for inferior survival in patients undergoing partial hepatectomy for HCC in a European cohort. Further studies are warranted to validate these findings.

Risk factors in deceased donor liver transplantation: a single centre experience

Deceased brain-dead donor liver transplantation (LT) is a high-risk intervention. The outcome depends on a large number of modifiable and non-modifiable factors. Objective: to analyze our own experience and identify preoperative and perioperative prognostic factors for poor outcomes in LT. Materials and methods. The study included 301 liver transplants performed between January 2016 and December 2021. Donor and recipient characteristics, intraoperative data, perioperative characteristics including laboratory test data, and the nature and frequency of complications were used for the analysis. Results. The 1-, 3- and 5-year recipient survival rates were 91.8%, 85.1%, and 77.9%, respectively; graft survival rates were 90.4%, 83.7%, and 76.7%, respectively. The most significant predictors of poor outcome of LT on the recipient side were biliary stents (HR 7.203, p < 0.01), acutely decompensated cirrhosis (HR 2.52, p = 0.02); in the postoperative period, non-surgical infectious complications (HR 4.592, p < 0.01) and number of reoperations (HR 4.063, p < 0.01). Donor creatinine level (HR 1.004, p = 0.01, one factor analysis; HR 1.004, p = 0.016, multivariate analysis) was the only reliable prognostic negative factor. Conclusion. LT taking into account established risk factors will improve surgery outcomes and help personalize the therapy for each patient.

Artificial intelligence and liver transplantation: Looking for the best donor-recipient pairing

Decision-making based on artificial intelligence (AI) methodology is increasingly present in all areas of modern medicine. In recent years, models based on deep-learning have begun to be used in organ transplantation. Taking into account the huge number of factors and variables involved in donor-recipient (D-R) matching, AI models may be well suited to improve organ allocation. AI-based models should provide two solutions: complement decision-making with current metrics based on logistic regression and improve their predictability. Hundreds of classifiers could be used to address this problem. However, not all of them are really useful for D-R pairing. Basically, in the decision to assign a given donor to a candidate in waiting list, a multitude of variables are handled, including donor, recipient, logistic and perioperative variables. Of these last two, some of them can be inferred indirectly from the team's previous experience. Two groups of AI models have been used in the D-R matching: artificial neural networks (ANN) and random forest (RF). The former mimics the functional architecture of neurons, with input layers and output layers. The algorithms can be uni- or multi-objective. In general, ANNs can be used with large databases, where their generalizability is improved. However, they are models that are very sensitive to the quality of the databases and, in essence, they are black-box models in which all variables are important. Unfortunately, these models do not allow to know safely the weight of each variable. On the other hand, RF builds decision trees and works well with small cohorts. In addition, they can select top variables as with logistic regression. However, they are not useful with large databases, due to the extreme number of decision trees that they would generate, making them impractical. Both ANN and RF allow a successful donor allocation in over 80% of D-R pairing, a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease, balance of risk score, and survival outcomes following liver transplantation scores. Many barriers need to be overcome before these deep-learning-based models can be included for D-R matching. The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.

BAR Score Performance in Predicting Survival after Living Donor Liver Transplantation: A Single-Center Retrospective Study

METHODS

146 adult liver transplant recipients were included. Univariate and multivariate analyses were used to determine the independent predictors of survival at 3 months, 1 year, and 5 years. The receiver operating characteristic (ROC) curve for the BAR score was plotted, and the area under the ROC curve (AUROC) was calculated. Kaplan-Meier curve and log-rank test were used to compare survival above and below the best cutoff values.

RESULTS

The mean age was 52.45 ± 8.54 years, and 59.6% were males. The survival rates were 89, 78.8, and 72% at 3 months, 1 year, and 5 years, respectively. The BAR score demonstrated a clinically significant value in the prediction of 3-month (AUROC = 0.89), 1-year (AUROC = 0.76), and 5-year survival (AUROC = 0.71). Among the investigated factors associated with survival, BAR score <10 points was the only independent predictor of 3-month (OR 7.34, p < 0.0001), 1-year (OR 3.37, p=0.001), and 5-year survival (OR 2.83, p=0.044).

CONCLUSIONS

BAR is a simple and easily applicable scoring system that could significantly predict short- and long-term survival after LDLT. A large multicenter study is warranted to validate our results in the Egyptian population.

The Role of Sarcopenia and Myosteatosis in Short- and Long-Term Outcomes Following Curative-Intent Surgery for Hepatocellular Carcinoma in a European Cohort

Simple Summary

Recent studies have shown that pathological changes of body composition, in particular reduced muscle mass (sarcopenia) and impaired muscle quality (myosteatosis), are linked to poor outcomes in a variety of clinical conditions. Hepatocellular carcinoma (HCC) is the most frequent primary malignant tumor of the liver in the Western hemisphere and remains a prominent cause of cancer-associated mortality. The present study investigates the prognostic value of alterations in body composition in predicting perioperative morbidity, mortality and long-term oncological outcome in HCC using preoperative computed-tomography-based segmentation. Our study found supporting evidence for the relevance of muscle quality over quantity in a European population and verifies the predictive role of myosteatosis in patients suffering from HCC, with a particularly significant value in the earlier perioperative phase.

Abstract

Alterations of body composition, especially decreased muscle mass (sarcopenia) and impaired muscle quality (myosteatosis), are associated with inferior outcomes in various clinical conditions. The data of 100 consecutive patients who underwent partial hepatectomy for hepatocellular carcinoma (HCC) at a German university medical centre were retrospectively analysed (May 2008–December 2019). Myosteatosis and sarcopenia were evaluated using preoperative computed-tomography-based segmentation. We investigated the predictive role of alterations in body composition on perioperative morbidity, mortality and long-term oncological outcome. Myosteatotic patients were significantly inferior in terms of major postoperative complications (Clavien–Dindo ≥ 3b; 25% vs. 5%, p = 0.007), and myosteatosis could be confirmed as an independent risk factor for perioperative morbidity in multivariate analysis (odds ratio: 6.184, confidence interval: 1.184–32.305, p = 0.031). Both sarcopenic and myosteatotic patients received more intraoperative blood transfusions (1.6 ± 22 vs. 0.3 ± 1 units, p = 0.000; 1.4 ± 2.1 vs. 0.3 ± 0.8 units, respectively, p = 0.002). In terms of long-term overall and recurrence-free survival, no statistically significant differences could be found between the groups, although survival was tendentially worse in patients with reduced muscle density (median survival: 41 vs. 60 months, p = 0.223). This study confirms the prognostic role of myosteatosis in patients suffering from HCC with a particularly strong value in the perioperative phase and supports the role of muscle quality over quantity in this setting. Further studies are warranted to validate these findings.

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.

Clinical value and limitations of the preoperative C-reactive-protein-to-albumin ratio in predicting post-operative morbidity and mortality after deceased-donor liver transplantation: a retrospective single-centre study

Liver transplantation is still associated with a high risk of severe complications and post-operative mortality. This study examines the predictive value of the preoperative C-reactive-protein-to-albumin ratio (CAR) regarding perioperative morbidity and mortality in deceased-donor liver transplantation (DDLT) recipients. In total, 390 DDLT recipients between 05/2010 and 03/2020 were eligible. Predictive abilities of CAR were examined through receiver operating characteristic curve (ROC) analyses. Groups were compared using parametric and non-parametric tests as appropriate. Independent risk factors for morbidity and mortality were identified using uni- and multivariable logistic regression analyses. A good predictive ability for CAR was shown regarding perioperative morbidity (comprehensive complication index ≥75, Clavien-Dindo score ≥4a) and 12-month mortality, with an ideal cut-off of CAR = 26%. Patients with CAR>26% had significantly higher median CCI scores (60 vs. 43, P < 0.001), longer intensive care unit (ICU, 5 vs. 4 days, P < 0.001) and hospital (28 vs. 21 days, P < 0.001) stays and higher 12-month mortality rates (20% vs 6%, P < 0.001). Multivariable analyses identified CAR>26%, pre-OLT inpatient hospitalization (including ICU) and post-operative red blood cell transfusions as independent predictors of severe cumulative morbidity (CCI≥75). Preoperative CAR might be a reliable additional tool to predict perioperative morbidity and mortality in DDLT recipients.

Various myosteatosis selection criteria and their value in the assessment of short- and long-term outcomes following liver transplantation

Body composition and myosteatosis affect clinical outcomes in orthotopic liver transplantation (OLT). Here we aimed to compare the value and limitations of various selection criteria to define pre-transplant myosteatosis in the assessment of short- and long-term outcomes following OLT. We retrospectively analyzed the data of 264 consecutive recipients who underwent deceased donor OLT at a German university medical centre. Myosteatosis was evaluated by preoperative computed-tomography-based segmentation. Patients were stratified using muscle radiation attenuation of the whole muscle area (L3Muslce-RA), psoas RA (L3Psoas-RA) and intramuscular adipose tissue content (IMAC) values. L3Muslce-RA, L3Psoas-RA and IMAC performed well without major differences and identified patients at risk for inferior outcomes in the group analysis. Quartile-based analyses, receiver operating characteristic curve and correlation analyses showed a superior association of L3Muslce-RA with perioperative outcomes when compared to L3Psoas-RA and L3IMAC. Long-term outcome did not show any major differences between the used selection criteria. This study confirms the prognostic role of myosteatosis in OLT with a particularly strong value in the perioperative phase. Although, based on our data, L3Muscle-RA might be the most suitable and recommended selection criterion to assess CT-based myosteatosis when compared to L3Psoas-RA and L3IMAC, further studies are warranted to validate these findings.

[Sarcopenia and Cachexia-associated Risk in Surgery]

Cachexia is defined as a multifactorial syndrome characterised by involuntary progressive weight loss due to a decrease in skeletal muscle mass, with or without a reduction in adipose tissue. The breakdown of muscle tissue is known as sarcopenia. This is clinically defined as loss of muscle mass and/or muscle strength, with loss of muscle strength being more important than muscle mass. Cachexia is responsible for the death of at least 20% of all cancer patients. The incidence in these patients varies, depending on the type of disease, between 80% for patients with gastric and pancreatic cancer, 50% for patients with lung, colon and prostate cancer, and about 40% for patients with breast cancer or leukemia. It is often difficult to distinguish between tumour-associated cachexia and cachexia caused by side effects and complications of oncological therapy. The main clinical feature of cachexia is involuntary weight loss, but this does not always manifest itself clinically, making it much more difficult to identify patients at risk. Not only the long-term outcome of the patient is influenced by cachexia and sarcopenia. Immediate postoperative complication rates (morbidity) are also increased and have profound effects on the burden of disease and the suffering of patients after surgical treatment. Cachexia, sarcopenia and myosteatosis are therefore highly relevant parameters for everyday clinical practice, which have a significant influence on the postoperative outcome of the patient. Several tools have been developed to aid the identification of patients with nutritional risk, i.e. involuntary weight loss, reduced muscle strength and physical condition. Such measures should be a part of our daily clinical routine to ensure the identification of patients with the highest postoperative risk. Novel preconditioning treatment may be beneficial to certain patient groups to reduce postoperative morbidity.

Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation

Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a fair comparison, the United Network for Organ Sharing database was used with 4 different end-points (3 months, and 1, 2 and 5 years), with a total of 39, 189 D-R pairs and 28 donor and recipient variables. Modelling techniques were divided into two groups: 1) classical statistical methods, including Logistic Regression (LR) and Naïve Bayes (NB), and 2) standard machine learning techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB) or Support Vector Machines (SVM), among others. The methods were compared with standard scores, MELD, SOFT and BAR. For the 5-years end-point, LR (AUC = 0.654) outperformed several machine learning techniques, such as MLP (AUC = 0.599), GB (AUC = 0.600), SVM (AUC = 0.624) or RF (AUC = 0.644), among others. Moreover, LR also outperformed standard scores. The same pattern was reproduced for the others 3 end-points. Complex machine learning methods were not able to improve the performance of liver allocation, probably due to the implicit limitations associated to the collection process of the database.

Prognostic role of the Donor Risk Index, the Eurotransplant Donor Risk Index, and the Balance of Risk score on graft loss after liver transplantation

This study aimed to identify cutoff values for donor risk index (DRI), Eurotransplant (ET)-DRI, and balance of risk (BAR) scores that predict the risk of liver graft loss. MEDLINE and Web of Science databases were searched systematically and unrestrictedly. Graft loss odds ratios and 95% confidence intervals were assessed by meta-analyses using Mantel-Haenszel tests with a random-effects model. Cutoff values for predicting graft loss at 3 months, 1 year, and 3 years were analyzed for each of the scores. Measures of calibration and discrimination used in studies validating the DRI and the ET-DRI were summarized. DRI ≥ 1.4 (six studies, n = 35 580 patients) and ET-DRI ≥ 1.4 (four studies, n = 11 666 patients) were associated with the highest risk of graft loss at all time points. BAR > 18 was associated with the highest risk of 3-month and 1-year graft loss (n = 6499 patients). A DRI cutoff of 1.8 and an ET-DRI cutoff of 1.7 were estimated using a summary receiver operator characteristic curve, but the sensitivity and specificity of these cutoff values were low. A DRI and ET-DRI score ≥ 1.4 and a BAR score > 18 have a negative influence on graft survival, but these cutoff values are not well suited for predicting graft loss.

The role of recipient myosteatosis in graft and patient survival after deceased donor liver transplantation

BACKGROUND

Myosteatosis is associated with perioperative outcomes in orthotopic liver transplantation (OLT). Here, we investigated the effects of body composition and myosteatosis on long-term graft and patient survival following OLT.

METHODS

Clinical data from 225 consecutive OLT recipients from a prospective database were retrospectively analysed (May 2010 to December 2017). Computed tomography-based lumbar skeletal muscle index (SMI) (muscle mass) and mean skeletal muscle radiation attenuation (SM-RA) (myosteatosis) were calculated using a segmentation tool (3D Slicer). Patients with low skeletal muscle mass (low SMI) and myosteatosis (low SM-RA) were identified using predefined and validated cut-off values.

RESULTS

The mean donor and recipient age was 55 ± 16 and 54 ± 12 years, respectively. Some 67% of the recipients were male. The probability of graft and patient survival was significantly lower in patients with myosteatosis compared with patients with higher SM-RA values (P = 0.011 and P = 0.001, respectively). Low skeletal muscle mass alone was not associated with graft and patient survival (P = 0.273 and P = 0.278, respectively). Dividing the cohort into quartiles, based on the values of SMI and SM-RA, resulted in significant differences in patient but not in graft survival (P = 0.011). Even though multivariable analysis identified low SM-RA as an important prognostic marker (hazard ratio: 2.260, 95% confidence interval: 1.177-4.340, P = 0.014), myosteatosis lost its significance when early mortality (90 days) was excluded from the final multivariable model. Patients with myosteatosis showed significantly higher all-cause mortality and in particular higher rates of deaths due to respiratory and septic complication (P = 0.002, P = 0.022, and P = 0.049, respectively).

CONCLUSIONS

Preoperative myosteatosis may be an important prognostic marker in patients undergoing deceased donor liver transplantation. The prognostic value of myosteatosis seems to be particularly important in the early post-operative phase. Validation in prospective clinical trials is warranted.

Research progress on hepatic machine perfusion

Nowadays, liver transplantation is the most effective treatment for end-stage liver disease. However, the increasing imbalance between growing demand for liver transplantation and the shortage of donor pool restricts the development of liver transplantation. How to expand the donor pool is a significant problem to be solved clinically. Many doctors have devoted themselves to marginal grafting, which introduces livers with barely passable quality but a high risk of transplant failure into the donor pool. However, existing common methods of preserving marginal grafts lead to both high risk of postoperative complications and high mortality. The application of machine perfusion allows surgeons to make marginal livers meet the standard criteria for transplant, which shows promising prospect in preserving and repairing donor livers and improving ischemia reperfusion injury. This review summarizes the progress of recent researches on hepatic machine perfusion.

Low Postoperative Platelet Counts Are Associated with Major Morbidity and Inferior Survival in Adult Recipients of Orthotopic Liver Transplantation

BACKGROUND

Platelets (PLT) play an essential functional role in cellular injury and liver regeneration following partial hepatectomy and orthotopic liver transplantation (OLT). Here, we investigated the association of postoperative PLT counts with short- and long-term outcomes in adult OLT recipients.

METHODS

Three hundred consecutive patients from our prospective OLT database were analyzed retrospectively (May 2010-November 2017). Ninety-day post-OLT complications were graded using the Clavien-Dindo (CD) classification and quantified by the comprehensive complication index (CCI). To determine the prognostic accuracy of PLT counts, the area under the receiver operating characteristic curve (AUROC) was calculated for major complications (CD ≥ 3b). Parametric and non-parametric tests were applied for subgroup analyses. Uni- and multivariable logistic regression analyses were performed to identify risk factors for major complications. Graft and patient survival were analyzed using the Kaplan-Meier method as well as uni- and multivariable Cox regression analyses.

RESULTS

Postoperative day 6 PLT counts < 70 × 10⁹/L (POD6-70) were identified as the best cutoff for predicting major complications (AUROC = 0.7; p < 0.001; Youden index 0.317). The stratification of patients into low- (n = 113) and high-PLT (n = 187) groups highlighted significant differences in major complications (CCI 68 ± 29 vs. 43 ± 28, p < 0.001); length of hospital and intensive care unit (ICU) stay (53 ± 43 vs. 31 ± 25, p < 0.001; 21 ± 29 vs. 7 ± 11, p < 0.001, respectively) and estimated procedural costs. POD6-70 was associated with inferior 5-year graft survival. Multivariable logistic regression analysis identified POD6-70 as an independent predictor of major complications (odds ratio 2.298, confidence intervals 1.179-4.478, p = 0.015).

CONCLUSION

In OLT patients, a PLT count on POD6 of less than 70 × 10⁹/L bears a prognostic significance warranting further investigations.

Ischemia-Reperfusion Injury in Marginal Liver Grafts and the Role of Hypothermic Machine Perfusion: Molecular Mechanisms and Clinical Implications

Ischemia-reperfusion injury (IRI) constitutes a significant source of morbidity and mortality after orthotopic liver transplantation (OLT). The allograft is metabolically impaired during warm and cold ischemia and is further damaged by a paradox reperfusion injury after revascularization and reoxygenation. Short-term and long-term complications including post-reperfusion syndrome, delayed graft function, and immune activation have been associated with IRI. Due to the current critical organ shortage, extended criteria grafts are increasingly considered for transplantation, however, with an elevated risk to develop significant features of IRI. In recent years, ex vivo machine perfusion (MP) of the donor liver has witnessed significant advancements. Here, we describe the concept of hypothermic (oxygenated) machine perfusion (HMP/HOPE) approaches and highlight which allografts may benefit from this technology. This review also summarizes clinical applications and the main aspects of ongoing randomized controlled trials on hypothermic perfusion. The mechanistic aspects of IRI and hypothermic MP-which include tissue energy replenishment, optimization of mitochondrial function, and the reduction of oxidative and inflammatory damage following reperfusion-will be comprehensively discussed within the context of current preclinical and clinical evidence. Finally, we highlight novel trends and future perspectives in the field of hypothermic MP in the context of recent findings of basic and translational research.

Myosteatosis to predict inferior perioperative outcome in patients undergoing orthotopic liver transplantation

Muscle wasting and alterations of body composition are linked to clinical outcomes in numerous medical conditions. The role of myosteatosis in posttransplant outcomes remains to be determined. Here we investigated skeletal muscle mass and myosteatosis as prognostic factors in patients undergoing orthotopic liver transplantation (OLT). The data of 225 consecutive OLT recipients from a prospective database were retrospectively analyzed (May 2010-December 2017). Computed tomography-based skeletal-muscle-index (muscle mass), visceral-fat-area (visceral adiposity), and mean skeletal-muscle-radiation-attenuation (myosteatosis) were calculated using a segmentation tool. Cut-off values of myosteatosis resulted in a good stratification of patients into low- and high-risk groups in terms of morbidity (Clavien-Dindo ≥3b). Patients with myosteatosis had significantly higher complication rates (90-day Comprehensive Complication Index 68 ± 32 vs 44 ± 30, P < .001) and also displayed significantly longer intensive care (18 ± 25 vs 11 ± 21 days, P < .001) and hospital stay (56 ± 55 vs 33 ± 24 days, P < .001). Estimated costs were 44% higher compared to patients without myosteatosis. Multivariable analysis identified myosteatosis as an independent prognostic factor for major morbidity (odds ratio: 2.772, confidence interval: 1.516-5.066, P = .001). Adding myosteatosis to the well-established Balance-of-Risk-(BAR) score resulted in an increased prognostic value compared to the original BAR score. Myosteatosis may be a useful parameter to predict perioperative outcome in patients undergoing OLT, supporting the role of muscle quality (myosteatosis) over quantity (muscle mass) in this setting.