CACS, CCTA and mCAD-LT score in the pre-transplant assessment of coronary artery disease and the prediction of post-transplant cardiovascular events.

BACKGROUND: The optimal cardiovascular assessment of liver transplant (LT) candidates is unclear. We aimed to evaluate the performance of CT-based coronary tests (coronary artery calcium score [CACS] and coronary CT angiography [CCTA]) and a modification of the CAD-LT score (mCAD-LT, excluding family history of CAD) to diagnose significant coronary artery disease (CAD) before LT and predict the incidence of post-LT cardiovascular events (CVE). METHODS: We retrospectively analysed a single-centre cohort of LT candidates who underwent non-invasive tests; invasive coronary angiography (ICA) was performed depending on the results of non-invasive tests. mCAD-LT was calculated in all patients. RESULTS: Six-hundred-and-thirty-four LT candidates were assessed and 351 of them underwent LT. CACS, CCTA and ICA were performed in 245, 123 and 120 LT candidates, respectively. Significant CAD was found in 30% of patients undergoing ICA. The AUROCs of mCAD-LT (.722) and CCTA (.654) were significantly higher than that of CACS (.502) to predict the presence of significant CAD. Specificity of the tests ranged between 31% for CCTA and 53% for CACS. Among patients who underwent LT, CACS ≥ 400 and mCAD-LT were independently associated with the incidence of CVE; in patients who underwent CCTA before LT, significant CAD at CCTA also predicted post-LT CVE. CONCLUSION: In this cohort, mCAD-LT score and CT-based tests detect the presence of significant CAD in LT candidates, although they tend to overestimate it. Both mCAD-LT score and CT-based tests classify LT recipients according to their risk of post-LT CVE and can be used to improve post-LT risk mitigation.

De Novo Malignancy After Liver Transplantation: Risk Assessment, Prevention, and Management-Guidelines From the ILTS-SETH Consensus Conference.

De novo malignancies (DNMs) following liver transplantation (LT) have been reported as 1 of the major causes of late mortality, being the most common cause of death in the second decade after LT. The overall incidence of DNMs is reported to be in the range of 3.1% to 14.4%, and the incidence is 2- to 3-fold higher in transplant recipients than in age- and sex-matched healthy controls. Long-term immunosuppressive therapy, which is the key in maintaining host tolerance and achieving good long-term outcomes, is known to contribute to a higher risk of DNMs. However, the incidence and type of DNM also depends on different risk factors, including patient demographics, cause of the underlying chronic liver disease, behavior (smoking and alcohol abuse), and pre-existing premalignant conditions. The estimated standardized incidence ratio for different DNMs is also variable. The International Liver Transplantation Society-Spanish Society of Liver Transplantation Consensus Conference working group on DNM has summarized and discussed the current available literature on epidemiology, risk factors, management, and survival after DNMs. Recommendations for screening and surveillance for specific tumors, as well as immunosuppression and cancer-specific management in patients with DNM, are summarized.

Noninvasive assessment of liver function.

PURPOSE OF REVIEW: It is our opinion that there is an unmet need in hepatology for a minimally or noninvasive test of liver function and physiology. Quantitative liver function tests define the severity and prognosis of liver disease by measuring the clearance of substrates whose uptake or metabolism is dependent upon liver perfusion or hepatocyte function. Substrates with high-affinity hepatic transporters exhibit high 'first-pass' hepatic extraction and their clearance measures hepatic perfusion. In contrast, substrates metabolized by the liver have low first-pass extraction and their clearance measures specific drug metabolizing pathways. RECENT FINDINGS: We highlight one quantitative liver function test, the dual cholate test, and introduce the concept of a disease severity index linked to clinical outcome that quantifies the simultaneous processes of hepatocyte uptake, clearance from the systemic circulation, clearance from the portal circulation, and portal-systemic shunting. SUMMARY: It is our opinion that dual cholate is a relevant test for defining disease severity, monitoring the natural course of disease progression, and quantifying the response to therapy.

Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study.

BACKGROUND & AIMS: There is an increasing discrepancy between the number of potential liver graft recipients and the number of organs available. Organ allocation should follow the concept of benefit of survival, avoiding human-innate subjectivity. The aim of this study is to use artificial-neural-networks (ANNs) for donor-recipient (D-R) matching in liver transplantation (LT) and to compare its accuracy with validated scores (MELD, D-MELD, DRI, P-SOFT, SOFT, and BAR) of graft survival. METHODS: 64 donor and recipient variables from a set of 1003 LTs from a multicenter study including 11 Spanish centres were included. For each D-R pair, common statistics (simple and multiple regression models) and ANN formulae for two non-complementary probability-models of 3-month graft-survival and -loss were calculated: a positive-survival (NN-CCR) and a negative-loss (NN-MS) model. The NN models were obtained by using the Neural Net Evolutionary Programming (NNEP) algorithm. Additionally, receiver-operating-curves (ROC) were performed to validate ANNs against other scores. RESULTS: Optimal results for NN-CCR and NN-MS models were obtained, with the best performance in predicting the probability of graft-survival (90.79%) and -loss (71.42%) for each D-R pair, significantly improving results from multiple regressions. ROC curves for 3-months graft-survival and -loss predictions were significantly more accurate for ANN than for other scores in both NN-CCR (AUROC-ANN=0.80 vs. -MELD=0.50; -D-MELD=0.54; -P-SOFT=0.54; -SOFT=0.55; -BAR=0.67 and -DRI=0.42) and NN-MS (AUROC-ANN=0.82 vs. -MELD=0.41; -D-MELD=0.47; -P-SOFT=0.43; -SOFT=0.57, -BAR=0.61 and -DRI=0.48). CONCLUSIONS: ANNs may be considered a powerful decision-making technology for this dataset, optimizing the principles of justice, efficiency and equity. This may be a useful tool for predicting the 3-month outcome and a potential research area for future D-R matching models.