Prognostic implication of early posttransplant hypercholesterolemia in liver transplantation for patients with hepatocellular carcinoma.

BACKGROUND: Hyperlipidemia is a common complication after liver transplantation (LT) and develops mostly in the early posttransplant period. Recently, some studies have reported a positive correlation between hyperlipidemia and favorable prognosis in patients with hepatocellular carcinoma (HCC) undergoing hepatectomy. This study aimed to evaluate the possibility of predicting prognosis in HCC patients receiving LT by early posttransplant dyslipidemia. METHODS: From January 2015 to December 2017, a total of 806 HCC patients from China Liver Transplant Registry database were retrospectively enrolled. The prognostic relevance of early posttransplant hypertriglyceridemia or hypercholesterolemia was examined using survival analysis, and subgroup analysis was implemented based on LT criteria. RESULTS: Early posttransplant hypercholesterolemia (EPHC) was independently inversely associated with the risk of recurrence [hazard ratio (HR) = 0.630; P = 0.022], but was not significantly correlated with the mortality. However, early posttransplant hypertriglyceridemia was not related to prognosis. Intriguingly, with further classification, we found that borderline EPHC (B-EPHC), instead of significant EPHC, was a predictor of lower risk for both recurrence (HR = 0.504; P = 0.006) and mortality (HR = 0.511; P = 0.023). Compared with non-EPHC patients, B-EPHC patients achieved significantly superior 1-year and 3-year tumor-free survival (89.6% and 83.7% vs. 83.8% and 72.7% respectively; P = 0.023), and 1-year and 3-year overall survival (95.8% and 84.8% vs. 94.6% and 77.6% respectively; P = 0.039). In the subgroup analysis, B-EPHC remained an independent predictor of better prognosis in patients beyond Milan criteria and those within Hangzhou criteria; whereas there was no significant relationship between B-EPHC and prognosis in patients within Milan criteria and those beyond Hangzhou criteria. More interestingly, patients beyond Milan criteria but within Hangzhou criteria were identified as the crucial subpopulation who benefited from B-EPHC (recurrence HR = 0.306, P = 0.011; mortality HR = 0.325, P = 0.031). CONCLUSIONS: B-EPHC could assist transplant teams in dynamically evaluating prognosis after LT for HCC as a postoperative non-oncological biomarker, especially in patients beyond Milan criteria but within Hangzhou criteria.

Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation.

BACKGROUND: Acute kidney injury (AKI) is a common complication after liver transplantation (LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach. METHODS: A total of 493 patients with donation after cardiac death LT (DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes (KDIGO). The clinical data of patients with AKI (AKI group) and without AKI (non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: The incidence of AKI was 35.7% (176/493) during the follow-up period. Compared with the non-AKI group, the AKI group showed a remarkably lower survival rate (P < 0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval (CI): 0.794-0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models (P < 0.001). CONCLUSIONS: The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT.

New-onset hyperglycemia immediately after liver transplantation: A national survey from China Liver Transplant Registry.

BACKGROUND: New-onset hyperglycemia (NOH) is a common phenomenon after liver transplantation (LT), but its impact on clinical outcomes has not yet been fully assessed. We aimed to evaluate the etiology and prognosis of NOH within 1 month after LT. METHODS: The data of 3339 adult patients who underwent primary LT from donation after citizen death between January 2010 and June 2016 were extracted from China Liver Transplant Registry database and analyzed. NOH was defined as fasting blood glucose ≥7.0 mmol/L confirmed on at least two occasions within the first post-transplant month with or without hypoglycemic agent. RESULTS: Of 3339 liver recipients, 1416 (42.4%) developed NOH. Recipients with NOH had higher incidence of post-transplant complications such as graft and kidney failure, infection, biliary stricture, cholangitis, and tumor recurrence in a glucose concentration-dependent manner as compared to non-NOH recipients (P < 0.05). The independent risk factors of NOH were donor warm ischemic time >10 min, cold ischemic time >10 h, anhepatic time >60 min, recipient model for end-stage liver disease score >30, moderate ascites and corticosteroid usage (P < 0.05). Liver enzymes (alanine aminotransferase and gamma-glutamyltranspeptidase) on post-transplant day 7 significantly correlated with NOH (P < 0.001). CONCLUSIONS: NOH leads to increased morbidity and mortality in liver recipients. Close surveillance and tight control of blood glucose are desiderated immediately following LT particularly in those with delayed graft function and receiving corticosteroid. Strategic targeting graft ischemic injury may help maintain glucose homeostasis.