Absolute lymphocyte count recovery at 1 month after transplantation predicts favorable outcomes of patients with hepatocellular carcinoma

A Novel Online Calculator Predicting Acute Kidney Injury After Liver Transplantation: A Retrospective Study

Acute kidney injury (AKI) after liver transplantation (LT) is a common complication, and its development is thought to be multifactorial. We aimed to investigate potential risk factors and build a model to identify high-risk patients. A total of 199 LT patients were enrolled and each patient data was collected from the electronic medical records. Our primary outcome was postoperative AKI as diagnosed and classified by the KDIGO criteria. A least absolute shrinkage and selection operating algorithm and multivariate logistic regression were utilized to select factors and construct the model. Discrimination and calibration were used to estimate the model performance. Decision curve analysis (DCA) was applied to assess the clinical application value. Five variables were identified as independent predictors for post-LT AKI, including whole blood serum lymphocyte count, RBC count, serum sodium, insulin dosage and anhepatic phase urine volume. The nomogram model showed excellent discrimination with an AUC of 0.817 (95% CI: 0.758-0.876) in the training set. The DCA showed that at a threshold probability between 1% and 70%, using this model clinically may add more benefit. In conclusion, we developed an easy-to-use tool to calculate the risk of post-LT AKI. This model may help clinicians identify high-risk patients.

Platelets level variability during the first year after liver transplantation in the risk prediction model for recipients mortality

INTRODUCTION AND OBJECTIVES

Many scoring systems in liver diseases use static values of liver function parameters. These parameters may change significantly in liver transplant (LTx) recipients over time due to various processes. The study was aimed at building a new model for survival prediction after LTx based on variability of selected parameters.

MATERIALS AND METHODS

The study included 450 LTx recipients who survived a minimum one year after transplantation. We analyzed liver enzymes and hematology parameters static values and their variability during the first year after transplantation. Modeling patients' survival was performed using Cox regression. Various sets of parameters (both static and variability and trends values) were tested to predict survival in our study group. Models' performance was measured using the concordance index.

RESULTS

The single predictors of the patients survival were the static values of AST with C-index 0.706 (0.5883-0.7494), ALT 0.6102 (0.4843-0.6857) and bilirubin 0.6224 (0.5537-0.6695). High prediction scores were observed for variability in creatinine 0.6023 (0.5409-0.6451), PLT 0.6350 (0.5491-0.7043), RBC 0.5689 (0.5065-0.6213) and WBC 0.6506 (0.5095-0.7124). Our best-fitted and proposed model for patients survival after LTx has C-index 0.8273 (IQR 0.7767-0.8649). The model uses the following indicators for mortality prediction: the static value of AST, variability measure of PLT and trend measures of WBC and PLT.

CONCLUSIONS

Adding variability and trend measures increases predictive accuracy in modeling patients survival after LTx. We propose a high-accuracy survival model in which variability and trend of PLT measures in the first year after transplantation are strong predictors of long-term mortality.

Serologic and molecular biomarkers for recurrence of hepatocellular carcinoma after liver transplantation: A systematic review and meta-analysis

INTRODUCTION

Recurrence after liver transplantation (LT) for hepatocellular carcinoma (HCC) is a major cause of mortality. Knowledge on biomarkers may contribute to better surveillance based on the patients' risk of recurrence. Reviewing the literature, we aimed to identify serological and molecular biomarkers for recurrence of hepatocellular carcinoma after liver transplantation.

METHODS

A literature search was performed in the databases PubMed and Scopus to identify observational studies evaluating serological or molecular biomarkers for recurrence of HCC after LT using adjusted analysis to correct for confounding.

RESULTS

Of 502 records, 69 mainly retrospective studies were included with a total of 15,213 patients. Of these, 41 studies were suitable for meta-analyses, which showed that the serum markers pre-transplant α-fetoprotein (AFP) (hazard ratio (HR) 2.69 [2.08-3.47]), pre-transplant des-gamma-carboxy prothrombin (DCP) (HR 5.99 [3.27-10.98]), and allelic imbalance in microsatellites in DNA of tumor tissue (HR 13.49 [3.17-57.30]) were related to recurrence.

CONCLUSIONS

AFP, DCP and allelic imbalance in microsatellites may be used to predict recurrence. Together with other modalities, biomarkers may be used in future transplantation criteria to optimize selection of suitable patients.