Metabolomics as a tool to predict the risk of decompensation or liver-related death in patients with compensated cirrhosis.

BACKGROUND AND AIMS: Patients with compensated cirrhosis with clinically significant portal hypertension (CSPH: HVPG > 10 mm Hg) have a high risk of decompensation. HVPG is, however, an invasive procedure not available in all centers. The present study aims to assess whether metabolomics can improve the capacity of clinical models in predicting clinical outcomes in these compensated patients. APPROACH AND RESULTS: This is a nested study from the PREDESCI cohort (an RCT of nonselective beta-blockers vs. placebo in 201 patients with compensated cirrhosis and CSPH), including 167 patients for whom a blood sample was collected. A targeted metabolomic serum analysis, using ultra-high-performance liquid chromatography-mass spectrometry, was performed. Metabolites underwent univariate time-to-event cox regression analysis. Top-ranked metabolites were selected using Log-Rank p -value to generate a stepwise cox model. Comparison between models was done using DeLong test. Eighty-two patients with CSPH were randomized to nonselective beta-blockers and 85 to placebo. Thirty-three patients developed the main endpoint (decompensation/liver-related death). The model, including HVPG, Child-Pugh, and treatment received ( HVPG/Clinical model ), had a C-index of 0.748 (CI95% 0.664-0.827). The addition of 2 metabolites, ceramide (d18:1/22:0) and methionine (HVPG/Clinical/Metabolite model), significantly improved the model's performance [C-index of 0.808 (CI95% 0.735-0.882); p =0.032]. The combination of these 2 metabolites together with Child-Pugh and the type of treatment received (Clinical/Metabolite model) had a C-index of 0.785 (CI95% 0.710-0.860), not significantly different from the HVPG-based models including or not metabolites. CONCLUSIONS: In patients with compensated cirrhosis and CSPH, metabolomics improves the capacity of clinical models and achieves similar predictive capacity than models including HVPG.

Implementation of a re-linkage to care strategy in patients with chronic hepatitis C who were lost to follow-up in Latin America.

To achieve WHO's goal of eliminating hepatitis C virus (HCV), innovative strategies must be designed to diagnose and treat more patients. Therefore, we aimed to describe an implementation strategy to identify patients with HCV who were lost to follow-up (LTFU) and offer them re-linkage to HCV care. We conducted an implementation study utilizing a strategy to contact patients with HCV who were not under regular follow-up in 13 countries from Latin America. Patients with HCV were identified by the international classification of diseases (ICD-9/10) or equivalent. Medical records were then reviewed to confirm the diagnosis of chronic HCV infection defined by anti-HCV+ and detectable HCV-RNA. Identified patients who were not under follow-up by a liver specialist were contacted by telephone or email, and offered a medical reevaluation. A total of 10,364 patients were classified to have HCV. After reviewing their medical charts, 1349 (13%) had undetectable HCV-RNA or were wrongly coded. Overall, 9015 (86.9%) individuals were identified with chronic HCV infection. A total of 5096 (56.5%) patients were under routine HCV care and 3919 (43.5%) had been LTFU. We were able to contact 1617 (41.3%) of the 3919 patients who were LTFU at the primary medical institution, of which 427 (26.4%) were cured at a different institutions or were dead. Of the remaining patients, 906 (76.1%) were candidates for retrieval. In our cohort, about one out of four patients with chronic HCV who were LTFU were candidates to receive treatment. This strategy has the potential to be effective, accessible and significantly impacts on the HCV care cascade.

Performance of pre-transplant criteria in prediction of hepatocellular carcinoma progression and waitlist dropout.

BACKGROUND & AIM: Liver transplantation (LT) selection models for hepatocellular carcinoma (HCC) have not been proposed to predict waitlist dropout because of tumour progression. The aim of this study was to compare the alpha-foetoprotein (AFP) model and other pre-LT models in their prediction of HCC dropout. METHODS: A multicentre cohort study was conducted in 20 Latin American transplant centres, including 994 listed patients for LT with HCC from 2012 to 2018. Longitudinal tumour characteristics, and patterns of progression were recorded at time of listing, after treatments and at last follow-up over the waitlist period. Competing risk regression models were performed, and model's discrimination was compared estimating Harrell's adapted c-statistics. RESULTS: HCC dropout rate was significantly higher in patients beyond (24% [95% CI 16-28]) compared to those within Milan criteria (8% [95% IC 5%-12%]; p < .0001), with a SHR of 3.01 [95% CI 2.03-4.47]), adjusted for waiting list time and bridging therapies (c-index 0.63 [95% CI 0.57; 0.69). HCC dropout rates were higher in patients with AFP scores >2 (adjusted SHR of 3.17 [CI 2.13-4.71]), c-index of 0.71 (95% CI 0.65-0.77; p = .09 vs Milan). Similar discrimination power for HCC dropout was observed between the AFP score and the Metroticket 2.0 model. In patients within Milan, an AFP score >2 points discriminated two populations with a higher risk of HCC dropout (SHR 1.68 [95% CI 1.08-2.61]). CONCLUSIONS: Pre-transplant selection models similarly predicted HCC dropout. However, the AFP model can discriminate a higher risk of dropout among patients within Milan criteria.

Failure in all steps of hepatocellular carcinoma surveillance process is frequent in daily practice.

INTRODUCTION AND OBJECTIVES: Failures at any step in the hepatocellular carcinoma (HCC) surveillance process can result in HCC diagnostic delays and associated worse prognosis. We aimed to estimate the prevalence of surveillance failure and its associated risk factors in patients with HCC in Argentina, considering three steps: 1) recognition of at-risk patients, 2) implementation of HCC surveillance, 3) success of HCC surveillance. METHODS: We performed a multi-center cross-sectional study of patients at-risk for HCC in Argentina seen between10.01.2018 and 10.30.2019. Multivariable logistic regression analysis was used to identify correlates of surveillance failure. RESULTS: Of 301 included patients, the majority were male (74.8%) with a mean age of 64 years old. At the time of HCC diagnosis, 75 (25%) patients were unaware of their diagnosis of chronic liver disease, and only 130 (43%) patients were under HCC surveillance. Receipt of HCC surveillance was significantly associated with follow-up by a hepatologist. Of 119 patients with complete surveillance, surveillance failure occurred in 30 (25%) patients. Surveillance failure was significantly associated with alpha fetoprotein ≥20 ng/mL (OR 4.0, CI 95% 1.43-11.55). CONCLUSIONS: HCC surveillance failure was frequent in all the evaluated steps. These data should help guide strategies to improve the implementation and results of HCC surveillance in our country.

Lenvatinib as first-line therapy for recurrent hepatocellular carcinoma after liver transplantation: Is the current evidence applicable to these patients?

Liver transplantation (LT) is one of the leading curative therapies for hepatocellular carcinoma (HCC). Despite recent optimization of transplant selection criteria, including alpha-feto protein, HCC recurrence after LT is still the leading cause of death in these patients. During the last decades, effective systemic treatments for HCC, including tyrosine kinase inhibitors and immunotherapy, have been approved. We describe the clinical scenario of a patient with recurrence of HCC five years after LT, who received lenvatinib as first-line systemic therapy to introduce systemic treatment options in this clinical setting. In this opinion review, we detail first and second-line systemic treatment options, focusing on those feasible for patients with recurrent HCC after LT. Several trials have evaluated new drugs to treat HCC patients in first and second-line therapy, but patients with recurrent HCC after LT have been excluded from these trials. Consequently, most of the evidence comes from observational retrospective studies. Whether tyrosine kinase inhibitors will remain the primary therapeutic approach in these patients, due to a relative contraindication for immunotherapy, may be clarified in the near future.

LI-RADS 4 or 5 categorization may not be clinically relevant for decision-making processes: A prospective cohort study.

INTRODUCTION AND OBJECTIVES: The liver imaging reporting data system (LI-RADS) for hepatocellular carcinoma (HCC) was proposed to standardize and enhance consensus of reporting. However, clinical utility of LI-RADS has not been evaluated in Latin America. We therefore sought to compare LI-RADS categories with histopathology findings in liver transplant (LT) explants in a regional center. MATERIALS AND METHODS: Prospective cohort study conducted between 2012 and 2018 in a single center from Argentina including patients with HCC listed for LT. LI-RADS definitions were applied to magnetic resonance images (MRI) or computed tomography (CT) abdominal scans at time of listing and at final pre-LT reassessment and compared to explant pathology findings; specifically, major nodule (NOD1). RESULTS: Of 130 patients with HCC listed for LT (96.1% with cirrhosis and 35.6% with hepatitis C virus infection), 72 underwent LT. Overall, 65% had imaging HCC diagnosis based on MRI (n = 84), 26% with CT (n = 34) and 9% (n = 12) with both methods. Among LT patients with pre-transplant imaging at our institution (n = 42/72), 69% of the NOD1 were LR-5, 21% LR-4 and 10% LR-3. Definite HCC diagnosis was 50% in LR-3 NOD1 (CI 18-90); none presented microvascular invasion. In LR-4 NOD1, HCC was confirmed in 89% (CI 59-98), of which 11% showed microvascular invasion; whereas in LR-5 NOD1 77% (CI 64-87) had confirmed HCC, 17% with microvascular invasion. CONCLUSIONS: LI-RADS was useful to standardize reports; however, no significant differences were observed between LR-4 and LR-5 HCC probability when compared to explant pathology.