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External Validation of Six Liver Functional Reserve Models to predict Posthepatectomy Liver Failure after Major Resection for Hepatocellular Carcinoma.

ABSTRACT: Objective: To validate and compare the predictive ability of albumin-bilirubin model (ALBI) with other 5 liver functional reserve models (APRI, FIB4, MELD, PALBI, King's score) for posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) who underwent major hepatectomy. Methods: Data of patients undergoing major hepatectomy for HCC from 4 hospitals between January 01, 2008 and December 31, 2019 were retrospectively analyzed. PHLF was evaluated according to the definition of the 50-50 criteria. Performances of six liver functional reserve models were determined by the area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analysis. Results: A total of 745 patients with 103 (13.8%) experienced PHLF were finally included in this study. Among six liver functional reserve models, ALBI showed the highest AUC (0.64, 95% CI: 0.58-0.69) for PHLF. All models showed good calibration and greater net benefit than treating all patients at a limit range of threshold probabilities, but the ALBI demonstrated net benefit across the largest range of threshold probabilities. Subgroup analysis also showed ALBI had good predictive performance in cirrhotic (AUC=0.63) or non-cirrhotic (AUC=0.62) patients. Conclusion: Among the six models, the ALBI model shows more accurate predictive ability for PHLF in HCC patients undergoing major hepatectomy, regardless of having cirrhosis or not.

SUBMITTER: Guo G

PROVIDER: S-EPMC8317527 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesPosthepatectomy liver failure (PHLF) is a severe complication of liver resection. We aimed to develop and validate a model based on extracellular volume (ECV) and liver volumetry derived from computed tomography (CT) for preoperative predicting PHLF in resectable hepatocellular carcinoma (HCC) patients.MethodsA total of 393 resectable HCC patients from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. A total of 281 patients from our hospital were randomly divided into a training cohort (n = 181) and an internal validation cohort (n = 100), and 112 patients from another hospital formed the external validation cohort. CT-derived ECV was measured on nonenhanced and equilibrium phase images, and liver volumetry was measured on portal phase images. The model is composed of independent predictors of PHLF. The under the receiver operator characteristic curve (AUC) and calibration curve were used to reflect the predictive performance and calibration of the model. Comparison of AUCs used the DeLong test.ResultsCT-derived ECV, measured future liver remnant (mFLR) ratio, and serum albumin were independent predictors for PHLF in resectable HCC patients. The AUC of the model was significantly higher than that of the ALBI score in the training cohort, internal validation cohort, and external validation cohort (all p < 0.001). The calibration curve of the model showed good consistency in the training cohort and the internal and external validation cohorts.ConclusionsThe novel model contributes to the preoperative prediction of PHLF in resectable HCC patients.Critical relevance statementThe novel model combined CT-derived extracellular volume, measured future liver remnant ratio, and serum albumin outperforms the albumin-bilirubin score for predicting posthepatectomy liver failure in patients with resectable hepatocellular carcinoma.Key points• CT-derived ECV correlated well with the fibrosis stage of the background liver. • CT-derived ECV and mFLR ratio were independent predictors for PHLF in HCC. • The AUC of the model was higher than the CT-derived ECV and mFLR ratio. • The model showed a superior predictive performance than that of the ALBI score.

Project description:BackgroundPosthepatectomy liver failure (PHLF) represents a life-threatening complication with limited therapeutic options. Neutrophils play a critical and dynamic role during regeneratory processes, but their role in human liver regeneration is incompletely understood, especially as underlying liver disease, detectable in the majority of patients, critically affects hepatic regeneration. Here we explored intrahepatic neutrophil accumulation and neutrophil extracellular traps (NETs) in patients with PHLF and validated the functional relevance of NETs in a murine partial hepatectomy (PHx) model.MethodsWe investigated the influx of neutrophils, macrophages, eosinophils, and mast cells and the presence of their respective extracellular traps in liver biopsies of 35 patients undergoing hepatectomy (10 patients with PHLF) before and after the initiation of liver regeneration by fluorescence microscopy. In addition, NET formation and neutrophil activation were confirmed by plasma analysis of 99 patients (24 patients with PHLF) before and up to 5 days after surgery. Furthermore, we inhibited NETs via DNase I in a murine PHx model of mice with metabolically induced liver disease.ResultsWe detected rapid intrahepatic neutrophil accumulation, elevated levels of myeloperoxidase release, and NET formation in regenerating human livers, with a significantly higher increase of infiltrating neutrophils and NETs in patients with PHLF. Circulating markers of neutrophil activation, including elastase, myeloperoxidase, and citrullinated histone H3, correlated with markers of liver injury. In a murine PHx model, we showed that the inhibition of NET accelerated hepatocyte proliferation and liver regeneration.ConclusionsPatients with PHLF showed accelerated intrahepatic neutrophil infiltration and NET formation, which were associated with liver damage. Further, we identified postsurgical myeloperoxidase levels as predictive markers for adverse outcomes and observed that blocking NETs in a murine PHx model accelerated tissue regeneration.

Project description:ObjectivesEffective and non-invasive biomarkers to predict and avoid posthepatectomy liver failure (PHLF) are urgently needed. This systematic review aims to evaluate the efficacy of gadoxetic acid-enhanced MRI-derived parameters as an imaging biomarker in preoperative prediction of PHLF.MethodsA systematic literature search was performed in the databases of PubMed/Medline, Web of Science, Embase, and Cochrane Library up to 11 December 2020. Studies evaluating the incidence of PHLF on patients who underwent hepatectomy with preoperative liver function assessment using gadoxetic acid-enhanced MRI were included. Data was extracted using pre-designed tables. The Quality In Prognostic Studies (QUIPS) tool was adopted to evaluate the risk of bias.ResultsA total of 15 studies were identified for qualitative synthesis and most studies were marked as low to moderate risk of bias in each domain of QUIPS. The most commonly used parameter was relative liver enhancement or its related parameters. The reported incidence of PHLF ranged from 3.9 to 40%. The predictive sensitivity and specificity of gadoxetic acid-enhanced MRI parameters varied from 75 to 100% and from 54 to 93% in ten reported studies. A majority of the studies revealed that the gadoxetic acid-enhanced MRI parameter was a predictor for PHLF.ConclusionsGadoxetic acid-enhanced MRI showed a high predictive capacity for PHLF and represents a promising imaging biomarker in prediction of PHLF. Multicenter, prospective trials with large sample size and reliable, unified liver function parameters are required to validate the efficacy of individual liver function parameters.Key points• There is an obvious heterogeneity of the published studies, not only in variance of MRI liver function parameters but also in indication and extent of the liver resection. • Signal intensity (SI)-based parameters derived from gadoxetic acid-enhanced MRI are the commonly used method for PHLF prediction. • Gadoxetic acid-enhanced MRI-derived parameters showed high predictive efficacy for PHLF and can potentially serve as a predictor for the incidence of PHLF.

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External Validation of Six Liver Functional Reserve Models to predict Posthepatectomy Liver Failure after Major Resection for Hepatocellular Carcinoma.

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