Posthepatectomy Liver Failure In Patients Research Articles

Does the Surgical Approach Affect the Incidence of Post-Hepatectomy Liver Failure in Cirrhotic Patients? An Analysis of the NSQIP Database.

Background: The association between surgical approach and post-hepatectomy liver failure (PHLF) in cirrhotic patients is poorly understood. We hypothesize that patients will have similar rates of liver failure regardless of whether they undergo minimally invasive liver resection (MILR) or open liver resection (OLR) in major liver resections. In contrast, there will be lower rates of PHLF in patients undergoing minor hepatectomy via the MILR approach.Methods: Propensity score matching was used to analyze regression by matching the MILR to the OLR cohort. Patient demographics from the American College of Surgeons National Surgical Quality Improvement Program, including race, age, gender, and ethnicity, were matched. Chronic obstructive pulmonary disease, congestive heart failure, smoking, hypertension, diabetes, renal failure, dyspnea, dialysis dependence, body mass index, and American Society of Anesthesiologists (ASA) classification (>ASA III) were among the preoperative patient characteristics subject to matching. PHLF (Grade A vs B. vs C) was our primary outcome measure.Results: A total of 2129 cirrhotic patients were included in the study. In the minor hepatectomy group, patients undergoing an OLR were more likely to get discharged to a facility (7.0% vs 4.4%; P = .03), had greater hospital length of stay (5 vs 3 days; P = .02), and had a greater need for invasive postoperative interventions (10.7% vs 4.6%; P < .01). They were also noted to have higher rates of organ space superficial surgical infections (SSIs) (7.3% vs 3.7%; P = .003), Clostridium difficile infection (.9% vs .1%; P = .05), renal insufficiency (2.1% vs .1%; P < .01), unplanned intubations (3.1% vs 1.4%; P = .03), and Grade C liver failure (2.3% vs .9%; P = .03).Conclusion: A higher incidence of PHLF grade C was found in patients undergoing OLR in the minor hepatectomy group. Therefore, in cirrhotic patients who can tolerate minimally invasive approaches, MILR should be offered to prevent postoperative complications as part of their optimization plan.

ALBI score combined with FIB-4 index to predict post-hepatectomy liver failure in patients with hepatocellular carcinoma

Post-hepatectomy liver failure (PHLF) is a potentially life-threatening complication following liver resection. Hepatocellular carcinoma (HCC) often occurs in patients with chronic liver disease, which increases the risk of PHLF. This study aimed to investigate the ability of the combination of liver function and fibrosis markers (ALBI score and FIB-4 index) to predict PHLF in patients with HCC. Patients who underwent hepatectomy for HCC between August 2012 and September 2022 were considered for inclusion. Multivariable logistic regression analysis was used to identify factors associated with PHLF, and ALBI score and FIB-4 index were combined based on their regression coefficients. The performance of the combined ALBI-FIB4 score in predicting PHLF and postoperative mortality was compared with Child–Pugh score, MELD score, ALBI score, and FIB-4 index. A total of 215 patients were enrolled in this study. PHLF occurred in 35 patients (16.3%). The incidence of severe PHLF (grade B and grade C PHLF) was 9.3%. Postoperative 90‐d mortality was 2.8%. ALBI score, FIB-4 index, prothrombin time, and extent of liver resection were identified as independent factors for predicting PHLF. The AUC of the ALBI-FIB4 score in predicting PHLF was 0.783(95%CI: 0.694–0.872), higher than other models. The ALBI-FIB4 score could divide patients into two risk groups based on a cut-off value of − 1.82. High-risk patients had a high incidence of PHLF of 39.1%, while PHLF just occurred in 6.6% of low-risk patients. Similarly, the AUCs of the ALBI-FIB4 score in predicting severe PHLF and postoperative 90-d mortality were also higher than other models. Preoperative ALBI-FIB4 score showed good performance in predicting PHLF and postoperative mortality in patients undergoing hepatectomy for HCC, superior to the currently commonly used liver function and fibrosis scoring systems.

Predictive value of NLR, Fib4, and APRI in the occurrence of liver failure after hepatectomy in patients with hepatocellular carcinoma.

Neutrophil-lymphocyte ratio (NLR), fibrosis index based on four factors (Fib4), aspartate aminotransferase-to-platelet ratio index (APRI) can be used for prognostic evaluation of hepatocellular carcinoma. However, no study has established an individualized prediction model for the prognosis of hepatocellular carcinoma based on these factors. To screen the factors that affect the prognosis of hepatocellular carcinoma and establish a nomogram model that predicts postoperative liver failure after hepatic resection in patients with hepatocellular carcinoma. In total, 220 patients with hepatocellular carcinoma treated in our hospital from January 2022 to January 2023 were selected. They were divided into 154 participants in the modeling cohort, and 66 in the validation cohort. Comparative analysis of the changes in NLR, Fib4, and APRI levels in 154 patients with hepatocellular carcinoma before liver resection and at 3 mo, 6 mo, and 12 mo postoperatively was conducted. Binary logistic regression to analyze the influencing factors on the occurrence of liver failure in hepatocellular carcinoma patients, roadmap prediction modeling, and validation, patient work characteristic curves (ROCs) to evaluate the predictive efficacy of the model, calibration curves to assess the consistency, and decision curve analysis (DCA) to evaluate the model's validity were also conducted. Binary logistic regression showed that Child-Pugh grading, Surgical site, NLR, Fib4, and APRI were all risk factors for liver failure after hepatic resection in patients with hepatocellular carcinoma. The modeling cohort built a column-line graph model, and the area under the ROC curve was 0.986 [95% confidence interval (CI): 0.963-1.000]. The patients in the validation cohort utilized the column-line graph to predict the probability of survival in the validation cohort and plotted the ROC curve with an area under the curve of the model of 0.692 (95%CI: 0.548-0.837). The deviation of the actual outcome curves from the calibration curves of the column-line plots generated by the modeling and validation cohorts was small, and the DCA confirmed the validity. NLR, Fib4, and APRI independently influence posthepatectomy liver failure in patients with hepatocellular carcinoma. The column-line graph prediction model exhibited strong prognostic capability, with substantial concordance between predicted and actual events.

Utility of Machine Learning in the Prediction of Post-Hepatectomy Liver Failure in Liver Cancer.

Posthepatectomy liver failure (PHLF) is a serious complication associated with high mortality rates. Machine learning (ML) has rapidly developed and may outperform traditional models in predicting PHLF in patients who have undergone hepatectomy. This study aimed to predict PHLF using ML and compare its performance with that of traditional scoring systems. The clinicopathological data of 334 patients who underwent liver resection were retrospectively collected. The Pycaret library, a simple, open-source machine learning library, was used to compare multiple classification models for PHLF prediction. The predictive performance of 15 ML algorithms was compared using the mean area under the receiver operating characteristic curve (AUROC) and accuracy, and the best-fit model was selected among 15 ML algorithms. Next, the predictive performance of the selected ML-PHLF model was compared with that of routine scoring systems, the albumin-bilirubin score (ALBI) and the fibrosis-4 (FIB-4) index, using AUROC. The best model was extreme gradient boosting (accuracy:93.1%; AUROC:0.863) among the 15 ML algorithms. As compared with ALBI and FIB-4, the ML PHLF model had higher AUROC for predicting PHLF. The novel ML model for predicting PHLF outperformed routine scoring systems.

Pre-operative evaluation of spontaneous portosystemic shunts as a predictor of post-hepatectomy liver failure in patients undergoing liver resection for hepatocellular carcinoma

Pre-operative evaluation of spontaneous portosystemic shunts as a predictor of post-hepatectomy liver failure in patients undergoing liver resection for hepatocellular carcinoma

An Interpretable Radiomics Model Based on Two-Dimensional Shear Wave Elastography for Predicting Symptomatic Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma.

The aim of this study was to develop and validate an interpretable radiomics model based on two-dimensional shear wave elastography (2D-SWE) for symptomatic post-hepatectomy liver failure (PHLF) prediction in patients undergoing liver resection for hepatocellular carcinoma (HCC). A total of 345 consecutive patients were enrolled. A five-fold cross-validation was performed during training, and the models were evaluated in the independent test cohort. A multi-patch radiomics model was established based on the 2D-SWE images for predicting symptomatic PHLF. Clinical features were incorporated into the models to train the clinical-radiomics model. The radiomics model and the clinical-radiomics model were compared with the clinical model comprising clinical variables and other clinical predictive indices, including the model for end-stage liver disease (MELD) score and albumin-bilirubin (ALBI) score. Shapley Additive exPlanations (SHAP) was used for post hoc interpretability of the radiomics model. The clinical-radiomics model achieved an AUC of 0.867 (95% CI 0.787-0.947) in the five-fold cross-validation, and this score was higher than that of the clinical model (AUC: 0.809; 95% CI: 0.715-0.902) and the radiomics model (AUC: 0.746; 95% CI: 0.681-0.811). The clinical-radiomics model showed an AUC of 0.822 in the test cohort, higher than that of the clinical model (AUC: 0.684, p = 0.007), radiomics model (AUC: 0.784, p = 0.415), MELD score (AUC: 0.529, p < 0.001), and ALBI score (AUC: 0.644, p = 0.016). The SHAP analysis showed that the first-order radiomics features, including first-order maximum 64 × 64, first-order 90th percentile 64 × 64, and first-order 10th percentile 32 × 32, were the most important features for PHLF prediction. An interpretable clinical-radiomics model based on 2D-SWE and clinical variables can help in predicting symptomatic PHLF in HCC.

Balance of Gata3 and Ramp2 in hepatocytes regulates hepatic vascular reconstitution in postoperative liver regeneration

Post-hepatectomy liver failure (PHLF) leads to poor prognosis in patients undergoing hepatectomy, with hepatic vascular reconstitution playing a critical role. However, the regulators of hepatic vascular reconstitution remain unclear. In this study, we aimed to investigate the regulatory mechanisms of hepatic vascular reconstitution and identify biomarkers predicting PHLF in patients undergoing hepatectomy. Candidate genes that were associated with hepatic vascular reconstitution were screened using adeno-associated virus vectors in Alb-Cre-CRISPR/Cas9 mice subjected to partial hepatectomy. The biological activities of candidate genes were estimated using endothelial precursor transfusion and associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) models. The level of candidates was detected in biopsies from patients undergoing ALPPS. Risk factors for PHLF were also screened using retrospective data. Downregulation of Gata3 and upregulation of Ramp2 in hepatocytes promoted the proliferation of liver sinusoidal endothelial cells and hepatic revascularization. Pigment epithelium-derived factor (PEDF) and vascular endothelial growth factor A (VEGFA) played opposite roles in regulating the migration of endothelial precursors from bone marrow and the formation of new sinusoids after hepatectomy. Gata3 restricted endothelial cell function in patient-derived hepatic organoids, which was abrogated by a Gata3 inhibitor. Moreover, overexpression of Gata3 led to higher mortality in ALPPS mice, which was improved by a PEDF-neutralizing antibody. The expression of Gata3/RAMP and PEDF/VEGFA tended to have a negative correlation in patients undergoing ALPPS. A nomogram incorporating multiple factors, such as serum PEDF/VEGF index, was constructed and could efficiently predict the risk of PHLF. The balance of Gata3 and Ramp2 in hepatocytes regulates the proliferation of liver sinusoidal endothelial cells and hepatic revascularization via changes in the expression of PEDF and VEGFA, revealing potential targets for the prevention and treatment of PHLF. In this study, we show that the balance of Gata3 and Ramp2 in hepatocytes regulates hepatic vascular reconstitution by promoting a shift from pigment epithelium-derived factor (PEDF) to vascular endothelial growth factor A (VEGFA) expression during hepatectomy- or ALLPS (associating liver partition and portal vein ligation for staged hepatectomy)-induced liver regeneration. We also identified serum PEDF/VEGFA index as a potential predictor of post-hepatectomy liver failure in patients who underwent hepatectomy. This study improves our understanding of how hepatocytes contribute to liver regeneration and provides new targets for the prevention and treatment of post-hepatectomy liver failure.

Impact of the Prolonged Intermittent Pringle Maneuver on Post-Hepatectomy Liver Failure: Comparison of Open and Laparoscopic Approaches.

The influence of prolonged intermittent Pringle maneuver (IPM) on post-hepatectomy liver failure (PHLF) remains unclear. We evaluated the impact of the prolonged IPM on PHLF in patients undergoing open and laparoscopic hepatectomy. We retrospectively included 546 patients who underwent hepatectomy using IPM. The patients were divided into open (n = 294) and laparoscopic (n = 252) groups. Odds ratios for PHLF occurrence were estimated in each group according to cumulative Pringle time (CPT). The cut-off value was set at CPT of 120min. Risk factors for PHLF were evaluated in the open and laparoscopic groups. Additionally, we analyzed the post-operative outcomes in the open and laparoscopic groups with CPT ≥ 120min and performed propensity score matching analysis based on PFLF-associated factors. In the open group, the risk of PHLF increased as CPT increased, particularly after 120min. However, in the laparoscopic group, PHLF did not occur at less than 60min, and the risk of PHLF was not significantly different at more than 60min. Multivariate analysis identified CPT ≥ 120min as an independent risk factor for PHLF in the open group (p < 0.001), but not in the laparoscopic group. Propensity score matching analysis showed that the PHLF rate was significantly lower in the laparoscopic group with CPT ≥ 120min (p = 0.027). The post-operative transaminase levels were significantly lower in the laparoscopic group with CPT ≥ 120min. Laparoscopic hepatectomy may cause less PHLF with prolonged IPM compared with open hepatectomy.

Inflammatory Markers Showed Significant Incremental Value for Predicting Post-Hepatectomy Liver Failure in Hepatocellular Carcinoma Patients.

Post-hepatectomy liver failure (PHLF) remains a complication with the potential risk of mortality for hepatocellular carcinoma (HCC) patients. The systemic inflammatory response (SIR) has been demonstrated to be associated with a bad prognosis of liver cirrhosis and tumors. This study aims to evaluate the incremental prognostic value of inflammatory markers in predicting PHLF in patients with HCC. Clinical characteristics and variables were retrospectively collected in 2824 patients diagnosed with HCC who underwent radical hepatectomy from the First Medical Center of the General Hospital of the People's Liberation Army. A recently published prognostic model for PHLF was used as the reference model. The increase in AUC (ΔAUC), integrated discrimination improvement (IDI), and the continuous version of the net reclassification improvement (NRI) were applied for quantifying the incremental value of adding the inflammatory markers to the reference model. A p value < 0.05 was considered statistically significant. The reference PHLF model showed acceptable prediction performance in the current cohort, with an AUC of 0.7492 (95%CI, 0.7191-0.7794). The calculated ΔAUC associated with procalcitonin (PCT) was the only one that was statistically significant (p < 0.05), with a value of 0.0044, and demonstrated the largest magnitude of the increase in AUC. The continuous NRI value associated with the systemic immune-inflammation index (SII) was 35.79%, second only to GPS (46.07%). However, the inflammatory markers of the new models with statistically significant IDI only included WBC count, lymphocyte count, and SII. IDI associated with SII, meanwhile, was the maximum (0.0076), which was consistent with the performance of using the ΔAUC (0.0044) to assess the incremental value of each inflammatory variable. Among a wide range of inflammatory markers, only PCT and SII have potential incremental prognostic value for predicting PHLF in patients with radical resectable HCC.

CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma

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.Graphical

Spleen volume is a predictor of posthepatectomy liver failure and short-term mortality for hepatocellular carcinoma.

The study aimed at retrospectively assessing the impact of spleen volume (SpV) on the development of posthepatectomy liver failure (PHLF) in patients who underwent hepatectomy for hepatocellular carcinoma (HCC). 152 patients with primary HCC who underwent hepatectomy (sectionectomy or more) were classified into PHLF and non-PHLF groups, and then the relationship between PHLF and SpV was assessed. SpV (cm3) was obtained from preoperative CT and standardized based on the patient's body surface area (BSA, m2). PHLF was observed in 39 (26%) of the 152 cases. SpV/BSA was significantly higher in the PHLF group, and the postoperative 1-year survival rate was significantly worse in the PHLF group than that in the non-PHLF group (p = 0.044). Multivariable analysis revealed SpV/BSA as a significant independent risk factor for PHLF. Using the cut-off value (160 cm3/m2), the 152 cases were divided into small SpV and large SpV groups. The incidence of PHLF was significantly higher in the large SpV group (p = 0.002), the liver failure-related mortality rate was also significantly higher in the large SpV group (p = 0.007), and the 1-year survival rate was significantly worse in the large SpV group (p = 0.035). These results suggest SpV as a predictor of PHLF and short-term mortality in patients who underwent hepatectomy for HCC. Moreover, SpV measurement is a simple and potentially useful method for predicting PHLF in patients with HCC.

A novel nomogram based on preoperative parameters to predict posthepatectomy liver failure in patients with hepatocellular carcinoma

BackgroundPosthepatectomy liver failure is one of the main causes of death in patients after hepatectomy. This study intends to establish a prediction model to predict the risk of posthepatectomy liver failure and provide a scientific basis for further reducing the incidence of posthepatectomy liver failure. MethodsThis was a retrospective analysis of 1,172 patients with hepatocellular carcinoma undergoing partial hepatectomy. Using univariate and multivariate logistic regression analyses and stepwise regression, a prediction model for posthepatectomy liver failure was established based on the independent risk factors for posthepatectomy liver failure and validated by bootstrapping with 100 resamples, and the receiver operating characteristic curve was used to evaluate the predictive value of the prediction model. ResultsThe incidence rate of posthepatectomy liver failure was 22.7% (266/1172). The results showed that the indocyanine green retention rate at 15 minutes (odds ratio = 1.05, P = .002), alanine transaminase (odds ratio = 1.02, P < .001), albumin rate (odds ratio = 0.92, P < .001), total bilirubin (odds ratio = 1.04, P < .001), prothrombin time (odds ratio = 2.44, P < .001), aspartate aminotransferase-neutrophil ratio (odds ratio = 0.95, P < .001), and liver fibrosis index (odds ratio = 1.35, P < .001) were associated with posthepatectomy liver failure. These 7 independent risk factors for posthepatectomy liver failure were integrated into a nomogram prediction model, the predictive efficiency for posthepatectomy liver failure (area under the curve = 0.818, 95% confidence interval 0.789-0.848) was significantly higher than in other predictive models with a liver fibrosis index (area under the curve = 0.651), indocyanine green R15 (area under the curve = 0.669), albumin-bilirubin score (area under the curve = 0.709), albumin-indocyanine green evaluation score (area under the curve = 0.706), model for end-stage liver disease score (area under the curve = 0.636), and Child‒Pugh (area under the curve = 0.551) (all P < .001). The risk of posthepatectomy liver failure in the high-risk posthepatectomy liver failure group (score ≥152) was higher than that in the posthepatectomy liver failure low-risk group (score <152). ConclusionThis study developed and validated a nomogram model to predict the risk of posthepatectomy liver failure before surgery that can effectively predict the risk of posthepatectomy liver failure in patients with hepatocellular carcinoma.

A radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma.

Post-hepatectomy liver failure (PHLF) is a fatal complication after liver resection in patients with hepatocellular carcinoma (HCC). It is of clinical importance to estimate the risk of PHLF preoperatively. This study aimed to develop and validate a prediction model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging to estimate the risk of PHLF in patients with HCC. A total of 276 patients were retrospectively included and randomly divided into training and test cohorts (194:82). Clinicopathological variables were assessed to identify significant indicators for PHLF prediction. Radiomics features were extracted from the normal liver parenchyma at the hepatobiliary phase and the reproducible, robust and non-redundant ones were filtered for modeling. Prediction models were developed using clinicopathological variables (Clin-model), radiomics features (Rad-model), and their combination. The PHLF incidence rate was 24% in the whole cohort. The combined model, consisting of albumin-bilirubin (ALBI) score, indocyanine green retention test at 15min (ICG-R15), and Rad-score (derived from 16 radiomics features) outperformed the Clin-model and the Rad-model. It yielded an area under the receiver operating characteristic curve (AUC) of 0.84 (95% confidence interval (CI): 0.77-0.90) in the training cohort and 0.82 (95% CI: 0.72-0.91) in the test cohort. The model demonstrated a good consistency by the Hosmer-Lemeshow test and the calibration curve. The combined model was visualized as a nomogram for estimating individual risk of PHLF. A model combining clinicopathological risk factors and radiomics signature can be applied to identify patients with high risk of PHLF and serve as a decision aid when planning surgery treatment in patients with HCC.

Unsupervised Machine Learning of MRI Radiomics Features Identifies Two Distinct Subgroups with Different Liver Function Reserve and Risks of Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

Objective: To identify subgroups of patients with hepatocellular carcinoma (HCC) with different liver function reserves using an unsupervised machine-learning approach on the radiomics features from preoperative gadoxetic-acid-enhanced MRIs and to evaluate their association with the risk of post-hepatectomy liver failure (PHLF). Methods: Clinical data from 276 consecutive HCC patients who underwent liver resections between January 2017 and March 2019 were retrospectively collected. Radiomics features were extracted from the non-tumorous liver tissue at the gadoxetic-acid-enhanced hepatobiliary phase MRI. The reproducible and non-redundant features were selected for consensus clustering analysis to detect distinct subgroups. After that, clinical variables were compared between the identified subgroups to evaluate the clustering efficacy. The liver function reserve of the subgroups was compared and the correlations between the subgroups and PHLF, postoperative complications, and length of hospital stay were evaluated. Results: A total of 107 radiomics features were extracted and 37 were selected for unsupervised clustering analysis, which identified two distinct subgroups (138 patients in each subgroup). Compared with subgroup 1, subgroup 2 had significantly more patients with older age, albumin–bilirubin grades 2 and 3, a higher indocyanine green retention rate, and a lower indocyanine green plasma disappearance rate (all p &lt; 0.05). Subgroup 2 was also associated with a higher risk of PHLF, postoperative complications, and longer hospital stays (&gt;18 days) than that of subgroup 1, with an odds ratio of 2.83 (95% CI: 1.58–5.23), 2.41(95% CI: 1.15–5.35), and 2.14 (95% CI: 1.32–3.47), respectively. The odds ratio of our method was similar to the albumin–bilirubin grade for postoperative complications and length of hospital stay (2.41 vs. 2.29 and 2.14 vs. 2.16, respectively), but was inferior for PHLF (2.83 vs. 4.55). Conclusions: Based on the radiomics features of gadoxetic-acid-enhanced MRI, unsupervised clustering analysis identified two distinct subgroups with different liver function reserves and risks of PHLF in HCC patients. Future studies are required to validate our findings.

Predicting symptomatic post-hepatectomy liver failure in patients with hepatocellular carcinoma: development and validation of a preoperative nomogram.

To develop and validate a nomogram based on liver stiffness (LS) for predicting symptomatic post-hepatectomy (PHLF) in patients with hepatocellular carcinoma (HCC). A total of 266 patients with HCC were enrolled prospectively from three tertiary referral hospitals from August 2018 to April 2021. All patients underwent preoperative laboratory examination to obtain parameters of liver function. Two-dimensional shear wave elastography (2D-SWE) was performed to measure LS. Three-dimensional virtual resection obtained the different volumes including future liver remnant (FLR). A nomogram was developed by using logistic regression and determined by receiver operating characteristic (ROC) curve analysis and calibration curve analysis, which was validated internally and externally. A nomogram was constructed with the following variables: FLR ratio (FLR of total liver volume), LS greater than 9.5kPa, Child-Pugh grade, and the presence of clinically significant portal hypertension (CSPH). This nomogram enabled differentiation of symptomatic PHLF in the derivation cohort (area under curve [AUC], 0.915), internal fivefold cross-validation (mean AUC, 0.918), internal validation cohort (AUC, 0.876) and external validation cohort (AUC, 0.845). The nomogram also showed good calibration in the derivation, internal validation, and external validation cohorts (Hosmer-Lemeshow goodness-of-fit test, p = 0.641, p = 0.06, and p = 0.127, respectively). Accordingly, the safe limit of the FLR ratio was stratified using the nomogram. An elevated level of LS was associated with the occurrence of symptomatic PHLF in HCC. A preoperative nomogram integrating LS, clinical and volumetric features was useful in predicting postoperative outcomes in patients with HCC, which might help surgeons in the management of HCC resection. A serial of the safe limit of the future liver remnant was proposed by a preoperative nomogram for hepatocellular carcinoma, which might help surgeons in 'how much remnant is enough in liver resection'. • An elevated liver stiffness with the best cutoff value of 9.5kPa was associated with the occurrence of symptomatic post-hepatectomy liver failure in hepatocellular carcinoma. • A nomogram based on both quality (Child-Pugh grade, liver stiffness, and portal hypertension) and quantity of future liver remnant was developed to predict symptomatic post-hepatectomy liver failure for HCC, which enabled good discrimination and calibration in both derivation and validation cohorts. • The safe limit of future liver remnant volume was stratified using the proposed nomogram, which might help surgeons in the management of HCC resection.

Current status of preoperative risk assessment for posthepatectomy liver failure in patients with hepatocellular carcinoma.

Liver resection is an effective therapeutic option for patients with hepatocellular carcinoma. However, posthepatectomy liver failure (PHLF) remains a major cause of hepatectomy-related mortality, and the accurate prediction of PHLF based on preoperative assessment of liver functional reserve is a critical issue. The definition of PHLF proposed by the International Study Group for Liver Surgery has gained acceptance as a standard grading criterion. Liver function can be estimated using a variety of parameters, including routine blood biochemical examinations, clinical scoring systems, dynamic liver function tests, liver stiffness and fibrosis markers, and imaging studies. The Child-Pugh score and model for end-stage liver disease scores are conventionally used for estimating liver decompensation, although the alternatively developed albumin-bilirubin score shows superior performance for predicting hepatic dysfunction. Indocyanine green clearance, a dynamic liver function test mostly used in Japan and other Asian countries, serves as a quantitative estimation of liver function reserve and helps determine indications for surgical procedures according to the estimated risk of PHLF. In an attempt to improve predictive accuracy, specific evaluation of liver fibrosis and portal hypertension has gained popularity, including liver stiffness measurements using ultrasonography or magnetic resonance elastography, as well as noninvasive fibrosis markers. Imaging modalities, including Tc-99m-labeled galactosyl serum albumin scintigraphy and gadolinium-enhanced magnetic resonance imaging, are used for preoperative evaluation in combination with liver volume. This review aims to provide an overview of the usefulness of current options for the preoperative assessment of liver function in predicting PHLF.

Optical Biomedical Imaging Reveals Criteria for Violated Liver Regenerative Potential.

To reduce the risk of post-hepatectomy liver failure in patients with hepatic pathologies, it is necessary to develop an approach to express the intraoperative assessment of the liver's regenerative potential. Traditional clinical methods do not enable the prediction of the function of the liver remnant. Modern label-free bioimaging, using multiphoton microscopy in combination with second harmonic generation (SHG) and fluorescence lifetime imaging microscopy (FLIM), can both expand the possibilities for diagnosing liver pathologies and for assessing the regenerative potential of the liver. Using multiphoton and SHG microscopy, we assessed the structural state of liver tissue at different stages of induced steatosis and fibrosis before and after 70% partial hepatectomy in rats. Using FLIM, we also performed a detailed analysis of the metabolic state of the hepatocytes. We were able to determine criteria that can reveal a lack of regenerative potential in violated liver, such as the presence of zones with reduced NAD(P)H autofluorescence signals. Furthermore, for a liver with pathology, there was an absence of the jump in the fluorescence lifetime contributions of the bound form of NADH and NADPH the 3rd day after hepatectomy that is characteristic of normal liver regeneration. Such results are associated with decreased intensity of oxidative phosphorylation and of biosynthetic processes in pathological liver, which is the reason for the impaired liver recovery. This modern approach offers an effective tool that can be successfully translated into the clinic for express, intraoperative assessment of the regenerative potential of the pathological liver of a patient.

Predictive Factors for Post-Hepatectomy Liver Failure in Patients with Cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a cancer of biliary tract with a different incidence across the globe. The northeast part of Thailand has the highest incidence of CCA. Post-hepatectomy liver failure (PHLF) is the most severe and life-threatening complication in CCA patients. The aim of this study was to identify factors related to PHLF incidence in patients with CCA. An analytical cross-sectional study was performed in a university hospital in northeast Thailand between January 1, 2014 and December 31, 2020. PHLF grading criteria was used according to the international study group of liver surgery definition for PHLF. Multiple Logistic Regression with a stepwise forward method was employed to identify the predictive factors related to PHLF. The study findings revealed that 185 patients developed PHLF, of whom 56.22% experience grade A, 36.76% grade B, and 7.06% grade C PHLF. Based on our findings, seventeen factors were significantly correlated with PHLF incidence, namely age, cholangiocarcinoma type, hepatectomy type, preoperative biliary drainage, cholangitis, Child-Turcotte-Pugh grade, operation time, total blood loss, total blood transfusion, level of serum albumin, total bilirubin, direct bilirubin, alanine transaminase, aspartate transaminase, alkaline phosphatase, prothrombin time, and level of international normalized ratio. Total bilirubin (adjusted OR=14.07, 95% CI 7.54-26.27), Child-Turcotte-Pugh grade (adjusted OR=3.34, 95% CI 1.43-7.81), total blood transfusion (adjusted OR=2.32, 95% CI 1.19-4.54), and operation time (adjusted OR=1.77, 95% CI 1.05-2.97) could significantly predict PHLF incidence with a positive predictive value of 86.03% and a negative predictive value of 80.23%, while the accuracy of prediction was 81.88%. The findings of this study identified total bilirubin, Child-Turcotte-Pugh grade, total blood transfusion, and operation time as clinical predictive factors of PHLF. Therefore, modification of these factors is recommended to reduce the probability of liver failure in CCA patients.

A Nomogram Based on Preoperative Lab Tests, BMI, ICG-R15, and EHBF for the Prediction of Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

Background: Liver cancer is one of the most common malignant tumors, and worldwide, its incidence ranks sixth, and its morality third. Post-hepatectomy liver failure (PHLF) is the leading cause of death in patients who have undergone liver resection. This retrospective study investigated the risk factors for PHLF by predicting and constructing an index to evaluate the risk. This was achieved by combining the lab tests with an indocyanine green (ICG) clearance test. Methods: The study analyzed 1081 hepatocellular carcinoma (HCC) patients who had received liver resection at Sun Yat-sen University Cancer Center between 2005 and 2020. The patients were divided into a PHLF group (n = 113) and a non-PHLF group (n = 968), according to the International Study Group of Liver Surgery (ISGLS) criteria. Receiver operating characteristics (ROC) curves were then used to estimate the optimal cut-off values. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors. Finally, a nomogram was constructed where the calibration plot, the areas under the ROC curve (AUC), and the decision curve analysis (DCA) showed good predictive ability. Results: Correlation analysis revealed that body mass index (BMI) was positively correlated with ICG-R15 and with effective hepatic blood flow (EHBF). Univariate and multivariate logistics regression analysis revealed that BMI, ICG-R15, international normalized ratio (INR), tumor size, hepatic inflow occlusion (HIO) time, and operation method were independent predictive factors for PHLF. When these factors and EHBF were included in the nomogram, the nomogram showed a good predictive value, with a C-index of 0.773 (95% Confidence Interval [CI]: 0.729-0.818). The INR had the largest ROC areas (AUC INR = 0.661). Among the variables, ICG-R15 (AUC ICG-R15 = 0.604) and EHBF (AUC EHBF = 0.609) also showed good predictive power. Conclusions: The risk of PHLF in HCC patients can be precisely predicted by this model prior to the operation. By integrating EHBF into the model, HCC patients at higher risk for PHLF can be identified more effectively.

Preoperative Predictive Nomogram Based on Alanine Aminotransferase, Prothrombin Time Activity, and Remnant Liver Proportion (APART Score) to Predict Post-Hepatectomy Liver Failure after Major Hepatectomy

Introduction: Post-hepatectomy liver failure (PHLF) is a serious complication associated with major hepatectomies. An accurate prediction of PHLF is necessary to determine the feasibility of major hepatectomy. This study aimed to assess the association between PHLF and preoperative laboratory and computed tomography (CT) findings. Methods: Medical records of 65 patients who underwent major hepatectomy and preoperative CT were retrospectively reviewed. We evaluated future remnant liver volume evaluation models and remnant liver hemodynamics, which were assessed by arterial enhancement fraction (AEF) by using preoperative CT. Variables, including CT findings, were compared between patients with and without PHLF after major hepatectomy, and the preoperative PHLF-predicting nomogram was constructed using multivariate logistic regression. Results: The PHLF group included 21 patients (32.3%). The AEF was not significantly different between the two groups. In the future remnant liver volume evaluation models, future remnant liver proportion (fRLP) had the highest concordance index (C-index) in the receiver operating characteristic curve analysis (C-index, 0.755). Multivariate analysis of preoperative evaluable factors revealed that alanine aminotransferase levels (p = 0.034), prothrombin time activity (p = 0.021), and fRLP (p = 0.012) were independent predictive factors of PHLF. A nomogram (APART score) was constructed using these three factors, with a receiver operating curve showing a C-index of 0.894. According to the APART score, scores of 51–60 indicated moderate risk (40.0%), and scores over 60 indicated a high risk of PHLF (83.3%) (p < 0.001). Discussion: The APART score may help predict PHLF in patients indicated for major hepatectomies.