A novel online calculator based on noninvasive markers (ALBI and APRI) for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma

Multivariable prognostic models for post-hepatectomy liver failure: An updated systematic review

BACKGROUND

Partial hepatectomy continues to be the primary treatment approach for liver tumors, and post-hepatectomy liver failure (PHLF) remains the most critical life-threatening complication following surgery.

AIM

To comprehensively review the PHLF prognostic models developed in recent years and objectively assess the risk of bias in these models.

METHODS

This review followed the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Three databases were searched from November 2019 to December 2022, and references as well as cited literature in all included studies were manually screened in March 2023. Based on the defined inclusion criteria, articles on PHLF prognostic models were selected, and data from all included articles were extracted by two independent reviewers. The PROBAST was used to evaluate the quality of each included article.

RESULTS

A total of thirty-four studies met the eligibility criteria and were included in the analysis. Nearly all of the models (32/34, 94.1%) were developed and validated exclusively using private data sources. Predictive variables were categorized into five distinct types, with the majority of studies (32/34, 94.1%) utilizing multiple types of data. The area under the curve for the training models included ranged from 0.697 to 0.956. Analytical issues resulted in a high risk of bias across all studies included.

CONCLUSION

The validation performance of the existing models was substantially lower compared to the development models. All included studies were evaluated as having a high risk of bias, primarily due to issues within the analytical domain. The progression of modeling technology, particularly in artificial intelligence modeling, necessitates the use of suitable quality assessment tools.

Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

Patients diagnosed with hepatocellular carcinoma (HCC) often present with multimorbidity, significantly contributing to adverse outcomes, particularly in-hospital mortality. This study aimed to develop a predictive nomogram to assess the impact of comorbidities on in-hospital mortality risk in HCC patients undergoing palliative locoregional therapy. We retrospectively analyzed data from 345 hospitalized HCC patients who underwent palliative locoregional therapy between January 2015 and December 2022. The nomogram was constructed using independent risk factors such as length of stay (LOS), hepatitis B virus (HBV) infection, hypertension, chronic obstructive pulmonary disease (COPD), anemia, thrombocytopenia, liver cirrhosis, hepatic encephalopathy (HE), N stage, and microvascular invasion. The model demonstrated high predictive accuracy with an AUC of 0.908 (95% CI: 0.859-0.956) for the overall dataset, 0.926 (95% CI: 0.883-0.968) for the training set, and 0.862 (95% CI: 0.728-0.994) for the validation set. Calibration curves indicated a strong correlation between predicted and observed outcomes, validated by statistical tests. Decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility in predicting in-hospital mortality. This nomogram offers a practical tool for personalized risk assessment in HCC patients undergoing palliative locoregional therapy, facilitating informed clinical decision-making and improving patient management.

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.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Preoperative alkaline phosphatase-to-platelet count ratio as a prognostic factor for hepatocellular carcinoma with microvascular invasion

OBJECTIVES

The association between platelet status and hepatocellular carcinoma (HCC) prognoses remains controversial. Herein, we aimed to clarify the prognostic value of multiple platelet-related biomarkers, including platelet count, platelet/lymphocyte ratio (PLR), aspartate aminotransferase to platelet ratio index (APRI), and alkaline phosphatase-to-platelet count ratio index (APPRI) in HCC with microvascular invasion (MVI) after curative resection or liver transplantation.

MATERIALS AND METHODS

A retrospective review of 169 patients with solitary HCC and MVI who underwent resection or liver transplantation between January 2015 and December 2018 was conducted. Preoperative clinical, laboratory, pathologic, and imaging data were collected and analyzed. Overall survival (OS) and disease-free survival (DFS) were defined as the clinical endpoints. Univariate and multivariate Cox proportional hazards regression analyses were conducted to investigate potential predictors of DFS and OS.

RESULTS

Multivariate Cox regression analyses revealed that maximum tumor diameter, poor cell differentiation, and APPRI were independent predictors of DFS; while poor cell differentiation, APRI, APPRI, prothrombin time, and alpha-fetoprotein were independent prognostic factors for OS. The 1-, 3-, and 5-year DFS rates were 66.90%, 48.40%, and 37.40% for patients with APPRI ≤0.74 and 40.40%, 24.20%,and 24.20% for patients with APPRI>0.74. The corresponding rates of OS over 1, 3, and 5 years were 92.40%, 88.10% and 77.70%, and 72.30%, 38.20%, and 19.10%, respectively. The DFS and OS rates of patients whose APPRI was more than 0.74 were substantially lower than those of patients whose APPRI was less than or equal to 0.74 (p = 0.002 and p < 0.001, respectively).

CONCLUSION

Elevated preoperative APPRI is a noninvasive, simple, and easily assessable parameter linked to poor prognosis in individuals with single HCC and MVI after resection or liver transplantation.

Predicting Post-Hepatectomy Liver Failure in HCC Patients: A Review of Liver Function Assessment Based on Laboratory Tests Scores

The assessment of liver function is crucial in predicting the risk of post-hepatectomy liver failure (PHLF) in patients undergoing liver resection, especially in cases of hepatocellular carcinoma (HCC) which is often associated with cirrhosis. There are currently no standardized criteria for predicting the risk of PHLF. Blood tests are often the first- and least invasive expensive method for assessing hepatic function. The Child-Pugh score (CP score) and the Model for End Stage Liver Disease (MELD) score are widely used tools for predicting PHLF, but they have some limitations. The CP score does not consider renal function, and the evaluation of ascites and encephalopathy is subjective. The MELD score can accurately predict outcomes in cirrhotic patients, but its predictive capabilities diminish in non-cirrhotic patients. The albumin-bilirubin score (ALBI) is based on serum bilirubin and albumin levels and allows the most accurate prediction of PHLF for HCC patients. However, this score does not consider liver cirrhosis or portal hypertension. To overcome this limitation, researchers suggest combining the ALBI score with platelet count, a surrogate marker of portal hypertension, into the platelet-albumin-bilirubin (PALBI) grade. Non-invasive markers of fibrosis, such as FIB-4 and APRI, are also available for predicting PHLF but they focus only on cirrhosis related aspects and are potentially incomplete in assessing the global liver function. To improve the predictive power of the PHLF of these models, it has been proposed to combine them into a new score, such as the ALBI-APRI score. In conclusion, blood test scores may be combined to achieve a better predictive value of PHLF. However, even if combined, they may not be sufficient to evaluate liver function and to predict PHLF; thus, the inclusion of dynamic and imaging tests such as liver volumetry and ICG r15 may be helpful to potentially improve the predictive capacity of these models.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

A comprehensive preoperative predictive score for post-hepatectomy liver failure after hepatocellular carcinoma resection based on patient comorbidities, tumor burden, and liver function: the CTF score

BACKGROUND

Post-hepatectomy liver failure (PHLF) is a dreaded complication following liver resection for hepatocellular carcinoma (HCC) with a high mortality rate. We sought to develop a score based on preoperative factors to predict PHLF.

METHODS

Patients who underwent resection for HCC between 2000 and 2020 were identified from an international multi-institutional database. Factors associated with PHLF were identified and used to develop a preoperative comorbidity-tumor burden-liver function (CTF) predictive score.

RESULTS

Among 1785 patients, 106 (5.9%) experienced PHLF. On multivariate analysis, several factors were associated with PHLF including high Charlson comorbidity index (CCI ≥ 5) (OR 2.80, 95%CI, 1.08-7.26), albumin-bilirubin (ALBI) (OR 1.99, 95%CI, 1.10-3.56), and tumor burden score (TBS) (OR 1.06, 95%CI, 1.02-1.11) (all p < 0.05). Using the beta-coefficients of these variables, a weighted predictive score was developed and made available online ( https://alaimolaura.shinyapps.io/PHLFriskCalculator/ ). The CTF score (c-index = 0.67) performed better than Child-Pugh score (CPS) (c-index = 0.53) or Barcelona clinic liver cancer system (BCLC) (c-index = 0.57) to predict PHLF. A high CTF score was also an independent adverse prognostic factor for survival (HR 1.61, 95%CI, 1.12-2.30) and recurrence (HR 1.36, 95%CI, 1.08-1.71) (both p = 0.01).

CONCLUSION

Roughly 1 in 20 patients experienced PHLF following resection of HCC. Patient (i.e., CCI), tumor (i.e., TBS), and liver function (i.e., ALBI) factors were associated with risk of PHLF. These preoperative factors were incorporated into a novel CTF tool that was made available online, which outperformed other previously proposed tools.

Current evidence on posthepatectomy liver failure: comprehensive review

INTRODUCTION

Despite important advances in many areas of hepatobiliary surgical practice during the past decades, posthepatectomy liver failure (PHLF) still represents an important clinical challenge for the hepatobiliary surgeon. The aim of this review is to present the current body of evidence regarding different aspects of PHLF.

METHODS

A literature review was conducted to identify relevant articles for each topic of PHLF covered in this review. The literature search was performed using Medical Subject Heading terms on PubMed for articles on PHLF in English until May 2022.

RESULTS

Uniform reporting on PHLF is lacking due to the use of various definitions in the literature. There is no consensus on optimal preoperative assessment before major hepatectomy to avoid PHLF, although many try to estimate future liver remnant function. Once PHLF occurs, there is still no effective treatment, except liver transplantation, where the reported experience is limited.

DISCUSSION

Strict adherence to one definition is advised when reporting data on PHLF. The use of the International Study Group of Liver Surgery criteria of PHLF is recommended. There is still no widespread established method for future liver remnant function assessment. Liver transplantation is currently the only effective way to treat severe, intractable PHLF, but for many indications, this treatment is not available in most countries.

Early postoperative serum aspartate aminotransferase for prediction of post-hepatectomy liver failure

Background

Post-hepatectomy liver failure (PHLF) is a serious complication of hepatectomy. The current criteria for PHLF diagnosis (ISGLS consensus) require laboratory data on or after postoperative day (POD) 5, which may delay treatment for patients at risk. The present study aimed to determine the associations between early postoperative (POD1) serum aminotransferase levels and PHLF.

Methods

The medical records of patients who underwent hepatectomy at Ramathibodi Hospital from January 2008 to December 2019 were retrospectively examined. Patients were classified into PHLF and non-PHLF groups. Preoperative characteristics, intraoperative findings, and early postoperative laboratory data (serum AST, ALT, bilirubin, and international normalized ratio (INR) on POD0 to POD5) were analyzed.

Results

A total of 890 patients were included, of whom 31 (3.4%) had PHLF. Cut-off points for AST of 260 U/L and ALT of 270 U/L on POD1 were predictive of PHLF. In multivariate analysis, AST > 260 U/L on POD1, ICG-R15, major hepatectomy, blood loss, and INR were independently associated with PHLF.

Conclusions

Early warning from elevated serum AST on POD1, before a definitive diagnosis of PHLF is made on POD5, can help alert physicians that a patient is at risk, meaning that active management and vigilant monitoring can be initiated as soon as possible.

A Web-Based Prediction Model for Estimating the Probability of Post-hepatectomy Major Complications in Patients with Hepatocellular Carcinoma: A Multicenter Study from a Hepatitis B Virus-Endemic Area

BACKGROUND

The identification of patients at high risk of developing postoperative complications is important to improve surgical safety. We sought to develop an individualized tool to predict post-hepatectomy major complications in hepatitis B virus (HBV)-infected patients with hepatocellular carcinoma (HCC).

METHODS

A multicenter database of patients undergoing hepatectomy for HCC were analyzed; 2/3 and 1/3 of patients were assigned to the training and validation cohorts, respectively. Independent risks of postoperative 30-day major complications (Clavien-Dindo grades III-V) were identified and used to construct a web-based prediction model, which predictive accuracy was assessed using C-index and calibration curves, which was further validated by the validation cohort and compared with conventional scores.

RESULTS

Among 2762 patients, 391 (14.2%) developed major complications after hepatectomy. Diabetes mellitus, concurrent hepatitis C virus infection, HCC beyond the Milan criteria, cirrhosis, preoperative HBV-DNA level, albumin-bilirubin (ALBI), and aspartate transaminase to platelet ratio index (APRI) were identified as independent predictors of developing major complications, which were used to construct the online calculator ( http://www.asapcalculate.top/Cal11_en.html ). This model demonstrated good calibration and discrimination, with the C-indexes of 0.752 and 0.743 in the training and validation cohorts, respectively, which were significantly higher than those conventional scores (the training and validation cohorts: 0.565 ~ 0.650 and 0.568 ~ 0.614, all P < 0.001).

CONCLUSIONS

A web-based prediction model was developed to predict the probability of post-hepatectomy major complications in an individual HBV-infected patient with HCC. It can be used easily in the real-world clinical setting to help management-related decision-making and early warning, especially in areas with endemic HBV infection.

The ALBI score: From liver function in patients with HCC to a general measure of liver function

The (albumin-bilirubin) ‘ALBI’ score is an index of ‘liver function’ that was recently developed to assess prognosis in patients with hepatocellular carcinoma, irrespective of the degree of underlying liver fibrosis. Other measures of liver function, such as model for end-stage liver disease (MELD) and Child-Pugh score, which were introduced for specific clinical scenarios, have seen their use extended to other areas of hepatology. In the case of ALBI, its application has been increasingly extended to chronic liver disease in general and in some instances to non-liver diseases where it has proven remarkably accurate in terms of prognosis. With respect to chronic liver disease, numerous publications have shown that ALBI is highly prognostic in patients with all types and stages of chronic liver disease. Outside of liver disease, ALBI has been reported as being of prognostic value in conditions ranging from chronic heart failure to brain tumours. Whilst in several of these reports, explanations for the relationship of liver function to a clinical condition have been proposed, it has to be acknowledged that the specificity of ALBI for liver function has not been clearly demonstrated. Nonetheless, and similar to the MELD and Child-Pugh scores, the lack of any mechanistic basis for ALBI’s clinical utility does not preclude it from being clinically useful in certain situations. Why albumin and bilirubin levels, or a combination thereof, are prognostic in so many different diseases should be studied in the future.

A Novel Nomogram for Prediction of Post-Hepatectomy Liver Failure in Patients with Resectable Hepatocellular Carcinoma: A Multicenter Study

Objective

To develop a nomogram for predicting post-hepatectomy liver failure (PHLF) in patients with resectable hepatocellular carcinoma (HCC) based on portal hypertension, the extent of resection, ALT, total bilirubin, and platelet count.

Methods

Patients with HCC hospitalized from January 2015 to December 2020 were included in a retrospective cohort study. 595 HCC patients were divided into a training cohort (n=416) and a validation cohort (n=179) by random sampling. Univariate and multivariable analyses were performed to identify the independent variables to predict PHLF. The nomogram models for predicting the overall risk of PHLF and the risk of PHLF B+C were constructed based on the independent variables. Comparisons were made by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) with traditional models, such as FIB-4 score, APRI score, CP class (Child-Pugh), MELD score (model of end-stage liver disease), and ALBI score (albumin-bilirubin) to analyze the accuracy and superiority of the nomogram.

Results

We discovered that portal hypertension (yes vs no) (OR=1.677,95% CI:1.817-4.083, p=0.002), the extent of liver resection (OR=1.872,95% CI:3.937-47.096, p=0.001), ALT (OR=1.003,95% CI:1.003-1.016, P=0.003), total bilirubin (OR=1.036,95% CI:1.031-1.184, p=0.005), and platelet count (OR= 1.004, 95% CI:0.982-0.998, p=0.020) were independent risk factors for PHLF using multifactorial analysis. The nomogram models were constructed using well-fit calibration curves for each of these five covariates. When compared to the FIB4, ALBI, MELD, and CP score, our nomogram models have a better predictive value for predicting the overall risk of PHLF or the risk of PHLF B+C. The validation cohort's results were consistent. DCA also confirmed the conclusion.

Conclusion

Our models, in the form of static nomogram or web application, were developed to predict PHLF overall risk and PHLF B+C risk in patients with HCC, with a high prediction sensitivity and specificity performance than other commonly used scoring systems.

A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma

Background

Treatments for patients with early‐stage hepatocellular carcinoma (HCC) include liver transplantation (LT), liver resection (LR), radiofrequency ablation (RFA), and microwave ablation (MWA), are critical for their long-term survival. However, a computational model predicting treatment-independent prognosis of patients with HCC, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed, to our best knowledge. The goal of this study is to identify prognostic factors associated with OS and RFS in patients with HCC and develop nomograms to predict them, respectively.

Methods

We retrospectively retrieved 730 patients with HCC from three hospitals in China and followed them up for 3 and 5 years after invasive treatment. All enrolled patients were randomly divided into the training cohort and the validation cohort with a 7:3 ratio, respectively. Independent prognostic factors associated with OS and RFS were determined by the multivariate Cox regression analysis. Two nomogram prognostic models were built and evaluated by concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), the Kaplan–Meier survival curve, and decision curve analyses (DCAs), respectively.

Results

Prognostic factors for OS and RFS were identified, and nomograms were successfully built. Calibration discrimination was good for both the OS and RFS nomogram prediction models (C-index: 0.750 and 0.746, respectively). For both nomograms, the AUC demonstrated outstanding predictive performance; the DCA shows that the model has good decision ability; and the calibration curve demonstrated strong predictive power. The nomograms successfully discriminated high-risk and low-risk patients with HCC associated with OS and RFS.

Conclusions

We developed nomogram survival prediction models to predict the prognosis of HCC after invasive treatment with acceptable accuracies in both training and independent testing cohorts. The models may have clinical values in guiding the selection of clinical treatment strategies.

A Novel Nomogram for Predicting Postoperative Liver Failure After Major Hepatectomy for Hepatocellular Carcinoma

Background

Post-hepatectomy liver failure (PHLF) is the most common cause of mortality after major hepatectomy in hepatocellular carcinoma (HCC) patients. We aim to develop a nomogram to preoperatively predict grade B/C PHLF defined by the International Study Group on Liver Surgery Grading (ISGLS) in HCC patients undergoing major hepatectomy.

Study Design

The consecutive HCC patients who underwent major hepatectomy at the Eastern Hepatobiliary Surgery Hospital between 2008 and 2013 served as a training cohort to develop a preoperative nomogram, and patients from 2 other hospitals comprised an external validation cohort. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify preoperative predictors of grade B/C PHLF. Multivariable logistic regression was utilized to establish a nomogram model. Internal and external validations were used to verify the performance of the nomogram. The accuracy of the nomogram was also compared with the conventional scoring models, including MELD and ALBI score.

Results

A total of 880 patients who underwent major hepatectomy (668 in the training cohort and 192 in the validation cohort) were enrolled in this study. The independent risk factors of grade B/C PHLF were age, gender, prothrombin time, total bilirubin, and CSPH, which were incorporated into the nomogram. Good prediction discrimination was achieved in the training (AUROC: 0.73) and validation (AUROC: 0.72) cohorts. The calibration curve also showed good agreement in both training and validation cohorts. The nomogram has a better performance than MELD and ALBI score models.

Conclusion

The proposed nomogram showed more accurate ability to individually predict grade B/C PHLF after major hepatectomy in HCC patients than MELD and ALBI scores.

Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

Background/Aims

Posthepatectomy liver failure (PHLF) is a major complication that increases mortality in patients with hepatocellular carcinoma after surgical resection. The aim of this retrospective study was to evaluate the utility of magnetic resonance elastography-assessed liver stiffness (MRE-LS) for the prediction of PHLF and to develop an MRE-LS-based risk prediction model.

Methods

A total of 160 hepatocellular carcinoma patients who underwent surgical resection with available preoperative MRE-LS data were enrolled. Clinical and laboratory parameters were collected from medical records. Logistic regression analyses were conducted to identify the risk factors for PHLF and develop a risk prediction model.

Results

PHLF was present in 24 patients (15%). In the multivariate logistic analysis, high MRE-LS (kPa; odds ratio [OR] 1.49, 95% confidence interval [CI] 1.12 to 1.98, p=0.006), low serum albumin (≤3.8 g/dL; OR 15.89, 95% CI 2.41 to 104.82, p=0.004), major hepatic resection (OR 4.16, 95% CI 1.40 to 12.38, p=0.014), higher albumin-bilirubin score (>-0.55; OR 3.72, 95% CI 1.15 to 12.04, p=0.028), and higher serum α-fetoprotein (>100 ng/mL; OR 3.53, 95% CI 1.20 to 10.40, p=0.022) were identified as independent risk factors for PHLF. A risk prediction model for PHLF was established using the multivariate logistic regression equation. The area under the receiver operating characteristic curve (AUC) of the risk prediction model was 0.877 for predicting PHLF and 0.923 for predicting grade B and C PHLF. In leave-one-out cross-validation, the risk model showed good performance, with AUCs of 0.807 for all-grade PHLF and 0. 871 for grade B and C PHLF.

Conclusions

Our novel MRE-LS-based risk model had excellent performance in predicting PHLF, especially grade B and C PHLF.

ALBI grade: Evidence for an improved model for liver functional estimation in patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) usually arises in the context of a chronically damaged liver. Liver functional estimation is of paramount importance in clinical decision making. The Child-Pugh score (CPS) can be used to categorise patients into 3 classes (A to C) based on the severity of liver functional impairment according to 5 parameters (albumin, bilirubin, prothrombin time, presence of ascites and hepatic encephalopathy). The albumin-bilirubin (ALBI) grade has emerged as an alternative, reproducible and objective measure of liver functional reserve in patients with HCC, defining worsening liver impairment across 3 grades (I to III). The ALBI score can identify different subgroups of patients with different prognoses across the diverse Barcelona Clinic Liver Cancer stages and CP classes, making it an appealing clinical predictor. In patients treated with potentially curative approaches (resection, transplantation, radiofrequency ablation, microwave ablation), ALBI grade has been shown to correlate with survival, tumour relapse, and post-hepatectomy liver failure. ALBI grade also predicts survival, toxicity and post-procedural liver failure in patients treated with transarterial chemoembolisation, radioembolisation, external beam radiotherapy as well as multi-kinase inhibitors (sorafenib, lenvatinib, cabozantinib, regorafenib) and immune checkpoint inhibitor therapy. In this review, we summarise the body of evidence surrounding the role of ALBI grade as a biomarker capable of optimising patient selection and therapeutic sequencing in HCC.

A Nomogram Based on Preoperative Inflammatory Indices and ICG-R15 for Prediction of Liver Failure After Hepatectomy in HCC Patients

Objective

To establish a nomogram based on inflammatory indices and ICG-R15 for predicting post-hepatectomy liver failure (PHLF) among patients with resectable hepatocellular carcinoma (HCC).

Methods

A retrospective cohort of 407 patients with HCC hospitalized at Xiangya Hospital of Central South University between January 2015 and December 2020, and 81 patients with HCC hospitalized at the Second Xiangya Hospital of Central South University between January 2019 and January 2020 were included in the study. Totally 488 HCC patients were divided into the training cohort (n=378) and the validation cohort (n=110) by random sampling. Univariate and multivariate analysis was performed to identify the independent risk factors. Through combining these independent risk factors, a nomogram was established for the prediction of PHLF. The accuracy of the nomogram was evaluated and compared with traditional models, like CP score (Child-Pugh), MELD score (Model of End-Stage Liver Disease), and ALBI score (albumin-bilirubin) by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

Cirrhosis (OR=2.203, 95%CI:1.070-3.824, P=0.030), prothrombin time (PT) (OR=1.886, 95%CI: 1.107-3.211, P=0.020), tumor size (OR=1.107, 95%CI: 1.022-1.200, P=0.013), ICG-R15% (OR=1.141, 95%CI: 1.070-1.216, P<0.001), blood loss (OR=2.415, 95%CI: 1.306-4.468, P=0.005) and AST-to-platelet ratio index (APRI) (OR=4.652, 95%CI: 1.432-15.112, P=0.011) were independent risk factors of PHLF. Nomogram was built with well-fitted calibration curves on the of these 6 factors. Comparing with CP score (C-index=0.582, 95%CI, 0.523-0.640), ALBI score (C-index=0.670, 95%CI, 0.615-0.725) and MELD score (C-ibasedndex=0.661, 95%CI, 0.606-0.716), the nomogram showed a better predictive value, with a C-index of 0.845 (95%CI, 0.806-0.884). The results were consistent in the validation cohort. DCA confirmed the conclusion as well.

Conclusion

A novel nomogram was established to predict PHLF in HCC patients. The nomogram showed a strong predictive efficiency and would be a convenient tool for us to facilitate clinical decisions.

Prognostic Value of the Albumin-Bilirubin Grade for the Prediction of Post-Hepatectomy Liver Failure: A Systematic Review and Meta-Analysis

(1) Introduction: Liver resection (LR) for hepatocellular carcinoma (HCC) is often burdened by life-threatening complications, such as post-hepatectomy liver failure (PHLF). The albumin-bilirubin (ALBI) score can accurately evaluate liver function and the long-term prognosis of HCC patients, including PHLF. We aimed to evaluate the diagnostic value of the ALBI grade in predicting PHLF in HCC patients undergoing LR. (2) Methods: MEDLINE, Embase, and Scopus were searched through January 17th, 2021. Studies reporting the ALBI grade and PHLF occurrence in HCC patients undergoing LR were included. The Odds Ratio (OR) prevalence with 95% confidence intervals (CI) was pooled, and the heterogeneity was expressed as I2. The quality of the studies was assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies). (3) Results: Seven studies met the inclusion criteria and were included in the analysis. A total of 5377 patients who underwent LR for HCC were considered, of whom 718 (13.4%) developed PHLF. Patients with ALBI grades 2 and 3 before LR showed increased rates of PHLF compared to ALBI grade 1 patients. The pooled OR was 2.572 (95% CI, 1.825 to 3.626, p < 0.001), with substantial heterogeneity between the studies (I2 = 69.6%) and no publication bias (Begg's p = 0.764 and Egger's p = 0.851 tests). All studies were at a 'low risk' or 'unclear risk' of bias. Univariate meta-regression analysis showed that heterogeneity was not dependent on the country of study, the age and sex of the participants, the definition of PHLF used, the rate of patients in Child-Pugh class A or undergoing major hepatectomy. (4) Conclusions: In this meta-analysis of published studies, individuals with ALBI grades of 2 and 3 showed increased rates of PHLF compared to ALBI grade 1 patients.

Development and Validation of a Nomogram Based on Noninvasive Liver Reserve and Fibrosis (PALBI and FIB-4) Model to Predict Posthepatectomy Liver Failure Grade B-C in Patients with Hepatocellular Carcinoma

Hepatectomy is currently one of the most effective treatments for hepatocellular carcinoma (HCC). However, postoperative liver failure (PHLF) is a serious complication and the leading cause of mortality in patients with HCC after hepatectomy. This study attempted to develop a novel nomogram based on noninvasive liver reserve and fibrosis models, platelet-albumin-bilirubin grade (PALBI) and fibrosis-4 index (FIB-4), able to predict PHLF grade B-C. This was a single-centre retrospective study of 574 patients with HCC undergoing hepatectomy between 2014 and 2018. The independent risk factors of PHLF were screened using univariate and multivariate logistic regression analyses. Multivariate logistic regression was performed using the training set, and the nomogram was developed and visualised. The utility of the model was evaluated in a validation set using the receiver operating characteristic (ROC) curve. A total of 574 HCC patients were included (383 in the training set and 191 for the validation set) and included PHLF grade B-C complications of 14.8, 15.4, and 13.6%, respectively. Overall, cirrhosis (P < 0.026, OR = 2.296, 95% confidence interval (CI) 1.1.02-4.786), major hepatectomy (P=0.031, OR = 2.211, 95% CI 1.077-4.542), ascites (P=0.014, OR = 3.588, 95% 1.299-9.913), intraoperative blood loss (P < 0.001, OR = 4.683, 95% CI 2.281-9.616), PALBI score >-2.53 (, OR = 3.609, 95% CI 1.486-8.764), and FIB-4 score ≥1.45 (P < 0.001, OR = 5.267, 95% CI 2.077-13.351) were identified as independent risk factors associated with PHLF grade B-C in the training set. The areas under the ROC curves for the nomogram model in predicting PHLF grade B-C were significant for both the training and validation sets (0.832 vs 0.803). The proposed nomogram predicted PHLF grade B-C among patients with HCC with a better prognostic accuracy than other currently available fibrosis and noninvasive liver reserve models.