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

A nomogram for predicting prognosis in patients with transjugular intrahepatic portosystemic shunt creation based on deep learning-derived spleen volume-to-platelet ratio

OBJECTIVES

The objective of our study was to develop a nomogram to predict post-transjugular intrahepatic portosystemic shunt (TIPS) survival in patients with cirrhosis based on CT images.

METHODS

This retrospective cohort study included patients who had received TIPS operation at the Wenzhou Medical University First Affiliated Hospital between November 2013 and April 2017. To predict prognosis, a nomogram and Web-based probability were developed to assess the overall survival (OS) rates at 1, 3, and 5 years based on multivariate analyses. With deep learning algorithm, the automated measurement of liver and spleen volumes can be realized. We assessed the predictive accuracy and discriminative ability of the nomogram using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

RESULTS

Age, total bilirubin, and spleen volume-to-platelet ratio (SVPR) were identified as the independent risk factors for OS. The nomogram was constructed based on the above risk factors. The C-index (0.80, 0.74, 0.70), ROC curve (area under curve: 0.828, 0.761, 0.729), calibration curve, and DCA showed that nomogram good at predictive value, stability, and clinical benefit in the prediction of 1-, 3-, 5-year OS in patients with TIPS creation.

CONCLUSIONS

We constructed a nomogram for predicting prognosis in patients with TIPS creation based on risk factors. The nomogram can help clinicians in identifying patients with poor prognosis, eventually facilitating earlier treatment and selecting suitable patients before TIPS.

ADVANCES IN KNOWLEDGE

This study developed the first nomogram based on SVPR to predict the prognosis of patients treated with TIPS. The nomogram could help clinician in non-invasive decision-making.

Interpretations of the Role of Plasma Albumin in Prognostic Indices: A Literature Review

This review assesses how publications interpret factors that influence the serum or plasma albumin (PA) level in prognostic indices, focusing on inflammation and nutrition. On PubMed, a search for "albumin AND prognosis" yielded 23,919 results. From these records, prognostic indices were retrieved, and their names were used as search strings on PubMed. Indices found in 10 or more original research articles were included. The same search strings, restricted to "Review" or "Systematic review", retrieved yielded on the indices. The data comprised the 10 latest original research articles and up to 10 of the latest reviews. Thirty indices had 294 original research articles (6 covering two indices) and 131 reviews, most of which were from recent years. A total of 106 articles related the PA level to inflammation, and 136 related the PA level to nutrition. For the reviews, the equivalent numbers were 54 and 65. In conclusion, more publications mention the PA level as a marker of nutrition rather than inflammation. This is in contrast to several general reviews on albumin and nutritional guidelines, which state that the PA level is a marker of inflammation but not nutrition. Hypoalbuminemia should prompt clinicians to focus on the inflammatory aspects in their patients.

The Prognostic Value of Preoperative Serum Markers and Risk Classification in Patients with Hepatocellular Carcinoma

BACKGROUND

Complex hepatocellular carcinoma (HCC) prognostic biomarkers have been reported in various studies. We aimed to establish biomarkers that could predict prognosis, and formulate a simple classification using non-invasive preoperative blood test data.

METHODS

We retrospectively identified 305 patients for a discovery cohort who had undergone HCC-related hepatectomy at four Japanese university hospitals between January 1, 2011 and December 31, 2013. Preoperative blood test parameter optimal cut-off values were determined using receiver operating characteristic curve analysis. Cox uni- and multivariate analyses were used to determine independent prognostic factors. Risk classifications were established using classification and regression tree (CART) analysis. Validation was performed with 267 patients from three other hospitals.

RESULTS

In multivariate analysis, α-fetoprotein (AFP, p < 0.001), protein induced by vitamin K absence or antagonist-II (PIVKA-II, p = 0.006), and C-reactive protein (CRP, p < 0.001) were independent prognostic factors for overall survival (OS). AFP (p = 0.007), total bilirubin (p = 0.001), and CRP (p = 0.003) were independent recurrent risk factors for recurrence-free survival (RFS). CART analysis results formed OS (CRP, AFP, and albumin) and RFS (PIVKA-II, CRP, and total bilirubin) decision trees, based on machine learning using preoperative serum markers, with three risk classifications. Five-year OS (low risk, 80.0%; moderate risk, 56.3%; high risk, 25.2%; p < 0.001) and RFS (low risk, 43.4%; moderate risk, 30.8%; high risk, 16.6%; p < 0.001) risks differed significantly. These classifications also stratified OS and RFS risk in the validation cohort.

CONCLUSION

Three simple risk classifications using preoperative non-invasive prognostic factors could predict prognosis.

Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma

Purpose

To investigate the risk factors for recurrence in patients with early-stage hepatocellular carcinoma (HCC) after minimally invasive treatment with curative intent, then to construct a prediction model based on Lasso-Cox regression and visualize the model built.

Methods

Clinical data were collected from 547 patients that received minimally invasive treatment in our hospital from January 1, 2012, to December 31, 2016. Lasso regression was used to screen risk factors for recurrence. Then we established Cox proportional hazard regression model and random survival forest model including several parameters screened by Lasso regression. An optimal model was selected by comparing the values of C-index, then the model was visualized and the nomogram was finally plotted.

Results

The variables screened by Lasso regression including age, gender, cirrhosis, tumor number, tumor size, platelet-albumin-bilirubin index (PALBI), and viral load were incorporated in the Cox model and random survival forest model (P<0.05). The C-index of these two models in the training sets was 0.729 and 0.708, and was 0.726 and 0.700 in the validation sets, respectively. So we finally chose Lasso-Cox regression model, and the calibration curve in the validation set performed well, indicating that the model built has a better predictive ability. And then a nomogram was plotted based on the model chosen to visualize the results.

Conclusions

The present study established a nomogram for predicting recurrence in patients with early-stage HCC based on the Lasso-Cox regression model. This nomogram was of some guiding significance for screening populations at high risk of recurrence after treatment, by which doctors can formulate individualized follow-up strategies or treatment protocols according to the predicted risk of relapse for patients to improve the long-term prognosis.

Albumin-bilirubin score is superior to platelet-albumin-bilirubin score and model for end-state liver disease sodium for predicting posthepatectomy liver failure

BACKGROUND

Risk stratification for patients undergoing hepatectomy can be attempted using established models. This study compares the platelet-albumin-bilirubin (PALBI) score with albumin-bilirubin (ALBI) and model for end-stage liver disease sodium (MELD-Na) for predicting posthepatectomy liver failure (PHLF) and 30-day mortality.

METHODS

The 2014-2018 NSQIP database was queried for patients who underwent elective hepatectomy. Multivariable logistic regressions assessed associations of posthepatectomy outcomes with patient and clinical characteristics. Predictive accuracy of the grading systems was evaluated using receiver operator characteristic (ROC) curves and calculating area under the curve (AUC).

RESULTS

Severe PHLF (Grade B/C) and mortality were present in 2.58% (N = 369) and 1.2% (N = 171) of patients who underwent hepatectomy (N = 13 925), respectively. ALBI Grade 2/3 had a stronger association with severe PHLF (odds ratio [OR] = 1.62, p < 0.01) and mortality (OR = 2.06, p < 0.005) than PALBI Grade 2/3 (OR = 1.14, p = 0.43 for PHLF and OR = 2.01, p < 0.005 for mortality) or MELD-Na ≥10 (OR = 1.29, p = 0.25 for PHLF and OR = 1.84, p < 0.03). ALBI had a higher AUC (0.671) than PALBI (0.625) and MELD-Na (0.627) for predicting severe PHLF. ALBI had a higher AUC (0.695) than PALBI (0.642) for predicting 30-day mortality.

CONCLUSIONS

ALBI was a more accurate predictor of severe PHLF and 30-day mortality than MELD-Na and PALBI for patients who underwent hepatectomy.

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.