2D shear wave elastography combined with MELD improved prognostic accuracy in patients with acute-on-chronic hepatitis B liver failure

Shear wave elastography-based deep learning model for prognosis of patients with acutely decompensated cirrhosis

PURPOSE

This study aimed to develop and validate a deep learning model based on two-dimensional (2D) shear wave elastography (SWE) for predicting prognosis in patients with acutely decompensated cirrhosis.

METHODS

We prospectively enrolled 288 acutely decompensated cirrhosis patients with a minimum 1-year follow-up, divided into a training cohort (202 patients, 1010 2D SWE images) and a test cohort (86 patients, 430 2D SWE images). Using transfer learning by Resnet-50 to analyze 2D SWE images, a SWE-based deep learning signature (DLswe) was developed for 1-year mortality prediction. A combined nomogram was established by incorporating deep learning SWE information and laboratory data through a multivariate Cox regression analysis. The performance of the nomogram was evaluated with respect to predictive discrimination, calibration, and clinical usefulness in the training and test cohorts.

RESULTS

The C-index for DLswe was 0.748 (95% CI 0.666-0.829) and 0.744 (95% CI 0.623-0.864) in the training and test cohorts, respectively. The combined nomogram significantly improved the C-index, accuracy, sensitivity, and specificity of DLswe to 0.823 (95% CI 0.763-0.883), 86%, 75%, and 89% in the training cohort, and 0.808 (95% CI 0.707-0.909), 83%, 74%, and 85% in the test cohort (both p < 0.05). Calibration curves demonstrated good calibration of the combined nomogram. Decision curve analysis indicated that the nomogram was clinically valuable.

CONCLUSIONS

The 2D SWE-based deep learning model holds promise as a noninvasive tool to capture valuable prognostic information, thereby improving outcome prediction in patients with acutely decompensated cirrhosis.

Research progress on short-term prognosis of acute-on-chronic liver failure

INTRODUCTION

Acute-on-chronic liver failure (ACLF) is a clinical syndrome characterized as a severe condition with rapid progression, poor therapeutic response and poor prognosis. Early and timely evaluation of the prognosis is helpful for providing appropriate clinical intervention and prolonging patient survival.

AREAS COVERED

Currently, there are no specific dynamic and comprehensive approaches to assess the prognosis of patients with ACLF. This article reviews the progress in evaluating the short-term prognosis of ACLF to provide future directions for more dynamic prospective large-scale multicenter studies and a basis for individualized and precise treatment for ACLF patients. We searched PubMed and Web of Science with the term 'acute on chronic liver failure' and 'prognosis.' There was no date or language restriction, and our final search was on 26 October 2022.

EXPERT OPINION

ACLF is a dynamic process, and the best prognostic marker is the clinical evolution of organ failure over time. New prognostic markers are developing not only in the fields of genetics and histology but also toward diversification combined with imaging. Determining which patients will benefit from continued advanced life support is a formidable challenge, and accurate short-term prognostic assessments of ACLF are a good approach to addressing this issue.

Factors other than fibrosis that increase measured shear wave velocity

Shear wave elastography (SWE) is now becoming an indispensable diagnostic tool in the routine examination of liver diseases. In particular, accuracy is required for shear wave propagation velocity measurement, which is directly related to diagnostic accuracy. It is generally accepted that the liver shear wave propagation velocity reflects the degree of fibrosis, but there are still few reports on other factors that increase the shear wave propagation velocity. In this study, we reviewed such factors in the literature and examined their mechanisms. Current SWE measures propagation velocity based on the assumption that the medium has a homogeneous structure, uniform density, and is purely elastic. Otherwise, the measurement is subject to error. The other (confounding) factors that we routinely experience are primarily: (1) Conditions that appear to increase the viscous component; and (2) Conditions that appear to increase tissue density. Clinically, the former includes acute hepatitis, congested liver, biliary obstruction, etc, and the latter includes diffuse infiltration of malignant cells, various storage diseases, tissue necrosis, etc. In any case, it is important to evaluate SWE in the context of the entire clinical picture.

A Prognostic Nomogram with High Accuracy Based on 2D-SWE in Patients with Acute-on-chronic Liver Failure

BACKGROUND AND AIMS

Acute-on-chronic liver failure (ACLF) is associated with very high mortality. Accurate prediction of prognosis is critical in navigating optimal treatment decisions to improve patient survival. This study was aimed to develop a new nomogram integrating two-dimensional shear wave elastography (2D-SWE) values with other independent prognostic factors to improve the precision of predicting ACLF patient outcomes.

METHODS

A total of 449 consecutive patients with ACLF were recruited and randomly allocated to a training cohort (n=315) or a test cohort (n=134). 2D-SWE values, conventional ultrasound features, laboratory tests, and other clinical characteristics were included in univariate and multivariate analysis. Factors with prognostic value were then used to construct a novel prognostic nomogram. Receiver operating curves (ROCs) were generated to evaluate and compare the performance of the novel and published models including the Model for End-Stage Liver Disease (MELD), MELD combined with sodium (MELD-Na), and Jin's model. The model was validated in a prospective cohort (n=102).

RESULTS

A ACLF prognostic nomogram was developed with independent prognostic factors, including 2D-SWE, age, total bilirubin (TB), neutrophils (Neu), and the international normalized ratio (INR). The area under the ROC curve (AUC) was 0.849 for the new model in the training cohort and 0.861 in the prospective validation cohort, which were significantly greater than those for MELD (0.758), MELD-Na (0.750), and Jin's model (0.777, all p <0.05). Calibration curve analysis revealed good agreement between the predicted and observed probabilities. The new nomogram had superior overall net benefit and clinical utility.

CONCLUSIONS

We established and validated a 2D-SWE-based noninvasive nomogram to predict the prognosis of ACLF patients that was more accurate than other prognostic models.

Potential Gut Microbiota Features for Non-Invasive Detection of Schistosomiasis

The gut microbiota has been identified as a predictive biomarker for various diseases. However, few studies focused on the diagnostic accuracy of gut microbiota derived-signature for predicting hepatic injuries in schistosomiasis. Here, we characterized the gut microbiomes from 94 human and mouse stool samples using 16S rRNA gene sequencing. The diversity and composition of gut microbiomes in Schistosoma japonicum infection-induced disease changed significantly. Gut microbes, such as Bacteroides, Blautia, Enterococcus, Alloprevotella, Parabacteroides and Mucispirillum, showed a significant correlation with the level of hepatic granuloma, fibrosis, hydroxyproline, ALT or AST in S. japonicum infection-induced disease. We identified a range of gut bacterial features to distinguish schistosomiasis from hepatic injuries using the random forest classifier model, LEfSe and STAMP analysis. Significant features Bacteroides, Blautia, and Enterococcus and their combinations have a robust predictive accuracy (AUC: from 0.8182 to 0.9639) for detecting liver injuries induced by S. japonicum infection in humans and mice. Our study revealed associations between gut microbiota features and physiopathology and serological shifts of schistosomiasis and provided preliminary evidence for novel gut microbiota-derived features for the non-invasive detection of schistosomiasis.

A model based on two-dimensional shear wave elastography for acute-on-chronic liver failure development in patients with acutely decompensated hepatitis B cirrhosis

BACKGROUND

To evaluate the accuracy of two-dimensional (2D) shear wave elastography (SWE), develop and validate a novel prognostic model in predicting acute-on-chronic liver failure (ACLF) development in patients with acutely decompensated hepatitis B cirrhosis.

METHODS

This prospective cohort study enrolled 221 patients in the First Affiliated Hospital of Nanchang University from September 2019 to January 2021, and randomly assigned them to the derivation and validation cohorts (7:3 ratio). Ultrasound, 2D SWE, clinical and laboratory data were collected, and outcome (ACLF developed) was recorded during a 90-day follow-up period. We evaluated the ability of 2D SWE to predict the outcome, developed a model for predicting ACLF development in the derivation cohort, and assessed the model in the validation cohort.

RESULTS

2D SWE values were significantly higher in patients with ACLF development (P<0.05). The accuracy of 2D SWE in predicting the outcome was better than that of serum parameters of liver fibrosis (all P<0.05). The SWE model for ACLF development had good calibration and discrimination [concordance index (C-index): 0.855 and 0.840 respectively] in derivation and validation cohorts, outperforming serum prognostic scores (all P<0.05).

CONCLUSIONS

The SWE model, superior to serum prognostic scores in predicting ACLF development, could be a noninvasive tool to guide the individual management of patients with acutely decompensated hepatitis B cirrhosis.

Shear-Wave Dispersion Slope of the Liver: Effect of Study Protocol and Ascites on the Measurement Applicability

This study aimed to evaluate the shear-wave dispersion (SWD) scanning protocol including the minimum number of measurements and better size of the region of interest (ROI), as well as the influence of ascites on the measurement applicability. Patients who had undergone serial SWD examinations between July 2019 and December 2020 were included. In patients with chronic liver disease (group A), two different ROI sizes were applied, and at least 10 measurements were repeated to determine the minimum number of measurements and better ROI size. In patients with liver failure (group B), failure and unreliable results were compared between patients with and without ascites. A minimum of five measurements when using a 20-mm ROI and six measurements when using a 10-mm ROI were required. Compared with using a 20-mm ROI, a 10-mm ROI showed a higher unreliable rate. The failure and unreliable rates of SWD in patients with ascites were significantly higher than those in patients without ascites. SWD examination required at least five measurements when using a 20-mm ROI and six measurements when using a 10-mm ROI. A larger ROI was associated with higher reliability, and ascites influenced the failure and reliability of the SWD measurement.

Comparison between preoperative two-dimensional shear wave elastography and indocyanine green clearance test for prediction of post-hepatectomy liver failure

Background

Post-hepatectomy liver failure (PHLF) is one of the most serious complications and major causes of liver resection mortality. The purpose of this study is to investigate and compare the performance of preoperative two-dimensional shear wave elastography (2D-SWE) and the indocyanine green (ICG) clearance test for the prediction of PHLF.

Methods

A total of 172 consecutive patients who were undergoing major liver resection were prospectively identified. Patients were evaluated by preoperative 2D-SWE and ICG clearance test. According to the International Study Group of Liver Surgery (ISGLS) recommendations, No PHLF, PHLF A, PHLF B, and PHLF C group classifications were defined. The differences in liver stiffness value (LSV) and ICG retention rate at 15 minutes (ICGR15) among the different PHLF classifications were investigated. The performance of LSV and ICGR15 for diagnosing different classifications of PHLF was compared.

Results

PHLF occurred in 43 (25.0%) patients, and 24 (14.0%) patients were grade A, 14 (8.1%) were grade B, and 5 (2.9%) were grade C. Both LSV and ICGR15 of the PHLF C group were significantly higher than those of the No PHLF group (P=0.025, P=0.001, respectively). According to univariate and multivariate logistic regression analysis, LSV and ICGR15 were significantly related to PHLF (P=0.051, P=0.084, respectively). For diagnosis of ≥ PHLF A, ≥ PHLF B, and ≥ PHLF C, the areas under the receiver operating characteristic curve (AUCs) for 2D-SWE were 0.624 [95% confidence interval (CI): 0.536-0.712, P=0.015], 0.699 (95% CI: 0.576-0.821, P=0.005), and 0.831 (95% CI: 0.737-0.925, P=0.01), respectively. The AUCs of the ICG clearance test were 0.631 (95% CI: 0.542-0.721, P=0.01), 0.570 (95% CI: 0.436-0.704, P=0.32), and 0.717 (95% CI: 0.515-0.920, P=0.098), respectively. The AUC of LSV for the diagnosis of ≥ PHLF A was comparable to that of ICGR15 (P=0.17). The AUCs of LSV were significantly higher than those of ICGR15 for the diagnosis of ≥ PHLF B (P=0.002) and C (P=0.038).

Conclusions

2D-SWE demonstrates the potential to aid in the prediction of the severity of PHLF. Our findings also suggest that the performance of 2D-SWE is better than the ICG clearance test.