A Phase 2 Randomized Trial of Survodutide in MASH and Fibrosis

Opportunistic Screening of Chronic Liver Disease with Deep Learning Enhanced Echocardiography

Importance

Chronic liver disease affects more than 1.5 billion adults worldwide, however the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; but this information is not leveraged.

Objective

To develop and evaluate a deep learning algorithm on echocardiography videos to enable opportunistic screening for chronic liver disease.

Design

Retrospective observational cohorts.

Setting

Two large urban academic medical centers.

Participants

Adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests, between July 4, 2012, to June 4, 2022.

Exposure

Deep learning model predictions from a deep-learning computer vision pipeline that identifies subcostal view echocardiogram videos and detects the presence of cirrhosis or steatotic liver disease (SLD).

Main Outcome and Measures

Clinical diagnosis by paired abdominal ultrasound or magnetic resonance imaging (MRI).

Results

A total of 1,596,640 echocardiogram videos (66,922 studies from 24,276 patients) from Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver, an automated pipeline that identifies high quality subcostal images from echocardiogram studies and detects the presence of cirrhosis or SLD. In the held-out CSMC test cohort, EchoNet-Liver was able to detect the presence of cirrhosis with an AUC of 0.837 (0.789 - 0.880) and SLD with an AUC of 0.799 (0.758 - 0.837). In a separate test cohort with paired abdominal MRIs, cirrhosis was detected with an AUC of 0.704 (0.689-0.718) and SLD was detected with an AUC of 0.726 (0.659-0.790). In an external test cohort of 106 patients (n = 5,280 videos), the model detected cirrhosis with an AUC of 0.830 (0.738 - 0.909) and SLD with an AUC of 0.768 (0.652 - 0.875).

Conclusions and Relevance

Deep learning assessment of clinical echocardiography enables opportunistic screening of SLD and cirrhosis. Application of this algorithm may identify patients who may benefit from further diagnostic testing and treatment for chronic liver disease.

EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD)

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed non-alcoholic fatty liver disease (NAFLD), is defined as steatotic liver disease (SLD) in the presence of one or more cardiometabolic risk factor(s) and the absence of harmful alcohol intake. The spectrum of MASLD includes steatosis, metabolic dysfunction-associated steatohepatitis (MASH, previously NASH), fibrosis, cirrhosis and MASH-related hepatocellular carcinoma (HCC). This joint EASL-EASD-EASO guideline provides an update on definitions, prevention, screening, diagnosis and treatment for MASLD. Case-finding strategies for MASLD with liver fibrosis, using non-invasive tests, should be applied in individuals with cardiometabolic risk factors, abnormal liver enzymes, and/or radiological signs of hepatic steatosis, particularly in the presence of type 2 diabetes (T2D) or obesity with additional metabolic risk factor(s). A stepwise approach using blood-based scores (such as FIB-4) and, sequentially, imaging techniques (such as transient elastography) is suitable to rule-out/in advanced fibrosis, which is predictive of liver-related outcomes. In adults with MASLD, lifestyle modification - including weight loss, dietary changes, physical exercise and discouraging alcohol consumption - as well as optimal management of comorbidities - including use of incretin-based therapies (e.g. semaglutide, tirzepatide) for T2D or obesity, if indicated - is advised. Bariatric surgery is also an option in individuals with MASLD and obesity. If locally approved and dependent on the label, adults with non-cirrhotic MASH and significant liver fibrosis (stage ≥2) should be considered for a MASH-targeted treatment with resmetirom, which demonstrated histological effectiveness on steatohepatitis and fibrosis with an acceptable safety and tolerability profile. No MASH-targeted pharmacotherapy can currently be recommended for the cirrhotic stage. Management of MASH-related cirrhosis includes adaptations of metabolic drugs, nutritional counselling, surveillance for portal hypertension and HCC, as well as liver transplantation in decompensated cirrhosis.

Pharmacotherapeutic options for metabolic dysfunction-associated steatotic liver disease: where are we today?

INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD) is defined by hepatic steatosis and cardiometabolic risk factors like obesity, type 2 diabetes, and dyslipidemia. Persistent metabolic injury may promote inflammatory processes resulting in metabolic dysfunction-associated steatohepatitis (MASH) and liver fibrosis. Mechanistic insights helped to identify potential drug targets, thereby supporting the development of novel compounds modulating disease drivers.

AREAS COVERED

The U.S. Food and Drug Administration has recently approved the thyroid hormone receptor β-selective thyromimetic resmetirom as the first compound to treat MASH and liver fibrosis. This review provides a comprehensive overview of current and potential future pharmacotherapeutic options and their modes of action. Lessons learned from terminated clinical trials are discussed together with the first results of trials investigating novel combinational therapeutic approaches.

EXPERT OPINION

Approval of resmetirom as the first anti-MASH agent may revolutionize the therapeutic landscape. However, long-term efficacy and safety data for resmetirom are currently lacking. In addition, heterogeneity of MASLD reflects a major challenge to define effective agents. Several lead compounds demonstrated efficacy in reducing obesity and hepatic steatosis, while anti-inflammatory and antifibrotic effects of monotherapy appear less robust. Better mechanistic understanding, exploration of combination therapies, and patient stratification hold great promise for MASLD therapy.