Gastroenterology
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General Hepatology
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Easy to apply, uses parameters commonly measured in outpatient and inpatient settings
by
Mike Bassett, Staff Writer, MedPage Today
October 25, 2024
A risk stratification algorithm may be able to predict which patients with advanced chronic liver disease are at risk of developing hepatocellular carcinoma (HCC), according to a large-scale multicenter study.
Among more than 2,300 patients with advanced chronic liver disease, a six-parameter algorithm, known as PLEASE, indicated that patients stratified as high risk had a cumulative risk of developing de novo HCC within 2 years of 15.6% versus 1.7% among those in the low-risk category, reported Jonel Trebicka, MD, PhD, of Münster University in Germany, and colleagues.
Comprising the six parameters were platelet count less than 150 × 109/L, liver stiffness measurement (LSM) greater than or equal to 15 kPa, age greater than or equal to 50 years, male sex, controlled/uncontrolled viral hepatitis, and presence of steatotic liver diseases. Patients were considered to be high risk if they had four or more parameters.
“Based on our new algorithm, we speculate that patients at high risk for de novo HCC should undergo more frequent HCC screening, whereas those at lower risk could be managed with a longer screening interval,” Trebicka and team wrote in NEJM Evidence . “This idea needs to be tested prospectively and our algorithm provides both the means and clinical equipoise to do so.”
“Overall, our algorithm is easy to apply, with five parameters (platelet count, etiology, age, sex, and elastography) that are commonly measured or recorded in patients with chronic liver disease, both in the outpatient and the inpatient setting,” they added.
In an editorial accompanying the study, Stephen L. Chan, MD, of the Chinese University of Hong Kong, and colleagues noted that existing screening programs for other cancer types have adopted risk-based screening approaches that have been shown to improve outcomes and cost-effectiveness.
Therefore this study “is timely in developing a validated algorithm to stratify patients into high- and low-risk categories of de novo HCC development, and thereby provides an important foundation for future prospective studies to examine the clinical effectiveness of risk-based surveillance.”
However, Chan and colleagues also pointed out that improvements in stratifying risk in HCC surveillance need to be accompanied by better adherence to surveillance.
For example, they noted that a U.S. multicohort study showed that just 14% of patients received semi-annual surveillance, and that two-thirds had no surveillance, prior to a diagnosis of HCC, with the lack of surveillance most likely due to the failure of patients to arrange surveillance and adhere to surveillance programs.
“Strategies to enhance adherence and increase awareness of the value of screening should also be integrated into the design of future risk-based surveillance programs, in order to truly realize their benefits in the real world,” they wrote.
For this study, Trebicka and colleagues included 2,340 patients from 17 centers in Germany and China. Patients were eligible if they had valid baseline LSM by two-dimensional shear-wave elastography, comprehensive baseline laboratory results with platelet count, and a minimum of 6 months of follow-up data available to monitor for the development of de novo HCC.
HCC developed in 5.4% of patients during a follow-up of 13.7 months. Higher LSM (HR 2.28, 95% CI 1.67-3.12), lower platelet count (HR 0.46, 95% CI 0.31-0.68), diagnosis of viral hepatitis (HR 6.80, 95% CI 3.71-12.49), steatotic liver disease (HR 2.07, 95% CI 1.09-3.95), male sex (HR 1.70, 95% CI 1.02-2.82), and older age (HR 1.04, 95% CI 1.02-1.06) were all found to be independently associated with a risk of developing de novo HCC in the multivariable regression model.
Trebicka and colleagues further validated their algorithm using LSM across different elastography techniques, as well as with a single-center cohort in Vienna, Austria, “indicating that the algorithm could be used in real clinical practice in different hospital settings, with differences in equipment and patients.”
Mike Bassett is a staff writer focusing on oncology and hematology. He is based in Massachusetts.
Disclosures
This study was supported by the German Research Foundation; the German Federal Ministry of Education and Research for the DEEP-HCC project; and the Hessian Ministry of Higher Education, Research, and the Arts for the ENABLE and ACLF-I cluster projects.
Trebicka had no disclosures.
Several co-authors reported relationships with industry.
Chan reported relationships with Celleron Therapeutics, Genor Biopharma, Ipsen, MSD, Novartis, Autem Therapeutics, AstraZeneca, Bayer, Bristol Myers Squibb, Eisai, Hutchmed, and Roche.
Co-editorialists reported multiple relationships with industry.
Primary Source
NEJM Evidence
Source Reference: Gu W, et al “Hepatocellular cancer surveillance in patients with advanced chronic liver disease” NEJM Evid 2024; DOI: 10.1056/EVIDoa2400062.
Secondary Source
NEJM Evidence
Source Reference: Chan LL, et al “A new predictive algorithm toward risk-based surveillance for liver cancer” NEJM Evid 2024; DOI: 10.1056/EVIDe2400344.
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