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A new risk score model for hepatocellular carcinoma using standard clinical data can better identify individuals at risk better than the current standard risk models.
The Fibrosis-4 Index (FIB-4) is used to identify adults who don’t have viral hepatitis or decompensated cirrhosis who are at risk of hepatocellular carcinoma (HCC), but a new risk score has been created using routinely available clinical data, according to a study in JAMA Network Open.1
Improving the understanding of risk can allow clinicians to better personalize treatment approaches or catch HCC early enough for treatment.
Because most risk models for HCC focus on patients who have viral hepatitis or diagnosed cirrhosis or use variables that are not routinely available in clinical care, the researchers sought to develop a better risk score for those patients who do not have viral hepatitis or cirrhosis.
In the US, viral HCC has decreased while HCC incidence due to chronic alcohol-associated liver disease and metabolic dysfunction–associated steatotic liver disease (MASLD) is increasing, the authors noted. More than a third (38%) of adults globally have MASLD, which is expected to increase to more than 55% by 2040.2
There are currently no treatments approved for MASLD, formerly known as nonalcoholic fatty liver disease. Instead, lifestyle changes are the only treatment available.3 MASLD can progress to metabolic dysfunction–associated steatohepatitis, for which there is now an FDA-approved therapy4; cirrhosis, and ultimately, HCC.
In addition, a substantial number of patients do not have cirrhosis at the time of HCC diagnosis or they have unrecognized cirrhosis. “Thus, current guidelines focusing on early HCC detection may be missing opportunities for screening, early detection, and primary prevention targeting modifiable risk factors in a high-risk population,” the authors noted.
They used the US Department of Veterans Affairs (VA) electronic health records to analyze demographic, clinical, laboratory, and diagnostic data for veterans aged 30 to 95 years. The study included more than 6.5 million veterans, 92.9% of whom were male with a median (IQR) age of 65 (54-74) years. The researchers divided data into development (n = 5,119,775) and validation (n = 1,389,513) samples.
Ultimately, 0.2% developed HCC. Of those who did develop HCC, 69.5% had a FIB-4 of 3.25 (considered a positive predictive value) or lower at baseline. Of the total cohort, 55.8% had a FIB-4 lower than 1.45 at baseline and 5.1% had a FIB-4 higher than 3.24.
Most of the patients had overweight or obesity. There was moderate-risk drinking in 11.3% of patients in the development sample and 10.8% in the validation sample and high-risk drinking in 21% and 2.25%, respectively.
They found:
The multivariable analysis of the development sample found FIB-4 was the most important factor, but diabetes and age were also important. They determined that for every 29 people screened, there would be 1 true-positive instance detected.
The risk score the authors developed included FIB-4, age, sex, race and ethnicity, body mass index, diabetes status, smoking status, and alcohol use. For every 23 people screened, there would be 1 true-positive detected. This model would increase cancers detected among those screened by 22.9%.
Nearly half (48.6%) of the individuals with a score higher than 58 on the model—which they identified as a threshold for screening—had a FIB-4 under 3.25, plus 19.5% were younger than age 65 years, 44.0% did not have diabetes, and more than half (56.7%) had reported no alcohol.
The final risk score model requires external validation before it can be applied to other settings since it was created using data from the VA health care system. The researchers also cautioned that “associations with age should be interpreted with care” because most of the HCC events that would occur for the oldest age had likely already occurred and this study excluded patients with prevalent HCC. Another limitation is the observation design of the study relying on data routinely collected by the electronic health record.
“With a better understanding of risk, clinicians can prioritize their approach to treatment and prevention of metabolic dysfunction–associated comorbidities and subsequent liver disease,” the authors wrote. “Clinicians can also provide patients with a more personalized explanation of HCC risk in the context of patients’ unique comorbidities and lifestyles.”
References
1. Ilagan-Ying YC, Gordon KS, Tate JP, et al. Risk score for hepatocellular cancer in adults without viral hepatitis or cirrhosis. JAMA Netw Open. 2024;7(11):e2443608. doi:10.1001/jamanetworkopen.2024.43608
2. Younossi AM, Kalligeros M, Henry L. Epidemiology of metabolic dysfunction-associated steatotic liver disease. Clin Mol Hepatol. Published online August 19, 2024. doi:10.3350/cmh.2024.0431
3. Nonalcoholic fatty liver disease (NAFLD). American Liver Foundation. Updated October 24, 2024. Accessed November 13, 2024. https://liverfoundation.org/liver-diseases/fatty-liver-disease/nonalcoholic-fatty-liver-disease-nafld/
4. Joszt L. FDA approves resmetirom, first treatment for NASH with liver fibrosis. AJMC®. March 14, 2024. Accessed November 13, 2024. https://www.ajmc.com/view/fda-approves-resmetirom-first-treatment-for-nash-with-liver-fibrosis