CLivD score predicts risk of chronic liver disease in general population

Use of the novel Chronic Liver Disease (CLivD) score can reliably identify individuals in the general population who are at risk of future advanced liver disease. In addition, risk estimation can be done by anyone through the internet or by using the related scoring sheets.

The CLivD score is a simple prediction model based on age, sex, alcohol use, waist-hip ratio, diabetes, and smoking, with or without measurement of the liver enzyme gamma-glutamyltransferase.

“The model identifies individuals at high risk and provides data to support lifestyle changes,” the researchers said. “At the primary healthcare level, the CLivD score can be used to identify individuals who should be referred for further liver assessment.”

Lead researcher Fredrik Aberg, from the Helsinki University Hospital, Finland, and his team carried out multivariable Cox regression analyses to develop prediction models for liver-related outcomes with and without laboratory measures (Model_lab and Model_non-lab) in 25,760 individuals aged 40–70 years.

They obtained their data from the Finnish population-based health examination surveys FINRISK 1992‒2012 and Health 2000 (derivation cohort). The prediction models were validated externally in the Whitehall II (n=5,058) and Copenhagen City Heart Study (CCHS; n=3,049) cohorts.

The absolute rate of incident liver outcomes had a range of 53‒144 per 100,000 person-years. The final prediction model included the following risk factors: age, sex, alcohol use (drinks/week), waist‒hip ratio, diabetes, and smoking. Model_lab also included gamma-glutamyltransferase values. [J Hepatol 2022;77:302-311]

Internally validated Wolbers’ C-statistics were 0.77 for Model_lab and 0.75 for Model_non-lab, while the 15-year area under the receiver operating characteristic curve (AUC) were 0.84 (95 percent confidence interval [CI], 0.75‒0.93) and 0.82 (95 percent CI, 0.74‒0.91), respectively.

The CLivD score was able to detect a small section (<2 percent) of the population with >10-percent absolute 15-year risk for liver events. Only 10 percent of all liver events occurred in participants in the lowest risk category.

Externally validated 15-year AUCs were 0.78 for Model_lab and 0.65 Model_non-lab in the CCHS cohort and 0.78 for Model_non-lab in the Whitehall II cohort.

“Liver disease often progresses silently without symptoms and thus the diagnosis is often delayed until severe complications occur and prognosis becomes poor,” the researchers said. “In order to identify individuals in the general population who have a high risk of developing severe liver disease in the future, we developed and validated a CLivD risk prediction score.”

Identifying individuals in the general population who are at risk of future liver disease can help inform them about liver-related hazards and how to reduce these risks. This knowledge can also promote healthy lifestyle changes among at-risk individuals, such as reducing harmful drinking, according to the researchers. [Br J Gen Pract 2013;63:e698-e705]

“Furthermore, the prediction model could help target lifestyle intervention resources and therapeutic decisions based on risk, and possibly also help assess response to such interventions,” they added.