Discussion
While the CLivD score was originally developed to predict future liver-related outcomes in the general population, the present study shows that it also holds promise in identifying both liver steatosis and advanced liver fibrosis. Furthermore, the integration of the CLivD score substantially improved the discrimination performance of FIB-4 in the detection of advanced liver fibrosis. By sequentially applying the CLivD and FIB-4 scores, we were able identify a subgroup of individuals in the US general population with a prevalence of advanced liver fibrosis as high as 55%. Conversely, approximately 47% of the population exhibited a CLivDlab score categorised as minimal risk, resulting in an exceptionally low prevalence of advanced fibrosis (<1.5%) regardless of the FIB-4 score and thus rendering FIB-4 testing unnecessary for this particular group. These findings underscore the pivotal role of the CLivD score as the initial step in early liver disease identification and emphasise its synergistic potential when combined with FIB-4.
Study strengths include the use of the nationally representative NHANES dataset, enhancing the generalisability of our results. The comprehensive data collection methods, including LSMs, laboratory tests and clinical assessments, ensured robust analyses. The use of established cutoffs for LSMs and risk scores further facilitated clear risk stratification.
However, we acknowledge certain limitations inherent to our study. The cross-sectional design restricts our ability to establish causal relationships, and the reliance on self-reported data, such as alcohol consumption and smoking, may introduce recall bias. Additionally, the use of surrogate markers for liver fibrosis rather than histological confirmation may introduce measurement error. Furthermore, our study focused on a specific age range (40–70 years), which may limit its generalisability to younger or older populations. This age range was selected because it corresponds to the demographic for which the CLivD score was originally designed.
The performance of CLivD for detecting advanced fibrosis was improved among participants without obesity. We speculate that this phenomenon may be attributed to the diminished accuracy of LSMs in individuals with obesity, thereby increasing the likelihood of a false positive result when LSM ≥12 kPa is observed in this subgroup. Obesity-related false positive LSM values are probably especially common when using lower LSM cutoffs such as 8 kPa. Higher LSM cutoffs (12–15 kPa) have recently been advocated for ruling in advanced liver disease.7
Previous research has highlighted the suboptimal discriminatory performance of the FIB-4 test in detecting advanced liver fibrosis and predicting liver-related outcomes in the unselected general population.9 10 These studies found that the sensitivity of FIB-4 was poor in unselected individuals but improved when applied to those with specific risk factors for liver disease. Our study revealed analogous results, demonstrating that FIB-4 performance improved along with increasing CLivD scores. However, these earlier studies primarily assessed the performance of the FIB-4 test and its variations based on pretest probability determined by various risk factors. They did not comprehensively evaluate the entire screening process, which includes how the target risk group is defined—an aspect essential to optimising screening effectiveness. There are both false positive and false negative rates associated with defining the target risk group.11
In our investigation, we discovered that when compared with a strategy of applying FIB-4 to all individuals, targeting FIB-4 testing specifically to those with CLivD scores falling into the low-risk/intermediate-risk/high-risk category led to an improvement in specificity while maintaining consistent sensitivity levels. This refined approach enhances the overall accuracy of liver fibrosis detection and highlights the significance of carefully defining the target risk group within the screening process.
Our findings align with current guidelines that discourage the use of non-invasive tests like FIB-4 for liver fibrosis screening in the unselected general population, particularly in individuals without risk factors.7 8 Instead, the recommendation is to target screening towards individuals with specific risk factors for liver disease to increase pretest probability.7 8 However, the challenge lies in identifying the ideal target group for screening. We previously found that defining the target group by the CLivD score was associated with substantially better sensitivity than defining the target group by traditional risk factors considered in isolation.11
One distinctive feature of the CLivD score is its reliance on widely available variables, with the CLivDnon-lab score not even requiring a single blood test. This accessibility means that individuals can easily calculate their CLivD score using a web calculator, even without healthcare contact. Implementing this sequential approach (CLivD→FIB-4) in population-based screening programmes for liver fibrosis holds the potential to optimise resource allocation, thereby reducing healthcare costs while effectively identifying those at risk. Since the CLivD score is a continuous variable, it allows choosing the cut-off according to the desired sensitivity and specificity level.
To comprehensively evaluate the financial implications of incorporating CLivD-based risk assessment into routine healthcare screening programmes, conducting cost-effectiveness analyses is imperative. While the CLivD score was originally derived in a Finnish population and externally validated in various populations,2 12 further investigation in diverse populations and different age groups is warranted to ensure its applicability.
In conclusion, our study sheds light on the potential of the CLivD score, extending its original prognostic scope to include the identification of liver steatosis and advanced fibrosis. When combined with FIB-4, it significantly enhances the accuracy of advanced fibrosis detection. These findings offer a pathway towards more efficient and targeted strategies in population-based liver fibrosis screening, ultimately leading to early intervention and improved patient outcomes.