Metabolically–dysfunction–associated steatotic liver disease (MASLD) is strongly linked to cardiometabolic disorders, and cardiovascular disease (CVD) remains the leading cause of mortality in these patients. However, it is unclear whether commonly used cardiovascular risk prediction tools accurately estimate risk in this population.
Metabolically–dysfunction–associated steatotic liver disease (MASLD) is strongly linked to cardiometabolic disorders, and cardiovascular disease (CVD) remains the leading cause of mortality in these patients. However, it is unclear whether commonly used cardiovascular risk prediction tools accurately estimate risk in this population. This study evaluated the performance of three widely used models—the Framingham Risk Score (FRS), Pooled Cohort Equation (PCE), and the American Heart Association PREVENT equation—in predicting cardiovascular events among patients with MASLD.
Using data from the TARGET-NASH real-world cohort, the study included 1,090 US adults aged ≥30 years with MASLD. Researchers assessed the ability of these models to predict 5–10 year cardiovascular risk and compared predicted risk with actual observed cardiovascular events.
The results showed that all commonly used tools performed poorly in this population. The FRS demonstrated weak discrimination (C-statistic 0.58), while the PREVENT model also showed limited predictive ability (C-statistic 0.60). In addition, both FRS and PCE showed significant calibration errors, meaning they overestimated risk in high-risk groups and underestimated risk in lower-risk groups. Importantly, the study also demonstrated that cardiovascular event rates increased progressively from MASLD to cirrhosis, suggesting that liver disease severity contributes independently to cardiovascular risk.
Overall, these findings indicate that standard cardiovascular risk calculators are inadequate for patients with MASLD. Given the high cardiovascular mortality in this population, new risk assessment tools incorporating liver disease–specific factors are urgently needed to improve prediction, prevention, and clinical management.
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