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Optimal population screening strategies for liver fibrosis associated with metabolic dysfunction-associated steatotic liver disease. | LitMetric

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is an important public health threat, potentially leading to chronic liver disease and liver cancer. Current guidelines recommend using the FIB-4 score for initial identification of subjects at risk of future complications. We formulate a novel population screening strategy based on the Steatosis-Associated Fibrosis Estimator (SAFE) score, recently developed for MASLD risk stratification in primary care.

Methods: We interrogated the National Health and Nutrition Examination Survey data, 2017-20, in which a sample of subjects representative of US civilian population underwent vibration controlled transient elastography (VCTE). The current guideline and a new, SAFE-based proposal were applied to these data to project the number of subjects to be diagnosed with liver fibrosis gauged by liver stiffness measurement (LSM), including significant (LSM ≥8kPa) and advanced (LSM ≥12kPa) fibrosis, as well as the number of VCTEs to be performed.

Results: In the survey data, 2,691 subjects, projecting to 75.8 million US adults, were found to have MASLD, of whom 11% had LSM 8-12kPa and 6% LSM ≥12kPa. When the current guideline was applied, 18.1 million VCTEs would be needed to diagnose 3.5 million subjects with LSM ≥8kPa and 1.7 million subjects with LSM ≥12kPa. In comparison, a new approach based on the SAFE score would detect 4.9 million with LSM ≥8kPa and 2.5 million subjects with LSM ≥12kPa (37% and 45% improvement over the current guideline, respectively), while requiring 5.0 million fewer VCTEs (28% reduction).

Conclusion: The proposed population risk stratification approach using the SAFE score is simpler and substantially more effective, yielding more subjects with liver fibrosis while requiring less resources compared to the currently recommended algorithm.

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http://dx.doi.org/10.14309/ajg.0000000000003268DOI Listing

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