METHODS: This was an individual participant data meta-analysis for the performance of NITs against liver biopsy for MASH+F2-4, MASH+F2-3 and MASH+F4. Index tests were the FibroScan-AST (FAST) score, liver stiffness measured using vibration-controlled transient elastography (LSM-VCTE), the fibrosis-4 score (FIB-4) and the NAFLD fibrosis score (NFS). Area under the receiver operating characteristics curve (AUROC) and thresholds including those that achieved 34% SFR were reported.
RESULTS: We included 2281 unique cases. The prevalence of MASH+F2-4, MASH+F2-3 and MASH+F4 was 31%, 24% and 7%, respectively. Area under the receiver operating characteristics curves for MASH+F2-4 were .78, .75, .68 and .57 for FAST, LSM-VCTE, FIB-4 and NFS. Area under the receiver operating characteristics curves for MASH+F2-3 were .73, .67, .60, .58 for FAST, LSM-VCTE, FIB-4 and NFS. Area under the receiver operating characteristics curves for MASH+F4 were .79, .84, .81, .76 for FAST, LSM-VCTE, FIB-4 and NFS. The sequential combination of FIB-4 and LSM-VCTE for the detection of MASH+F2-3 with threshold of .7 and 3.48, and 5.9 and 20 kPa achieved SFR of 67% and sensitivity of 60%, detecting 15 true positive cases from a theoretical group of 100 participants at the prevalence of 24%.
CONCLUSIONS: Sequential combinations of NITs do not compromise diagnostic performance and may reduce resource utilisation through the need of fewer LSM-VCTE examinations.
DESIGN: Individual patient data meta-analysis of studies evaluating LSM-VCTE against liver histology was conducted. FIB-4 and NFS were computed where possible. Sensitivity, specificity and area under the receiver operating curve (AUROC) were calculated. Biomarkers were assessed individually and in sequential combinations.
RESULTS: Data were included from 37 primary studies (n=5735; 45% women; median age: 54 years; median body mass index: 30 kg/m2; 33% had type 2 diabetes; 30% had advanced fibrosis). AUROCs of individual LSM-VCTE, FIB-4 and NFS for advanced fibrosis were 0.85, 0.76 and 0.73. Sequential combination of FIB-4 cut-offs (<1.3; ≥2.67) followed by LSM-VCTE cut-offs (<8.0; ≥10.0 kPa) to rule-in or rule-out advanced fibrosis had sensitivity and specificity (95% CI) of 66% (63-68) and 86% (84-87) with 33% needing a biopsy to establish a final diagnosis. FIB-4 cut-offs (<1.3; ≥3.48) followed by LSM cut-offs (<8.0; ≥20.0 kPa) to rule out advanced fibrosis or rule in cirrhosis had a sensitivity of 38% (37-39) and specificity of 90% (89-91) with 19% needing biopsy.
CONCLUSION: Sequential combinations of markers with a lower cut-off to rule-out advanced fibrosis and a higher cut-off to rule-in cirrhosis can reduce the need for liver biopsies.
METHODS: We analyzed data from 3004 individuals with biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD) across 29 Chinese and 9 international cohorts to validate the acMASH index and develop the acFibroMASH index. Additionally, we utilized the independent external data from a multi-national cohort of 9034 patients with MASLD to examine associations between the acFibroMASH index and the risk of LREs.
RESULTS: In the pooled global cohort, the acMASH index identified MASH with an area under the receiver operating characteristic curve (AUROC) of 0.802 (95% confidence interval [CI], 0.786-0.818). The acFibroMASH index (including the acMASH index plus liver stiffness measurement) accurately identified fibrotic MASH with an AUROC of 0.808 in the derivation cohort and 0.800 in the validation cohort. Notably, the AUROC for the acFibroMASH index was 0.835 (95% CI, 0.786-0.882), superior to that of the FAST score at 0.750 (95% CI, 0.693-0.800; P < .01) in predicting the 5-year risk of LREs. Patients with acFibroMASH >0.39 had a higher risk of LREs than those with acFibroMASH <0.15 (adjusted hazard ratio, 11.23; 95% CI, 3.98-31.66).
CONCLUSIONS: This multi-ethnic study validates the acMASH index as a reliable, noninvasive test for identifying MASH. The newly proposed acFibroMASH index is a reliable test for identifying fibrotic MASH and predicting the risk of LREs.
METHODS: Using the Qualtrics XM and WJX platforms, questionnaires were sent online to MAFLD-ICD-11 coding collaborators, authors of papers, and relevant association members.
RESULTS: A total of 890 international experts in various fields from 61 countries responded to the survey. We also achieved full coverage of provincial-level administrative regions in China. 77.1% of respondents agreed that MAFLD should be represented in ICD-11 by updating NAFLD, with no significant regional differences (77.3% in Asia and 76.6% in non-Asia, p = 0.819). Over 80% of respondents agreed or somewhat agreed with the need to assign specific codes for progressive stages of MAFLD (i.e. steatohepatitis) (92.2%), MAFLD combined with comorbidities (84.1%), or MAFLD subtypes (i.e., lean, overweight/obese, and diabetic) (86.1%).
CONCLUSIONS: This global survey by a collaborative panel of clinical, coding, health management and policy experts, indicates agreement that MAFLD should be coded in ICD-11. The data serves as a foundation for corresponding adjustments in the ICD-11 revision.