Analytical performance of the Enhanced Liver Fibrosis (ELF) Test on the Atellica IM Analyzer

Efficacy and safety of aldafermin in non-alcoholic steatohepatitis: A systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Non-alcoholic steatohepatitis (NASH) is an advanced subtype of non-alcoholic fatty liver disease (NAFLD). NASH prevalence is increasing exponentially and carries a high risk for disease progression, cirrhosis, and liver-related mortality. Aldafermin, a fibroblast growth factor 19 (FGF19) analog, is one of the evolving therapeutic agents with the potential to regulate multiple pathways involved in the pathogenesis of NASH. We aimed to investigate the efficacy and safety of aldafermin in patients with NASH.

METHODS

PubMed, Scopus, Cochrane Library, and Web of Science were searched till November 2023 to identify eligible randomized controlled trials (RCTs). Continuous data were pooled as mean difference (MD), while dichotomous data were pooled as risk ratios (RR) with a 95 % confidence interval. A subgroup meta-analysis was conducted to evaluate the efficacy of the two doses (1 mg and 3 mg) of aldafermin.

RESULTS

Four RCTs with a total of 491 patients were included. Aldafermin showed a dose-dependent improvement in the ≥30 % reduction in the liver fat content (RR: 2.16, 95 % CI [1.41 to 3.32]) and (RR: 5.00, 95 % CI [1.34 to 18.64]), alanine aminotransferase levels (MD: -19.79, 95 % CI [-30.28 to -9.3]) and (MD: -21.91, 95 % CI [-29.62 to -14.21]), aspartate aminotransferase levels (MD: -11.79, 95 % CI [-18.06 to -5.51]) and (MD: -13.9, 95 % CI [-18.59 to -9.21]), and enhanced liver fibrosis score (ELF) (MD: -0.13, 95 % CI [-0.29 to 0.02]) and (MD: -0.33, 95 % CI [-0.50 to -0.17]), in the 1 mg and 3 mg subgroups respectively. No significant differences were detected in the aldafermin group regarding histologic endpoints, lipid profile, metabolic parameters, and overall adverse effects, except for the increased occurrence of diarrhea in the aldafermin 3 mg subgroup.

CONCLUSION

Aldafermin is a promising well-tolerated therapeutic agent for NASH with evidence supporting its ability to reduce liver fat content, fibrosis serum biomarkers, and liver enzymes. However, its effectiveness in improving histologic fibrosis, while showing numerical trends, still lacks statistical significance. Larger and longer NASH trials are warranted to enhance the robustness of the evidence.

Analytical Performance Specifications for Input Variables: Investigation of the Model of End-Stage Liver Disease

BACKGROUND

Artificial intelligence models constitute specific uses of analysis results and, therefore, necessitate evaluation of analytical performance specifications (APS) for this context specifically. The Model of End-stage Liver Disease (MELD) is a clinical prediction model based on measurements of bilirubin, creatinine, and the international normalized ratio (INR). This study evaluates the propagation of error through the MELD, to inform choice of APS for the MELD input variables.

METHODS

A total of 6093 consecutive MELD scores and underlying analysis results were retrospectively collected. "Desirable analytical variation" based on biological variation as well as current local analytical variation was simulated onto the data set as well as onto a constructed data set, representing a worst-case scenario. Resulting changes in MELD score and risk classification were calculated.

RESULTS

Biological variation-based APS in the worst-case scenario resulted in 3.26% of scores changing by ≥1 MELD point. In the patient-derived data set, the same variation resulted in 0.92% of samples changing by ≥1 MELD point, and 5.5% of samples changing risk category. Local analytical performance resulted in lower reclassification rates.

CONCLUSIONS

Error propagation through MELD is complex and includes population-dependent mechanisms. Biological variation-derived APS were acceptable for all uses of the MELD score. Other combinations of APS can yield equally acceptable results. This analysis exemplifies how error propagation through artificial intelligence models can become highly complex. This complexity will necessitate that both model suppliers and clinical laboratories address analytical performance specifications for the specific use case, as these may differ from performance specifications for traditional use of the analyses.

Derivation and validation of Transform equations to convert historical Enhanced liver fibrosis (ELF) scores to modern equivalents

BACKGROUND AND AIMS

The Siemens Healthineers ELF™ Test was designed in 2004 with 2 algorithms to allow choice in histological alignment. Consequently, historical and modern algorithms are not fully harmonized, complicating comparisons involving early datasets. We derived transform equations to equate all ELF score versions, allowing historical data to be used in systematic reviews and meta-analyses.

METHODS

Historical ELF equations were graphed pairwise versus their modern equivalent to assess correlation and derive four transforms. Transforms were validated using multiple datasets and evaluated for median absolute bias, number of samples reflecting clinically significant bias, number of discordant samples, bias at established cutoffs, and regression slope and y-intercept.

RESULTS

Three transforms were validated equating Scheuer-aligned and/or age-included historical ELF equations (Immuno 1) to later equations aligned to Ishak and omitting age. A fourth transform corrected ADVIA Centaur® / Atellica® IM ELF scores miscalculated using the Scheuer Immuno 1 equation. Transformed data were well within allowable ELF bias limits.

CONCLUSIONS

All transforms enabled accurate comparison of ELF scores generated by all historical algorithms to the current ADVIA Centaur / Atellica IM Analyzer ELF score. The transforms presented in this report should be used in systematic reviews and meta-analyses to facilitate comparisons to historical data.