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

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 vers...

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Veröffentlicht in:Clinica chimica acta 2024-01, Vol.552, p.117696-117696, Article 117696
Hauptverfasser: Gee, Matthew F W, Palladino, Jr, Agostino, Levy, H Roma, De Vol, Edward, Kiaei, David
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Sprache:eng
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Zusammenfassung: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. 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. 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. 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.
ISSN:0009-8981
1873-3492
DOI:10.1016/j.cca.2023.117696