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...
| Publicado en: | Clinical Chemistry Vol. 70; no. 4; pp. 653 - 660 |
|---|---|
| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176610194&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176610194 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099147 10CS jtl: Clinical Chemistry issn: 00099147 maglogo: N pubinfo: dt: Apr2024 vid: 70 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 176610194 10.1093/clinchem/hvae019 176610194 ppf: 653 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Analytical Performance Specifications for Input Variables: Investigation of the Model of End-Stage Liver Disease. aug: au: Andersen, Eline S Röttger, Richard Brasen, Claus L Brandslund, Ivan affil: Department of Biochemistry and Immunology, Lillebaelt Hospital , Vejle , Denmark sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|