Setting analytical performance specifications using HbA1c as a model measurand
Field | Value | Language |
dc.contributor.author | Loh, Tze Ping | |
dc.contributor.author | Smith, Alison F. | |
dc.contributor.author | Bell, Katy J.L. | |
dc.contributor.author | Lord, Sarah J | |
dc.contributor.author | Ceriotti, Ferruccio | |
dc.contributor.author | Jones, Graham | |
dc.contributor.author | Bossuyt, Patrick | |
dc.contributor.author | Sandberg, Sverre | |
dc.contributor.author | Horvath, Andrea R | |
dc.date.accessioned | 2021-11-11T01:15:45Z | |
dc.date.available | 2021-11-11T01:15:45Z | |
dc.date.issued | 2021 | en_AU |
dc.identifier.uri | https://hdl.handle.net/2123/26852 | |
dc.description.abstract | Analytical performance specifications (APS) for measurands describe the minimum analytical quality requirements for their measurement. These APS are used to monitor and contain the systematic (trueness/bias) and random errors (precision/imprecision) of a laboratory measurement to ensure the results are “fit for purpose” in informing clinical decisions about managing a patient’s health condition. In this review, we highlighted the wide variation in the setting of APS, using different levels of evidence, as recommended by the Milan Consensus, and approaches. The setting of a priori defined outcome-based APS for HbA1c remains challenging. Promising indirect alternatives seek to link the clinical utility of HbA1c and APS by defining statistical confidence for interpreting the laboratory values, or through simulation of clinical performance at varying levels of analytical performance. APS defined based on biological variation estimates in healthy individuals using the current formulae are unachievable by nearly all routine laboratory methods for HbA1c testing. On the other hand, the APS employed in external quality assurance programs have been progressively tightened, and greatly facilitate the improved quality of HbA1c testing. Laboratories should select the APS that fits their intended clinical use and should document the data and rationale underpinning those selections. Where possible common APS should be adopted across a region or country to facilitate the movement of patients and patient data across health care facilities. | en_AU |
dc.language.iso | en | en_AU |
dc.publisher | Elsevier | en_AU |
dc.relation.ispartof | Clinica Chimica Acta | en_AU |
dc.rights | Copyright All Rights Reserved | en_AU |
dc.subject | Analytical performance specification | en_AU |
dc.subject | Bias | en_AU |
dc.subject | Imprecision | en_AU |
dc.subject | External quality assurance | en_AU |
dc.subject | Proficiency testing | en_AU |
dc.subject | Quality goal | en_AU |
dc.subject | Quality control | en_AU |
dc.title | Setting analytical performance specifications using HbA1c as a model measurand | en_AU |
dc.type | Article | en_AU |
dc.subject.asrc | 1103 Clinical Sciences | en_AU |
dc.subject.asrc | 1117 Public Health and Health Services | en_AU |
dc.identifier.doi | 10.1016/j.cca.2021.10.016 | |
usyd.faculty | SeS faculties schools::Faculty of Medicine and Health::Sydney School of Public Health | en_AU |
usyd.citation.volume | 523 | en_AU |
usyd.citation.spage | 407 | en_AU |
usyd.citation.epage | 414 | en_AU |
workflow.metadata.only | Yes | en_AU |
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