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dc.contributor.authorLiu, Jiakai
dc.contributor.authorTan, Chin Hon
dc.contributor.authorBadrick, Tony
dc.contributor.authorLoh, Tze Ping
dc.date.accessioned2020-02-17T01:10:56Z
dc.date.available2020-02-17T01:10:56Z
dc.date.issued2018
dc.identifier.issn0009-9120
dc.identifier.doi10.1016/j.clinbiochem.2017.10.009
dc.identifier.urihttp://hdl.handle.net/10072/391544
dc.description.abstractIntroduction: An increase in analytical imprecision (expressed as CVa) can introduce additional variability (i.e. noise) to the patient results, which poses a challenge to the optimal management of patients. Relatively little work has been done to address the need for continuous monitoring of analytical imprecision. Methods: Through numerical simulations, we describe the use of moving standard deviation (movSD) and a recently described moving sum of outlier (movSO) patient results as means for detecting increased analytical imprecision, and compare their performances against internal quality control (QC) and the average of normal (AoN) approaches. Results: The power of detecting an increase in CVa is suboptimal under routine internal QC procedures. The AoN technique almost always had the highest average number of patient results affected before error detection (ANPed), indicating that it had generally the worst capability for detecting an increased CVa. On the other hand, the movSD and movSO approaches were able to detect an increased CVa at significantly lower ANPed, particularly for measurands that displayed a relatively small ratio of biological variation to CVa. Conclusion: The movSD and movSO approaches are effective in detecting an increase in CVa for high-risk measurands with small biological variation. Their performance is relatively poor when the biological variation is large. However, the clinical risks of an increase in analytical imprecision is attenuated for these measurands as an increased analytical imprecision will only add marginally to the total variation and less likely to impact on the clinical care.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom112
dc.relation.ispartofpageto116
dc.relation.ispartofjournalClinical Biochemistry
dc.relation.ispartofvolume52
dc.subject.fieldofresearchMedical biochemistry and metabolomics
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchcode3205
dc.subject.fieldofresearchcode3202
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsMedical Laboratory Technology
dc.subject.keywordsQuality control
dc.subject.keywordsMoving standard deviation
dc.titleMoving standard deviation and moving sum of outliers as quality tools for monitoring analytical precision
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationLiu, J; Tan, CH; Badrick, T; Loh, TP, Moving standard deviation and moving sum of outliers as quality tools for monitoring analytical precision, Clinical Biochemistry, 2018, 52, pp. 112-116
dcterms.dateAccepted2017-10-15
dc.date.updated2020-02-17T01:08:11Z
gro.hasfulltextNo Full Text
gro.griffith.authorBadrick, Tony C.


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