Average of Patient Deltas: Patient-Based Quality Control Utilizing the Mean Within-Patient Analyte Variation.
BACKGROUND: Because traditional QC is discontinuous, laboratories use additional strategies to detect systematic error. One strategy, the delta check, is best suited to detect large systematic error. The moving average (MA) monitors the mean patient analyte value but cannot equitably detect systemat...
| Publicado en: | Clinical Chemistry Vol. 67; no. 7; pp. 1019 - 1030 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2021
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| 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=152594979&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152594979 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099147 10CS jtl: Clinical Chemistry issn: 00099147 maglogo: N pubinfo: dt: Jul2021 vid: 67 iid: 7 pid: 622 pub: Oxford University Press / USA artinfo: ui: 152594979 10.1093/clinchem/hvab057 152594979 ppf: 1019 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Average of Patient Deltas: Patient-Based Quality Control Utilizing the Mean Within-Patient Analyte Variation. aug: au: Cembrowski, George S. Qian Xu Cervinski, Mark A. affil: Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada sug: ab: BACKGROUND: Because traditional QC is discontinuous, laboratories use additional strategies to detect systematic error. One strategy, the delta check, is best suited to detect large systematic error. The moving average (MA) monitors the mean patient analyte value but cannot equitably detect systematic error in skewed distributions. Our study combines delta check and MA to develop an average of deltas (AoD) strategy that monitors the mean delta of consecutive, intrapatient results. METHODS: Arrays of the differences (delta) between paired patient results collected within 20-28 h of each other were generated from historical data. AoD protocols were developed using a simulated annealing algorithm in MatLab (Mathworks) to select the number of patient delta values to average and truncation limits to eliminate large deltas. We simulated systematic error by adding bias to arrays for plasma albumin, alanine aminotransferase, alkaline phosphatase, amylase, aspartate aminotransferase, bicarbonate, bilirubin (total and direct), calcium, chloride, creatinine, lipase, sodium, phosphorus, potassium, total protein, and magnesium. The average number of deltas to detection (ANDED) was then calculated in response to induced systematic error. RESULTS: ANDED varied by combination of assay and AoD protocol. Errors in albumin, lipase, and total protein were detected with a mean of 6 delta pairs. The highest ANDED was calcium, with a positive 0.6-mg/dL shift detected with an ANDED of 75. However, a negative 0.6-mg/dL calcium shift was detected with an ANDED of 25. CONCLUSIONS: AoD detects systematic error with relatively few paired patient samples and is a patient-based QC technique that will enhance error detection. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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