Journal Information
Journal ID (publisher-id): BM
Journal ID (nlm-ta): Biochem Med (Zagreb)
Title: Biochemia Medica
Abbreviated Title: Biochem. Med. (Zagreb)
ISSN (print): 1330-0962
ISSN (electronic): 1846-7482
Publisher: Croatian Society of Medical Biochemistry and Laboratory Medicine
Article Information
Copyright statement: ©Croatian Society of Medical Biochemistry and Laboratory Medicine.
Copyright: 2018, Croatian Society of Medical Biochemistry
License (open-access):
This is an Open Access article distributed under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Date received: 02 April 2019
Date accepted: 17 July 2019
Publication date (print and electronic): 15 October 2019
Volume: 29
Issue: 3
Electronic Location Identifier: 030710
Publisher ID: bm-29-3-030710
DOI: 10.11613/BM.2019.030710
Optimizing moving average control procedures for small-volume laboratories: can it be done?
Author notes:
[*] Corresponding author: veralukic.lab@gmail.com
Introduction
Moving average (MA) means calculating the average value from a set of patient results and further using that value for analytical quality control purposes. The aim of this study was to examine whether the selection, optimization and validation of MA procedures can be performed using the already described bias detection simulation method and whether it is possible to select appropriate MA procedures for a laboratory with a small daily testing volume.
Materials and methods
The study was done on four analytes: creatinine, potassium, sodium and albumin. All patient results of these tests processed during six months were taken from the laboratory information system. Using the MA Generator software, different MA procedures were analysed. Different inclusion criteria, calculation formulas, batch sizes and weighting factors were tested. Selection of optimal MA procedures was based on their ability to detect simulated biases of different sizes. After optimization, the validation of MA procedures was done. The results were presented by bias detection curves and MA validation charts.
Keywords: quality control; moving average; bias detection simulation; MA Generator software
