Implementation and application of moving average as continuous analytical quality control instrument demonstrated for 24 routine chemistry assays

Implementation and application of moving average as continuous analytical quality control... AbstractBackground:General application of a moving average (MA) as continuous analytical quality control (QC) for routine chemistry assays has failed due to lack of a simple method that allows optimization of MAs. A new method was applied to optimize the MA for routine chemistry and was evaluated in daily practice as continuous analytical QC instrument.Methods:MA procedures were optimized using an MA bias detection simulation procedure. Optimization was graphically supported by bias detection curves. Next, all optimal MA procedures that contributed to the quality assurance were run for 100 consecutive days and MA alarms generated during working hours were investigated.Results:Optimized MA procedures were applied for 24 chemistry assays. During this evaluation, 303,871 MA values and 76 MA alarms were generated. Of all alarms, 54 (71%) were generated during office hours. Of these, 41 were further investigated and were caused by ion selective electrode (ISE) failure (1), calibration failure not detected by QC due to improper QC settings (1), possible bias (significant difference with the other analyzer) (10), non-human materials analyzed (2), extreme result(s) of a single patient (2), pre-analytical error (1), no cause identified (20), and no conclusion possible (4).Conclusions:MA was implemented in daily practice as a continuous QC instrument for 24 routine chemistry assays. In our setup when an MA alarm required follow-up, a manageable number of MA alarms was generated that resulted in valuable MA alarms. For the management of MA alarms, several applications/requirements in the MA management software will simplify the use of MA procedures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clinical Chemistry and Laboratory Medicine (CCLM) de Gruyter

Implementation and application of moving average as continuous analytical quality control instrument demonstrated for 24 routine chemistry assays

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Publisher
De Gruyter
Copyright
©2017 Walter de Gruyter GmbH, Berlin/Boston
ISSN
1437-4331
eISSN
1437-4331
D.O.I.
10.1515/cclm-2016-0696
Publisher site
See Article on Publisher Site

Abstract

AbstractBackground:General application of a moving average (MA) as continuous analytical quality control (QC) for routine chemistry assays has failed due to lack of a simple method that allows optimization of MAs. A new method was applied to optimize the MA for routine chemistry and was evaluated in daily practice as continuous analytical QC instrument.Methods:MA procedures were optimized using an MA bias detection simulation procedure. Optimization was graphically supported by bias detection curves. Next, all optimal MA procedures that contributed to the quality assurance were run for 100 consecutive days and MA alarms generated during working hours were investigated.Results:Optimized MA procedures were applied for 24 chemistry assays. During this evaluation, 303,871 MA values and 76 MA alarms were generated. Of all alarms, 54 (71%) were generated during office hours. Of these, 41 were further investigated and were caused by ion selective electrode (ISE) failure (1), calibration failure not detected by QC due to improper QC settings (1), possible bias (significant difference with the other analyzer) (10), non-human materials analyzed (2), extreme result(s) of a single patient (2), pre-analytical error (1), no cause identified (20), and no conclusion possible (4).Conclusions:MA was implemented in daily practice as a continuous QC instrument for 24 routine chemistry assays. In our setup when an MA alarm required follow-up, a manageable number of MA alarms was generated that resulted in valuable MA alarms. For the management of MA alarms, several applications/requirements in the MA management software will simplify the use of MA procedures.

Journal

Clinical Chemistry and Laboratory Medicine (CCLM)de Gruyter

Published: Jul 26, 2017

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