Patient-based real-time quality control gains traction in clinical chemistry
Patient-based real-time quality control (PBRTQC) is gaining traction, with respected laboratories in the US and Europe starting to adopt the approach.
Early adopters have found it more reliable and economical than conventional QC, says Professor Tony Badrick, CEO of The Royal College of Pathologists Quality Assurance Programs.
With real-time QC, the laboratory uses a simulation program to calculate an optimal patient block size, usually about 30, to identify exceptions for each type of test based on a rolling average, mean, median or another statistical parameter. Professor Badrick explains that this is used to detect changes caused by introduced bias or imprecision.
The software within the laboratory instrument is then programmed to flag exceptions. Haematologists have been using patient-based QC since the 1970s, but it is relatively new to clinical chemistry. Professor Badrick says it overcomes some of the inherent challenges with modern conventional QC. The advantages include:
“With conventional QC, you won’t see the problem until you run a QC sample, which can be four or more hours later, whereas with PBRTQC you will most likely detect the bias earlier,” says Professor Badrick, who is a prolific author on laboratory QC.
“I think people have to understand the problems with conventional QC. It has flaws, including that the material is non-commutable. It doesn’t always behave the same way that patient samples behave.”
The potential for human error is another risk.
“Often the synthetic sample has to be reconstituted, so there can be problems with the way it is made up, or you might get the material mixed up or leave the lid off, allowing evaporation and a change of concentration.”
But real-time QC doesn’t work for every type of test.
“Paediatric and oncology assays are not appropriate for this approach. Also, it isn’t appropriate for small runs. It is easier to run conventional QC in that case rather than wait until you have enough samples for a patient average to be available.”
One of the difficulties with patient-based real-time QC is figuring out the different subpopulations for each test. For example; inpatients, outpatients, males, females, geriatrics and community screening.
“You have to manipulate the data so that you are removing extreme patient values which will distort the patient mean/median,” says Professor Badrick.
When PBRTQC is implemented, it is common for early adopters to use a hybrid approach.
“They may start the instrument up first thing in the morning and run conventional QC to check the instrument is in control. Then, they will start running their patients until they have enough samples to use patient-based real-time QC to do the control on the instrument,” says Professor Badrick.
“If the real-time QC flags that something’s wrong, I might use conventional QC to troubleshoot the problem.”
Professor Badrick is aware of laboratories in the US that have saved about 80% of its conventional QC material by running the hybrid system.
The most significant risk with real-time QC is that people might adopt an algorithm without it being correctly validated.
“You have to ensure that you have the right block size and that you are including and excluding the right patients.
“There are no guidelines yet, but there is a lot of literature out there. We and others have been producing guidance papers.”
To respond to this trend Abbott AlinIQ AMS has developed a comprehensive package that provides all the parameters to establish and run real time QC in a laboratory.2 The initiative of including PBRTQC in the AMS module has seen Abbott invited to participate as an industry representative on the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) subcommittee on Patient Based Real Time Quality Control (PBRTQC).
Abbott will provide the opportunity for the IFCC to further study the benefits of installing and monitoring laboratory performance based on the principles of PBRTQC rather than traditional QC methods.
Abbott is currently finalising multi party agreements with the IFCC subcommittee to install real time QC into client laboratories. These initiatives will lead to joint publication of the outcomes documenting the implementation process, and derived benefits.
This process will enable Abbott to further consider how we will look at incorporating real time QC into our informatics solution in the future and in turn support the enablement in laboratories across Australia and New Zealand.
A list of papers Professor Badrick has worked on:
1Verification of out-of-control situations detected by "average of normal" approach’. Jiakai Liu, Chin Hon Tan, Tze Ping Loh, Tony Badrick. Clin Biochem 49(16-17), 1248-1253; 2016. https://www.corelaboratory.abbott/tl/ous/technical-library.html#OperationsManuals p114-116
2Moving sum number of positive patient results as a quality control tool. Tze Ping Loh, Jiakai Liu, Chin Hon Tan, Tony Badrick. CCLM 55(11); 1709-1714:2017.
3Using next generation electronic medical records for laboratory quality monitoring. Loh TP, Badrick T. JLPM 2: 61: 2017
4Moving standard deviation and moving sum of outliers as quality tools for monitoring analytical precision. Tze Ping Loh, Jiakai Liu, Chin Hon Tan, Tony Badrick. Clin Biochem 52(2); 112-116: 2018
5Missed detection of significant positive and negative shifts in gentamicin assay: implications for routine laboratory quality practices. Gary Koerbin, Jiakai Liu, Alex Eigenstetter, Chin Hon Tan, Tony Badrick, Tze Ping. Biochemia Medica 28(1): doi: 10.11613/BM.2018.010705; 2018
6A primer on patient-based quality control techniques. Tony Badrick, Mark Cervinski, Tze Ping Loh. Clin Biochem 2019; 64: 1-5.
7Recommendations for laboratory informatics specifications needed for the application of patient-based real time quality control. Tze Ping Loh, Andreas Bietenbeck, Mark A Cervinski, Alex Katayev, Huub H van Rossum, Tony Badrick. Clin Chim Acta 2019; 495: 625-629.
8Recommendations for laboratory informatics specifications needed for the application of patient-based real time quality control. Tze Ping Loh, Andreas Bietenbeck, Mark A Cervinski, Alex Katayev, Huub H van Rossum, Tony Badrick. Clin Chim Acta 2019; 495: 625-629.
9A direct comparison of patient based real-time quality control techniques: The importance of the analyte distribution and transformation. Smith J, Badrick T, Bowling F. Ann Clin Biochem 57(3): 206-2014: 2020
10Recommendation for performance verification of patient-based real time quality control. Tze Ping Loh, Andreas Bietenbeck, Mark A Cervinski, Alex Katayev, Huub H van Rossum, Tony Badrick. CCLM 58(8); 1205-1213: 2020; DOI: 10.1515/cclm-2019-1024
11Patient-based quality control for glucometer: using moving average and moving sum of outliers. Lim, CY, Badrick T, Loh TP. Biochem Medica 30(2), 2020. doi.org/10.11613/BM.2020.020709
12Understanding Patient-Based Real-Time Quality Control using Simulation Modeling. Bietenbeck A, Cervinski MA, Katayev A, Loh TP, van Rossum HH, Badrick T. Clin Chem 66(8); 1072-1083: 2020.
13Implementation of Patient-Based Real Time Quality Control. Badrick T, Bietenbeck A, Katayev A, Loh TP, van Rossum HH, Cervinski MA. Crit Rev Clin Lab Sc. 2020. DOI: 10.1080/10408363.2020.1765731
14Patient Based Real Time Quality Control – Q&A. Tony Badrick, Andreas Bietenbeck, Alex Katayev, Huub H van Rossum, Mark A Cervinski, and Tze Ping Loh. Clin Chem 66(9); 1140-1145: 2020.
15Asking why: moving beyond error detection to failure mode and effects analysis. Chun Yee Lim, Tze Ping Loh, Tony Badrick. JPLM 2020 doi: 10.21037/jlpm-20-26.
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