Optimization of a Moving Averages Program Using a Simulated Annealing Algorithm: The Goal is to Monitor the Process Not the Patients

• Published in: Clinical chemistry (Baltimore, Md.) Vol. 62; no. 10; pp. 1361 - 1371
• Main Authors: Ng, David, Polito, Frank A, Cervinski, Mark A
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-10-2016
• Subjects: Algorithms; Biological variation; Frequency distribution; Goals; Hematology; Humans; Laboratories; Monitoring, Physiologic; Oncology; Optimization; Patient Safety; Patients; Pediatrics
• ISSN: 0009-9147; 1530-8561
• DOI: 10.1373/clinchem.2016.257055

The patient moving average (MA) is a QC strategy using the mean patient result to continuously monitor assay performance. Developing sensitive MA protocols that rapidly detect systematic error (SE) is challenging. We compare MA protocols established using a previously published report as a guide and demonstrate the use of a simulated annealing (SA) algorithm to optimize MA protocol performance.
Using 400 days of patient data, we developed MA protocols for 23 assays. MA protocols developed using a previously published report and our SA algorithm were compared using the average number of patient samples affected until error detection (ANPed).
Comparison of the strategies demonstrated that protocols developed using the SA algorithm generally proved superior. Some analytes such as total protein showed considerable improvement, with positive SE equal to 0.8 g/dL detected with an ANPed of 135 samples using the previously published method whereas the SA algorithm detected this SE with an ANPed of 18. Not all analytes demonstrated similar improvement with the SA algorithm. Phosphorus, for instance, demonstrated only minor improvements, with a positive SE of 0.9 mg/dL detected with an ANPed of 34 using the previously published method vs an ANPed of 29 using the SA algorithm. We also demonstrate an example of SE detection in a live environment using the SA algorithm derived MA protocols.
The SA algorithm-developed MA protocols are currently in use in our laboratory and they rapidly detect SE, reducing the number of samples requiring correction and improving patient safety.