Impact of combining data from multiple instruments on performance of patient-based real-time quality control

Abstract

Introduction:It is unclear what is the best strategy for applying patient-based real-time quality control (PBRTQC) algorithm in the presence of multiple instruments. This simulation study compared the error detection capability of applying PBRTQC algorithms for instruments individually and in combination using serum sodium as an example. Materials and methods: Four sets of random serum sodium measurements were generated with differing means and standard deviations to represent four simulated instruments. Moving median with winsorization was selected as the PBRTQC algorithm. The PBRTQC parameters (block size and control limits) were optimized and applied to the four simulated laboratory data sets individually and in combination. Results: When the PBRTQC algorithm were individually optimized and applied to the data of the individual simulated instruments, it was able to detect bias several folds faster than when they were combined. Similarly, the individually applied algorithms had perfect error detection rates across different magnitudes of bias, whereas the error detection rates of the algorithm applied on the combined data missed smaller biases. The performance of the individually applied PBRTQC algorithm performed more consistently among the simulated instruments compared to when the data were combined. Discussion: While combining data from different instruments can increase the data stream and hence, increase the speed of error detection, it may widen the control limits and compromising the probability of error detection. The presence of multiple instruments in the data stream may dilute the effect of the error when it only affects a selected instrument.

Next-Generation Patient-Based Real-Time Quality Control Models

“…Therefore, the actual performance of the subgroup-specific model was overestimated. A similar phenomenon can be observed when using separate models for tests performed on multiple instruments in the same laboratory [10].…”

“…These models focus on specific errors that conventional and earlier patient-based QC models may not optimally detect. Increased computing power coupled with advanced IT capabilities has enabled the real-time application of these models (i.e., before a result is released), aiding the optimization of parameters according to local laboratory data characteristics through simulation [ 9 , 10 ]. Such an in-silico analysis significantly enhances the understanding of how parameters interact and affect PBRTQC performance.…”

Assessment of patient based real‐time quality control on comparative assays for common clinical analytes

“…First, we set up individual optimal PBRTQC models with combined patient data other than the data from the analyzers. As stated by Zhou et al 24 the performance of PBRTQC could be better if data were applied using separate instruments. In addition, in the simulation process, the intellectual QC rules to assess the biased data points in the chart were built in the software and were confidential for the corporation.…”