A direct comparison of patient-based real-time quality control techniques: The importance of the analyte distribution

Patient-based real-time quality control (PBRTQC) techniques have been described in clinical chemistry for over 50 years. PBRTQC has a number of advantages over traditional quality control including commutability, cost and the opportunity for real-time monitoring. However, there are few systematic investigations assessing how different PBRTQC techniques perform head-to-head.

In this study, we compare moving averages with and without truncation and moving medians. For analytes with skewed distributions such as alanine aminotransferase and creatinine, we also investigate the effect of Box-Cox transformation of the data. We assess the ability of each technique to detect simulated analytical bias in real patient data for multiple analytes and to retrospectively detect a real analytical shift in a creatinine and urea assay.

For analytes with symmetrical distributions, we show that error detection is similar for a moving average with and without four standard deviation truncation limits and for a moving median. In contrast to analytes with symmetrically distributed results, moving averages perform poorly for right skewed distributions such as alanine aminotransferase and creatinine and function only with a tight upper truncation limit. Box-Cox transformation of the data both improves the performance of moving averages and allows all data points to be used. This was also confirmed for retrospective detection of a real analytical shift in creatinine and urea.

Our study highlights the importance of careful assessment of the distribution of patient results for each analyte in a PBRTQC program with the optimal approaches dependent on whether the patient result distribution is symmetrical or skewed.