Jiakai Liu1, Chin Hon Tan1, Tony Badrick2, Tze Ping Loh3. 1. Department of Industrial and Systems Engineering, National University of Singapore, Singapore. 2. Faculty of Health Sciences and Medicine, Bond University, Queensland, Australia. 3. Department of Laboratory Medicine, National University Hospital, Singapore; Biomedical Institute for Global Health Research and Technology, National University of Singapore, Singapore. Electronic address: tploh@hotmail.com.
Abstract
INTRODUCTION: An increase in analytical imprecision (expressed as CVa) can introduce additional variability (i.e. noise) to the patient results, which poses a challenge to the optimal management of patients. Relatively little work has been done to address the need for continuous monitoring of analytical imprecision. METHODS: Through numerical simulations, we describe the use of moving standard deviation (movSD) and a recently described moving sum of outlier (movSO) patient results as means for detecting increased analytical imprecision, and compare their performances against internal quality control (QC) and the average of normal (AoN) approaches. RESULTS: The power of detecting an increase in CVa is suboptimal under routine internal QC procedures. The AoN technique almost always had the highest average number of patient results affected before error detection (ANPed), indicating that it had generally the worst capability for detecting an increased CVa. On the other hand, the movSD and movSO approaches were able to detect an increased CVa at significantly lower ANPed, particularly for measurands that displayed a relatively small ratio of biological variation to CVa. CONCLUSION: The movSD and movSO approaches are effective in detecting an increase in CVa for high-risk measurands with small biological variation. Their performance is relatively poor when the biological variation is large. However, the clinical risks of an increase in analytical imprecision is attenuated for these measurands as an increased analytical imprecision will only add marginally to the total variation and less likely to impact on the clinical care.
INTRODUCTION: An increase in analytical imprecision (expressed as CVa) can introduce additional variability (i.e. noise) to the patient results, which poses a challenge to the optimal management of patients. Relatively little work has been done to address the need for continuous monitoring of analytical imprecision. METHODS: Through numerical simulations, we describe the use of moving standard deviation (movSD) and a recently described moving sum of outlier (movSO) patient results as means for detecting increased analytical imprecision, and compare their performances against internal quality control (QC) and the average of normal (AoN) approaches. RESULTS: The power of detecting an increase in CVa is suboptimal under routine internal QC procedures. The AoN technique almost always had the highest average number of patient results affected before error detection (ANPed), indicating that it had generally the worst capability for detecting an increased CVa. On the other hand, the movSD and movSO approaches were able to detect an increased CVa at significantly lower ANPed, particularly for measurands that displayed a relatively small ratio of biological variation to CVa. CONCLUSION: The movSD and movSO approaches are effective in detecting an increase in CVa for high-risk measurands with small biological variation. Their performance is relatively poor when the biological variation is large. However, the clinical risks of an increase in analytical imprecision is attenuated for these measurands as an increased analytical imprecision will only add marginally to the total variation and less likely to impact on the clinical care.
Keywords:
ANPed; Abbreviations; Analytical coefficient of variation; Analytical error; AoN; Average number of patient results affected before error detection; Average of normal; CV; CVa; Coefficient of variation; Erroneous; Imprecision; Laboratory Management; Laboratory error; Moving average; Moving standard deviation; Moving sum; Moving sum of outlier; QC; Quality control; SD; Spurious; analytical coefficient of variation; average number of patient results affected before error detection; average of normal; coefficient of variation; movSO; moving SD; moving standard deviation; moving sum of outlier; quality control; standard deviation