Internal quality control: Moving average algorithms outperform Westgard rules.

INTRODUCTION: Internal quality control (IQC) is traditionally interpreted against predefined control limits using multi-rules or 'Westgard rules'. These include the commonly used 1:3s and 2:2s rules. Either individually or in combination, these rules have limited sensitivity for detection of systematic errors. In this proof-of-concept study, we directly compare the performance of three moving average algorithms with Westgard rules for detection of systematic error. METHODS: In this simulation study, 'error-free' IQC data (control case) was generated. Westgard rules (1:3s and 2:2s) and three moving average algorithms (simple moving average (SMA), weighted moving average (WMA), exponentially weighted moving average (EWMA); all using ±3SD as control limits) were applied to examine the false positive rates. Following this, systematic errors were introduced to the baseline IQC data to evaluate the probability of error detection and average number of episodes for error detection (ANEed). RESULTS: From the power function graphs, in comparison to Westgard rules, all three moving average algorithms showed better probability of error detection. Additionally, they also had lower ANEed compared to Westgard rules. False positive rates were comparable between the moving average algorithms and Westgard rules (all <0.5%). The performance of the SMA algorithm was comparable to the weighted algorithms forms (i.e. WMA and EWMA). CONCLUSION: Application of an SMA algorithm on IQC data improves systematic error detection compared to Westgard rules. Application of SMA algorithms can simplify laboratories IQC strategy.

Vibration motor-integrated low-cost, miniaturized system for rapid quantification of red blood cell aggregation.

Human red blood cells (RBCs) aggregate under low shear conditions, which significantly modulates flow resistance and tissue perfusion. A higher aggregation tendency in blood thus serves as an important clinical indicator for the screening of cardiovascular disorders. Conventional ways of measuring RBC aggregation still require large sample volumes, cumbersome manual procedures, and expensive benchtop systems. These inconvenient and high-cost measurement methods hamper their clinical applicability. Here, we propose a low-cost, miniaturized system to overcome the limitations of these methods. Our system utilizes a coin vibration motor (CVM) to generate a localized vortex for disaggregating RBCs in a disposable fluidic chip. The design of the chip was optimized with fluid dynamics simulations to ensure sufficient shear flow in the localized vortex for RBC disaggregation. The time-dependent increase in light transmittance from an LED light source through the plasma gap while the RBCs re-aggregate is captured with a CMOS camera under stasis conditions to quantify the level of RBC aggregation. Our CVM-based aggregometer was validated against a commercial benchtop system for human blood samples under physiological and pathological conditions, and showed an excellent performance with a high intraclass correlation coefficient of 0.995. In addition, we were able to achieve a rapid measurement (<4 min) with the CVM-based aggregometer, requiring only a 6 µl blood sample. These illustrate the potential of our CVM-based aggregometer for low-cost point-of-care diagnostics without compromising the measurement sensitivity.