Mapping point-of-care performance using locally-smoothed median and maximum absolute difference curves.

BACKGROUND

The goal is to introduce visual performance mapping efficient for establishing acceptance criteria and facilitating decisions regarding the utility of hospital point-of-care devices. This approach uniquely reveals the quality of performance locally, as opposed to globally.

METHODS

After presenting theoretical foundations, this study illustrates the approach by applying it to six hospital glucose meter systems (GMSs) using clinical multi-center (n=2767) and multi-system (n=613, n=100) observations.

RESULTS

LS MAD curves identified breakouts, that is, points where the locally-smoothed median absolute difference (LS MAD) curve exceeds the recommended error tolerance limit of 5 mg/dL (0.28 mmol/L). LS maximum absolute difference (MaxAD) breakthroughs, which occur where the LS MaxAD curve exceeds the 99th percentile of MaxADs from x=30-200 mg/dL (1.67-11.10 mmol/L), showed extreme error locations. A multi-sensor interference- and hematocrirt-correcting GMS displayed a flat LS MAD curve until it reached a breakout of 179 mg/dL (9.94 mmol/L) and generated breakthroughs that could affect bedside decision-making, but less erratically than other systems with inadequate performance for hospital critical care. We discovered Class I (meter high, reference low) and Class II (converse) discrepant values in some systems. Class I errors could lead to inappropriate insulin dosing and hypoglycemic episodes in tight glucose control.

CONCLUSIONS

LS MAD-MaxAD curves help assess the performance of point-of-care testing. Visual mapping of systematic and random errors locally over the entire analyte measurement range in a single integrated display is an advantage when considering the adverse impact of zones of poor quantitative performance on specific clinical applications, threshold-driven bedside decisions and the care of critically ill patients.