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Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Ed. by Gillery, Philippe / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter / Tate, Jillian R.

IMPACT FACTOR increased in 2015: 3.017
Rank 5 out of 30 in category Medical Laboratory Technology in the 2014 Thomson Reuters Journal Citation Report/Science Edition

SCImago Journal Rank (SJR) 2015: 0.873
Source Normalized Impact per Paper (SNIP) 2015: 0.982
Impact per Publication (IPP) 2015: 2.238

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

1 / Nam K. Tran1 / Harpreet Singh1

1Point-of-Care Testing Center for Teaching and Research (POCT·CTR), Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, CA, USA

Corresponding author: Gerald J. Kost, MD, PhD, MS, FACB, POCT·CTR, Pathology and Laboratory Medicine, 3455 Tupper Hall, School of Medicine, University of California, Davis, CA 95616, USA Phone: +1-530-574-3945, Fax: +1-530-752-4548

Citation Information: Clinical Chemistry and Laboratory Medicine. Volume 49, Issue 10, Pages 1637–1646, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: 10.1515/CCLM.2011.655, October 2011

Publication History



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.

Keywords: bandwidth; breakout; breakthrough; discrepant value; erroneous result; glucose meter; International Standards Organization (ISO) 15197; tight glucose control (TGC); tolerance limit; visual logistics

Citing Articles

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Gerald J. Kost, Pratheep Katip, Ket Vansith, and Henok Negash
Point of Care: The Journal of Near-Patient Testing & Technology, 2013, Volume 12, Number 1, Page 1
Chloe S. Tang, William J. Ferguson, Richard F. Louie, John Tuan H. Vy, Stephanie L. Sumner, and Gerald J. Kost
Point of Care: The Journal of Near-Patient Testing & Technology, 2012, Volume 11, Number 3, Page 147

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