Literature DB >> 32416173

An approach to selecting auto-verification limits and validating their error detection performance independently for pre-analytical and analytical errors.

Huub H van Rossum1.   

Abstract

BACKGROUND: Auto-verification limits are widely used to trigger confirmatory actions to enable detection of pre-analytical, analytical and post-analytical errors. An approach is presented for validating auto-verification limit performance in a laboratory-specific manner, independently for pre-analytical and analytical error detection.
METHODS: To evaluate this approach, MA Generator (www.huvaros.com) was used to run error-detection simulations using various upper-limit checks (ULC) and lower-limit checks (LLC). Pre-analytical error detection was defined as triggering of a limit check alarm within one erroneous result. Analytical error detection was defined as triggering a limit check alarm within the scheduled internal QC measurement interval, both with ≥97.5% probability. Furthermore, the limit check alarm rates were obtained.
RESULTS: Pre-analytical error detection and rapid detection of larger analytical errors by limit checks outperformed moving average quality control at the cost of a significantly larger number of alarms. A pre-analytical error detection by LLC and ULC of ≥-55% and >60%, ≥-10% and ≥20%, and ≥-40% and ≥50% and an analytical error detection of ≥-4% and ≥15%, ≥-3% and ≥4% and ≥-30% and ≥25% were obtained for hemoglobin, sodium and calcium, respectively.
CONCLUSIONS: The obtained ULC and LLC alarm rate and error detection performance, enabled substantiated selection of optimal auto-verification limits and validation thereof.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Analytical; Analytical quality control; Limit check; Moving average; Patient-based real-time QC; Pre-analytical; Quality assurance

Mesh:

Substances:

Year:  2020        PMID: 32416173     DOI: 10.1016/j.cca.2020.05.026

Source DB:  PubMed          Journal:  Clin Chim Acta        ISSN: 0009-8981            Impact factor:   3.786


  2 in total

1.  An Objective Approach to Deriving the Clinical Performance of Autoverification Limits.

Authors:  Tze Ping Loh; Rui Zhen Tan; Chun Yee Lim; Corey Markus
Journal:  Ann Lab Med       Date:  2022-09-01       Impact factor: 4.941

2.  Development and implementation of an LIS-based validation system for autoverification toward zero defects in the automated reporting of laboratory test results.

Authors:  Di Jin; Qing Wang; Dezhi Peng; Jiajia Wang; Bijuan Li; Yating Cheng; Nanxun Mo; Xiaoyan Deng; Ran Tao
Journal:  BMC Med Inform Decis Mak       Date:  2021-06-02       Impact factor: 2.796

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.