Literature DB >> 24362233

Influence of analytical bias and imprecision on the number of false positive results using Guideline-Driven Medical Decision Limits.

Per Hyltoft Petersen1, George G Klee2.   

Abstract

BACKGROUND: Diagnostic decisions based on decision limits according to medical guidelines are different from the majority of clinical decisions due to the strict dichotomization of patients into diseased and non-diseased. Consequently, the influence of analytical performance is more critical than for other diagnostic decisions where much other information is included. The aim of this opinion paper is to investigate consequences of analytical quality and other circumstances for the outcome of "Guideline-Driven Medical Decision Limits". TERMS: Effects of analytical bias and imprecision should be investigated separately and analytical quality specifications should be estimated accordingly. BIOLOGICAL VARIATION AND ANALYTICAL PERFORMANCE: Use of sharp decision limits doesn't consider biological variation and effects of this variation are closely connected with the effects of analytical performance. Such relationships are investigated for the guidelines for HbA1c in diagnosis of diabetes and in risk of coronary heart disease based on serum cholesterol. The effects of a second sampling in diagnosis give dramatic reduction in the effects of analytical quality showing minimal influence of imprecision up to 3 to 5% for two independent samplings, whereas the reduction in bias is more moderate and a 2% increase in concentration doubles the percentage of false positive diagnoses, both for HbA1c and cholesterol. FREQUENCY OF FOLLOW-UP LABORATORY TESTS: An alternative approach comes from the current application of guidelines for follow-up laboratory tests according to clinical procedure orders, e.g. frequency of parathyroid hormone requests as a function of serum calcium concentrations. Here, the specifications for bias can be evaluated from the functional increase in requests for increasing serum calcium concentrations. PROBABILITY FUNCTION FOR DIAGNOSES: In consequence of the difficulties with biological variation and the practical utilization of concentration dependence of frequency of follow-up laboratory tests already in use, a kind of probability function for diagnosis as function of the key-analyte is proposed.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Analytical bias; Analytical imprecision; Analytical quality specifications; Clinical guidelines; Clinical outcome; Probability function

Mesh:

Substances:

Year:  2013        PMID: 24362233     DOI: 10.1016/j.cca.2013.12.014

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


  5 in total

Review 1.  Toward a Framework for Outcome-Based Analytical Performance Specifications: A Methodology Review of Indirect Methods for Evaluating the Impact of Measurement Uncertainty on Clinical Outcomes.

Authors:  Alison F Smith; Bethany Shinkins; Peter S Hall; Claire T Hulme; Mike P Messenger
Journal:  Clin Chem       Date:  2019-08-23       Impact factor: 8.327

Review 2.  Methods, units and quality requirements for the analysis of haemoglobin A1c in diabetes mellitus.

Authors:  Ilkka Penttilä; Karri Penttilä; Päivi Holm; Harri Laitinen; Päivi Ranta; Jukka Törrönen; Rainer Rauramaa
Journal:  World J Methodol       Date:  2016-06-26

3.  Investigation of 2 models to set and evaluate quality targets for hb a1c: biological variation and sigma-metrics.

Authors:  Cas Weykamp; Garry John; Philippe Gillery; Emma English; Linong Ji; Erna Lenters-Westra; Randie R Little; Gojka Roglic; David B Sacks; Izumi Takei
Journal:  Clin Chem       Date:  2015-03-03       Impact factor: 8.327

4.  The influence of sample freezing at - 80 °C for 2-12 weeks on glycated haemoglobin (HbA1c) concentration assayed by HPLC method on Bio-Rad D-10® auto analyzer.

Authors:  Katarzyna Bergmann; Grazyna Sypniewska
Journal:  Biochem Med (Zagreb)       Date:  2016-10-15       Impact factor: 2.313

5.  Using Sigma metrics to establish analytical product performance requirements and optimize analytical performance of an in vitro diagnostic assay using a theoretical total PSA assay as an example.

Authors:  Victoria Petrides; Sharon Schneider
Journal:  Biochem Med (Zagreb)       Date:  2018-06-15       Impact factor: 2.313

  5 in total

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