Calculation of limits for significant bidirectional changes in two or more serial results of a biomarker based on a computer simulation model

Lund, Flemming; Petersen, Per Hyltoft; Fraser, Callum G.; Sölétormos, György

doi:10.1177/0004563214555163

Cited by 28 publications

(35 citation statements)

References 8 publications

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“…In this case, the RCV has to be used for a change between two [2,39,40] or more [41] consecutive results. The formula for a change between two results is: RCV = z*2…”

Section: Use Of Databases In Laboratory Medicinementioning

confidence: 99%

Rationale for using data on biological variation

Ricós¹,

Álvarez²,

Perich³

et al. 2015

Clinical Chemistry and Laboratory Medicine (CCLM)

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Abstract:The aims of this study are: 1) to use the data included in the biological variation (BV) database to address the usability of BV estimates; and 2) to use different examples from the authors' laboratories to illustrate the use and the usefulness of BV data in laboratory medicine. The BV database is an essential tool for laboratory management. Examples of application of data derived from BV are given in this paper, such as analytical performance specifications that have been included in various quality control software designed to optimize operative rules; also they have been incorporated as acceptability limits in external quality assurance reports. BV data from pathological status are of utmost interest for monitoring patients and differences between the intra-individual coefficients of variation (CV I ) estimated from healthy and patients are shown. However, for a number of analytes there are limited data available and for many there are no data, consequently new studies should be encouraged at an international level. In addition, developing international criteria to evaluate publications dealing with the estimation of BV components would be of the utmost interest. We are ready and willing to collaborate with such worthy initiatives. The first EFLM strategic conference on analytical performance specifications is an excellent opportunity for debating these ideas.

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“…In this case, the RCV has to be used for a change between two [2,39,40] or more [41] consecutive results. The formula for a change between two results is: RCV = z*2…”

Section: Use Of Databases In Laboratory Medicinementioning

confidence: 99%

Rationale for using data on biological variation

Ricós¹,

Álvarez²,

Perich³

et al. 2015

Clinical Chemistry and Laboratory Medicine (CCLM)

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“…The proposed approach works independently from the criterion applied to set the quality specifications. They can be based on true or false classification [13] as in case of total cholesterol or HbA 1c , biological variation [11] or state-of-the-art [14].…”

Section: Discussionmentioning

confidence: 99%

How to define a significant deviation from the expected internal quality control result

Ceriotti

Brugnoni

Mattioli

2015

Clinical Chemistry and Laboratory Medicine (CCLM)

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Background: Internal quality control (IQC) is an everyday practice described in several documents. Its planning requires the definition of quality goals and a documentation system able to provide alarms as soon as the goals are not reached. We propose the use of the uncertainty approach to develop an effective alarm system. Methods: The use of the uncertainty information to verify the conformity to specifications is described. A top-down approach to the definition of the uncertainty of the method is described. Once the uncertainty is calculated, the complete measurement result (result ± expanded uncertainty) is compared with the maximum permissible error (quality goal). An alternative and more immediate presentation is obtained defining an "acceptance zone" derived from the maximum permissible error reduced on either sides by expanded uncertainty. This approach is applied to two analytes: glucose and creatinine. Results:The relationship between quality goal and expanded uncertainty defines the width of the acceptance zone; if uncertainty is equal or larger than the quality goal, the goal is not attainable. Conclusions:The proposed approach uses an information, expanded uncertainty, that each laboratory seeking ISO 15189 accreditation should already have. The data presentation is immediate and easy to interpret allowing a direct comparison between the performance of the method and the quality goals.

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Section: Introductionmentioning

confidence: 99%

“…[6]. Several manuscripts can be found in this field, for example [3,[9][10][11]. The approach defines criteria for normal variation in two sequential measurements, which mathematically are often defined as The calculation of RCVs for many different laboratory outcomes is quite simple since all laboratories know approximately the analytical and intra-individual variabilities for medical laboratory results from samples from the healthy population.…”

mentioning

confidence: 99%

A note on the calculation of reference change values for two consecutive normally distributed laboratory results

Regis

Postma

Heuvel

2017

Chemometrics and Intelligent Laboratory Systems

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Regis, M.; Postma, Th.A.; van den Heuvel, E.R. Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication Citation for published version (APA):Regis, M., Postma, T. A., & van den Heuvel, E. R. (2017). A note on the calculation of reference change values for two consecutive normally distributed laboratory results. Chemometrics and Intelligent Laboratory Systems, 171, 102-111. DOI: 10.1016/j.chemolab.2017.10.008 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. it is shown that positive shifts in the individual average health condition are difficult to detect, while it is much easier to identify negative shifts. IntroductionMedical laboratory results are traditionally compared with normal reference limits, i.e. ranges of values that are expected for healthy persons. They are typically defined by the lower and upper 5% quantiles of a reference group, i.e. subjects for which no morbidity is assessed [7]. These population-based reference ranges are mere cut-off values and can lead to false positives and false negatives. The classification of a normal measurement does not guarantee that the value is normal for the specific patient and alternatively an abnormal measurement does not necessarily imply disease alert, in particular when the value is close to the critical threshold. The reason is that measurements in individuals are affected by true condition's shifts, but also by some other inherent causes, such as pre-analytical, analytical, between-and within-subject biological variations [5]. Population-based reference ranges do not separate these sources of variation.The considerations ...

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Calculation of limits for significant bidirectional changes in two or more serial results of a biomarker based on a computer simulation model

Abstract: The first result should be multiplied by the appropriate factors for increase and decrease to give the limits for a significant bidirectional change in several consecutive measurements.

Cited by 28 publications

References 8 publications

Rationale for using data on biological variation

Rationale for using data on biological variation

How to define a significant deviation from the expected internal quality control result

A note on the calculation of reference change values for two consecutive normally distributed laboratory results

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