The role of uncertainty regarding the results of screening immunoassays in blood establishments

Pereira, Paulo; Westgard, James O.; Encarnação, Pedro; Seghatchian, Jerard; Sousa, Gracinda de

doi:10.1016/j.transci.2015.02.015

Cited by 18 publications

(9 citation statements)

References 11 publications

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“…It has been very well illustrated by Pereira et al, that ELISA-based screening test for TTI in blood banks does involve a certain amount of uncertainty especially around the cutoff zone used for calculating the reactive samples. 11 Hence, they have emphasized on the measurement of this uncertainty around the cut-off zone in the form of grey zone sample testing. Presently, there are no such existing guidelines for grey zone sample testing in any regulatory authority in India and most of the blood banks in India follow the strategy I of one-time ELISA testing as screening procedure as per WHO guidelines.…”

Section: Discussionmentioning

confidence: 99%

Effect of grey zone sample testing of transfusion transmissible infectious diseases on safety of blood-experience of a tertiary care referral teaching hospital blood bank from South India

Anitha¹,

Babu

Praveen

et al. 2017

Int J Res Med Sci

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Background: Grey zone samples with optical density (OD) lying between cut-off OD and 10% below the cut-off OD (cut-off OD × 0.9) were identified during routine transfusion transmissible infectious disease (TTIs) screening. Enzyme-linked immunosorbent assay (ELISA) used for this purpose can sometimes fail to detect blood donors who are recently infected or possessing the low viremia. Estimation of a grey zone in ELISA testing and repeat testing of grey zone samples can further help in reducing the risks of TTI in countries where nucleic acid amplification testing for TTIs is not feasible.Methods: On performing repeat ELISA testing on grey zone samples in duplicate, the samples showing both OD values below grey zone were marked nonreactive, and samples showing one or both OD value in the grey zone were marked indeterminate. The samples on repeat testing showing one or both OD above cut-off value were labelled reactive.Results: Of the 21,908 blood donors screened during the study period, a total of 144 blood donors were found to be in grey zone. On repeat testing of these grey zone samples, 35 (24.30%) were found to be reactive for TTIs.Conclusions: Estimation of grey zone samples with repeat testing can further enhance the safety of blood transfusion in resource poor developing nations where more sophisticated and sensitive methods such as nucleic acid amplification test (NAT) is not available in all the blood banks.

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Section: Discussionmentioning

confidence: 99%

Effect of grey zone sample testing of transfusion transmissible infectious diseases on safety of blood-experience of a tertiary care referral teaching hospital blood bank from South India

Anitha¹,

Babu

Praveen

et al. 2017

Int J Res Med Sci

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“…Pereira et al, 2015 [179,180] Determination of polybrominated diphenyl ethers in water by a simple dispersive liquid-liquid microextraction-gas chromatography-mass spectrometry method. Validation parameters, including the calculation of the expanded uncertainty associated to the results in the range of quantification is included.…”

Section: Paoloni Et Al 2016 [176]mentioning

confidence: 99%

A Practical Way to ISO/GUM Measurement Uncertainty for Analytical Assays Including In-House Validation Data

González¹,

Herrador²,

GarcíaAsuero³

et al. 2018

Quality Control in Laboratory

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In this contribution, we outline the estimation of measurement uncertainty of analytical assays in a practical way, according to the so-called reconciliation paradigm, by considering the heritance of uncertainties according to the ISO Guide to the expression of uncertainty in measurement (ISO/GUM) approach and the accuracy (bias and precision) study coming from the in-house method validation. A cause and effect analysis is performed by using the Ishikawa diagram or fishbone plot, consisting of a hierarchical structure reaching a final outcome that is the analytical result. The procedure is illustrated with a case study. This procedure may be very suitable for processing data in accreditation of routine assays.

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“…The laboratorian should understand that several uncertainty components are immeasurable in Uncertainty Approach, for example, diagnostic uncertainty in tests with binary results (positive/negative). For further details about other sources of uncertainty, please refer to [18], and for diagnostic uncertainty, please refer to [19].…”

Section: Introduction To the "Guide To The Expression Of Uncertaintymentioning

confidence: 99%

Uncertainty of Measurement in Medical Laboratories

Pereira¹

2016

New Trends and Developments in Metrology

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The Guide to the Expression of Uncertainty in Measurement GUM is not systematically used in medical laboratories, for what the laboratorian should understand the Uncertainty "pproach and its importance to recognize the level of realism of results. This chapter presents, discusses, and recommends the models fulfilling GUM principles. "n example is given to a single test for an easier understanding of the determination of measurement uncertainty. "ll the practice uses a freeware. Results with larger measurement uncertainty intervals have a significant probability of being unrealistic, arising a high risk of the uncorrected clinical decision. " flow chart to the selection of models for the determination of measurement uncertainty in a medical laboratory is recommended.

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The role of uncertainty regarding the results of screening immunoassays in blood establishments

Cited by 18 publications

References 11 publications

Effect of grey zone sample testing of transfusion transmissible infectious diseases on safety of blood-experience of a tertiary care referral teaching hospital blood bank from South India

Effect of grey zone sample testing of transfusion transmissible infectious diseases on safety of blood-experience of a tertiary care referral teaching hospital blood bank from South India

A Practical Way to ISO/GUM Measurement Uncertainty for Analytical Assays Including In-House Validation Data

Uncertainty of Measurement in Medical Laboratories

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