1. Introduction to the problem/question
Case study: Determination of measurement uncertainty
and diagnostic uncertainty in screening immunoassays
Paulo Pereira
Quality Management Department,
Portuguese Institute of Blood and Transplantation, Lisbon, Portugal
Introduction to the problem/question
Objectives
The determination of measurement uncertainty is not systematically performed in screening immunoassays, remaining a complex challenge when required, such as in an ISO test’s accreditation. Commonly the laboratorian does not focus the measurement on the decision value (cutoff), making the estimation useless considering the role of measurement uncertainty in the post-transfusion and post-transplantation safety. Also the estimation is commonly focused in measurement uncertainty, when the impact of the analytical uncertainty sources is not so clinically significant as the classical Bayesian estimations.
This poster briefly presents a novel flow chart to select models to evaluate uncertainty in screening immunoassays’ results considering not only the effect of analytical uncertainty but also the effect on the diagnostic accuracy.
Methodology
The measurement uncertainty is used to determine the “rejection zone” (an alternative concept of “grey zone”) fulfilling GUM principles. The seronegative window period and the Bayesian estimators determine the uncertainty caused by biological sources: it is determined the seronegative window period, diagnostic sensitivity, specificity, and area under ROC curve (AUC) when infected and healthy subjects samplings are available. Otherwise, it is determined the agreement of results. The flowchart illustrates the steps to select a model1.
Results
The determinations are performed to an immunoassay to detect anti-HCV. Results equal or higher than a ratio of 0.70 will be in the ‘”rejection zone”, and the corresponding blood components or candidature to tissue donor are declared not compliant and are rejected2. The seronegative period is 97 days (biological bias with impact in diagnostic accuracy/uncertainty)3. The diagnostic uncertainty of the results is considered 5% for diagnostic sensitivity and specificity considering a 95% CI. The AUC uncertainty result is analogous to the previous outcome4.
Conclusions
References
The GUM principles are applicable to determine measurement uncertainty used to define the “rejection zone”. The uncertainty associated to biological sources is expressed by the alpha and beta error (or secondary FPR and FNR) in diagnostic sensitivity and specificity, respectively. Both evaluations has a clinically significant role to the post-transfusion and post-transplantation safety since measure the risk of failure in the correct expression of results. For further details about the selection and estimations of uncertainty, please refer to references.
[1] Pereira P (2016). Uncertainty of measurement in medical laboratories. In Luigi Cocco (Ed.), Measurement Systems. Rijeka: Intech. ISBN
[2] Pereira P, Magnusson B, Theodorsson E, Westgard J, Encarnação P (2015). Measurement uncertainty as a tool for evaluating the “grey-zone” to reduce the false negatives in immunochemical screening of blood donors for infectious diseases. Accred Qual Assur, 21(1):25-32, DOI: /s x..
[3] Pereira P, Westgard J, Encarnação P, Seghatchian J (2014). Analytical model for calculating indeterminate results interval of screening tests, the effect on seroconversion window period: a brief evaluation of the impact of uncertain results on the blood establishment budget. Transfusion and Apheresis Science, 51(2):126–31, DOI: /j.transci
[4] Pereira P, Westgard J, Encarnação P, Seghatchian J (2015). Evaluation of the measurement uncertainty in screening immunoassays in blood establishments: Computation of diagnostic accuracy models. Transfus Apher Sci , 52(1):35-41, DOI: /j.transci