ISSUES IN ACCREDITATION
Measurement uncertainty: the view from haematology and haemostasis
The assessment of uncertainty of measurement is relatively new to workers in laboratory medicine. Here, Stephen MacDonald looks at the issue, which was the subject of his Congress presentation last September.
Laboratory analytical processes contribute 7.3% of laboratory errors in clinical laboratory diagnostic assays,1
significantly less than the
pre- and post-analytical phases. Until very recently, laboratory quality management focused on reducing errors associated with non-analytical phases, while largely ignoring analytical error.
Why do we need to measure uncertainty? Recently, International Organization for Standardization (ISO) and Clinical Pathology Accreditation (CPA) standards have introduced the concept of uncertainty of measurement (or measurement uncertainty [MU]) in laboratory tests and its application to the clinical utility of results generated. The CPA (2009) standard F3.3 states: “The laboratory shall determine the
uncertainty of results, where relevant and possible”. Searching the literature shows neither guidance nor consensus for how clinical haematology laboratories should assess uncertainty of measurement. United Kingdom laboratory accreditation agencies require all data generated in the clinical laboratory to be retained electronically for the purpose of audit and traceability. While archiving laboratory data is commonplace, analysis of this information is infrequently performed in the clinical laboratory. Validation, performance assessment and clinical utility of laboratory results are dependent on assessment of these data.
Concept of uncertainty In the context of medical laboratories, the determination of MU is commonly associated with assessment of the accuracy and
precision of our assays. This concept is not unfamiliar to laboratory scientists. Assays are provided with performance characteristics that are ratified and adjusted as an ongoing process in any laboratory. On a daily basis, performance of assays within these limits is monitored using internal quality control (IQC). Suitability for use is assessed using acceptance and quality control charts. Assay, analyte and laboratory-specific criteria can be put in place as necessary. Measurement uncertainty is not defined solely by the variability of repeated measurements and is therefore different from that of which we are accustomed. However, these performance characteristics may be incorporated into MU calculations. At its simplest, MU is a means of reporting
a result to our users and informing them how confident we are that the result is in the region of the value we are reporting. Two parameters are needed in order to quantify uncertainty; one is the interval and the other the confidence level. This is demonstrated most easily with a
simplified example. We take an arbitrary laboratory result of, for example, 100 units. Following our assessment of MU we could assert that the result is 100±10% with 95% confidence. This means that we are 95% certain that the ‘true’ value of what we are measuring is between 90 and 110 units, even though we have measured it as 100. The result could equally be reported with an absolute (eg 10 units) value rather than a relative (%) uncertainty. Which is used depends on the assessment of the assay, and the presence, or not, of proportional error and its associated uncertainty. We must be aware that we cannot
Common examples of familiar measurands. THE BIOMEDICAL SCIENTIST AUGUST 2016
measure the true value of any measurand with our assays. We can only give the result that we have measured and the information of how ‘certain’ we are that it is near to that value. Therefore, we are defining the quality of the laboratory result. It is in this form that MU will be most usefully employed in the medical laboratory. It gives laboratory
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