Description of a generally applicable model for the evaluation of uncertainty of measurement in clinical chemistry.
There is a growing pressure on clinical chemistry laboratories to conform to quality standards that require the evaluation and expression of the uncertainty of results of measurement. Nevertheless, there is some reluctance to accept the uncertainty concept in the analytical community due to difficulty in evaluating uncertainty in practice. For example, often the uncertainty of some uncertainty components is not known very well in clinical chemistry measurements, such as those associated with matrix effects or with the values of the calibrators. Moreover, it is not clear how to interpret uncertainty in relation to diagnostic criteria, reference ranges and other decision limits in clinical chemistry practice. Hence, the value of reporting the uncertainty of the measurement result is not obvious. In this paper it is suggested a relatively simple, logical procedure for evaluating measurement uncertainty based on the principles in the Guide for the Expression of Uncertainty of Measurement (GUM). The measurement process is partitioned into elements that are well known to the analyst, namely sampling, calibration, and analysis. The corresponding model function expresses the result of a measurement as the value obtained by the analytical procedure multiplied by the correction factors for sampling bias, for bias caused by the calibrators, and for other types of bias. Under normal conditions, when the measurement procedure is validated and corrected for all known bias, the expected value of each correction factor is one. The uncertainty that remains with regard to sampling, manufacturing of calibrators and other types of bias is combined with the analytical imprecision to yield a combined uncertainty of a result of measurement. The advantages of this approach are: (i) Data from the method validation, internal quality control and from participation in external quality control schemes can be used as input in the uncertainty evaluation process. (ii) The partition of the measurement into well-defined tasks highlights the different responsibilities of the clinical chemistry laboratory and of the manufacturer of reagents and calibrators. (iii) The approach can be used to harmonize the uncertainty evaluation process, which is particularly relevant for laboratories seeking accreditation under ISO 17025. The application of the proposed model is demonstrated by evaluating the uncertainty of a result of a measurement of prolactin in human serum. In the example it is shown how to treat the uncertainty associated with a calibrator supplied with a commercial analytical kit, and how to evaluate the uncertainty associated with matrix effects.
Kristiansen
J
J
The National Institute of Occupational Health, Copenhagen, Denmark. jkr@ami.dk
D015982:Bias; D001774:Blood Chemical Analysis / Q000592:standards / Q000706:statistics & numerical data; D002624:Chemistry, Clinical / Q000592:standards / Q000706:statistics & numerical data*; D019963:Clinical Chemistry Tests / Q000592:standards / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D011388:Prolactin / Q000097:blood / Q000592:standards; D011786:Quality Control; D011863:Radioimmunoassay / Q000592:standards / Q000706:statistics & numerical data; D012015:Reference Standards
D011388:Prolactin