Reprint of "Influence of analytical bias and imprecision on the number of false positive results using Guideline-Driven Medical Decision Limits"

A New Concept for Reference Change Values-Regression to the Population Mean

BACKGROUND

Reference change values (RCV) are used to indicate a change in analyte concentration that is unlikely to be due to random variation in the patient or the measurement. Current theory describes RCV relative to a first measurement result (X1). We investigate an alternative view predicting the starting point for RCV calculations from X1 and its location in the reference interval.

METHODS

Data for serum sodium, calcium, and total protein from the European Biological Variation study and from routine clinical collections were analyzed for the effect of the position of X1 within the reference interval on the following result from the same patient. A model to describe the effect was determined, and an equation to predict the RCV for a sample in a population was developed.

RESULTS

For all data sets, the midpoints of the RCVs were dependent on the position of X1 in the population. Values for X1 below the population mean were more likely to be followed by a higher result, and X1 results above the mean were more likely to be followed by lower results. A model using population mean, reference interval dispersion, and result diagnostic variation provided a good fit with the data sets, and the derived equation predicted the changes seen.

CONCLUSIONS

We have demonstrated that the position of X1 within the reference interval creates an asymmetrical RCV. This can be described as a regression to the population mean. Adding this concept to the theory of RCVs will be an important consideration in many cases.

The use of error and uncertainty methods in the medical laboratory

Error methods - compared with uncertainty methods - offer simpler, more intuitive and practical procedures for calculating measurement uncertainty and conducting quality assurance in laboratory medicine. However, uncertainty methods are preferred in other fields of science as reflected by the guide to the expression of uncertainty in measurement. When laboratory results are used for supporting medical diagnoses, the total uncertainty consists only partially of analytical variation. Biological variation, pre- and postanalytical variation all need to be included. Furthermore, all components of the measuring procedure need to be taken into account. Performance specifications for diagnostic tests should include the diagnostic uncertainty of the entire testing process. Uncertainty methods may be particularly useful for this purpose but have yet to show their strength in laboratory medicine. The purpose of this paper is to elucidate the pros and cons of error and uncertainty methods as groundwork for future consensus on their use in practical performance specifications. Error and uncertainty methods are complementary when evaluating measurement data.

Strategies to define performance specifications in laboratory medicine: 3 years on from the Milan Strategic Conference

Measurements in clinical laboratories produce results needed in the diagnosis and monitoring of patients. These results are always characterized by some uncertainty. What quality is needed and what measurement errors can be tolerated without jeopardizing patient safety should therefore be defined and specified for each analyte having clinical use. When these specifications are defined, the total examination process will be "fit for purpose" and the laboratory professionals should then set up rules to control the measuring systems to ensure they perform within specifications. The laboratory community has used different models to set performance specifications (PS). Recently, it was felt that there was a need to revisit different models and, at the same time, to emphasize the presuppositions for using the different models. Therefore, in 2014 the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) organized a Strategic Conference in Milan. It was felt that there was a need for more detailed discussions on, for instance, PS for EQAS, which measurands should use which models to set PS and how to set PS for the extra-analytical phases. There was also a need to critically evaluate the quality of data on biological variation studies and further discussing the use of the total error (TE) concept. Consequently, EFLM established five Task Finish Groups (TFGs) to address each of these topics. The TFGs are finishing their activity on 2017 and the content of this paper includes deliverables from these groups.

The robustness of diagnostic tests for GH deficiency in adults

Since the 1970s, GH treatment has been an important tool in paediatric endocrinology for the management of growth retardation. It is now accepted that adults with severe GH deficiency (GHD) demonstrate impaired physical and psychological well-being and may benefit from replacement therapy with recombinant human GH. There is, however, an ongoing debate on how to diagnose GHD, especially in adults. A GH response below the cut-off limit of a GH-stimulation test is required in most cases for establishing GHD in adults. No 'gold standard' GH-stimulation test exists, but some GH stimulation tests may be more robust to variations in patient characteristics such as age and gender, as well as to pre-test conditions like heat exposure due to a hot bath or bicycling. However, body mass index (BMI) is negatively associated with GH-responses to all available GH-stimulation tests and glucocorticoid treatment, including conventional substitution therapy, influences the GH-responses. Recently, the role of IGF-I measurements in the clinical decision making has been discussed. The aim of this review is to discuss the available GH-stimulation tests. In this author's opinion, tests which include growth-hormone-releasing hormone (GHRH) tend to be more potent and robust, especially the GHRH+arginine test which has been proven to be of clinical use. In contrast, the insulin tolerance test (ITT) and the glucagon test appear to have too many drawbacks.