Making the most of a patient's laboratory data: optimisation of signal-to-noise ratio.

All results in laboratory medicine are compared to some reference for interpretation. This reference may be a previous result from the same patient, a reference population--either healthy or diseased, or both--or a decision limit recommended by an expert group. The aim for the medical laboratory is to improve the signal-to-noise ratio by increasing the signal or reducing the noise. This presentation deals with the more general tools for reduction of the noise component, and focuses on biological within-subject variation, reference intervals and decision models. Regarding biological within-subject variation, the estimation of reference change value (RCV) as a yardstick for judging measured differences within the patient over time is an important tool. Here, only type 1 errors are usually applied, but type 2 errors should also be taken into consideration. Moreover, variance homogeneity is assumed for the application of RCV, but this assumption is not always fulfilled, and erroneous interpretations may be introduced. A tool for comparison of different and more complicated algorithms applied to serial measurements is computer simulation (e.g. on data from tumour markers). In order to reduce the noise component from reference intervals, partitioning according to relevant subgroups is a tool, and useful criteria for judging whether subgroups should be combined are reported. Geographical and racial differences may cause different reference distributions (e.g. plasma proteins), but it has been possible to establish common reference intervals for 25 common components in Caucasians in the five Nordic countries. Transformation of data and presentation of accumulated ranked values in rankit plots where Gaussian (or log-Gaussian) distributions show up as straight lines is a valuable tool for interpretation of the distributions and comparison of subgroups. In this way it is often possible to isolate a low-risk group which fits a log-Gaussian distribution. In case of thyroid autoantibodies the distributions look biphasic, even after all possible rule-out criteria have been exhausted, but a composite model makes it possible to extract a reference population from the mixture. The classical decision model is bimodal, reflecting an assumption of two independent but overlapping distributions, with a clear but unknown prevalence for the disease. When a decision limit (cut-off point) is applied, the percentages of false positive and false negative results will be determined, but the underlying prevalence is unchanged. In contrast to this, the unimodal distributions cover a continuum of probabilities for a certain disease (risk) and the decision limit is arbitrarily chosen. Thus, the disease or risk is defined by the measured component. Consequently, the decision limit directly defines the prevalence, and this limit can be different over time and geography as has been the case for cholesterol or glucose.