Improved monitoring of differences in serial laboratory results.

The results of investigations in laboratory medicine are used for many purposes. Most are performed for monitoring individuals in acute situations and following the improvement or deterioration of chronic disease. In diagnosis, case-finding, and screening, conventional population-based reference intervals are relevant, and they are partitioned if necessary according to age, sex, or other important characteristics. In spite of the advantages of fixed decision limits, these intervals remain the mainstay of interpreting numerical results. Such tools are of very limited use, however, in evaluating serial results obtained for an individual. Withinindividual biological variation is well known to be much smaller than between-individual variation for nearly all substances assayed in laboratory medicine (1 ). Each individual has a range of values that span only a part of the reference interval. In consequence, individuals can have important changes in results when they all lie within the reference interval. Such changes will usually be considered unremarkable by both laboratory professionals and clinicians, and thus ignored. In addition, results can change from inside to outside the interval (and vice versa) without having clinical importance. Laboratories conventionally flag results outside the reference limits, probably provoking some unnecessary follow-up activity, if only a repetition of the investigation (2 ). Katzmann and coworkers in the current issue of the Journal have tackled the difficult problem of establishing criteria for monitoring changes in monoclonal protein concentrations (3 ). For some laboratory investigations, expert groups have recommended numerical criteria for interpreting changes. For the abnormal proteins in serum and urine investigated by Katzmann and colleagues (3 ), the guidelines from such expert groups state that reductions in the monoclonal protein in serum of at least 25% and 50% are considered minimal and partial responses, respectively, and the corresponding responses for urine monoclonal protein require at least 50% and 90% decreases (4, 5 ). As for many guidelines throughout medicine on the use of numerical data, these recommendations are round numbers. Guidelines on the imprecision required for investigations are replete with such numbers as 3%, 5%, 10%, and the like. Other examples from guidelines on clinical monitoring are statements that changes in serum troponin concentration of 20% (or 30%) are clinically important (6 ). Although such roundnumber guidelines may be based on considerable professional experience and may be easy to remember, they seem empirical. More objective strategies are required for interpreting test results in monitoring. The use of the reference change value (RCV) has been advocated as a most appropriate tool for monitoring individuals (7 ). This strategy is based on the thesis that to decide whether test results have changed in an important way (perhaps informing the clinician that the individual is improving or deteriorating), they have to have changed by more than the difference expected from the inherent sources of variation. These sources are preanalytical variation, analytical imprecision (CVA), and within-individual biological imprecision (CVI). If preanalytical variation has been minimized (2 ), then the RCV can be calculated as: RCV 2 Z (CVA 2 CVI ), where Z is the number of SDs appropriate to the probability. Thus, RCVs are very simple to derive for commonly performed investigations, because every laboratory should know CVA from internal QC programs and CVI data are available (8 ). An additional argument for the use of the RCV in monitoring is that, for many analytes, the estimates of CVI are constant over time, geography, and analytical method (7 ), and in health and chronic, stable disease (9 ); thus, RCVs can be used by all, everywhere. There are disadvantages to this approach, however, and they have been discussed recently by Cooper and colleagues (10 ). These disadvantages include the possibilities that (a) statistical information may overwhelm clinicians, (b) use of the Z score may deny clinical judgment, (c) RCV may be dependent on test frequency, (d) some biological variation may be depen1 Centre for Research into Cancer Prevention and Screening, University of Dundee, Dundee, Scotland. * Address correspondence to the author at: Centre for Research into Cancer Prevention and Screening, Ninewells Hospital and Medical School, Dundee DD1 9SY, Scotland. Fax 441382425679; e-mail [email protected]. Received September 3, 2011; accepted September 13, 2011. Previously published online at DOI: 10.1373/clinchem.2011.175026 2 Nonstandard abbreviations: RCV, reference change value; CVA, analytical imprecision; CVI, within-individual imprecision; M-spike, monoclonal protein spike. Clinical Chemistry 57:12 000 – 000 (2011) Editorials