Background: Interpretation of serial data in monitoring of patients is usually performed by use of the ‘reference change value’ (RCV). While this tool for interpretation of measured differences is simple and clear, there are a number of drawbacks attached to the uncritical use of this concept. It is a dichotomised interpretation of continuous data using a fixed probability without any counter hypothesis. Therefore, a tool for better understanding and interpretation of measured differences in monitoring is needed.
Theory: The concept of sensitivity, specificity, likelihood ratios and odds used for diagnostic test evaluations is applied to monitoring by substituting measured concentrations with measured differences. Thus, two frequency distributions of differences are assumed, one for a stable, steady-state, situation and one for a certain change. Values exceeding a measured difference will thus represent ‘false change’ for the stable and ‘true change’ for the change and the ratio between these will define the likelihood. By making the hypothesis of change variable and equal to the actual difference, the distribution corresponding to the true changes for the measured difference varies with this. Consequently, the likelihood ratio for change increases with growing measured difference and when used together with the pre-test odds or pre-test probability, the post-test odds and post-test probability, related to the clinical situation, can be calculated.
Results: One example is acute intermittent porphyria, where increasing excretion of porphobilinogen is characteristic for an attack. The within-subject biological variation is estimated to 25%, which for two measurements gives a variation of 35% for measured differences. Three pre-test probabilities are assumed and illustrate that post-test odds and probability depends on both the pre-test probability and the measured difference. A second example is monitoring women in a follow-up after treatment of breast cancer, using the tumour marker CA 15-3. The within-subject biological variation is estimated to 14.9% and for differences 21% (2½×14.9 due to two measurements). Here, the monitoring is totally scheduled and the frequency of progression depends on the time after treatment. Thus, the pre-test probability varies with time so that a certain measured difference with a given likelihood ratio will result in varying post-test odds depending on time.
Conclusions: The concept presented here expands the earlier concept of RCVs by making it possible to have an estimate of the post-test odds for a certain difference to occur based on likelihood ratios and pre-test odds.
Clin Chem Lab Med 2008;46:157–64.