The influence of analytical bias on diagnostic misclassifications

Quality specifications for analytical imprecision and bias based on ‘the state of the art’, ‘biology’ and ‘analysis of clinical situations’ have been proposed by several scientists. Most interesting is the assessment of ‘diagnostic misclassifications’ based on direct evaluation of the consequences of analytical bias on the percentage of false positives and false negatives from a clinical decision situation, or based on the percentage of healthy individuals outside each reference limit when common reference intervals are used. With use of graphical or computer simulations assuming increasing (positive or negative) analytical bias, the expected percentage of misclassifications can be estimated — and, for the error for which the outcome (the fraction of misclassifications) is considered unacceptable, the maximum allowable analytical bias can be defined. An overview is given of previous proposals for specification of allowable analytical bias, and new examples are presented: (i) for S-transferrin, an analytical bias of + 10% will increase the percentage of healthy individuals with measured concentration values above the upper reference limit from 2.5 to 10%; (ii) the percentage of healthy men with concentration values for S-cholesterol above 6.2 mmol/1 (240 mg/dl) will vary between 25 and 85% for analytical bias from − 1.0 to + 1.0 mmol/l (± 16%); (iii) for glycated haemoglobin, two examples are given which illustrate the effect of analytical bias on the risk of retinopathy and so-called ‘microalbuminuria’ for measured values identical to the target 7.5% and 10.1% glycated haemoglobin, respectively. It is concluded that analytical bias may have significant impact on diagnostic performance, better standardization is needed, and quality specifications for allowable analytical bias should be based on medical usefulness criteria or, if such data are not available, on biological criteria.