Clinical outcome estimates based on treatment target limits of laboratory tests: proposal for a plot visualizing effects and differences of medical target setting exemplified by glycemic control in diabetes

: In modern medicine many laboratory measurements are used as a surrogate for clinical outcome. There is therefore a need for models to quantify this approach. The target plot presented here is based on the difference plot. The difference between two measurements is plotted against the initial measurement, of which the first measurement is the index measurement (M ) and the second the outcome measurement (M ). A vertical line separates normal vs. increased M concentrations according to a selected target concentration, and a horizontal line describes the situation of no change. A target line (M ) with a slope equal to −1 and indicating the selected target concentration is drawn through the crossing point. The six outcome areas thus refer to the combined effect of M and M . A target plot taking the biological and analytical variation in consideration is considered. : The target plot addresses sources of outcome categories, and calculates the percentage in each of these categories according to a defined target value. Hemoglobin A (HbA ) in diabetes in patients from Vejle County, Denmark was taken as an example. : Different strategies for target setting for HbA were investigated: the Danish Diabetes Association (DDA) target of 5.84% HbA , the target from the American Diabetes Association (ADA) of 7.0% HbA , and 6.62% HbA , which is the 99.9 centile of the Danish national reference interval. All three strategies can be validated from the target plot for differences in surrogate clinical outcome in spite of identical patient material. Use of the ADA target (the highest nominal value) reveals the lowest percentage of index values above the target, and of those the highest percentage outcome values of the surrogate measure reached the target. : The target plot allows detailed stratification of outcome based on surrogate parameters and assessment of risk of disease based on the selected target. The need to standardize outcome targets in diabetes to allow comparison of quality of treatment is obvious.