Aim: To investigate the effect of composite analytical bias and imprecision in the measurements of fasting plasma-glucose (fPG) for diagnosis of diabetes mellitus and estimation of risk of development and progression of retinopathy using measurements of Haemoglobin A1C (HbA1C%).
Materials and methods: Data on biological within-subject variation for fPG (5.7% and 4.9%) and HbA1C% (1.9%) from literature and data on fPG for a 'low-risk' population (regarding diabetes) from own investigations (ln-values of mean=1.6781 approximately geometric mean population=5.36 mmol/L and standard deviation=0.0891 approximately CV population=8.9%). Further, guidelines for diagnosis of diabetes (two consecutive measurements of fPG above 7.0 mmol/L) were obtained from literature as also the risk of development of and progression of retinopathy using measurements of HbA1C (a change in risk of 44% for a change in HbA1C% of 10%). It was assumed that each individual had values which over a short time had a Gaussian distribution about a biological set-point. Calculations of the effect of analytical bias and imprecision were performed by linear addition of bias and squared addition of imprecision to the squared error-free biological distribution. Composite variations of bias and imprecision were obtained by varying assumed imprecision and calculating the maximum acceptable bias for the stated situation.
Results: Two diagnostic examples are described for fPG and one for risk related to HbA1C%. Firstly, the risk of diabetes as a function of set-point and bias and imprecision was investigated, using functions where the probability of two measurements above 7.0 mmol/L was plotted against biological set-points, resulting in a S-shaped curve with a 25% probability for a set-point equal to 7.0 mmol/L. Here, a maximum 5% probability of classifying an individual with a set-point of 6.4 mmol/L (upper reference limit for the 'low-risk' population) as diabetic was used to calculate the analytical quality specifications. Comparably, the 5% probability of misclassifying a diabetic with fPG of 8.0 mmol/L was investigated, and both specifications were illustrated in an imprecision-bias plot. Secondly, the percentage of 'low-risk' individuals which would be falsely diagnosed as diabetic was calculated, and this percentage was plotted as a function of bias for different assumed values of imprecision. Thirdly, the confidence intervals for a certain risk-difference for HbA1C% of 5% or 10% was used to draw an imprecision-bias plot for different assumed changes and probabilities.
Discussion: Analytical quality taking the demands for bias and imprecision in account are obtainable in laboratories, but may be questionable for use of capillary blood and POCT instruments with considerable consequences for the number of individuals classified as diabetics, and thereby for the economy etc.
Conclusion: For clinical settings, with so clear recommendations and descriptions of risk curves as in diabetes, it is relatively easy to estimate the analytical quality specifications according to the highest level of the model hierarchy, when relevant probabilities for the events are assumed.