Reference limits of high-sensitive cardiac troponin T indirectly estimated by a new approach applying data mining. A special example for measurands with a relatively high percentage of values at or below the detection limit

Abstract

A new model for the indirect estimation of reference limits (RLs) has been proposed recently and was coined TMC approach (truncated minimum chi-square estimation) which can be performed with R statistic. A spline function is applied to the RLs to get a continuous function if age is graphically presented vs. the RLs avoiding artificial “jumps” between different age groups. Most indirect models assume a power normal distribution and fail if this assumption is not fulfilled as e.g. if a relatively high percentage of measured values is below the detection limit and the data are distributed extremely skewed. This problem is handled by the TMC model. High-sensitive cardiac troponin T (hs cTnT) was chosen as an example. The hs cTnT concentration in serum or plasma is well accepted as a valuable marker in the diagnosis of acute myocardial infarction. Currently, the 99th percentile derived from a “healthy” subpopulation is the decision limit recommended by consensus groups. However, this decision limit is questioned by several authors for many reasons. In the present report, the 97.5th and the 99th percentile limits were reinvestigated by the TMC model with different subpopulations stratified according to age and sex and were finally compared to presently recommended decision limits. In summary, the generally recommended 99th percentile as a fixed decision limit should be reconsidered. It is suggested to apply more specific reference limits stratified for age and sex instead of a fixed decision limit.

Keywords

• age partitioning
• data mining
• diurnal variation
• indirect reference limits
• sex partitioning
• tmc approach
• troponin t