Band 45, Heft 2 - Special issue: Indirect Reference Limits; Issue Editor: Rainer Haeckel

Reference intervals (RIs) can be determined by direct and indirect procedures. Both approaches identify a reference population from which the RIs are defined. The crucial difference between direct and indirect methods is that direct methods select particular individuals after individual anamnesis and medical examination have confirmed the absence of pathological conditions. These individuals form a reference subpopulation. Indirect methods select a reference subpopulation in which the individuals are not identified. They isolate a reference population from a mixed population of patients with pathological and non-pathological conditions by statistical reasoning. At present, the direct procedure internationally recommended is the “gold standard”. It has, however, the disadvantage of high expenses which cannot easily be afforded by most medical laboratories. Therefore, laboratories adopt RIs established by direct methods from external sources requiring a high responsibility for transference problems which are usually neglected by most laboratories. These difficulties can be overcome by indirect procedures which can easily be performed by most laboratories without causing economic problems. The present review focuses on indirect approaches. Various procedures are presented with their benefits and limitations. Preliminary simulation studies indicate that more recently developed concepts are superior to older approaches.

The indirect approach to defining reference intervals operates ‘ a posteriori ’, on stored laboratory data. It relies on being able to separate healthy and diseased populations using one or both of clinical techniques or statistical techniques. These techniques are also fundamental in a priori , direct reference interval approaches. The clinical techniques rely on using clinical data that is stored either in the electronic health record or within the laboratory database, to exclude patients with possible disease. It depends on the investigators understanding of the data and the pathological impacts on tests. The statistical technique relies on identifying a dominant, apparently healthy, typically Gaussian distribution, which is unaffected by the overlapping populations with higher (or lower) results. It depends on having large databases to give confidence in the extrapolation of the narrow portion of overall distribution representing unaffected individuals. The statistical issues involved can be complex, and can result in unintended bias, particularly when the impacts of disease and the physiological variations in the data are under appreciated.

Reference intervals depend on the distribution of results within a reference population and can be influenced by subclinical disease. Functional reference limits present an opportunity to derive clinically relevant reference limits from routinely collected data sources, which consist of mixed populations of unhealthy and healthy groups. Serum ferritin is a good example of the utility of functional reference limits. Several studies have identified clinically relevant reference limits through examining the relationship between serum ferritin and erythrocyte parameters. These ferritin functional limits often represent the inflection point at which erythrocyte parameters change significantly. Comparison of ferritin functional reference limits with those based on population distributional reference limits reveals that the lower reference limit may fall below the point at which patients become clinically unwell. Functional reference limits may be considered for any biomarker that exhibits a correlated relationship with other biomarkers.

Significant variation in reported reference intervals across healthcare centers and networks for many well-standardized laboratory tests continues to exist, negatively impacting patient outcomes by increasing the risk of inappropriate and inconsistent test result interpretation. Reference interval harmonization has been limited by challenges associated with direct reference interval establishment as well as hesitancies to apply currently available indirect methodologies. The Truncated Maximum Likelihood (TML) method for indirect reference interval establishment developed by the German Society of Clinical Chemistry and Laboratory Medicine (DGKL) presents unique clinical and statistical advantages compared to traditional indirect methods (Hoffmann and Bhattacharya), increasing the feasibility of developing indirect reference intervals that are comparable to those determined using a direct a priori approach based on healthy reference populations. Here, we review the application of indirect methods, particularly the TML method, to reference interval harmonization and discuss their associated advantages and disadvantages. We also describe the CSCC Reference Interval Harmonization Working Group’s experience with the application of the TML method in harmonization of adult reference intervals in Canada.

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.

The glucose concentration in plasma or serum is one of the most often requested analytical values in laboratory medicine. Whereas the analytical part of the glucose determination is well standardised, the standardisation of the pre-examination part (pre-analytical phase) is not sufficiently solved, yet. In view of the present controversial discussion regarding the most efficient prevention of pre-analytical glycolysis, the question arises whether the economical and logistic expenses for inhibiting glycolysis determining random glucose concentration are justified. In hospitals with adequate logistics (e.g. pneumatic tube systems for blood tubes) to guarantee a blood sample transport time of about 1 – 2 h, plasma or serum without prevention of glycolysis can be applied for random glucose concentrations if the reference limits are estimated by the laboratory. If such logistics are not available, especially in primary care services, either plasma or serum samples or whole blood in special tubes with anti-glycolytic additives may be sent to the laboratory.

Objectives Indirect methods for the estimation of Reference Limits (RLs) use large data pools stored in modern laboratory information’s systems. To avoid correlation between observations repeated results from each patient should be excluded. Some data pools obtained are anonymized, and thereafter the data cannot be re-identified. The effect of the procedure of data selection on the estimations is not investigated yet. Methods We considered four parameters. Data sets were enclosed from two sources: a university hospital and a laboratory primarily reflecting a patient population from medical practitioners. Four algorithms were used for data selection, which generate first, last, all and non-repeated values. RLs were estimated through these data sets and compared. Results This study showed the broader reference range estimated by indirect methods if using the whole data set compared to first/last values or non-repeated values. Conclusions The use of all data without a filtering step results in a significant bias whereas the choice of first or last values has nearly no impact. The exclusion of repeated measurements results in narrower RLs. This influence confine the use of anonymous data sets where filtering is impossible for the estimation of RLs by indirect methods.

Objectives Plasma glucose concentrations exhibit a pronounced daytime-dependent variation. The oscillations responsible for this are currently not considered in the determination of reference limits (RL) and decision limits. Methods We characterized the daily variation inherent in large-scale laboratory data from two different university hospitals (site 1 n=513,682, site 2 n=204,001). Continuous and distinct RL for daytime and night were estimated. Diurnal characteristics of glucose concentrations were further investigated by quantile regression analyses introducing age and cosinor-functions as predictors in the model. Results Diurnal variations expressed as amplitude/Midline Estimating Statistic of Rhythm (MESOR) ratio, averaged 7.7% (range 5.9–9.3%). The amplitude of glucose levels decreased with increasing concentrations. Between 06:00 and 10:00 h an average decrease of 4% has to be considered. Nocturnal glucose samples accounted for only 5% of the total amount but contributed to 19.5% of all findings over 11.1 mmol/L. Partitioning of RL between day and night is merely justified for the upper reference limit. The nocturnal upper RLs for both genders differed from those obtained during the day by 11.0 and 10.6% at site 1 and by 7.6 and 7.5% at site 2. Conclusions We conclude that indirect approaches to estimate upper RL of random plasma glucose concentrations require stratification concerning the time of sample collection.

It has been observed that the estimation of reference intervals of leukocytes in whole venous blood leads to higher upper reference limits (uRLs) with indirect methods than has been reported in the literature determined by direct approaches. This phenomenon was reinvestigated with a newer, more advanced indirect method, and could be confirmed. Furthermore, a diurnal variation was observed with lower values during the morning and higher values in the late afternoon and at night. This observation can explain why indirect approaches using samples collected during 24 h lead to higher uRLs than direct methods applied on samples collected presumably in the morning.

Objectives Mean platelet volume (MPV), platelet distribution width (PDW), and plateletcrit (PCT) possess diagnostic and prognostic capabilities in a variety of diseases. We aimed to establish reference intervals (RI) for platelet indices (PI) in seniors. Methods We established direct and indirect RI for MPV, PDW, and PCT in selected reference individuals aged 60 years and older. Abnormal PI were assessed in a population-based setting in the Principality of Liechtenstein, where 37.7% of the whole nation’s population aged 60 years and older had PI determined by hematology analyzers from Sysmex (Horgen, Switzerland). Results Among 689 female and 542 male participants, MPV and PDW did not exhibit age- and gender-specific differences, whereas PCT in females also displayed no age-specific differences. Age- and sex-independent RI were 9.3–12.5 fl for MPV and 10.1–16.7% for PDW, whereas the age-independent RI for PCT in women was 0.18–0.37. In males, age-specific RI for PCT were 0.16–0.30 (age 60–69), 0.15–0.33 (age 70–79), and 0.14–0.33 (age 80 and older). The population-based frequency of abnormal PI results was 0.8% (MPV), 1.1% (PDW), and 24.4% (PCT). Conclusions Applying novel RI for PI reveals that only approximately 1% of patients exhibit abnormal MPV and PDW. Abnormal PCT is observed much more frequently.

Objectives Most laboratories have difficulties to determine their own reference intervals for the diagnostic evaluation of patient results by direct methods. Therefore, data is often just taken from the literature or package inserts of the analytical tests. Methods The section on Reference Limits of the German Society for Clinical Chemistry and Laboratory Medicine (DGKL) first uploaded the Reference Limit Estimator (RLE) as an R-program with MS Excel-interface on the DGKL home page and now this tool is implemented in the commercial Laboratory Information System OPUS::L (OSM AG Essen, Germany). We used this OPUS::L “Population specific Reference Limits” tool online with our laboratory database. First calculations were done using the example of lipase. Results The manufacturer’s original reference interval for lipase 12–53 U/L (adults) was changed to age dependent upper reference limits of <41 U/L (<20 years), <60 U/L (20–80 years) and <70 U/L (>80 years). Conclusions By means of the OPUS::L “Population specific Reference Limits” tool we were able to establish our laborarotry specific reference interval for plasma lipase activity. The new reference limits helped to solve an old problem of implausible low elevated lipase values.