Search

Objectives Reference intervals of total cholesterol concentrations in plasma and of their fractions low-density lipoprotein (LDL)-, high-density lipoprotein (HDL)-and non-HDL concentrations are seldom studied with respect to the relevance of age and sex. Therefore, the effect of age and sex on the reference intervals was reinvestigated with 2 indirect procedures. Methods As an indirect approach, the truncated minimum chi-square method was applied. All analyses were performed by computer programs available. The script published on the homepage of the German Society of Clinical Chemistry and Laboratory Medicine (DGKL) allows to derive a continuous age dependency of reference intervals together with their confidence and equivalence limits. The results of this approach were compared with those obtained by an indirect method developed more recently, the refineR algorithm. Results In the present study, the upper reference limits of total cholesterol varied from 5.1 to 7.8 mmol/L (197–302 mg/dL) depending on various biological variables (as age, sex, inpatients versus outpatients). These upper limits increased with age. Differences between sexes can be neglected except for the age above 80 years. The pattern of reference limits of LDL cholesterol and non-HDL cholesterol paralleled those of total cholesterol. The reference limits of HDL cholesterol were higher in women than in men but were independent of age. Conclusions Reference limits for the concentrations of total cholesterol and their fractions LDL-, HDL-and non-HDL concentrations should be stratified for age and sex.

The concept of reference change values (RCVs) for diagnosis and monitoring of diseases has become well established. Several models habe been developed, e. g. one assuming a normal distribution and another one for a log-normal distribution. RCV values calculated for some measurands with both models are compared with each other and led to similar results. A few examples led to RCV values which are not plausible for diagnostic purposes. Although statistical concepts of RCV values are well established, their clinical relevance remains questionable at least for some measurands. Studies with clinicians are required whether RCVs are of practical usefulness.

Epidemiology and potential risk factors for cystic fibrosis arthropathy (CFA) were studied in a relevant cystic fibrosis (CF) patient cohort.Cohort study of patients included in the German CF registry in 2016-2017. Descriptive analysis, exploratory tests and multivariable logistic regression were used to assess prevalence of CFA and associated potential risk factors for adult patients with/without chronic Pseudomonas aeruginosa infection.6069 CF patients aged from 0 to 78 years were analysed. CFA was observed in 4.9% of the patients. Prevalence was significantly higher in adult patients (8.4%) compared to patients18 years (0.7%; p0.0001). Logistic regression analyses in adult patients (n=3319) showed that CFA was significantly associated with increasing age (OR=1.04; 95% CI: 1.02-1.05; p0.0001), female gender (OR=2.10; 95%CI:1.52-2.90; p0.0001), number of hospitalizations (OR=1.24; 95%CI:1.12-1.36; p0.0001), chronic P. aeruginosa infection (OR=1.83; 95%CI:1.28-2.61; p=0.0009), CF-related diabetes (OR=1.69; 95%CI:1.23-2.33; p=0.0013), pancreatic insufficiency (OR=2.39; 95%CI:1.28-4.46; p=0.0060) and sinusitis/polyps (OR=1.91; 95%CI:1.39-2.62; p0.0001). In a subgroup analysis of adults without chronic P. aeruginosa infection (n=1550) CFA was also significantly associated with increasing age, female gender, increasing number of hospitalizations, pancreatic insufficiency as well as sinusitis/polyps; antimycotic treatment associated only in this subgroup while association with CF-related diabetes was not significant.CFA is a frequent and clinically relevant co-morbidity particularly in adult CF patients. CFA is significantly more common in patients with chronic P. aeruginosa colonization but associations with other indicators for a more severe disease course were identified regardless of P. aeruginosa colonization status.

Reference intervals are established either by direct or indirect approaches. Whereas the definition of direct is well established, the definition of indirect is still a matter of debate. In this paper, a general definition that covers all indirect models presently in use is proposed. With the upcoming popularity of indirect models, it has become evident that further partitioning strategies are required to minimize the risk of patients’ false classifications. With indirect methods, such partitions are much easier to execute than with direct methods. The authors believe that the future of reference interval estimation belongs to indirect models with big data pools either from one laboratory or combined from several regional centres (if necessary). Independent of the approach applied, the quality assurance of the pre-analytical and analytical phase, considering biological variables and other confounding factors, is essential.

Objectives There are generally two major reasons for the comparison of reference intervals (RIs): when externally determined RIs (from the literature or provided by a manufacturer) are compared with presently used intra-laboratory RIs and when indirectly estimated RIs are compared with directly established RIs. Discrepancies within these comparisons may occur for two reasons: 1. the pre-analytical and/or analytical conditions do not agree and/or 2. biological variables influencing the establishment of RIs have not been considered adequately. If directly and indirectly estimated reference intervals (RIs) are compared with each other, they very often agree. Sometimes, however, a comparison may differ, with the reason for any discrepancy not being further studied. A major reason for differences in the comparison of RIs is that the requirement for stratification has been neglected. Methods The present report outlines the consequences to RI comparison if stratification is neglected during RI determination with the main variables affecting RIs being sex and age. Alanine aminotransferase was chosen as an example in which the RIs depend on both these factors. Results Both direct and indirect approaches lead to erroneous RIs if stratification for variables which are known to affect the estimation of RIs is not performed adequately. However, failing to include a required stratification in procedures for directly determined RIs affects the outcome in a different way to indirectly determined RIs. Conclusions The resulting difference between direct and indirect RIs is often misinterpreted as an incorrect RI estimation of the indirect method.

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.

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.

We report, to our knowledge for the first time, on distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). Linear regression, which is a simple and well-interpretable algorithm in machine learning and statistics, is utilized. The algorithm is trained using as features the Brillouin frequency shifts and linewidths of the fiber’s multipeak Brillouin spectrum. To assess and improve the effectiveness of the regression algorithm, we make use of machine learning concepts to estimate the model’s uncertainties and select the features that contribute most to the model’s performance. In addition to relative humidity, the model is also able to simultaneously provide distributed temperature information addressing the well-known cross-sensitivity effects.