Estimation of the celiac disease prevalence in Denmark and the diagnostic value of HLA-DQ2/DQ8.

The aims of the present study were to estimate the celiac disease (CD) prevalence in Denmark and the relevance of the test for human leukocyte antigen DQ2 and DQ8 haplotypes (HLA-DQ2/DQ8) for diagnosing CD. The plasma IgA transglutaminase antibody (TGA-IgA) and HLA-DQ2/DQ8 tests should normally be positive for a CD diagnosis. First, we estimated the CD and HLA-DQ2/DQ8 prevalence in the available blood samples collected at the North Zealand Hospital. Out of a total of 9754 patients, with symptoms suggestive of CD (such as malabsorption, diarrhea, steatorrhea, ion-deficiency anemia, and weight loss or growth failure), 153 patients had TGA-IgA positive results (i.e. TGA-IgA > 10 U/mL). In this cohort, the prevalence of CD was 0.912% and the prevalence of HLA-DQ2/DQ8 positive patients was estimated to be 62%. Based on the distribution of HLA-DQ2/DQ8 positive individuals in the general Danish population, a calculation of CD prevalence in Denmark was found to be maximum 0.77%. Second, we analysed for the HLA-DQ2/DQ8 haplotypes in 293 positive plasma TGA-IgA samples. Nearly all (99%) of the CD patients and non-CD patients were HLA-DQ2/DQ8 positive.

Plasma creatinine medians from patients partitioned by gender and age used as a tool for assessment of analytical stability at different concentrations.

Background Monthly medians of patient results are useful in assessment of analytical quality in medical laboratories. Separate medians by gender makes it possible to generate two independent estimates of contemporaneous errors. However, for plasma creatinine, reference intervals (RIs) are different by gender and also higher over 70 years of age. Methods Daily, weekly and monthly patient medians were calculated from the raw data of plasma creatinine concentrations for males between 18 and 70 years, males >70 years, females between 18 and 70 years and females >70 years. Results The medians of the four groups were all closely associated, with similar patterns. The mean of percentage bias from each group defined the best estimate of bias. The maximum half-range (%) of the bias evaluations provided an estimate of the uncertainty comparable to the analytical performance specifications: thus, bias estimates could be classified as optimum, desirable or minimum quality. Conclusions Medians by gender and age are useful in assessment of analytical stability for plasma creatinine concentration ranging from 60 to 90 µmol/L. The daily medians are valuable in rapid detection of large systematic errors, the weekly medians in detecting minor systematic errors and monthly medians in assessment of long-term analytical stability.

Gender-partitioned patient medians of serum albumin requested by general practitioners for the assessment of analytical stability.

BACKGROUND: Recently, the use of separate gender-partitioned patient medians of serum sodium has revealed potential for monitoring analytical stability within the optimum analytical performance specifications for laboratory medicine. The serum albumin concentration depends on whether a patient is sitting or recumbent during phlebotomy. We therefore investigated only examinations requested by general practitioners (GPs) to provide data from sitting patients. METHODS: Weekly and monthly patient medians of serum albumin requested by GP for both male and female patients were calculated from the raw data obtained from three analysers in the hospital laboratory on examination of samples from those >18 years. The half-range of medians were applied as an estimate of the maximum bias. Further, the ratios between the two medians were calculated (females/males). RESULTS: The medians for male and female patients were closely related despite considerable variation due to the current analytical variation. This relationship was confirmed by the calculated half-range for the monthly ratio between the genders of 0.44%, which surpasses the optimum analytical performance specification for bias of serum albumin (0.72%). The weekly ratio had a half-range of 1.83%, which surpasses the minimum analytical performance specifications of 2.15%. CONCLUSIONS: Monthly gender-partitioned patient medians of serum albumin are useful for monitoring of long-term analytical stability, where the gender medians are two independent estimates of changes in (delta) bias: only results requested by GP are of value in this application to ensure that all patients are sitting during phlebotomy.

Separate patient serum sodium medians from males and females provide independent information on analytical bias.

BACKGROUND: During monitoring of monthly medians of results from patients undertaken to assess analytical stability in routine laboratory performance, the medians for serum sodium for male and female patients were found to be significantly related. METHODS: Daily, weekly and monthly patient medians of serum sodium for both male and female patients were calculated from results obtained on samples from the population >18 years on three analysers in the hospital laboratory. The half-range of medians was applied as an estimate of the maximum bias. Further, the ratios between the two medians were calculated. RESULTS: The medians of both genders demonstrated dispersions over time, but they were closely connected in like patterns, which were confirmed by the half-range of the ratios of medians for males and females that varied from 0.36% for daily, 0.14% for weekly and 0.036% for monthly ratios over all instruments. CONCLUSIONS: The tight relationship between the gender medians for serum sodium is only possible when raw laboratory data are used for calculation. The two patient medians can be used to confirm both and are useful as independent estimates of analytical bias during constant calibration periods. In contrast to the gender combined median, the estimate of analytical bias can be confirmed further by calculation of the ratios of medians for males and females.

Decrease by 50% of plasma IgA tissue transglutaminase antibody concentrations within 2 months after start of gluten-free diet in children with celiac disease used as a confirming diagnostic test.

BACKGROUND: Histological examination of small bowel biopsies is normally the gold standard for the diagnosis of celiac disease (CD). The objective of this study was to investigate whether the rate of decreases in elevated plasma IgA tissue transglutaminase antibody (IgA-tTG) and/or IgG deamidated gliadin peptides antibody (IgG - DGP) concentrations could be used as a confirming test for CD in children on a gluten-free diet (GFD) when biopsy was omitted in the diagnostic process. METHODS: In this retrospective study we compared children (≤18 years old) with a CD-confirming biopsy (n = 16) to children without a biopsy (n = 18). After initiation of GFD the antibody half-life (the time (T½) when the antibody concentration is 50% decreased) was determined in all children. RESULTS: Children with a biopsy (IgA-tTG, T½ = 1.9 months; IgG - DGP, T½ = 2.2 months) and children without a biopsy (IgA-tTG, T½ = 1.6 months; IgG - DGP, T½ = 2.7 months) had comparable T½ (mean) results (p < 0.05) supporting that all children had the CD diagnosis. CONCLUSIONS: When biopsy was omitted a rapid rate of decrease in CD antibody concentrations confirmed the CD diagnosis in children on GFD. The half-lives (T½) of IgA-tTG were less than 2 months in CD children.

Mapping of HLA- DQ haplotypes in a group of Danish patients with celiac disease.

BACKGROUND: A cost-effective identification of HLA- DQ risk haplotypes using the single nucleotide polymorphism (SNP) technique has recently been applied in the diagnosis of celiac disease (CD) in four European populations. The objective of the study was to map risk HLA- DQ haplotypes in a group of Danish CD patients using the SNP technique. METHODS: Cohort A: Among 65 patients with gastrointestinal symptoms we compared the HLA- DQ2 and HLA- DQ8 risk haplotypes obtained by the SNP technique (method 1) with results based on a sequence specific primer amplification technique (method 2) and a technique used in an assay from BioDiagene (method 3). Cohort B: 128 patients with histologically verified CD were tested for CD risk haplotypes (method 1). Patients with negative results were further tested for sub-haplotypes of HLA- DQ2 (methods 2 and 3). RESULTS: Cohort A: The three applied methods provided the same HLA- DQ2 and HLA- DQ8 results among 61 patients. Four patients were negative for the HLA- DQ2 and HLA- DQ8 haplotypes (method 1) but were positive for the HLA- DQ2.5-trans and HLA- DQ2.2 haplotypes (methods 2 and 3). Cohort B: A total of 120 patients were positive for the HLA- DQ2.5-cis and HLA- DQ8 haplotypes (method 1). The remaining seven patients were positive for HLA- DQ2.5-trans or HLA- DQ2.2 haplotypes (methods 2 and 3). One patient was negative with all three HLA methods. CONCLUSIONS: The HLA- DQ risk haplotypes were detected in 93.8% of the CD patients using the SNP technique (method 1). The sensitivity increased to 99.2% by combining methods 1 - 3.

Interpretation of increments in serial tumour biomarker concentrations depends on the distance of the baseline concentration from the cut-off.

BACKGROUND: In the management of breast cancer, several algorithms for interpretation of measured concentrations during monitoring have been introduced, without objective evaluation of their performance regarding the distance of the starting concentration from the cut-off concentration. METHODS: A computer simulation model has developed using parameters for the tumour biomarker CA 15-3 regarding biological variation and different rates of increase during progressive disease. Seven different algorithms, which include the cut-off point in the calculations, are applied to the simulated data corresponding to 1000 surrogate patients. Steady-state variation (CV-within-subject=14.9%) is based on Gaussian random numbers and an exponential increase in tumour growth (λ=0.009, 0.021, and 0.090 kU/day). RESULTS: The main outcome of the simulations was that low starting concentrations (baseline concentrations) delay the detection of progressive disease (true-positive), whereas, baseline concentrations just below the cut-off value results in false-positive results of progression during steady-state situations. The algorithms investigated show varying susceptibility for baseline concentrations approaching the cut-off. Thus, three of the algorithms show > 90% false-positives and three algorithms < 5% false-positives when baseline concentrations were just below the cut-off point. CONCLUSIONS: Based on the simulations, it is possible to select the optimal algorithms for early detection of progressive disease and a low percentage of false-positives.

A dynamic reference change value model applied to ongoing assessment of the steady state of a biomarker using more than two serial results.

BACKGROUND: Reference change values are used to assess the significance of a difference in two consecutive results from an individual. Reference change value calculations provide the limits for significant differences between two results due to analytical and inherent biological variations. Often more than two serial results are available. Using the reference change value concept on more than two measurements results in an increased number of false-positive results. This problem has been solved for both uni- and bidirectional differences through use of wider limits when additional results are included. METHODS: Based on normally (Gaussianly) distributed simulated data, a dynamic reference change value model was developed using more than two results and total coefficients of variation. The dynamic reference change value model includes validation of a set-point as the mean of the four first serial results and additional results are assessed for compliance to the steady state with the same set-point. Furthermore, the dynamic reference change value model compensates for increasing false-positive results with subsequent results. The dynamic reference change value model was designed to calculate significant limits for bidirectional differences. RESULTS: Reference change factors were calculated for multiplication of the mean of previous results to create the limits for significant differences. The reference change factors are provided as a function of number of results and total coefficients of variation both in tables and in figures. CONCLUSIONS: The dynamic reference change value model is appropriate for ongoing assessment of the steady state of a biomarker using more than two serial results.

Valid analytical performance specifications for combined analytical bias and imprecision for the use of common reference intervals.

Background Many clinical decisions are based on comparison of patient results with reference intervals. Therefore, an estimation of the analytical performance specifications for the quality that would be required to allow sharing common reference intervals is needed. The International Federation of Clinical Chemistry (IFCC) recommended a minimum of 120 reference individuals to establish reference intervals. This number implies a certain level of quality, which could then be used for defining analytical performance specifications as the maximum combination of analytical bias and imprecision required for sharing common reference intervals, the aim of this investigation. Methods Two methods were investigated for defining the maximum combination of analytical bias and imprecision that would give the same quality of common reference intervals as the IFCC recommendation. Method 1 is based on a formula for the combination of analytical bias and imprecision and Method 2 is based on the Microsoft Excel formula NORMINV including the fractional probability of reference individuals outside each limit and the Gaussian variables of mean and standard deviation. The combinations of normalized bias and imprecision are illustrated for both methods. The formulae are identical for Gaussian and log-Gaussian distributions. Results Method 2 gives the correct results with a constant percentage of 4.4% for all combinations of bias and imprecision. Conclusion The Microsoft Excel formula NORMINV is useful for the estimation of analytical performance specifications for both Gaussian and log-Gaussian distributions of reference intervals.

Analytical performance specifications for changes in assay bias (Δbias) for data with logarithmic distributions as assessed by effects on reference change values.

Background The distributions of within-subject biological variation are usually described as coefficients of variation, as are analytical performance specifications for bias, imprecision and other characteristics. Estimation of specifications required for reference change values is traditionally done using relationship between the batch-related changes during routine performance, described as Δbias, and the coefficients of variation for analytical imprecision (CVA): the original theory is based on standard deviations or coefficients of variation calculated as if distributions were Gaussian. Methods The distribution of between-subject biological variation can generally be described as log-Gaussian. Moreover, recent analyses of within-subject biological variation suggest that many measurands have log-Gaussian distributions. In consequence, we generated a model for the estimation of analytical performance specifications for reference change value, with combination of Δbias and CVA based on log-Gaussian distributions of CVI as natural logarithms. The model was tested using plasma prolactin and glucose as examples. Results Analytical performance specifications for reference change value generated using the new model based on log-Gaussian distributions were practically identical with the traditional model based on Gaussian distributions. Conclusion The traditional and simple to apply model used to generate analytical performance specifications for reference change value, based on the use of coefficients of variation and assuming Gaussian distributions for both CVI and CVA, is generally useful.

Different percentages of false-positive results obtained using five methods for the calculation of reference change values based on simulated normal and ln-normal distributions of data.

Background Reference change values provide objective tools to assess the significance of a change in two consecutive results for a biomarker from an individual. The reference change value calculation is based on the assumption that within-subject biological variation has random fluctuation around a homeostatic set point that follows a normal (Gaussian) distribution. This set point (or baseline in steady-state) should be estimated from a set of previous samples, but, in practice, decisions based on reference change value are often based on only two consecutive results. The original reference change value was based on standard deviations according to the assumption of normality, but was soon changed to coefficients of variation (CV) in the formula (reference change value = ± Z c 2½ c CV). Z is being dependent on the desired probability of significance, which also defines the percentages of false-positive results. The aim of this study was to investigate false-positive results using five different published methods for calculation of reference change value. Methods The five reference change value methods were examined using normally and ln-normally distributed simulated data. Results One method performed best in approaching the theoretical false-positive percentages on normally distributed data and another method performed best on ln-normally distributed data. The commonly used reference change value method based on two results (without use of estimated set point) performed worst both on normally distributed and ln-normally distributed data. Conclusions The optimal choice of method to calculate reference change value limits requires knowledge of the distribution of data (normal or ln-normal) and, if possible, knowledge of the homeostatic set point.

Calculation of limits for significant unidirectional changes in two or more serial results of a biomarker based on a computer simulation model.

BACKGROUND: Reference change values (RCVs) were introduced more than 30 years ago and provide objective tools for assessment of the significance of differences in two consecutive results from an individual. However, in practice, more results are usually available for monitoring, and using the RCV concept on more than two results will increase the number of false-positive results. Therefore, a simple method is needed to interpret the significance of a difference when all available serial biomarker results are considered. METHODS: A computer simulation model using Excel was developed. Based on 10,000 simulated data from healthy individuals, a series of up to 20 results from an individual was generated using different values for the within-subject biological variation plus the analytical variation. Each new result in this series was compared to the initial measurement result. These successive serial relative differences were computed to give limits for significant unidirectional differences with a constant cumulated maximum probability of both 95% (P < 0.05) and 99% (P < 0.01). RESULTS: Factors used to multiply the first result from an individual were calculated to create the limits for constant cumulated significant differences. The factors were shown to become a simple function of the number of results and the total coefficient of variation. CONCLUSIONS: To interpret unidirectional differences in two or more serial results of a biomarker, the limits for significances are easily calculated using the presented factors. The first result is multiplied by the appropriate factor for increase or decrease, which gives the limits for a significant difference.

Calculation of limits for significant bidirectional changes in two or more serial results of a biomarker based on a computer simulation model.

BACKGROUND: Reference change values provide objective tools to assess the significance of a change in two consecutive results of a biomarker from an individual. However, in practice, more results are usually available and using the reference change value concept on more than two results will increase the number of false positive results. METHODS: A computer simulation model was developed using Excel. Based on 10,000 simulated measurements among healthy individuals, a series of up to 20 results of a biomarker from each individual was generated using different values for the within-subject biological variation plus the analytical variation. Each new result in this series was compared to the initial result. These successive serial differences were computed to give limits for significant bidirectional changes with constant cumulated maximum probabilities of 95% (p < 0.05) and 99% (p < 0.01). RESULTS: From an individual factors used to multiply the first result were calculated to create limits for constant cumulated significant changes. The factors were shown to become a function of the number of results included and the total coefficient of variation. CONCLUSIONS: The first result should be multiplied by the appropriate factors for increase and decrease to give the limits for a significant bidirectional change in several consecutive measurements.

Monitoring performance of progression assessment criteria for cancer antigen 125 among patients with ovarian cancer compared by computer simulation.

BACKGROUND: Cancer antigen 125 (CA125) is used to monitor tumor burden among patients with advanced serous epithelial ovarian cancer. The purpose is to compare the monitoring performance of seven previously proposed criteria. MATERIALS & METHODS: The CA125 assessment criteria were applied to simulated datasets. We investigated the ability to provide information on CA125 increments as well as their robustness against false positive signals. RESULTS: For baseline concentrations above cut-off, the best performing criterion was based on a confirmed increment ≥2.5-times the nadir concentration. For baseline concentrations below cut-off, the best performing criterion was based on a confirmed increment from ≤ cut-off to >two-times cut-off. DISCUSSION: Computer simulation models may be useful for a preclinical validation of criteria to be investigated in clinical trials.

Visual clone identification of Penicillium commune isolates.

A method for visual clone identification of Penicillium commune isolates was developed. The method is based on images of fungal colonies acquired after growth on a standard medium and involves a high degree of objectivity, which in future studies will make it possible for non-experts to perform a qualified identification of different species as well as clones within a species. A total of 77 P. commune isolates from a cheese dairy were 3-point inoculated on Yeast Extract Sucrose (YES) agar and incubated for 7 days at 25 degrees C. After incubation, the isolates were classified into groups containing the same genotype determined by DNA fingerprinting (AFLP). Each genotype also has a specific phenotype such as different colony colours. By careful image acquisition, colours were measured in a reproducible way. Prior to image analysis, each image was corrected with respect to colour, geometry and self-illumination, thereby gaining a set of directly comparable images. A method for automatic extraction of a given number of concentric regions was used. Using the positions of the regions, a number of relevant features--capturing colour and colour-texture from the surface of the fungal colonies--was extracted for further analysis. We introduced the Jeffreys-Matusitas (JM) distance between the feature distributions to express the similarity between regions in two colonies, and to evaluate the overall (weighted) similarity. The nearest neighbour (NN) classification rule was used. On a dataset from 137 isolates, we obtained a "leave-one-out" cross-validation identification rate of approximately 93-98% compared with the result of DNA fingerprinting.

Criteria to interpret cancer biomarker increments crossing the recommended cut-off compared in a simulation model focusing on false positive signals and tumour detection time.

BACKGROUND: Several criteria have been proposed to interpret increments in serological cancer biomarker concentrations starting from low baseline concentrations crossing the cut-off. None of the criteria have been compared for their ability to signal tumour growth when ≤2% false positive results are accepted. METHODS: The cancer biomarker Tissue Polypeptide Antigen was used as an example. Seven criteria to interpret increments in concentrations were investigated by computer simulations. Firstly, for each criterion, we identified a baseline concentration stratified for three levels of biological variation providing ≤2% false positive signals of tumour growth during one year of monitoring. Secondly, combining the steady state concentrations with rates of marker increase during tumour growth allowed calculation of the lengths of tumour detection times for each criterion. RESULTS: The number of false positive marker signals depended on the baseline concentration, the magnitude of biological variation, and the magnitude of the required increment defined in the criterion. The lengths of the tumour detection times also depended on the rates of marker increase. CONCLUSIONS: The results suggest that different types of criteria should be used within different intervals of below cut-off level concentrations if the rate of false positive signals of marker increments should be kept ≤2%.

Assessing HER2 amplification by IHC, FISH, and real-time polymerase chain reaction analysis (real-time PCR) following LCM in formalin-fixed paraffin embedded tissue from 40 women with ovarian cancer.

We compare HER2 receptor amplification analysis by immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and real-time polymerase chain reaction (real-time PCR) DNA copy-number assay following laser capture microdissection (LCM) in formalin-fixed paraffin embedded tissue from 40 women with verified ovarian cancer. We speculate that LCM should result in a more accurate assessment of HER2 amplification in our real-time PCR assay compared with IHC and FISH. HER2 overexpression measured by IHC, FISH, or real-time PCR was found in 5.0%, 5.0%, and 22.5%, respectively. HER2 negative results measured by IHC, FISH, or real-time PCR were found in 95%, 92.5%, and 60.0%, respectively. Analysis failed for IHC, FISH, or real-time PCR in 0%, 2.5%, or 17.5% of cases. Concordance between IHC and FISH, IHC and real-time PCR, or FISH and real-time PCR were 89.7%, 72.7%, or 78.1%, respectively. Only few ovarian cancer patients were HER2 overexpressed measured by IHC or FISH and thus could be eligible for antibody-based therapy with trastuzumab (Herceptin). Interestingly, we find an increased number of HER2 positive patients by real-time PCR analysis on microdissected cancer cells, suggesting a number of HER2 positive patients not detected by current methods. Thus, the concept of quantitative measurement of HER2 on microdissected cancer cells should be explored further.