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.

Associations between biomarkers at discharge and co-morbidities and risk of readmission after community-acquired pneumonia: a retrospective cohort study.

To investigate whether hemoglobin, white blood cell count (WBC), urea, sodium, albumin, and C-reactive protein at discharge in patients hospitalized for community-acquired pneumonia (CAP) are associated with 30-day readmission. This study is a retrospective cohort study, which included all adult patients discharged after hospitalization for CAP from three Danish hospitals between January 2011 and July 2012. The outcome was all-cause, unplanned, 30-day readmission. Biomarker concentrations at discharge were transformed into binary variables by using either upper or lower quartiles as cut-off; the upper quartile was used for WBC, urea, and C-reactive protein, and the lower quartile was used for hemoglobin, sodium, and albumin. The study population consisted of 1149 patients. One hundred eighty-four (16.0%) patients were readmitted. Independent risk factors of readmission were WBC ≥ 10.6 cells × 109/L (hazard ratio 1.50; 95% CI, 1.07-2.11) and albumin <32 g/L (hazard ratio 1.78; 95% CI, 1.24-2.54) at discharge and the presence of ≥ 2 co-morbidities (hazard ratio 1.74; 95% CI, 1.15-2.64). When WBC, albumin, and co-morbidities were combined into a risk-stratification tool, there was a step-wise increase in risk of readmission for patients with 1, 2, or 3 risk factors with hazard ratios of 1.76 (95% CI, 1.25-2.49), 2.59 (95% CI, 1.71-3.93), and 6.15 (95% CI 3.33-11.38), respectively. WBC ≥ 10.6 cells × 109/L and albumin < 32 g/L at discharge and the presence of ≥ 2 co-morbidities were independently associated with increased risk of 30-day readmission.

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%.

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.