Volume 41, Issue s1 - Translations

Background: The overnight dexamethasone suppression test (DST) is routinely used in establishing the diagnosis of Cushing’s syndrome. However, factors such as variable resorption and increased metabolism of dexamethasone (DXMS) could lead to false positive results. The aim of our study was to evaluate the influence of sex, body mass index (BMI) and age on the DXMS metabolism. Methods: In total, 183 subjects were enrolled in a regular 2 mg DST. Patients with Cushing’s syndrome, depression, renal or hepatic insufficiency and patients treated with liver enzyme modulating pharmaceuticals were excluded from this study, so that 72 patients and 66 healthy subjects were analyzed. Nocturnal blood withdrawals were performed in 11 adult subjects at 1:00 AM, 3:00 AM, 5:00 AM, 7:00 AM, 8:00 AM to evaluate the kinetic of DXMS and cortisol. Results: In the DST DXMS levels demonstrated a high variation at 8:00 AM, that was inversely correlated with BMI (r=−0.24, p=0.045). Furthermore, DXMS levels determined in patients with a BMI<30 kg/m 2 (n=29) were significantly higher than values of obese patients with a BMI>30 kg/m 2 (n=43) (1.66±0.71 ng/mL vs. 1.31±0.57 ng/mL, p=0.026). Cortisol levels, measured at the same time, were neither correlated with DXMS nor with BMI values. In the kinetic study, maximal DXMS concentration was measured at approximately 3:00 AM. At 8:00 AM, median of DXMS was reduced to 59.1% and was correlated directly with the DXMS AUC (r=0.75, p=0.013). However, there was no correlation between the cortisol levels at 8:00 AM and the nocturnal DXMS concentrations. Conclusions: In the DST BMI of individual patients could modulate the resorption rate and metabolism of DXMS but appears to have no impact on cortisol levels. Therefore, obesity should not be a cause of falsely positive results in the DST.

Due to a change of provider at Zentrum für Diagnostik at Klinikum Chemnitz a comprehensive validation of the STA R Max ® analyser from Stago was undertaken. Alongside intra-assay and inter-assay determinations, a broad range of comparative measurements using pooled patient plasma were also carried out against the previous routine method (BCS XP from Siemens). Results obtained from routine and emergency sample testing are presented in this publication. Good to very good results were observed which allowed for a swift switchover in systems. Furthermore, user friendliness, reagents, turnaround times (TAT) and general susceptibilities of the new system were evaluated. After various adaptations to the diagnostic process, the transition to routine operation successfully took place.

Renal transplantation is the treatment-of-choice for a significant number of patients with end-stage renal disease. Prophylaxis, diagnosis and treatment of infections are cornerstones in the management of transplant patients. There are a number of opportunistic and rare pathogens in the immunosuppressed transplant patient population, whose early detection is essential for an optimized and targeted treatment. As the immunosuppressive regimen is adopted after transplantation and due to a potentially delayed reactivation of latent diseases, certain infections can occur in defined time intervals following transplantation. The present review summarizes the common and some of the rare diseases caused by the broad microbiological spectrum in kidney transplant recipients and the respective therapeutic options.

Background: With regard to the German E-Health Law of 2016, the German Society for Clinical Chemistry and Laboratory Medicine (DGKL) has been invited to develop a standard procedure for the storage and transmission of laboratory results. We suggest the commonly used z-transformation. Methods: This method evaluates by how many standard deviations (SDs) a given result deviates from the mean of the respective reference population. We confirm with real data that laboratory results of healthy individuals can be adjusted to a normal distribution by logarithmic transformation. Results: Thus, knowing the lower and upper reference limits LL and UL, one can transform any result x into a zlog value using the following equation: zlog=(log(x)–(log(LL)+log(UL))/2)·3.92/(log(UL)–log(LL))$\eqalign{ {\rm{zlog}} = & {\rm{(log(x)}}-{\rm{(log(LL)}} + {\rm{log(UL))/2)\cdot3}}{\rm{.92/(log(UL)}} \cr -{\bf{ }}{\rm{log(LL))}} \cr} $ The result can easily be interpreted, as its reference interval (RI) is –1.96 to +1.96 by default, and very low or high results yield zlog values around –5 and +5, respectively. For intuitive data presentation, the zlog values may be transformed into a continuous color scale, e.g. from blue via white to orange. Using the inverse function, any zlog value can then be translated into the theoretical result of an analytical method with another RI: (1)x=LL0.5−zlog/3.92⋅UL0.5+zlog/3.92$${\rm{x}} = {\rm{L}}{{\rm{L}}^{0.5 - {\rm{zlog}}/3.92}} \cdot {\rm{U}}{{\rm{L}}^{0.5 + {\rm{zlog}}/3.92}}$$ Conclusions: Our standardization proposal can easily be put into practice and may effectively contribute to data quality and patient safety in the frame of the German E-health law. We suggest for the future that laboratories should provide the zlog value in addition to the original result, and that the data transmission protocols (e.g. HL7, LDT) should contain a special field for this additional value.