Analyzing differences in the costs of treatment across centers within economic evaluations

Objectives: Assessments of health technologies increasingly include economic evaluations conducted alongside clinical trials. One particular concern with economic evaluations conducted alongside clinical trials is the generalizability of results from one setting to another. Much of the focus relating to this topic has been on the generalizability of results between countries, However, the characteristics of clinical trial design require further consideration of the generalizability of cost data between centers within a single country, which could be important in decisions about adoption of the new technology. Methods: We used data from a multicenter clinical trial conducted in the United Kingdom to assess the degree of variation in costs between patients and between treatment centers and the determinants of the degree of such variation. Results: The variation between patients was statistically significant for both the experimental and conventional treatments. However, the degree of variation between centers was only statistically significant for the experimental treatment. Such variation appeared to be a result of hospital practice, such as pay ment mechanisms for staff and provision of hostel accommodation, rather than variations in physical resource use or substantive differences in cost structure. Conclusions: Multicenter economic evaluations are necessary for determining the variations in hospital practice and characteristics that can in turn determine the generalizability of study results to other settings. Such analyses can identify issues that may be important in adopting a new health technology. Analysis is required of similar large multicenter trials to confirm these conclusions

Multicenter Performance Evaluation of MALDI-TOF MS for Rapid Detection of Carbapenemase Activity in Enterobacterales: The Future of Networking Data Analysis With Online Software

In this study, we evaluate the performance of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for rapid detection of carbapenemase activity in Enterobacterales in clinical microbiology laboratories during a multicenter networking validation study. The study was divided into three different stages: “software design,” “intercenter evaluation,” and “clinical validation.” First, a standardized procedure with an online software for data analysis was designed. Carbapenem resistance was detected by measuring imipenem hydrolysis and the results were automatically interpreted using the Clover MS data analysis software (Clover BioSoft, Spain). Second, a series of 74 genotypically characterized Enterobacterales (46 carbapenemase-producers and 28 non carbapenemase-producers) were analyzed in 8 international centers to ensure the reproducibility of the method. Finally, the methodology was evaluated independently in all centers during a 2-month period and results were compared with the reference standard for carbapenemase detection used in each center. The overall agreement rate relative to the reference method for carbapenemase resistance detection in clinical samples was 92.5%. The sensitivity was 93.9% and the specificity, 100%. Results were obtained within 60 min and accuracy ranged from 83.3 to 100% among the different centers. Further, our results demonstrate that MALDI-TOF MS is an outstanding tool for rapid detection of carbapenemase activity in Enterobacterales in clinical microbiology laboratories. The use of a simple in-house procedure with online software allows routine screening of carbapenemases in diagnostics, thereby facilitating early and appropriate antimicrobial therapy.Instituto de Salud Carlos III (ISCIII

Eu3+ multicenter formation and luminescent properties of Ca3Sc2Si3O12:Eu and Ca2YScMgSiO12:Eu single crystalline films

The work is dedicated to the investigation of Eu3+ multicenter formation and luminescent properties of Ca3Sc2Si3O12:Eu (CSSG:Eu) and Ca2YScMgSiO12:Eu (CYMSSG:Eu) single crystalline film (SCF) phosphors, grown by liquid phase epitaxy method onto Gd3Ga2.5Al2.5O12 (GAGG) and Y3Al5O12 (YAG) substrates, respectively. We have found notable differences in the luminescent properties of CSSG:Eu and CYMSSG:Eu SCFs caused by the Eu3+ multicenter formation in both garnets due to the different local surrounding of Eu3+ ions in the dodecahedral positions by the non-isovalent Sc3+/Mg2+ and Si4+ cations in the octahedral and tetrahedral positions of garnet hosts, respectively. A feature of the Eu3+ center creation in CYMSSG:Eu garnet in comparison with CSSG:Eu counterpart is the possibility of localization of Eu3+ ions in dodecahedral sites of both Ca2+ and Y3+ cations. However, based on the obtained results, we have presupposed preferable localization of the Eu3+ ions mainly in the Y3+ positions of this garnet

Non-supersymmetric extremal multicenter black holes with superpotentials

Using the superpotential approach we generalize Denef's method of deriving and solving first-order equations describing multicenter extremal black holes in four-dimensional N = 2 supergravity to allow non-supersymmetric solutions. We illustrate the general results with an explicit example of the stu model.Comment: 17 pages, v2: some clarifications adde

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample