In the Dutch Arthroplasty Register (LROI), the product and batch number of prosthetic components and cement are registered for traceability. Registration of the product number provides... Show moreIn the Dutch Arthroplasty Register (LROI), the product and batch number of prosthetic components and cement are registered for traceability. Registration of the product number provides opportunities to extend the information about a specific prosthesis. All product numbers used from the beginning of the registration in 2007 were characterized to develop and maintain an implant library.The Scientific Advisory Board developed a core-set that contains the most important characteristics needed to form an implant library. The final core-set contains the brand name, type, coating and material of the prosthesis. In total, 35 676 product numbers were classified, resulting in a complete implant library of all product numbers used in the LROI.To improve quality of the data and increase convenience of registration, the LROI implemented barcode scanning for data entry into the database. In 2017, 82% of prosthetic components and cement stickers had a GS1 barcode. The remaining product stickers used HIBCC barcodes and custom-made barcodes.With this implant library, implants can be grouped for analyses at group level, e.g. evaluation of the effect of a material of a prosthesis on survival of the implant. Apart from that, the implant library can be used for data quality control within the LROI database.The implant library reduces the registration burden and increases accuracy of the database. Such a system will facilitate new designs (learning from the past) and thus improve implant quality and ultimately patient safety. Show less
Several anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD... Show moreSeveral anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD patients from controls. These anatomical MRI measures have so far mainly been used separately. The full potential of anatomical MRI scans for AD diagnosis might thus not yet have been used optimally. In this study, we therefore combined multiple anatomical MRI measures to improve diagnostic classification of AD. For 21 clinically diagnosed AD patients and 21 cognitively normal controls, we calculated (i) cortical thickness, (ii) cortical area, (iii) cortical curvature, (iv) grey matter density, (v) subcortical volumes, and (vi) hippocampal shape. These six measures were used separately and combined as predictors in an elastic net logistic regression. We made receiver operating curve plots and calculated the area under the curve (AUC) to determine classification performance. AUC values for the single measures ranged from 0.67 (cortical thickness) to 0.94 (grey matter density). The combination of all six measures resulted in an AUC of 0.98. Our results demonstrate that the different anatomical MRI measures contain complementary information. A combination of these measures may therefore improve accuracy of AD diagnosis in clinical practice. Hum Brain Mapp 37:1920-1929, 2016. (c) 2016 Wiley Periodicals, Inc. Show less
