Open Access
Generic implant classification enables comparison across implant designs: the Dutch Arthroplasty Register implant library
Author(s) -
Geke Denissen,
Liza N. van Steenbergen,
Wouter T Lollinga,
Nico Verdonschot,
B.W. Schreurs,
Rob G H H Nelissen
Publication year - 2019
Publication title -
efort open reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.588
H-Index - 22
ISSN - 2396-7544
DOI - 10.1302/2058-5241.4.180063
Subject(s) - barcode , implant , product (mathematics) , computer science , database , medicine , mathematics , surgery , geometry , operating system
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 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. Cite this article: EFORT Open Rev 2019;4 DOI: 10.1302/2058-5241.4.180063