The risk of mechanical failure of modular revision hip stems is frequently mentioned in the literature, but little is currently known about the actual clinical failure rates of this type of prosthesis. The current retrospective long-term analysis examines the distal and modular failure patterns of the Prevision hip stem from 18 years of clinical use. A design improvement of the modular taper was introduced in 2008, and the data could also be used to compare the original and the current design of the modular connection. We performed an analysis of the Prevision modular hip stem using the manufacturer’s vigilance database and investigated different mechanical failure patterns of the hip stem from January 2004 to December 2022.Aims
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Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction. A group of 1,000 randomly selected clinical and hospital notes from eight different surgeons were collected for patients undergoing primary arthroplasty between 2012 and 2018. In all, 19 preoperative, 17 operative, and two postoperative variables of interest were manually extracted from these notes. A NLP algorithm was created to automatically extract these variables from a training sample of these notes, and the algorithm was tested on a random test sample of notes. Performance of the NLP algorithm was measured in Statistical Analysis System (SAS) by calculating the accuracy of the variables collected, the ability of the algorithm to collect the correct information when it was indeed in the note (sensitivity), and the ability of the algorithm to not collect a certain data element when it was not in the note (specificity).Aims
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