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
Methods
Hip fracture patients have high morbidity and mortality. Patient-reported outcome measures (PROMs) assess the quality of care of patients with hip fracture, including those with chronic cognitive impairment (CCI). Our aim was to compare PROMs from hip fracture patients with and without CCI, using the Norwegian Hip Fracture Register (NHFR). PROM questionnaires at four months (n = 34,675) and 12 months (n = 24,510) after a hip fracture reported from 2005 to 2018 were analyzed. Pre-injury score was reported in the four-month questionnaire. The questionnaires included the EuroQol five-dimension three-level (EQ-5D-3L) questionnaire, and information about who completed the questionnaire.Aims
Methods
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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