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Article Abstract

The German Arthroplasty Registry (EPRD) recorded almost 100,000 femoral neck fractures between 2013 and 2023. The aim of this study was to identify survival rates and risk factors for failure in individuals with femoral neck fractures. A dataset of 97,410 cases from the EPRD was analyzed. We compared hemiarthroplasty (HA) and total hip arthroplasty (THA) using machine learning algorithms (MLAs) and statistical modeling approaches. For the MLA, the dataset was partitioned into training and test sets, with iterative feature selection and hyperparameter search. Predictive models were developed using XGBoost classifiers. Based on the feature importance, we performed LASSO regression to assess the odds ratios for key predictors of implant failure. The failure rate was 3.7% for HAs and 5.6% for THAs, with a peak six weeks after surgery. LASSO regression revealed six risk factors for failure: non-cemented stem fixation (OR: 1.022, 95% CI: 1.019-1.026), treatment type (THA vs. HA; OR: 1.013, 95% CI: 1.010-1.016), time to discharge (OR: 1.006, 95% CI: 1.006-1.006), male sex (OR: 1.003, 95% CI: 1.000-1.005), age (OR: 0.999, 95% CI: 0.999-0.999), and day of surgery (weekday vs. weekend/holiday; OR: 1.004, 95% CI: 1.002-1.008). Longer hospital stays, male sex, and surgeries performed on weekends or holidays were associated with higher failure risks, while cemented fixation and hemiarthroplasty showed protective effects. Given that the overall failure rate was only 4.5%, even a 1-2% change in odds represents a very large clinical effect.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11900098PMC
http://dx.doi.org/10.3390/jcm14051457DOI Listing

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