首页|Swiss Federal Institute of Technology Zurich (ETH) Reports Findings in Hip Fract ure (Fracture prediction in a Swiss cohort)
Swiss Federal Institute of Technology Zurich (ETH) Reports Findings in Hip Fract ure (Fracture prediction in a Swiss cohort)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Hip Fracture is the su bject of a report. According to news originating from Zurich, Switzerland, by Ne wsRx correspondents, research stated, "Fracture prediction is essential in manag ing patients with osteoporosis, and is an integral component of many fracture pr evention guidelines. We aimed to identify the most relevant clinical fracture ri sk factors in contemporary populations by training and validating short- and lon g-term fracture risk prediction models in two cohorts." Our news journalists obtained a quote from the research from the Swiss Federal I nstitute of Technology Zurich (ETH), "We used traditional and machine learning s urvival models to predict risks of vertebral, hip and any fractures on the basis of clinical risk factors, T-scores and treatment history among participants in a nationwide Swiss osteoporosis registry (N = 5944 postmenopausal women, median follow-up of 4.1 years between January 2015 and October 2022; a total of 1190 fr actures during follow-up). The independent validation cohort comprised 5474 post menopausal women from the UK Biobank with 290 incident fractures during follow-u p. Uno's C-index and the time-dependent area under the receiver operating charac teristics curve were calculated to evaluate the performance of different machine learning models (Random survival forests and eXtreme Gradient Boosting). In the independent validation set, the C-index was 0.74 [0.58, 0.86 ] for vertebral fractures, 0.83 [0.7, 0.94 ] for hip fractures and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estim ations of up to 7 years. In comparison, the 10- year fracture probability calcula ted with FRAX? Switzerland was 0.60 [0.55, 0.64] for major osteoporotic fractures and 0.62 [0.49, 0.74] for hip fractures. The most important variables identified with Shapley additive explanations (SHAP) values were age, T-scores and prior fractures, while number of falls was an important predictor of hip fractures. Performances of both trad itional and machine learning models showed similar C-indices."
ZurichSwitzerlandEuropeCyborgsEm erging TechnologiesHealth and MedicineHip FractureMachine LearningRisk a nd Prevention
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