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Published on: September 14, 2017
Distal radius sections offer accurate and precise estimates of forearm fracture load
Seyedmahdi Hosseinitabatabaei1, Chantal E Kawalilak2, Matthew P McDonald2
1Division of Biomedical Engineering, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada.
Background:
Forearm fracture risk can be estimated via factor-of-risk: the ratio of applied impact force to forearm fracture load. Simple techniques are available for estimating impact force associated with a fall; estimating forearm fracture load is more challenging. Our aim was to assess whether failure load estimates of sections of the distal radius (acquired using High-Resolution peripheral Quantitative Computed Tomography and finite element modeling) offer accurate and precise estimates of forearm fracture load.
Methods:
We scanned a section of the distal radius of 19 cadaveric forearms (female, mean age 83.7, SD 8.3), and 34 women (75.0, 7.7). Sections were converted to finite element models and failure loads were acquired for different failure criteria. We assessed forearm fracture load using experimental testing simulating a fall on the outstretched hand. We used linear regression to derive relationships between ex vivo forearm fracture load and finite element derived distal radius failure load. We used derived regression coefficients to estimate forearm fracture load, and assessed explained variance and prediction error. We used root-mean-squared coefficients of variation to assess in vivo precision errors of estimated forearm fracture load.
Findings:
Failure load estimates of sections of the distal radius, used in conjunction with derived regression coefficients, explained 89-90% of the variance in experimentally-measured forearm fracture load with prediction errors <6.8% and precision errors <5.0%.
Interpretation:
Failure load estimates of distal radius sections can reliably estimate forearm fracture load experienced during a fall. Forearm fracture load estimates can be used to improve factor-of-risk predictions for forearm fracture.
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