A potential effective treatment for prevention of osteoporotic hip fractures is augmentation of the mechanical properties of the femur by injecting it with bone cement. This therapy, however, is only in research stage and can benefit substantially from computational simulations to optimize the pattern of cement injection. Some studies have considered a patient-specific planning paradigm for Osteoporotic Hip Augmentation (OHA). Despite their biomechanical advantages, customized plans require advanced surgical systems for implementation. Other studies, therefore, have suggested a more generalized injection strategy. The goal of this study is to investigate as to whether the additional computational overhead of the patient-specific planning can significantly improve the bone strength as compared to the generalized injection strategies attempted in the literature. For this purpose, numerical models were developed from high resolution CT images (n = 4). Through finite element analysis and hydrodynamic simulations, we compared the biomechanical efficiency of the customized cement-based augmentation along with three generalized injection strategies developed previously. Two series of simulations were studied, one with homogeneous and one with inhomogeneous material properties for the osteoporotic bone. The customized cement-based augmentation inhomogeneous models showed that injection of only 10 ml of bone cement can significantly increase the yield load (79.6%, P < 0.01) and yield energy (199%, P < 0.01) of an osteoporotic femur. This increase is significantly higher than those of the generalized injections proposed previously (23.8% on average). Our findings suggest that OHA can significantly benefit from a patient-specific plan that determines the pattern and volume of the injected cement.
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http://dx.doi.org/10.1016/j.jbiomech.2019.07.012 | DOI Listing |
Importance: Fragility fractures result in significant morbidity.
Objective: To review evidence on osteoporosis screening to inform the US Preventive Services Task Force.
Data Sources: PubMed, Embase, Cochrane Library, and trial registries through January 9, 2024; references, experts, and literature surveillance through July 31, 2024.
Brain Spine
March 2024
Consultant Orthopaedic Surgeon, San Carlo Borromeo Hospital, Via Pio II 3, Milano, Italy.
Introduction: Bisphosphonates are commonly used to prevent osteoporotic fractures. Many randomized controlled trials have proved the efficacy of bisphosphonates, showing their ability to increase bone mineral density and decrease the risk of hip and vertebral fractures. Atypical, bisphosphonate-related fractures concerning the femur have been widely described and a list of primary and secondary clinical and radiographic criteria are used in order to achieve diagnosis.
View Article and Find Full Text PDFUlus Travma Acil Cerrahi Derg
January 2025
Department of Biochemistry, Gulhane Faculty of Pharmacy, University of Health Sciences, Ankara-Türkiye.
Background: Osteoporosis is characteristically defined as a decrease in bone density and mass, accompanied by the deterioration of bone structure, which increases bone fragility and the risk of fractures. Osteoporosis frequently develops with age. In high-risk populations, oxidative damage is a common pathological condition.
View Article and Find Full Text PDFTunis Med
December 2024
Orthopaedic Department, Fattouma Bourguiba Hospital, Monastir Faculty of Medicine, University of Monastir, Tunisia.
Objectives: To evaluate survival after osteoporotic fractures of the upper femur and determine its associated factors.
Methods: Cross-sectional study of patients hospitalized for fractures of the upper end of the femur during 2020 at Monastir University Hospital. Outcome was determined up to 2 years.
PLOS Digit Health
January 2025
Department of Geriatric Medicine, The Canberra Hospital, ACT Health, Canberra, Australia.
Osteoporotic hip fractures (HFs) in the elderly are a pertinent issue in healthcare, particularly in developed countries such as Australia. Estimating prognosis following admission remains a key challenge. Current predictive tools require numerous patient input features including those unavailable early in admission.
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