Menu
Novel Data-based Screening for Hip Fracture Risk and Patient Specific Treatment Selection
Short Summary
A hip fracture can have severe consequences for the individual including a 50% risk of morbidity and a 25 % mortality within the first 12 months post-fracture. Current clinical methods that are used to identify individuals at high risk of fracturing their hip only consider the skeletal health of the person, which influences the force that is needed to break the hip. However, the vast majority of hip fractures in the elderly do not occur due to natural loading on the femur, but are the result of a fall from standing height or lower. This project is a collaboration between ETH-Zurich and the Icelandic Heart Association, Kopavogur, Iceland. The goal is to use machine learning to develop a novel algorithm for assessing the risk of fracturing the hip based on a combination of the patient data available at the Icelandic Heart Association and mechanical models developed at ETH Zurich. This algorithm will then be used in the second part of the project to explore the efficacy of available preventive treatment options for the individual.
Goals
The goal of this project to develop a tool that will allow for the assessment of the risk of hip fracture of an individual based on patient data that is related to the risk of falling, the force applied to the femur in the event of a fall, and the structural strength of the skeleton.
A second goal is to use this novel tool to assess the efficacy of different preventive treatments in reducing the risk of hip fracture.
Significance
Hip fractures are a common injury in the elderly with often crippling consequences. Improved injury risk assessment and a better selection of preventive treatment would allow insure a better protection of these individual’s lives and quality of life.
Background
The current clinical standard for the assessment of hip fracture risk is based on osteoporosis, a disease that is characterized by a severe loss in bone mass, which results in a structural weakness of the skeleton. This metric is lacking sensitivity and specificity. A combination of parameters which are related to the structural strength of the femur and parameters, which are related to the likelihood and severity of atypical loading could improve the assessment of hip fracture risk.
Publications
Patents / Startups
Publications
- Biesso et al. Dependency of Impact Forces and Strains on Muscle Activation during Falls in the Elderly, European Society for Biomechanics 2021, Milano, Italy
- Catani et al. Dependency of Femoral Impact Forces on Posture and Fall Direction assessed with Biofidelic Finite Element Models, European Society for Biomechanics 2021, Milano, Italy
- Fleps et al. FEM-derived Femoral Strength is a Better Predictor of Hip Fracture Risk than BMD in the AGES RS Cohort, European Society for Biomechanics 2020, July 12th-15th, Online
- Fleps et al. Non-Linear FE is more Predictive of Femoral Fractures than BMD and Linear FE in the AGES RS cohort, American Society for Bone and Mineral Research, Seattle, September 11-14th, 2020
- Galliker et al. The influence of Fall direction and Hip Protector Padding on Fracture Risk quantified with FE models, European Society for Biomechanics 2021, Milano, Italy
Patents / Startups
Transition Postdoc Fellowship Project
Dr. Ingmar Fleps
ETH Zurich
Consortium
- Icelandic Heart Association, Kopavogur, Iceland
Status
Completed
- 2nd Call, TPdF
Funded by
