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A patient-specific computational technique to predict spine injury risks associated with physical activities

一种针对患者的计算技术，用于预测与体力活动相关的脊柱损伤风险

基本信息

批准号：
10214330
负责人：
Asghar Rezaei
金额：
$ 14.65万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-15 至 2026-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10214330
关键词：
Activities of Daily Living Address Affect Applications Grants Articular Range of Motion Bone Diseases Cadaver Cervical Characteristics Chest Clinical Computational Technique Computer Models Data Defect Deformity Development Elderly Electromyography Equilibrium Exercise Face Failure Finite Element Analysis Floor Fracture Future Goals Guidelines Image In Vitro Injury Knowledge Lateral Lead Lifting Magnetic Resonance Imaging Measures Mechanics Metastatic Neoplasm to the Bone Methods Modeling Motion Movement Muscle Musculoskeletal Diseases Neoplasm Metastasis Neurologic Deficit Neurologic Symptoms Osteoporosis Osteoporotic Outcome Pain Pathologic Patients Persons Physical activity Population Prevention Process Property Quality of life Recommendation Research Risk Risk Assessment Robot Safety Site Spinal Spinal Fractures Spinal Injuries System Techniques Testing Translating Validation Vertebral column Weight-Bearing state Work X-Ray Computed Tomography age related arm base bone career development cohort comorbidity computerized tools evidence based guidelines experience experimental study fracture risk gait examination healthy aging human old age (65+)improved in vivo kinematics novel strategies older patient osteoporosis with pathological fracture patient population pressure response risk prediction skills spine bone structure tool

项目摘要

PROJECT SUMMARY/ABSTRACT Vertebral fracture is the most common type of osteoporotic fracture. Spine is also the most common site of bone metastasis, leading to pathologic vertebral fractures. While performing activities of daily living is an essential part of healthy aging, pathologic and non-pathologic vertebral fractures can occur during these activities in metastatic or osteoporotic spines. These fractures cause pain and neurologic manifestations, affecting quality of life. Currently, there is no objective clinical technique that can assess bone fracture risks associated with physical activities. To fill the gap, we propose a patient-specific computational technique to quantitatively evaluate spine injury risks associated with physical activities. This novel approach will enable clinicians to reliably recommend safe and individualized activities to elderly populations. To achieve this, we will obtain motions and muscle activity outcomes from an elderly patient cohort using video motion analysis. These data will be used as input for kinematic motion analyses and mechanical testing on cadaveric lumbar spines, to create and validate our computational models. The rationale for this project is that a QCT/FEA process that can mimic physical activities will be able to reduce vertebral fractures and improve quality of life in elderly patient populations. Our long-term goal is to develop reliable computational techniques to enable earlier injury risk predictions in elderly patients with musculoskeletal diseases. Our overall objective, in this application, is to develop a patient-specific quantitative computed tomography-based finite element analysis (QCT/FEA) method that can assess both kinematic motions and fracture characteristics of the spine, to estimate fracture risks of physical activities. To achieve the overall objective, the following three independent specific aims will be accomplished: 1) to obtain lumbar range of motion and muscle response outcomes in an elderly patient population during five physical movements; 2) to perform kinematic testing on cadaveric spines to measure intradiscal pressures –using a novel approach– during physical movements; and also mechanical testing on spine segments to measure intradiscal pressure at fracture; and 3) to develop and validate QCT/FEA models of the lumbar spine to estimate spine injury risks. This research will lead to the development of a computational tool that can assign a risk score associated with physical activities. Further, this work will provide preliminary data for future R01 grant proposals to predict fracture risks associated with physical movements and exercises in patient populations.

项目总结/文摘