A Hybrid Risk Model for Hip Fracture Prediction

Abstract

Hip fracture has long been considered as the most serious consequence of osteoporosis, which includes chronic pain, disability, and even death. In the elderly population, a femur fracture is very common. It is assessed that 50% of women aged 50 or older may experience a hip fracture in their remaining life. Hip fracture is among the most common injuries and can lead to substantial morbidity and mortality. In the US alone, over 250,000 hip fractures occur each year and this number is expected to double by the year 2040. Statistics indicate that over 20% of people who experience a hip fracture die within one year and only 25% have a total recovery. Femur fractures are now becoming a major social and economic burden on the health care system. In practice, it is very difficult to predict the femur fracture risks. One of the main reasons is that there is not a robust and easy-to-get measure to quantify the strength of the bone. Clinicians use bone mineral density (BMD) as an indicator of osteoporosis and fracture risk. Several studies showed that BMD cannot be used alone to identify bone strength. In fact, the majority of patients who suffer from fractures have normal or even higher BMD scores. There are a large number of risk factors that contribute to the occurrence of femur fracture, which should also be involved in predicting hip fracture risks. For example, age, weight, height, ethnicity and so on. Some of the factors might not have been identified yet. Thus, there will be a high level of uncertainty in the clinical dataset, which makes it difficult to construct and validate a hip risk prediction model. The objective of the dissertation is to construct an improved hip fracture risk prediction model. Due to the difficulty of obtaining experimental or clinical data, computational simulations might help increase the predictive ability of the risk model. In this research, the hip fracture risk model is based on a support vector machine (SVM) trained using a clinical dataset from the Women's Health Initiative (WHI). In order to improve the SVM-based hip fracture risk model, data from a fully parameterized finite element (FE) model is used to supplement the clinical dataset. This FE model allows one to simulate a wide range of geometries and material properties in the hip region, and provides a measure of risk based on mechanical quantities (e.g., strain). This dissertation presents new approaches to fuse the clinical data with the FE data in order to improve the predictive capability of the hip fracture risk prediction model. Two approaches are introduced in this dissertation to construct a hybrid risk model: an "augmented space" approach and a "computational patients" approach. This work has led to the construction of a new online hip fracture risk calculator with free access.