A New Hip Fracture Risk Index Derived from FEA -Computed Proximal Femur Fracture Loads and Energies -to-Failure Xuewei Cao1 Joyce H. Keyak2 Sigurdur Sigurdsson3 Chen Zhao4 Weihua Zhou4

2025-04-27 0 0 647.41KB 27 页 10玖币

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A New Hip Fracture Risk Index Derived from FEA-Computed Proximal

Femur Fracture Loads and Energies-to-Failure

Xuewei Cao1,#, Joyce H. Keyak2,#, Sigurdur Sigurdsson3, Chen Zhao4, Weihua Zhou4,

Anqi Liu5, Thomas Lang6, Hong-Wen Deng5, Vilmundur Gudnason3,7,*, Qiuying Sha1,*

1 Department of Mathematical Sciences, Michigan Technological University, Houghton,

Michigan, USA

2 Department of Radiological Sciences, Department of Biomedical Engineering, and

Department of Mechanical and Aerospace Engineering, University of California, Irvine,

CA, USA

3 Icelandic Heart Association Research Institute, Kópavogur, Iceland

4 Department of Applied Computing, Michigan Technological University, Houghton, MI,

USA

5 Center for Bioinformatics and Genomics, School of Medicine, Tulane University, New

Orleans, LA, USA

6 Department of Radiology and Biomedical Imaging, University of California, San

Francisco, CA, USA

7 University of Iceland, Reykjavik, Iceland

# Both authors contributed equally

* Corresponding authors:

Qiuying Sha, Department of Mathematical Sciences, Michigan Technological University,

Houghton, Michigan 49931, USA. E-mail: qsha@mtu.edu

Vilmundur Gudnason, Icelandic Heart Association Research Institute, Kópavogur,

Iceland. E-mail: v.gudnason@hjarta.is

Abstract

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based

patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and the

three-dimensional distribution of bone density in the proximal femur to compute the force

(fracture load) and energy necessary to break the proximal femur in a particular loading

condition. Although the fracture loads and energies-to-failure for different loading

conditions are individually associated with incident hip fracture, and are mutually

correlated, they each provide different structural information about the proximal femur that

can influence a subject’s overall fracture risk. To obtain a more robust measure of fracture

risk, we used principal component analysis (PCA) to develop a global FEA-computed

fracture risk index that incorporates the FEA-computed yield and ultimate failure loads

and energies-to-failure in four loading conditions (single-limb stance and impact from a

fall onto the posterior, posterolateral, and lateral aspects of the greater trochanter) of 110

hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-

Reykjavik study. We found that the first principal component (PC1) of the FE parameters

was the only significant predictor of hip fracture (p-value < 0.001); we refer to PC1 as the

global FEA-computed fracture risk index. To evaluate this fracture risk index, we

considered the areal bone mineral density (aBMD) and six covariates including age, sex,

height, weight, health status, and bone medication status. Using a logistic regression

model, we determined if prediction performance for hip fracture using PC1 differed from

that using FE parameters combined by stratified random resampling with respect to hip

fracture status. We also compared the performance of predicting hip fracture by the

logistic regression model including PC1 with the FRAX model. The results showed that

the average of the area under the receiver operating characteristic curve (AUC) using

PC1 (0.776) was always higher than that using all FE parameters combined (0.737) in

the male subjects (p-value < 0.001). The AUC of PC1 and AUC of the FE parameters

combined were not significantly different in the female subjects (p-value = 0.211) or in all

subjects (p-value = 0.159). The AUC values using PC1 (0.754 in all subjects, 0.825 in

males and 0.71 in females) were greater than the respective values using FRAX (0.651

in the whole sample, 0.705 in males and 0.623 in females) with p-value < 0.01. Therefore,

the global FEA-computed fracture risk index based on PCA includes information about

hip fracture incidence beyond that of FRAX.

Keywords: Hip fracture risk, Principal component analysis, Bone strength, Finite element

analysis, Osteoporosis

Introduction

Osteoporosis is among the most common and costly metabolic bone diseases [1], in

which the density and quality of bone are reduced. It is characterized by excessive

skeletal fragility and susceptibility to low-trauma fracture among the elderly [2-4], causing

bones to become weak and brittle and greatly increasing the risk of fracture [5,6]. The

incidence and prevalence of osteoporosis increases with age and is related to many

factors, such as gender, weight, height, and medical/medication history [7,8]. The

increasingly elderly population and the rise in fracture incidence have made osteoporosis

a major public health issue in the U.S. and around the world. Osteoporosis affects about

25% of women aged ≥ 65 and about 5% of men aged ≥ 65 [9]. The economic burden of

osteoporosis has been estimated at between $17 billion and $20.3 billion in the US alone

(2020 data) [10].

Although osteoporosis can affect any bone in the human body, osteoporotic

fractures of the proximal femur are the most devastating outcome of the disease, often

signaling an end to independent living in the functional elderly. Each year over 300,000

older people in the U.S.—many of those 65 and older—are hospitalized for hip fracture.

5-10% of patients experience a recurrent hip fracture [11]. Hip fractures are invariably

associated with chronic pain, reduced mobility, disability, and an increasing degree of

dependence [12]. Hip fractures cause the most morbidity among all fractures; reported

mortality rates are up to 20-24% in the first year after a hip fracture [13,14], and a greater

risk of dying may persist for at least 5 years afterwards [15]. Loss of function and

independence among survivors are profound; 40% are unable to walk independently and

60% require assistance a year later [16]. Because of these losses, 33% are totally

dependent or in a nursing home in the year following a hip fracture [17,18]. Less than half

of those who survive the hip fracture regain their previous level of function [12]. Fracture

risk assessment and risk stratification through screening are necessary to reduce the

incidence of hip fracture.

Areal bone mineral density (aBMD) of the proximal femur assessed by dual energy

X-ray absorptiometry (DXA) is the accepted clinical parameter for the diagnosis of

osteoporosis of the hip [19-21]. The aBMD is compared with that of a reference group to

obtain the number of standard deviations from the mean aBMD of a gender- and ethnicity-

matched healthy population, i.e., the T-score [22,23]. Although low DXA aBMD is

associated with bone weakness and fragility fracture [24], DXA is a 2D-projection

technique that poorly accounts for bone geometry and size, and it cannot separately

evaluate the cortical and trabecular bone [25]; all of these factors influence the integrity

of the proximal femur and, therefore, the risk of fracture. Thus, DXA aBMD provides

limited information about skeletal factors on fracture risk.

Quantitative CT (QCT) imaging is one of the most powerful methods for assessing

bone quality in the proximal femur; after three-dimensional (3D) segmentation of the

proximal femur, features such as volumetric bone mineral density (BMD) of the cortical

and trabecular bone and bone geometry can be computed. Therefore, QCT is better than

DXA for evaluating fracture risk [26]. Patient-specific finite element analysis (FEA) from

QCT images incorporates bone geometry, cortical thickness, and the three-dimensional

distribution of bone density in the proximal femur to compute the force (fracture load)

necessary to break the proximal femur in a particular loading condition. The fracture load

can be defined as the force at the onset of fracture (the yield strength) or the maximum

force the proximal femur withstands before complete fracture (the ultimate strength or

load capacity), and are calculated using FEA with linear or nonlinear material properties,

respectively. FEA-computed fracture loads are the most robust measure of proximal

femoral structural integrity and, therefore, hip fracture risk [27,28]. In particular, the

ultimate strength or load capacity of the proximal femur which is computed with nonlinear

FEA is associated with incident hip fracture in men and women, and in men even after

controlling for aBMD [8].

Finite element models compute the force necessary to break the proximal femur

when forces and other boundary conditions are applied to reflect a specific loading

condition, such as single-limb stance during walking or impact onto the greater trochanter

from a fall in a specific direction [8]. Although the FEA-computed yield or ultimate

strengths for different loading conditions are individually associated with incident hip

fracture, and are mutually correlated, each yield and ultimate strength for each loading

condition provides different structural information about the proximal femur that

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ANewHipFractureRiskIndexDerivedfromFEA-ComputedProximalFemurFractureLoadsandEnergies-to-FailureXueweiCao1,#,JoyceH.Keyak2,#,SigurdurSigurdsson3,ChenZhao4,WeihuaZhou4,AnqiLiu5,ThomasLang6,Hong-WenDeng5,VilmundurGudnason3,7,*,QiuyingSha1,*1DepartmentofMathematicalSciences,MichiganTechnologicalUniversi...

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A New Hip Fracture Risk Index Derived from FEA -Computed Proximal Femur Fracture Loads and Energies -to-Failure Xuewei Cao1 Joyce H. Keyak2 Sigurdur Sigurdsson3 Chen Zhao4 Weihua Zhou4

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