Modeling of Proximal Humeral Fractures
BMEN 611: Computational Modeling in Biomechanics
Fall 2023-2024
Presented by: Joanna Ghaddar
To: Dr. Jason Amatoury
Optimization of the Proximal Humeral Fractures Surgeries in terms of Implant Stability and
Post-operative Complications
Introduction
Proximal humeral fractures (PHF) are the third most frequent type of bone fractures, with their
incidence significantly increasing around the world [3]. Following PHF, a variety of procedures,
including intramedullary nailing and plating, and internal fixation can be implemented. The choice
of treatment is affected by various factors like the characteristics of the patient, radiographic
manifestations, concurrent injuries, and fracture severity [6]. Also, the fractured bone itself may
exhibit a complex mechanical behavior upon implant construct fixation during surgery (primary
stability) and post-surgery (secondary stability). Due to this, such procedures are characterized by
a high rate of bone fixation failure and surgical-technique-related complications [7]. Collectively,
this poses a challenge for surgeons and drives research demand for more comprehensive and
personalized preoperative planning and surgical design techniques.
This report encompasses a thorough investigation of PHF, employing a triadic modeling
framework comprising computational, biomechanical, and human models. These methodological
approaches shed light on evaluating current surgical procedures through finding optimal implants
configuration and minimizing post-surgical complications.
1. Computational Model
A computational framework based on finite element analysis (FEA) to systematically evaluate
osteoporotic fracture fixation is developed. This model studies the impact of cement augmentation
of screw heads in PHF using a test kit shown in Figure 1. It also compares different implant
configurations using seven fracture types 2 and 3 part well- and mal-reduced gap and wedge.
Several steps are followed for the finite element model generation:
a. Model geometry: Bone shape, fracture patterns, implant geometry and positioning.
b. Meshing and material property assignment: (using Simpleware software) Loading of
subject-specific items and simplification of bone anisotropy and gap geometries (for
example: neglecting polymethylmethacrylate embedding at the shaft and the head).
c. Interface configurations: Bone-bone, plate – bone, and plate – screws interactions.
d. Loading and boundary conditions: Utilizing three experimental and three physiological
loading modes as shown in Figure 2.
e. Analysis and post-processing: Measuring the outcome, which is the averaged compressive
strain in peri-screw bone region.
f. Validation of the simulation framework: Stimulating the test kit to predict previously
published results with loading mode and implant configuration by Unger et al. [2] where
the validated measure was peri-implant bone strain.
The main finding of this model shows that utilizing cannulated screws for augmentation appears
to be a practical approach for improving the anchoring of implants/screws in the humeral head.
The enhancement in screw stability significantly increases as bone mineral density decreases. [1].
2
Figure 1: Depiction of the parts and layout of the test-kit framework (on the left), which includes an outline of the virtual
testing machine's operation and the Database's contribution in model creation. This tool is comprised of three primary
components: a pre-processing module responsible for generating finite element models for chosen subjects based on the
Study group criteria, an analysis module that conducts finite element analysis utilizing an external solver, and a postprocessing module that extracts the desired results (on the right) [1].
Figure 2: Depiction of the test kit's three distinct loading conditions
(lower row), replicating biomechanical test configurations from
previously published studies (upper row). Two of these loading protocols
were based on Unger et al.'s research [2], where axial loading on
Proximal Humeral bone head was employed to induce varus bending (a,
d) and torsional loading (b, e). The third loading mode is modeled after
varus bending experiments but with less restricted boundary conditions
on the humeral head (c, f). [1].
3
2. Biomechanical Model
A biomechanical in vitro study investigating the stability of screw augmentation in a locking plate
at poor bone quality region in PHF model is developed. The study utilized a pair of twelve freshly
frozen cadaveric proximal humerus bones, taken from humans and devoid of any soft tissues. A
plate was used to fix a model with a three-part fracture model and a metaphyseal defect as shown
in Figure 3. Outcomes measured were bone mineral density (BMD) using quantitative computed
tomography and local bone quality using mechanical means (DensiProbe). Furthermore, varusbending test with cyclic loading was performed, followed by gradual increase in the magnitude of
the upper load until the point of failure in the screw-bone fixation was reached as shown in Figure
4.
The main finding of this model is that two screws augmentation in a region of humeral head with
poor bone quality performs nearly as effective as using four screws with twice the quantity of bone
cement. [4].
Figure 3: The illustration shows two specimens, using X-ray,
with a three-part PHF with a 10 mm gap in the metaphyseal
region. On the right side, both corresponding screws were
reinforced as per reference. [4].
Figure 4: The illustration shows
the loading in varus bending
test set up. Load application is
indicated by red arrow. The
head fragment is mounted on a
cushioned ball and is free to
rotate. [4].
3. Human Model
A uniform surgical procedure for stabilization and fixation of PHF utilizing locking plate to reduce
complications related to surgery is developed. Fifty patients with an average age of 63 years were
employed for this study and all treated by the same surgeon using a standard surgical protocol by
Minkus et al. [5] as shown in Figure 5. Constant-Murley Score (CS), Subjective Shoulder Value
(SSV), and X-ray images were conducted for the clinical follow-up examinations, which lasted up
to 24 months. No complications related to surgery or soft tissues were identified during the
postoperative radiologic evaluation. Whereas, avascular necrosis, a secondary biological
complication, was largely responsible for the main complications.
4
The main finding of the model is that such a standardized surgical approach enhances fixation by
improving primary stability and helps prevent complications associated with the surgical
techniques in patients with proximal humeral fractures (PHF) [8].
Figure 5: Illustration of the surgical procedure for locking plate osteosynthesis is as follows: The
patient is positioned in a specific position (a), with an image intensifier positioned to enable
anteroposterior view (b) and axial views (c, d). The fracture site is exposed (e). Tuberosities are
prepared for fixation (f). K-wires are percutaneously and retrogradely drilled up (g,h). The head
fragments are repositioned and reduced (i), and K-wires are advanced until reaching the
subchondral bone (j,k). The defect is augmented with allograft (l). The bicep tendon is addressed
(m), followed by tuberosities fixation (n). A locking plate is placed in position (o) and secured with
K-wires (p). Drilling vertically or from the highest point to the lowest point (q) is followed by the
positioning of screws (r). The final assessment for stability is performed in situ (s) and using the
image
intensifier
in
the
anteroposterior
(t)
and
axial
views
(u).[https://creativecommons.org/licenses/by/4.0/], from [5].
Discussion
The computational model utilizing finite element analysis allowed us to study the effects of screws
and cement augmentation with respect to bone density. Nevertheless, the testing apparatus was
limited to evaluating angle-stable plate fixation and was constrained to a specific group of leftsided bones. Also, four-part fractures were not included, as it was limited to two and three bone
fractures and to only three basic movements. However, this limitation can be addressed by
broadening the model to incorporate intramedullary nailing or exploring other innovative
approaches to bone fixation and to re-mesh the model to be inclusive to more patients. Although,
it is worth noting that re-meshing can be a very time-consuming process.
On the other hand, the biomechanical study showed that targeted cement augmentation of screws
with identification of local bone density in a PHF model resulted in a significant enhancement in
initial stability. This was done through mechanical testing that can assess axial, bending, and
torsional loads applied to a single fracture line. However, this study suffered from small sample
size and did not consider the negative side-effects on the bone cartilages nor the interaction of bone
cement with it which should be focused on in future research. It also lacked models of four-part
fractures like the computational model.
5
Furthermore, in both studies only bone mineral density (BMD) was assessed although various
mechanical properties of bones like stress, strain and elastic stiffness would serve as good
parameter for the assessment of the surgical procedure design.
On the other side, the human model focused on reducing the complications of post-surgery,
tackling a side that cannot be analyzed in the previous models. However, through this model very
important parameters like pain management, tissue variability, inflammatory response, and
functional recovery were not tackled.
In summary, computational models exhibit the strength of providing precise numerical
simulations, allowing for detailed analysis of fracture patterns and the evaluation of new treatment
strategies. They offer scalability and repeatability, making them invaluable tools for research and
surgical planning. However, their limitations lie in their dependency on accurate input data and
assumptions, which may not always reflect real-world complexities. Biomechanical models, on
the other hand, excel in replicating physical conditions, offering insights into load-bearing
capacities and the impact of implant placement. Nonetheless, they may oversimplify the complex
biomechanics of the human body, potentially leading to less accurate predictions. Lastly, humanbased models, including clinical trials, provide invaluable insights into real-world fracture
scenarios and treatment outcomes. They capture individual variations, but are constrained by
ethical considerations, limited availability of specimens, and the challenge of reproducing
standardized conditions. Thus, a comprehensive understanding of proximal humeral bone fractures
necessitates considering the combined strengths of these three models while acknowledging their
inherent limitations as discussed in Table 1.
Table 1: Table showing the strengths and limitations of Computational, Biomechanical, and Human Model.
6
Conclusion
Treating PHF has always been subject to controversy due to various factors ranging from bone
geometry, architecture and material properties in the fractured area, the specific implant
configuration and characteristics, the interaction between the bone and the implant, the
postoperative loading profile, and finally the patient’s biological response. That is why modeling
proximal humerus fractures is crucial for advancing surgical techniques and treatment strategies,
as it allows for a better understanding of such mechanical and biological complexities involved.
The intricacy of finite element analysis (FEA) models is precisely what makes them appealing
while examining such systems like these. However, FEA without validation remains a theoretical
construct lacking empirical evidence to support its accuracy and applicability in the real-world.
Validation can be done in biomechanical models like cadaveric human specimen and then in actual
human models. That is why the three models presented in this report go hand in hand for the
development of enhanced implants and personalized preoperative planning leading to optimized
fracture management, reduced fixation failures, and post-surgical complications.
References
1.
2.
3.
4.
5.
6.
7.
8.
Varga, P., et al., Validated computational framework for efficient systematic evaluation of
osteoporotic fracture fixation in the proximal humerus. Med Eng Phys, 2018. 57: p. 29-39.
Unger, S., et al., The effect of in situ augmentation on implant anchorage in proximal
humeral head fractures. Injury, 2012. 43(10): p. 1759-1763.
Baker, H.P., et al., Management of Proximal Humerus Fractures in Adults—A
Scoping Review. Journal of Clinical Medicine, 2022. 11(20): p. 6140.
Röderer, G., et al., Biomechanical in vitro assessment of screw augmentation in locked
plating of proximal humerus fractures. Injury, 2013. 44(10): p. 1327-1332.
Minkus, M. and M. Scheibel, Open reduction, retention, and fixation of proximal humeral
fractures using a locking plate osteosynthesis. Obere Extremität, 2019. 14(2): p. 136-138.
Rojas, J.T., M.S. Rashid, and M.A. Zumstein, How to treat stiffness after proximal humeral
fractures? EFORT open reviews, 2023. 8(8): p. 651-661.
Kennon, J.C., et al., Primary reverse shoulder arthroplasty: how did medialized and
glenoid-based lateralized style prostheses compare at 10 years? Journal of Shoulder and
Elbow Surgery, 2020. 29(7): p. S23-S31.
Kwisda, S., et al., A Standardized Operative Protocol for Fixation of Proximal Humeral
Fractures Using a Locking Plate to Minimize Surgery-Related Complications. Journal of
Clinical Medicine, 2023. 12(3): p. 1216.
7