Computational Modeling & Simulation has the ability to revolutionize the orthopedic device industry by reducing and in some instances eliminating the need for benchtop testing and clinical trials. Dr. Afshari shared his experience in establishing the credibility of computational models for product design and development purposes, and highlighted was that modeling fits with the regulatory and standards framework.
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Computational Modeling & Simulation in Orthopedics: Tools to Comply in an Evolving Field
1.
2. OMTEC 2019
Chicago, Illinois
June 11-13, 2019
Payman Afshari, PhD
Sr. Principal Engineer
DePuy Synthes Spine
Johnson and Johnson
COMPUTATIONAL MODELING AND SIMULATION ROLE IN
REGULATORY DECISION MAKING AND EVIDENCE GENERATION
3. COMPUTATIONAL MODELING AND SIMULATION
(CM&S), A TOOL TO HELP MAKE BETTER DECISIONS
“A good decision is based on knowledge
and not on numbers”
Plato
427 BCE
“Love is a serious mental disease”
He also said
5. MAJOR ORGANIZATIONS ADVANCING ROLE OF M&S
IN REGULATORY DECISION MAKING
• Has identified an important role for computational modeling in
its strategic priorities since 2011
• Medical Device Innovation Consortium (2012)
• Work collectively to accelerate MedTech innovation from concept to
commercialization by improving the processes for development, regulatory
assessment, and reimbursement review of medical technologies
• Avicenna Alliance (2016)
• A global organization that brings together healthcare stakeholders with the
goal of making in silico medicine standard practice in healthcare
A global organization that brings together healthcare stakeholders
With the goal of making in silico medicine standard practice in healthcareMorrison, Tina, et al, “Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories” Frontiers
in Medicine 2018, 5
6. 5
A 501(c)(3) non-profit public-private partnership aimed to benefit patients by advancing medical device regulatory science,
established in 2012.
Work collectively to accelerate MedTech innovation from concept to commercialization by improving the processes for
development, regulatory assessment, and reimbursement review of medical technologies.
MDIC board includes the director of FDA/CDRH, the director of Coverage and Analysis at CMS, C-level executives representing
patient organizations, non-profits, and industry.
MDIC works on science, not policy or lobbying; MDIC work is complementary to trade associations such as AdvaMed and MDMA.
Accelerate Progress
Achieve Results
WORKING COOPERATIVELY
to re-engineer pre-competitive
technology innovation
REDUCING TIME
and resources needed for new
technology development,
assessment, and review
HELPING PATIENTS
gain access to new medical
technologies sooner
Align Resources
Accelerate Progress
Achieve Results
MDIC HIGHLIGHTS
60 participating
member
organizations
10+ Projects
have been
initiated
Leading resource on issues
important to the Medtech
innovation ecosystem
Congressional testimony
on modernizing clinical
trials
Over $35m funding from
grants and contracts for
Program initiatives.
WWW.MDIC.ORG
What is MDIC?
7. The Avicenna Alliance
Our Mission
Dramatically accelerate medical innovation and its practical
implementation,
To ensure safe, affordable and profitable health care
Through the large scale adoption of in silico modeling
(Computer modeling & simulation, CM&S)
European Parliament, September 4, 2018
US Senate with the FDA, May 17, 2017
A global organization that brings together healthcare stakeholders
With the goal of making in silico medicine standard practice in healthcare through a
collaborative ecosystem of patients, clinicians, academics, industries, policy makers,
regulators & payers
• A market focused partnership of healthcare industries and
researchers set up at the request of the European Commission
• Origins in two EU initiatives:
1. VPH Institute
2. Avicenna project: a “Roadmap for in silico medicine”
8. REGULATORY EVIDENCE GENERATION PARADIGM
Current valid scientific sources of
evidence for Regulatory Decision
Making.
Human Clinical Trials
Animal Testing
Benchtop Testing
Modeling and Simulation
Orthopedics
9. M&S RESULTS AS REGULATORY EVIDENCE
Credibility is the trust, through the collection of
evidence, in the predictive capability of a
computational model for a context of use.
Stakeholders
How can I trust this
model?
Is this device safe?
Did we pick the right WC?
What if the model is
wrong?
Can we use
Simulation?
How do we know if
your model is credible?
Is there a guidance
document we can use?
Lack of a guidance on evaluating the credibility of
computational modeling and simulation motivated FDA
and ASME™, in partnership with the medical device
industry and software providers, to develop a standard.
Device
Original Concept, Jeff Bodner Medtronic
11. • ASME V&V 40
ASME V&V 40 FRAMEWORK
• Provides a framework for
1. Establishing credibility goals for a computational
model for a context of use (COU) based on
model risk
2. Assessing the relevance and adequacy of
completed V&V activities
[1] Reprinted from ASME V&V 40-2018, by permission of The
American Society of Mechanical Engineers. All rights reserved.
12. ASME V&V 40 STANDARD –
MAIN BODY DETAILS THE PROCESS
Guides a team through the risk-informed credibility assessment framework,
to determine HOW MUCH verification and validation (V&V) is necessary to
support using a computational model for a context of use (COU).
13. The question of interest describes the specific question, decision or
concern that is being addressed.
Context of use defines the specific role and scope of the computational
model used to inform that decision.
QUESTION OF INTEREST AND CONTEXT OF USE
14. Model risk is the possibility that the model may
lead to a false/incorrect conclusion about device
performance, resulting in adverse outcomes.
- Model influence is the contribution of the
computational model to the decision relative to
other available evidence.
- Decision consequence is the significance of an
adverse outcome resulting from an incorrect
decision.
RISK ASSESSMENT
15. Model credibility refers to the trust in the
predictive capability of the computational
model for the COU.
Trust can be established through the
collection of V&V evidence and by
demonstrating the applicability of the
V&V activities to support the use of the
CM for the COU.
Goals for each credibility factor are
based on model risk.
CREDIBILITY GOALS
16. • Is the standard practical?
• Has it been implemented in an actual regulatory submission?
DePuy Synthes Spine Success Story in implementing V&V 40
• Background
Clinician needs to scan a patient implanted with a metallic spinal device.
Question of Interest:
Could the patient implanted with the device be harmed by the
RF induced temperature rise during a MRI scan?
Answer:
Check the MRI Safety Label of the device
V&V 40 IN ACTION
17. MR CONDITIONAL LABELING FOR RF HEATING
• Under the scan conditions defined the <device name> is
expected to produce a maximum temperature rise of less
than <specific value>ºC after 15 minutes of continuous
scanning
∆T@15 Minutes
Question of
Interest
18. APPLYING THE V&V FRAMEWORK TO RF HEATING
COMPUTATIONAL MODEL Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
Establishing the framework
Creating the Virtual RF Coil
Assessment Activity
19. FRAME WORK
CONTEXT OF USE
• Computational model (CM) to be used to predict the temperature
increase within a specified confidence interval due to the
presence of a passive metallic spinal implant inside an ASTM
F2182 Phantom scanned in a 1.5T and 3T MR Scanner.
• CM is an ASTM F2182 RF Coil/Phantom Replicator
(A virtual RF Coil)
• CM&S will be the sole source of evidence to inform the MRI
labeling parameters for safe RF exposure (see Risk Profile).
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
20. RISK PROFILE
Model Influence: High
CM&S will be the sole source of evidence to
inform the MRI labeling parameters for safe
RF exposure.
Decision Consequence: Low/Mid
Spinal implants are:
• Anchored in bony tissues of spine
• Encapsulated by scar tissue, proximity to fat,
muscle and other soft tissues
• No major vasculature or neural impingements
• No historical complaints reported
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
21. MODEL AND CALIBRATION
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
Frequency Power
Output = ∆T@15 Minutes
22. UNCERTAINTY QUANTIFICATION (BENCHTOP)
Description Units
Variation
(+/-)
Source
P/S
Shape
Divisor
-1 0 1
Sensitivity
Coef. (ci)
R
2
Standard
Uncert. (ui)
[°C]
Standard
Uncert.
[% Nom.]
Experimental Uncertainty
EM
Gel - Electrical conductivity S/m 0.047 [1] P Rect. 1.73 10.323 10.11 8.7776 -16.440 0.851 -0.446 4.4%
Gel - Electric permitivity (n/a) 11.55 [1] P Rect. 1.73 9.9868 10.11 10.508 0.023 0.915 0.150 1.5%
TAV - Electrical conductivity S/m 5.71E+04 * P Normal 1 10.08 10.11 10.05 -2.63E-07 0.250 -0.015 0.1%
Thermal
Gel - Thermal conductivity W/m·K 0.1 * P Normal 1 11.525 10.11 9.0555 -12.348 0.993 -1.235 12.2%
Gel - Density Kg/m3
100 * P Normal 1 10.41 10.11 9.8468 -0.003 0.999 -0.282 2.8%
TAV - Specific heat capacity J/Kg·K 52.63 * P Normal 1 10.113 10.11 10.106 -6.65E-05 0.993 -0.003 0.0%
TAV - Density Kg/m3
443 * P Normal 1 10.113 10.11 10.106 -7.90E-06 0.993 -0.003 0.0%
TAV - Thermal conductivity W/m·K 0.67 * P Normal 1 10.173 10.11 10.049 -0.093 1.000 -0.062 0.6%
Test setup
Probe sensing location mm 0.5 [2] P Rect. 1.73 10.308 10.11 9.6506 -0.657 0.950 -0.190 1.9%
[Implant] X-axis displacement mm 1 [2] P Normal 1 9.9432 10.11 10.245 0.151 0.996 0.151 1.5%
[Implant] Y-axis displacement mm 1 [2] P Normal 1 10.096 10.11 10.047 -0.024 0.549 -0.024 0.2%
[Implant] Z-axis displacement mm 1 [2] P Normal 1 10.182 10.11 10.072 -0.055 0.969 -0.055 0.5%
[Implant] X-axis rotation ° 1 [2] P Normal 1 10.004 10.11 10.152 0.074 0.941 0.074 0.7%
[Implant] Y-axis rotation ° 1 [2] P Normal 1 9.9185 10.11 10.313 0.197 1.000 0.197 2.0%
[Implant] Z-axis rotation ° 1 [2] P Normal 1 10.072 10.11 10.152 0.040 0.999 0.040 0.4%
[Implant] Tolerance (dia.) mm 0.1 * P Normal 1 9.9185 10.11 10.082 0.818 0.625 0.082 0.8%
[Phantom] X-axis displacement mm 1 [2] P Normal 1 9.9794 10.11 10.051 0.036 0.300 0.036 0.4%
[Phantom] Y-axis displacement mm 1 [2] P Normal 1 10.241 10.11 10.115 -0.063 0.720 -0.063 0.6%
[Phantom] Z-axis displacement mm 1 [2] P Normal 1 10.087 10.11 10.1 0.006 0.318 0.006 0.1%
Temp. probe meas. system °C 0.5 [2] S Rect. 1.73 N/A 10.11 N/A 1.000 N/A 0.289 2.9%
Results
[°C] [%/Nom.] [°C] [%/Nom.]
Comb. Stand. Uncert. (uc) 1.40 14% 1.46 14%
Coverage factor (k) -2.85 -28%
Expanded Uncertainty (U) 2.79 28% 2.86 28%
Calculated (3T)
Proportional Stand.
Simulated (3T)
Proportional
Parameter / Uncertainty Contributor PDF CM&S Results [dT °C] (Coded) Calculation
0.58 U @ 97.5 %ile (p=95%)
[°C]
0.29 uc @ 1 St. Dev
2 U @ 2.5 %ile (p=95%)
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
23. Computational Model Error
• Boundary conditions
• Domain discretization
• Convergence
• User error
• Hardware dependencies, HPC / OS
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
UNCERTAINTY QUANTIFICATION (MODEL)
24. EXPLORE VALIDATION SPACE
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
25. MODEL UNCERTAINTY PROFILE
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
26. CREDIBILITY ASSESSMENT
Define COU
Credibility
Goals
Question of
Interest
Assess Risk
Is Model
Credible for the
COU
Yes
Document
Revise COU, Model, ...
No
Develop Computational Model
EM Model
Thermal Model Temperature
UQ - CM
User Error
Numerical Solver
Hardware
Discretization
UQ - Experimental
Material Properties
Positioning
Location
Probe Sensing Location
Probe Measurement Error
Evaluate Validation
Space
Calibration
Frequency
Tuning
Define Validation
Space / Portfolio
Temperature
First Attempt did not meet the
credibility requirements
A new method and tighter
convergence was implemented
The model met the credibility
requirements
Received 510(k)
Clearance with no
Deficiencies!
Is the model credible for the Context of Use?
27. ADVANCING M&S ACCEPTABILITY THROUGH COLLABORATION
STANDARDS, GUIDANCE DOCUMENTS AND BEST PRACTICES
Collaboration
in advancing
CM&S in
Orthopedics
ASME V&V 40 Working Groups:
• End to End Example (Tibial Tray)
• Solution Verification (Hip Stem)
• Using Real World Data (Tibial Tray)
• Patient Specific (3D Printed Femoral Cage)
• F2077 IBF Cage
• F2182 RF Heating
• F1717 Static Compression
• F-2996 Standard Practice for Finite
Element Analysis (FEA) of Non-Modular
Metallic Orthopaedic Hip Femoral Stems
• STM WK59162- New Test Method for
Finite Element Analysis (FEA) of Metallic
Orthopaedic Total Knee Tibial Components
In Progress
In Progress
28. Geometry
Material
BC
Software
Hardware
….
CHALLENGES IN IMPLEMENTING VVUQ
Uncertainty Quantification:
• Quantitative
characterization of
predictive capability of
both computational and
real world models.
• Probabilistic in nature; it
could require significant
resources to develop it.
Geometry,
Equipment
Procedures
Patient Data
Imaging
….
Clinical Data
Animal Testing
Benchtop Testing
Computational
Model
30. TAKEAWAYS
• The Role of CM&S as a powerful predictive tool impacting all aspects of
product life cycle is expected to grow.
• CM&S is being recognized by the world’s leading regulatory agencies as the
fourth paradigm of evidence generation.
• FDA is leading and promoting the effort in developing standards and
guidance documents to be used in regulatory submission.
• ASME V&V 40 Standard is a practical document that can be the conduit to
communicate the credibility of the CM&S to all the stakeholders in their
decision making and regulatory submissions.
• The burden of developing UQ can be reduced through collaboration with
all the stakeholders.
31. THANK YOU FOR YOUR ATTENTION
“Knowledge which is acquired under
compulsion obtains no hold on the mind”