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Computational Modeling & Simulation in Orthopedics: Tools to Comply in an Evolving Field

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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. 1. 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
  2. 2. 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
  3. 3. CM&S Applications Concept Evaluation Design Optimization Failure Analysis Manufacturing Physiological and Biomechanical Licensing and Acquisitions Regulatory Evidence Generation Design Decision Manufacturing Processing Decision Clinical Decision Business Decision Regulatory Decision CM&S AS A DECISION MAKING TOOL IN THE PRODUCT LIFE CYCLE Support Due Diligence Technical Team • In-Silico device performance Evaluation to internal and external standards. • Explore design performance characteristics of the subject device. Results of CM&S to inform a regulatory decision • Worst Case Selection • Performance Characterization • Safety Labeling • CAPA Regulatory Evidence Generation
  4. 4. 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
  5. 5. 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?
  6. 6. 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”
  7. 7. 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
  8. 8. 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
  9. 9. COLLABORATING TO CREATE A STANDARD
  10. 10. • 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.
  11. 11. 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).
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. • 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
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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)
  23. 23. 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
  24. 24. 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
  25. 25. 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?
  26. 26. 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
  27. 27. 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
  28. 28. UQ COLLABORATION
  29. 29. 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.
  30. 30. THANK YOU FOR YOUR ATTENTION “Knowledge which is acquired under compulsion obtains no hold on the mind”

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