The goal of health care systems

Transcription

1 Computational biodesign cardiovascular devices Prof. Pascal Verdonck IBiTech - Institute Biomedical Technology Ghent University The goal of health care systems Primary goal of health care policy = to maximize the health of the population within the limits of the available resources, and within an ethical framework built on equity and solidarity principles. Report of the Belgian EU Presidency, adopted by the EU Council of Ministers of Health in Dec

2 A great day: market approval ) Concept Develop ment Feasibility Testing -Verification -Process optimization Human Tests -(Validation) A new dimension in the cycle: The Who is the customer? Physician Patient Post Market Surveillance Full Market Release (Europe) customer Health care system Reimbursement Insurance company Regulatory bodies. The wisdom of the customer Risk Management User friendliness Quality control Development of the device: start from the end! The Wisdom of the Crowd: A group of (divers) individuals has always more intellect than one expert th Aristoteles, 4 c BC 2

3 High quality design requires ongoing user input! ) User Feedback / Interactions Concept Feasibility Develop ment Testing -Verification -Process optimization Human Tests -(Validation) Post Market Surveillance Full Market Release (Europe) Risk Management RV LV RA AO LA 3

4 Computational Fluid Dynamics CFD Computational Fluid Dynamics Transport phenomena: fundamental principles of mass, momentum and energy conservation expressed in algebraic, differential or integral representation Numerical description in space and time Design tool in aerospace, automotive and process industry 4

5 The role of computational fluid dynamics for heart valve design Kris Dumont, Benjamin Vandersmissen, Sebastiaan Annerel, Patrick Segers, Jan Vierendeels, Pascal Verdonck 5

6 Experimental model Experimental model Torsie Hoek draden Draden ifv torsie 80 Draden ifv torsie Lineair (Draden ifv torsie) y = -2,7869x + 117,

7 Experimental model Model 1 Model 2 Model 3 7

8 Experimental model a b Experimental model 8

9 CFD & Heart Valves FSI Hemodynamic Results 9

10 Aim of the Study Comparison of ATS Open Pivot Valve in mitral versus aortic position Applying a dedicated fluid-structure interaction (FSI) code K. Dumont, J.M.A. Stijnen, J. Vierendeels, F.N. van de Vosse and P. Verdonck. Validation of a fluid-structure interaction model of a heart valve using the dynamic mesh method in Fluent. Computer Methods in Biomechanics and Biomedical Engineering: 7: , K. Dumont, J. Vierendeels, P. Segers, G. Van Nooten and P. Verdonck. Predicting ATS Open PivotTM Heart Valve Performance with Computational Fluid Dynamics Journal of Heart Valve Disease: 14(3): , J. Vierendeels, K. Dumont and PR. Verdonck. Stabilization of a fluid-structure coupling procedure for rigid body motion. AIAA journal: 43(12) , Derive clinical relevant parameters, shear stress distribution using our FSI code. 10

11 Aim of the Study Comparison of ATS Open Pivot Valve and St Jude Regent Valve with computational fluid dynamics CFD Applying a dedicated fluid-structure interaction (FSI) code K. Dumont, J.M.A. Stijnen, J. Vierendeels, F.N. van de Vosse and P. Verdonck. Validation of a fluid-structure interaction model of a heart valve using the dynamic mesh method in Fluent. Computer Methods in Biomechanics and Biomedical Engineering: 7: , K. Dumont, J. Vierendeels, P. Segers, G. Van Nooten and P. Verdonck. Predicting ATS Open PivotTM Heart Valve Performance with Computational Fluid Dynamics Journal of Heart Valve Disease: 14(3): , J. Vierendeels, K. Dumont and PR. Verdonck. Stabilization of a fluid-structure coupling procedure for rigid body motion. AIAA journal: 43(12) , Derive clinical relevant parameters, shear stress distribution and platelet activation using our FSI code. 11

12 Introduction (a) 22mm AP ATS Open Pivot Valve Geometry (b) 21mm SJM Regent Valve Geometry FSI Hemodynamic Results 12

13 Particle Track Results: Forward Flow click on figure to play movie Particle Track Results: Forward Flow Stress Acumulation during Laminar Forward Flow percentage of particles percentage of particles 100 Different scale in percentage axis ATS SJM ATS SJM 0 accum ulation intervals in dyne.s/cm 2 13

14 Particle Track Results: Regurgitation Flow click on figure to play movie Particle Track Results: Regurgitation Flow Stress Accumulation during Laminar Regurgitation Flow 3 90 activated percentage of particles Hellums Threshold percentage of particles 100 Different scale in percentage axis ATS SJM ATS SJM accum ulation intervals in dyne.s/cm 2 14

15 The role of Finite Element Analysis for stent design Peter Mortier, Matthieu De Beule, Benedict Verhegghe, Pascal Verdonck The achilles heel of stenting is restenosis 1 day follow-up 180 day follow-up 15

16 Balloon expandable stents Jostent M Jostent Graft Jostent Bifurcation Cross Flex LC MiniCrown Penta Pixel Rstent Tenax Tensum 2 Terumo Tetra Velocity Vision ACS RX Duet Cordis Self expandable wire stents 16

17 Drug eluting stents Courtesy t Veer Restenosis in 5 to 30% of the treated lesions because of: Mechanical vascular injury Non-uniform strut distribution 17

18 Mechanical vascular injury Courtesy Migliavacca B: Dogboning Courtesy Squire, MIT, 2000 A: Balloon artery contact Stent expansion in a non-uniform ends-first manner (dogboning) causing high local stresses Mechanical vascular injury C: Foreshortening ΔL Courtesy Squire, MIT, 2000 Courtesy Fortimedix 18

19 Restenosis after DES partially related to non uniform strut distribution due to Courtesy Hwang et, Circulation A: Inadaquate vessel support B: Sub-optimal drug delivery Why computer simulations? Experimental approach is difficult because of small scale Simulations can accelerate device design Simulations provide additional information (FDA, CE approval) 19

20 Virtual design loop Traditional approach Our approach Parametric adaptable CAD model Parametric adaptable mesh Mesh generation FEA FEA ADVANTAGES: - two steps instead of three - can be fully automated Virtual Analysis of the mechanical properties Ideal stent = low foreshortening ΔL ΔL 20

21 Virtual analysis of the mechanical properties Ideal stent = low elastic recoil D = 3.3 mm D = 3.2 mm Virtual analysis of the mechanical properties Ideal stent = high flexibility 21

22 Virtual analysis of the mechanical properties Ideal stent = no dogboning Courtesy Migliavacca Virtual analysis of the mechanical properties Mortier et al., J. Biom. Eng., 2008 Foreshortening Elastic recoil Flexibility Dogboning Can be used to: - Compare different existing stent designs - Develop new stent designs 22

23 Personalized medicine Personalized medicine 23

24 Personalized medicine Personalized medicine Stress [MPa] 24

25 Opening of sidebranch Opening of sidebranch 25

26 Opening of sidebranch Taxus Liberté Endeavor Cypher Select (Boston Scientific) (Medtronic) (Cordis, J&J) Gelijkaardig productconcept! From design to clinic Tools are available and validated to analyse a priori stent properties and mechanical behavior Material properties SS316L, CoCr, Mg, Nitinol + Dedicated stent design Stent requirements Flexibility Radial strength, 26

27 Simulation based pre-operative planning REPORT CT images (preoperative) 3D reconstruction Finite element simulation of TAVI Patent application pending Simulation based preoperative planning: case TAVI Peter Mortier, Matthieu De Beule, Benedict Verhegghe, Pascal Verdonck 27

28 Limitations of current TAVI planning tools Current planning is based on an evaluation of the aortic anatomy using CT imaging Evaluating the anatomy gives only limited insights into the risk on complications related to device / host interaction Aortic Regurgitation (AR) Annular rupture Coronary obstruction Conduction problems Messika-Zeitoun et al., 2010, JACC Limitations of current TAVI planning tools case study: John Doe 28

29 Limitations of current TAVI planning tools case study: John Doe Dcirc = 22.8 mm Limitations of current TAVI planning tools case study: John Doe Moderate AR 29

30 Limitations of current TAVI planning tools case study: John Doe Moderate AR Could this have been predicted??? Solution: simulation-based TAVI planning FEops developed a web-based pre-operative planning service, called TM TAVIguide, which combines pre-operative CT imaging with advanced computer simulations allowing to predict stent frame deformation and paravalvular regurgitation TM TAVIguide is not yet commercially available 30

31 Solution: simulation-based TAVI planning FEops developed a web-based pre-operative planning service, called TM TAVIguide, which combines pre-operative CT imaging with advanced computer simulations allowing to predict stent frame deformation and paravalvular regurgitation ADVANTAGES Unique pre-operative insights Optimal device size, type and position selection Optimal patient selection TM TAVIguide is not yet commercially available Validation of predicted frame deformation by comparison with post-operative MSCT MSCT-post Dmax at inflow (33 pts) Mean difference +0.6mm; SD 1.1mm 6 Difference (Model - MSCT) [mm] Model prediction Mean (Model, MSCT) [mm] Data from EMC Rotterdam; UZA Antwerp and ICPS Massy 31

32 Validation of predicted calcium displacement by comparison with post-operative MSCT Model prediction MSCT-post Calcium coronary ostia distance (33 pts) Mean difference +1.0mm; SD 2.0mm Difference (Model - MSCT) [mm] Mean (Model, MSCT) [mm] Data from EMC Rotterdam; UZA Antwerp and ICPS Massy Prediction of frame deformation Prediction of frame malapposition Prediction of AR 32

33 Prediction of aortic regurgitation Model (ml/s) vs contrast aortography (Sellers) 52 pts included, further enrolling p < 0.01 Predicted aortic regurgitation [ml/s] Contrast aortography Data from EMC Rotterdam Effect of valve sizing: John Doe 26mm CoreValve Predicted AR = 16 ml/s Moderate AR 33

34 Effect of valve sizing: John Doe Large impact of device size! Model predicts 70% reduction of aortic regurgitation 26mm CoreValve Predicted AR = 16 ml/s 29mm CoreValve Predicted AR = 6 ml/s Effect of valve sizing: Small impact of device size! 26mm CoreValve Predicted AR = 4 ml/s 29mm CoreValve Predicted AR = 1 ml/s 34

35 Effect of valve positioning: Large impact of device position! Depth: 10mm Predicted AR = 6 ml/s Depth: 4mm Predicted AR = 15 ml/s Conclusions Computer simulations can predict frame deformation, calcium displacement and AR, and can assist in further increasing the safety and efficacy of TAVI This technology can provide unique insights helpful for personalised medicine device sizing patient selection device positioning selection of device type Acknowledgement: Prof Dr Peter de Jaegere (EMC Rotterdam) Prof Dr Johan Bosmans (UZA, Antwerp) Dr Thierry Lefevre (ICPS, Massy) 35

36 A good head and a good heart are always a fantastic combination Nelson Mandela 36