The goal of health care systems Primary goal of health care policy - - PDF document

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The goal of health care systems Primary goal of health care policy - - PDF document

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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

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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.

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Report of the Belgian EU Presidency, adopted by the EU Council of Ministers of Health in Dec 2010

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A great day: market approval

A new dimension in the cycle: The customer

Concept Feasibility Develop ment Testing

  • Verification
  • Process
  • ptimization

Human Tests

  • (Validation)

Post Market Surveillance Full Market Release (Europe) )

Who is the customer ?

  • Physician
  • Patient
  • 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”

Aristoteles, 4 th c BC

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High quality design requires ongoing user input !

Concept Feasibility Develop ment Testing

  • Verification
  • Process
  • ptimization

Human Tests

  • (Validation)

Post Market Surveillance Full Market Release (Europe) )

Risk Management

User Feedback / Interactions

LV LA RA RV AO

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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

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Kris Dumont, Benjamin Vandersmissen, Sebastiaan Annerel, Patrick Segers, Jan Vierendeels, Pascal Verdonck

The role of computational fluid dynamics for heart valve design

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Experimental model

Torsie Hoek draden 26° 45° 22° 55° 17° 70° y = -2,7869x + 117,05 10 20 30 40 50 60 70 80 10 15 20 25 30

Draden ifv torsie

Draden ifv torsie Lineair (Draden ifv torsie)

Experimental model

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Experimental model

Model 1 Model 2 Model 3

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3 a b 7 2 1 6 4 9 8 5

Experimental model Experimental model

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CFD & Heart Valves FSI Hemodynamic Results

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Aim of the Study

  • Comparison of ATS Open Pivot Valve in mitral versus aortic

position

  • Applying a dedicated fluid-structure interaction (FSI) code

1.

  • 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: 139-146, 2004.

2.

  • 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): 394-399, 2005. 3.

  • J. Vierendeels, K. Dumont and PR. Verdonck. Stabilization of a fluid-structure coupling

procedure for rigid body motion. AIAA journal: 43(12) 2549-2557, 2005.

  • Derive clinical relevant parameters, shear stress distribution

using our FSI code.

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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

1.

  • 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: 139-146, 2004.

2.

  • 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): 394-399, 2005. 3.

  • J. Vierendeels, K. Dumont and PR. Verdonck. Stabilization of a fluid-structure coupling

procedure for rigid body motion. AIAA journal: 43(12) 2549-2557, 2005.

  • Derive clinical relevant parameters, shear stress distribution

and platelet activation using our FSI code.

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Introduction

(a) 22mm AP ATS Open Pivot Valve Geometry (b) 21mm SJM Regent Valve Geometry

FSI Hemodynamic Results

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Particle Track Results: Forward Flow

click on figure to play movie

Particle Track Results: Forward Flow

Stress Acumulation during Laminar Forward Flow

1 20 30 40 50 60 70 80 90 1 00 0 - 5 5 -1 5 1 5 - 25 25 - 35

accumulation intervals in dyne.s/cm 2 percentage of particles

0.2 0.4 0.6 0.8 1 1 .2 1 .4 1 .6 1 .8 2

percentage of particles ATS SJM ATS SJM

Different scale in percentage axis

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Particle Track Results: Regurgitation Flow

click on figure to play movie

Particle Track Results: Regurgitation Flow

Stress Accumulation during Laminar Regurgitation Flow

1 20 30 40 50 60 70 80 90 1 00 0 - 5 5 - 1 5 1 5 - 25 25 - 35 35 - 45 45 - 55 55 - 65

accumulation intervals in dyne.s/cm 2 percentage of particles

0.5 1 1 .5 2 2.5 3

percentage of particles ATS SJM ATS SJM

Hellums Threshold

Different scale in percentage axis

activated

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Peter Mortier, Matthieu De Beule, Benedict Verhegghe, Pascal Verdonck

The role of Finite Element Analysis for stent design

The achilles heel of stenting is restenosis …

1 day follow-up 180 day follow-up

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Balloon expandable stents …

ACS RX Duet Vision Velocity Tetra Terumo Tensum 2 Tenax Rstent Pixel Penta MiniCrown Cross Flex LC Jostent Bifurcation Jostent Graft Jostent M Cordis

Self expandable wire stents …

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Drug eluting stents …

Courtesy ‘t Veer

Restenosis in 5 to 30% of the treated lesions …

… because of:

  • Mechanical vascular injury
  • Non-uniform strut distribution
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Mechanical vascular injury

A: “Balloon – artery” contact

Courtesy Squire, MIT, 2000 Courtesy Migliavacca

B: “Dogboning” Stent expansion in a non-uniform ends-first manner (dogboning) causing high local stresses

Mechanical vascular injury

Courtesy Squire, MIT, 2000 Courtesy Fortimedix Δ L

C: “Foreshortening”

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Restenosis after DES partially related to non uniform strut distribution due to

A: Inadaquate vessel support B: Sub-optimal drug delivery

Courtesy Hwang et , Circulation

Why computer simulations?

Experimental approach is difficult because of small scale Simulations can accelerate device design Simulations provide additional information (FDA, CE approval)

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Virtual design loop

Traditional approach Parametric adaptable CAD model Mesh generation FEA Our approach Parametric adaptable mesh FEA ADVANTAGES:

  • two steps instead of three
  • can be fully automated

Analysis of the mechanical properties

  • Ideal stent = low foreshortening

Δ L Δ L

Virtual

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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
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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
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Personalized medicine Personalized medicine

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Personalized medicine

Stress [MPa]

0.4 0.3 0.2 0.1 0.0

Personalized medicine

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Opening of sidebranch Opening of sidebranch

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Gelijkaardig productconcept!

Taxus Liberté

(Boston Scientific)

Cypher Select

(Cordis, J&J)

Endeavor

(Medtronic)

Opening of sidebranch

Tools are available and validated to analyse a priori stent properties and mechanical behavior

From design to clinic

Stent requirements

+

Material properties Dedicated stent design

SS316L, CoCr, Mg, Nitinol Flexibility Radial strength,…

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Simulation based pre-operative planning

REPORT

CT images (pre-

  • perative)

3D reconstruction Finite element simulation of TAVI

Patent application pending

Peter Mortier, Matthieu De Beule, Benedict Verhegghe, Pascal Verdonck

Simulation based preoperative planning: case TAVI

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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

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Dcirc = 22.8 mm

Limitations of current TAVI planning tools – case study: John Doe

Moderate AR

Limitations of current TAVI planning tools – case study: John Doe

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Could this have been predicted???

Limitations of current TAVI planning tools – case study: John Doe

Moderate AR

Solution: simulation-based TAVI planning

FEops developed a web-based pre-operative planning service, called TAVIguide TM , which combines pre-operative CT imaging with advanced computer simulations allowing to predict stent frame deformation and paravalvular regurgitation

TAVIguide TM is not yet commercially available

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Solution: simulation-based TAVI planning

FEops developed a web-based pre-operative planning service, called TAVIguide TM , 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

TAVIguide TM is not yet commercially available

Validation of predicted frame deformation by comparison with post-operative MSCT

Model prediction MSCT-post

  • 6
  • 4
  • 2

2 4 6 18 20 22 24 26 28 30 32

Mean (Model, MSCT) [mm]

Dmax at inflow (33 pts) Mean difference +0.6mm; SD 1.1mm

Difference (Model - MSCT) [mm] Data from EMC Rotterdam; UZA Antwerp and ICPS Massy

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Validation of predicted calcium displacement by comparison with post-operative MSCT

Model prediction MSCT-post

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  • 8
  • 4

4 8 12 4 8 12 16 20

Mean (Model, MSCT) [mm] Difference (Model - MSCT) [mm]

Calcium – coronary ostia distance (33 pts) Mean difference +1.0mm; SD 2.0mm

Data from EMC Rotterdam; UZA Antwerp and ICPS Massy

Prediction of frame deformation Prediction of frame malapposition Prediction of AR

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10 20 30 40 50 60 70 0+1 2+3 Predicted aortic regurgitation [ml/s] Contrast aortography

52 pts included, further enrolling p < 0.01

Data from EMC Rotterdam

Prediction of aortic regurgitation Model (ml/s) vs contrast aortography (Sellers)

Moderate AR

Effect of valve sizing: John Doe

26mm CoreValve Predicted AR = 16 ml/s

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26mm CoreValve 29mm CoreValve

Model predicts 70% reduction of aortic regurgitation

Predicted AR = 6 ml/s Predicted AR = 16 ml/s

Effect of valve sizing: John Doe Large impact of device size!

26mm CoreValve 29mm CoreValve Predicted AR = 1 ml/s Predicted AR = 4 ml/s

Effect of valve sizing: Small impact of device size!

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Depth: 10mm Depth: 4mm

Predicted AR = 15 ml/s Predicted AR = 6 ml/s

Effect of valve positioning: Large impact of device position!

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)

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“A good head and a good heart are always a fantastic combination”

Nelson Mandela