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Diastolic biomarkers

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Diastolic biomarkers

  1. 1. DIASTOLIC BIOMARKERS THROUGH MODEL PERSONALIZATION Dr Pablo Lamata Lecturer & Sir Henry Dale Fellow QBIO - Bilbao, 17th February 2015
  2. 2. • Introduction • The problem and the hypothesis • Methods & results • Discussion INDEX
  3. 3. INTRODUCTION
  4. 4. MEDICAL IMAGING
  5. 5. COMPUTATIONAL MODELLING Video courtesy of Dr. Nordsletten, King’s College of London
  6. 6. COMPUTATIONAL MODELLING
  7. 7. CLINICAL PULL AND TECHNICAL PUSH What’s needed? • Prevention (screening) • Diagnosis • Treatment Tech. offer? • Predictions • Biomarkers • Treatment tools
  8. 8. WHAT CAN WE OFFER? Imaging • Anatomy and function • Objective metrics • Biased, noisy? • Reproducibility? Modelling • Physiological understanding • Predictions • Assumptions? • Validity?
  9. 9. THE VISION Patient Therapy Images and observations
  10. 10. THE PROBLEM AND THE HYPOTHESIS
  11. 11. HF with Normal Ejection Fraction • Evidence of abnormal filling caused by stiffer myocardium, delayed relaxation, impaired atrio- ventricular conduit function. Diagnostic surrogates [Maeder09]: • Lab: natruiretic peptides • Echo: ratio early/late filling • Catheters: LV pressure Stratification: on-going challenge [Maeder09] PROBLEM: MANAGEMENT OF HEART FAILURE (HF) [Maeder09] Maeder and Kaye, “Heart Failure With Normal Left Ventricular Ejection Fraction,” J. Am. Coll. Cardiol. 2009
  12. 12. Is this heart relaxing well? • Catheter, PV loop, exponential fitting • Echo: filling waves OR tissue mapping, ratio STRATIFY DIASTOLIC HEART FAILURE
  13. 13. IS THE HEART FILLING WELL? Catheter, PV loop: exponential fitting Coupling between relaxation and stiffness P V Passive elastic Active fibre relaxation Total LV pressure
  14. 14. Assessment of fundamental mechanisms improves management of HF HYPOTHESIS Clinical data Diastolic biomarkers -Compliance -Relaxation
  15. 15. Make your model to reproduce the observation • Capture the inherent constitutive and physiological parameters PERSONALIZATION IN A NUTSHELL
  16. 16. METHODS & RESULTS
  17. 17. METHODS OVERVIEW 2. Motion tracking 1. Clinical measurements 3. Mechanical simulation 4. Parameter identification
  18. 18. Need two ingredients • Deformation • Pressure Issues • Availability • SNR • Range • Synchrony 1. CLINICAL MEASUREMENTS
  19. 19. Key ingredients: • A similarity metric • A solution space (transformation space) • An optimizer 2. MOTION TRACKING (IMAGE REGISTRATION) Frame N Frame 1 Measure similarity Apply Transformation Optimise M over T Similarity metric (M) Transformation (T)
  20. 20. Assumptions • Incompressibility • Quasi-static FEM: • Mass and momentum conservation • Principle of virtual work 3. MECHANICAL SIMULATION
  21. 21. • Match deformations! • Only in LV free wall • Boundary conditions • Apex and base from data • Optimiser • Brute force or sequential 4. PARAMETER IDENTIFICATION
  22. 22. Tissue stiffness: not unique, but clear differences between health and disease DIASTOLIC BIOMARKERS: STIFFNESS (I) α=C2+C3+C4
  23. 23. Break uniqueness: observe inflation through different times (in-silico proof) DIASTOLIC BIOMARKERS: STIFFNESS (II)
  24. 24. Real data: with active tension! If not accounted, as filling progresses, fibre stiffness decreases DIASTOLIC BIOMARKERS: STIFFNESS (III)
  25. 25. Estimate decay active tension, but identifiability still not solved DIASTOLIC BIOMARKERS: DECAYING AT
  26. 26. TWO CHALLENGES How to uncouple the decaying active tension (AT) and passive stiffness Reduce invasiveness (catheter pressure sensor)
  27. 27. METHOD TO UNCOUPLE STIFFNESS/AT (I) 6 unknowns 4 data points Additional constraints [7] • End diastole: null active tension • Positive, and monotonically decaying active tension Criterion to choose reference configuration
  28. 28. UNCOUPLE STIFFNESS/AT (II) Criterion to choose reference configuration
  29. 29. ROUTE FOR NON- INVASIVENESS (I) Stiffness = f(deform., pressure) • LV filling pressure: only catheter Two aims [8]: • Hypothesis: P = f(V) • Characterise impact of pressure offset errors
  30. 30. ROUTE FOR NON- INVASIVENESS (II) Literature surrogate • Able to differentiate stiffness • Stiffness = f(ejection fraction) • Unable to different. active tension
  31. 31. ROUTE FOR NON- INVASIVENESS (III) Able to recover pressure offset errors • Need temporal resolution! No pressure offset With pressure offset
  32. 32. DISCUSSION: OVERLOAD OR BENEFIT?
  33. 33. Data driven: • “Clean” pressure • Exponential fit Model driven • (as explained) • Higher significance, reproducibility BUT: • Assumptions • Tedious DATA VS. MODEL DRIVEN APPROACH
  34. 34. Right choice of complexity for each research question! In general, models bring • In-silico experimentation • Data enhancement and unveil biomarkers • Predictions of clinical outcome ADDED VALUE
  35. 35. KEY POINTS Clinical motivation: myocardial stiffness and relaxation are important Methods: FEM to reproduce the observation (pressure and deformation) Results: biomarkers for diastolic heart failure Clinical data Diastolic biomarkers -Stiffness -Relaxation
  36. 36. Key references • [Xi11] “Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced- order unscented Kalman filter” J Mech. Behav. Biomed. Mat. • [Xi13] “Diastolic functions from clinical measurements.” Med. image Anal. • [Xi14] “Understanding the need of LV pressure” Biomechs & Mod Mechanobiology Acknowledgements • Dr. Jihae Xi • Prof Nic Smith • Dr. Steven Niederer • Dr. David Nordsletten • Dr. Sander Land REFERENCES AND ACKNOWLEDGEMENTS

Editor's Notes

  • General hypothesis: wealth of clinical information, use of computational models to unveil more robust and accurate biomarkers
  • Need of multidisciplinary language

    Do not try to “over-engineer” the solutions

    Importance of building confidence, bounce your ideas off your colleagues!
  • Try to get the best of these two worlds, Observations and models

    Models: get metrics, patient selection, intervention planning, unveil mechanisms
  • The huge potential of the combination of models and images (observations)
  • The huge potential of the combination of models and images (observations)
  • Thank you for the introduction, and for the opportunity to present my ideas and goals.

    The problem that I will address is the management of HF, a major health issue in the UK, which brings annual costs of £0.75 billion to our health system. The lifetime risk of developing HF is one in five, it is expected that 3 of us will develop some form of this disease.

    HF is the clinical condition in which the heart is not able to pump enough blood to meet the body demands. Two actions govern the mechanical pump function of the heart, ejection and filling. The scope of my work focuses on the second, which relates to the condition of HFNEF, that affects half of the population with HF (the other half have systolic HF). HFNEF patients have abnormal filling, caused by a stiffer myocardium, a delayed relaxation, or an impaired atrio-ventricular counduit function.

    Current diagnostic clinical guidelines for HFNEG use these surrogates to characterise an impaired filling of the ventricles: 1,2,3

    The problem is that the characterization and stratification of patients is an on-going challenge. One of the fundamental reasons for it is that current diagnostic metrics are only surrogates of the mechanisms that impair ventricular filling. In my work, I plan to bridges this gap, estimating the fundamental mechanical properties that govern diastolic filling.

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