Dr. Pablo Lamata presented on using computational modeling and personalized simulations to derive diastolic biomarkers from clinical measurements that can help manage heart failure. The modeling approach involves tracking motion from medical images, running mechanical simulations, and identifying tissue parameters to reproduce observed pressures and deformations. Results demonstrated biomarkers for myocardial stiffness and relaxation that differentiate healthy versus diseased states. Challenges include uncoupling stiffness from decaying active tension and reducing invasiveness, but the models provide insights, data enhancement, and predictions to aid research into diastolic heart failure.
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. Is this heart relaxing well?
• Catheter, PV loop, exponential fitting
• Echo: filling waves OR tissue mapping, ratio
STRATIFY DIASTOLIC
HEART FAILURE
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. Assessment of fundamental mechanisms improves
management of HF
HYPOTHESIS
Clinical
data
Diastolic biomarkers
-Compliance
-Relaxation
15. Make your model to reproduce the observation
• Capture the inherent constitutive and physiological
parameters
PERSONALIZATION IN
A NUTSHELL
18. Need two ingredients
• Deformation
• Pressure
Issues
• Availability
• SNR
• Range
• Synchrony
1. CLINICAL
MEASUREMENTS
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)
21. • Match deformations!
• Only in LV free wall
• Boundary conditions
• Apex and base from data
• Optimiser
• Brute force or sequential
4. PARAMETER
IDENTIFICATION
22. Tissue stiffness: not unique, but clear differences between
health and disease
DIASTOLIC BIOMARKERS:
STIFFNESS (I)
α=C2+C3+C4
23. Break uniqueness: observe inflation through different times
(in-silico proof)
DIASTOLIC BIOMARKERS:
STIFFNESS (II)
24. Real data: with
active tension!
If not accounted, as
filling progresses,
fibre stiffness
decreases
DIASTOLIC BIOMARKERS:
STIFFNESS (III)
25. Estimate decay active tension, but identifiability still not
solved
DIASTOLIC BIOMARKERS:
DECAYING AT
26. TWO CHALLENGES
How to uncouple the decaying active
tension (AT) and passive stiffness
Reduce invasiveness (catheter
pressure sensor)
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
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. ROUTE FOR NON-
INVASIVENESS (II)
Literature surrogate
• Able to differentiate
stiffness
• Stiffness = f(ejection
fraction)
• Unable to different. active
tension
31. ROUTE FOR NON-
INVASIVENESS (III)
Able to recover pressure offset errors
• Need temporal resolution!
No pressure offset With pressure offset
33. Data driven:
• “Clean” pressure
• Exponential fit
Model driven
• (as explained)
• Higher
significance,
reproducibility
BUT:
• Assumptions
• Tedious
DATA VS. MODEL
DRIVEN APPROACH
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. 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. 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
Hinweis der Redaktion
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.
