diastolic biomarkers
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Improving the stratification power of cardiac ventricular shape
diastolic biomarkers through model personalizationDr Pablo LamataLecturer & Sir Henry Dale Fellow
QBIO - Bilbao, 17th February 2015
General hypothesis: wealth of clinical information, use of computational models to unveil more robust and accurate biomarkers1
IntroductionThe problem and the hypothesisMethods & resultsDiscussionIndex
INTRODUCTION
Medical imaging
Computational modelling
Video courtesy of Dr. Nordsletten, Kings College of London
Computational modelling
Clinical pull and technical push
Whats needed?Prevention (screening)DiagnosisTreatment
Tech. offer?PredictionsBiomarkersTreatment tools
Need of multidisciplinary language
Do not try to over-engineer the solutions
Importance of building confidence, bounce your ideas off your colleagues!7
What can we offer?ImagingAnatomy and functionObjective metrics
Biased, noisy?Reproducibility?ModellingPhysiological understandingPredictions
Assumptions?Validity?
Try to get the best of these two worlds, Observations and models
Models: get metrics, patient selection, intervention planning, unveil mechanisms8
The vision
Patient Therapy
Images and observations
The problem and the hypothesis
HF with Normal Ejection FractionEvidence of abnormal filling caused by stiffer myocardium, delayed relaxation, impaired atrio-ventricular conduit function.Diagnostic surrogates [Maeder09]:Lab: natruiretic peptidesEcho: ratio early/late fillingCatheters: LV pressureStratification: 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
11
Is this heart relaxing well?Catheter, PV loop, exponential fittingEcho: filling waves OR tissue mapping, ratioStratify diastolic heart failure
The huge potential of the combination of models and images (observations)12
Is the heart filling well?Catheter, PV loop: exponential fittingCoupling between relaxation and stiffness
PVPassive elasticActive fibre relaxationTotal LV pressure
The huge potential of the combination of models and images (observations)13
Assessment of fundamental mechanisms improves management of HFHypothesis
Clinical data
Diastolic biomarkersComplianceRelaxation
Make your model to reproduce the observationCapture the inherent constitutive and physiological parametersPersonalization in a nutshell
Methods & results
Methods overview
2. Motion tracking1. Clinical measurements
3. Mechanical simulation4. Parameter identification
Need two ingredientsDeformation PressureIssuesAvailabilitySNRRangeSynchrony
1. Clinical measurements
Key ingredients:A similarity metricA solution space (transformation space)An optimizer
2. Motion tracking (image registration)Frame NFrame 1MeasuresimilarityApply TransformationOptimise M over TSimilarity metric (M)Transformation (T)
AssumptionsIncompressibilityQuasi-staticFEM: Mass and momentum conservationPrinciple of virtual work3. Mechanical simulation
Match deformations!Only in LV free wallBoundary conditionsApex and base from dataOptimiser Brute force or sequential4. Parameter identification
Tissue stiffness: not unique, but clear differences between health and diseaseDiastolic biomarkers: stiffness (I)
=C2+C3+C4
Break uniqueness: observe inflation through different times (in-silico proof)Diastolic biomarkers: stiffness (II)
Real data: with active tension!
If not accounted, as filling progresses, fibre stiffness decreasesDiastolic biomarkers: stiffness (III)
Estimate decay active tension, but identifiability still not solvedDiastolic biomarkers: decaying AT
Two challengesHow to uncouple the decaying active tension (AT) and passive stiffness
Reduce invasiveness (catheter pressure sensor)
Method to uncouple stiffness/AT (I)
6 unknowns
4 data points
Additional constraints [7]End diastole: null active tensionPositive, and monotonically decaying active tensionCriterion to choose reference configuration
Uncouple stiffness/AT (II)
Criterion to choose reference configuration
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
Route for non-invasiveness (II)Literature surrogateAble to differentiate stiffnessStiffness = f(ejection fraction)Unable to different. active tension
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. 30
Route for non-invasiveness (III)Able to recover pressure offset errorsNeed temporal resolution!
No pressure offsetWith pressure offset
Discussion: overload or benefit?
Data driven: Clean pressureExponential fitModel driven(as explained)Higher significance, reproducibility
BUT:AssumptionsTediousData vs. model driven approach
Right choice of complexity for each research question!
In general, models bringIn-silico experimentationData enhancement and unveil biomarkersPredictions of clinical outcome
added value
Key pointsClinical 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 biomarkersStiffnessRelaxation
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
AcknowledgementsDr. Jihae XiProf Nic SmithDr. Steven NiedererDr. David NordslettenDr. Sander LandReferences and acknowledgements
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