diastolic biomarkers

36
DIASTOLIC BIOMARKERS THROUGH MODEL PERSONALIZATION Dr Pablo Lamata Lecturer & Sir Henry Dale Fellow QBIO - Bilbao, 17 th February 2015

Upload: cmib

Post on 14-Apr-2017

175 views

Category:

Engineering


1 download

TRANSCRIPT

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