Computational Modelling of Personalized Hemodynamic Response to Valve Replacement Surgery in Heart Failure Patients
Denis Menshykau (1), Thomas Wendl (1), Katrin Coböken (1), Sebastian Schneckener(1), Roland Loosen(1) and Thomas Eissing (1)
(1) Bayer AG, Systems Pharmacology & Medicine, Leverkusen, Germany
Objectives: A Cardiovascular (CV) Systems Pharmacology Platform (SPP) is being developed, with the aim to predict whole-body hemodynamic response to pharmacological or other interventions [1]. The capabilities of CV SPP are exemplified here by modelling hemodynamic changes in patients enrolled into a publically funded and currently conducted clinical study (SMART) on heart failure (HF) patients with aortic stenosis (AS). Patients enrolled into SMART study undergo aortic valve (AV) replacement surgery.
Methods: The CV SPP includes a detailed description of the CV physiology and is developed in the Open Systems Pharmacology Suite [1,3,4]. To model HF patients enrolled into the SMART study, the model was modified to account for non-linear resistance in AV [4]. Based on the individual patient data before/after the surgery the CV model was individually fitted to recapitulate pressure gradients across AV as well as other hemodynamic characteristics. Mechanistic modelling of the CV system was complimented with data-driven approaches to identify covariates and significant changes in biomarkers.
Results: Analysis of CV model parameters demonstrates that patients with aortic stenosis – in contrast to healthy individuals - have elevated peripheral resistance, reduced arterial compliance and altered myocardium function. The pressure-volume loops inferred from the patient data demonstrate that AV replacement drastically reduces systolic pressure in the left ventricle. Overall inter-individual variability of parameters describing heart mechanics was larger than those describing vasculature. Statistically significant (Welch’s test) and physiologically meaningful reduction in cardiac mass within a few months after the surgery was also observed. We finally discuss approaches to predict outcomes of AV replacement surgery.
Conclusions: Personalized computational models for the CV system of HF patients before and after AV replacement surgery are developed. Analysis of model parameters inferred from the patient data provides additional information about the changes in the CV system induced by the surgery. Using the identified hemodynamic changes in the current patient population, the CV model enables predictions of valve replacement surgery outcome in AV stenosis patients. This, in turn, will facilitate an informed adjustment of the concomitant pharmacologic medication. Data-driven analysis complements modelling for the variables, currently not accessible with mechanistic model.
References:
[1] Coböken et al. Development of a Cardiovascular Systems Pharmacology Platform, PAGE-Meeting Poster 2015
[2] http://www.sys-med.de/en/demonstrators/smart/
[3] Eissing T. et al. A computational system biology software platform for the multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front. Physio. (2011)
[4] www.open-systems-pharmacology.org
[5] Garcia et al. Analytical modeling of the instantaneous pressure gradient across the aortic valve. J Biomechanics (2005)