Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction

Abstract

Diastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition may not be associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II pulmonary hypertension (2018 Nice classification). This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to as heart failure with preserved ejection fraction, HFpEF), showing significant correlation with associated mortality. However, accurate measurements of this quantity are typically obtained through invasive catheterization and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently wide pathologic spectrum, the identifiability of the model's parameters, and the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity, we show that systolic, diastolic, and wedge pulmonary pressures can be estimated on average within 8, 6, and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.

An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle

“…Computational modeling, combined with invasive or non-invasive measurements, can forecast both the onset and worsening of cardiovascular disease [1][2][3]. More recently, multiscale models that account for cardiovascular physiology across multiple spatial scales have been developed [4,5].…”

“…In addition to structural identifiability, parameters can also be practically identifiable if they can be uniquely determined from limited and/or noisy data. Structural identifiability assesses the model's structure, and is determined using algebraic manipulations of the model [13][14][15] or by inferring parameters using noise-free, model generated data [1,16]. Parameters that are deemed structurally identifiable can be assessed for practical identifiability in the presence of noisy and limited data.…”

“…Several authors have considered parameter identifiability in the context of cardiovascular modeling [1,15,17,18]. Pironet et al [15] pursued a structural identifiability analysis on a sixcompartment model of the cardiovascular system.…”

“…A follow up investigation by Pironet et al used local sensitivity and profile likelihood analyses to conclude that only subset of parameters were practically identifiable from swine data in the vena cava, aorta, and left ventricle (LV) [17]. The studies by Colunga et al [18] and Harrod et al [1] used models including the LV, RV, and both the systemic and the pulmonary circulations. The former [18] used local sensitivity analysis and Markov chain Monte Carlo (MCMC) methods to deduce identifiable parameter subsets given limited data from heart transplant patients.…”

Framework for patient-specific simulation of hemodynamics in heart failure with counterpulsation support

“…Patient-specific lumped parameter computational models provide a low-cost option for proof-of concept testing of innovative treatment options in interaction with the cardiovascular system. Lumped parameter computational models are efficient ( 24 ) and are particularly suitable for testing in heterogeneous patient populations as each component can be tuned to match anatomical and hemodynamic data in a patient-specific manner ( 25 , 26 ). Given the heterogeneity of the HFpEF population, a patient-specific approach is particularly important to allow an extension to a patient cohort in the future.…”