Abstract
AbstractDiastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition is not 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 with or without mitral valve stenosis induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II (2018 Nice classification) pulmonary hypertension. 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. Accurate measurements of this quantity, however, 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, identifiability of its parameters, to 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.
Publisher
Cold Spring Harbor Laboratory