Abstract
Heart failure is treatable, but in the United Kingdom, the 1-, 5- and 10-year mortality rates are 24.1, 54.5 and 75.5%, respectively. The poor prognosis reflects, in part, the lack of specific, simple and affordable diagnostic techniques; the disease is often advanced by the time a diagnosis is made. Previous studies have demonstrated that certain metrics derived from pressure–velocity-based wave intensity analysis are significantly altered in the presence of impaired heart performance when averaged over groups, but to date, no study has examined the diagnostic potential of wave intensity on an individual basis, and, additionally, the pressure waveform can only be obtained accurately using invasive methods, which has inhibited clinical adoption. Here, we investigate whether a new form of wave intensity based on noninvasive measurements of arterial diameter and velocity can detect impaired heart performance in an individual. To do so, we have generated a virtual population of two-thousand elderly subjects, modelling half as healthy controls and half with an impaired stroke volume. All metrics derived from the diameter–velocity-based wave intensity waveforms in the carotid, brachial and radial arteries showed significant crossover between groups—no one metric in any artery could reliably indicate whether a subject’s stroke volume was normal or impaired. However, after applying machine learning to the metrics, we found that a support vector classifier could simultaneously achieve up to 99% recall and 95% precision. We conclude that noninvasive wave intensity analysis has significant potential to improve heart failure screening and diagnosis.
Highlights
Heart failure (HF) is a broad spectrum of disease in which the heart is unable to supply blood at the rate required by the body
It is often stratified by ejection fraction (EF): heart failure with reduced ejection fraction (≤40%, HFrEF), where there is usually a decrease in stroke volume (SV) due to a failure of intrinsic inotropy or loss of functional heart muscle; and heart failure with preserved ejection fraction (≥50%, HFpEF), where there is often a decrease in SV because the end diastolic volume (EDV) has reduced due to loss of ventricular compliance
Eight metrics were used to characterise the waves: the magnitudes of the systolic, diastolic and reflected (S, D, and R) waves (SWI, DWI and RWI, respectively); the wave energies of Separating waves into their forwards and backwards components did not improve the model’s performance, nor did stratifying the data by age or sex; we only present the results obtained from the unseparated waves for the entire dataset
Summary
Heart failure (HF) is a broad spectrum of disease in which the heart is unable to supply blood at the rate required by the body. Simulations were performed using the PulseWaveSolver utility of Nektar++ (Cantwell et al, 2015), which solves the 1D equations of blood flow using a high-order discontinuous Galerkin method with a spectral/hp-element discretisation This reduced-order modelling can accurately solve for the arterial area, velocity and pressure waveforms in complex arterial networks with reasonable computational cost (Alastruey et al, 2011; Sherwin et al, 2003) and has been validated against in-vitro (Alastruey et al, 2011; Matthys et al, 2007) and in-vivo (Mynard and Smolich, 2015; Olufsen et al, 2000; Pedregosa et al, 2011) data. An SVM was chosen after it performed best in preliminary tests against other classification algorithms
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