Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients

The correlation between subendocardial viability ratio and the degree of coronary artery stenosis in patients with coronary heart disease and its predictive value for the incidence of short-term cardiovascular events

OBJECTIVES

This study aimed to analyze the ability of subendocardial viability ratio (SEVR) to predict the degree of coronary artery stenosis and the relationship between SEVR and the incidence of short-term cardiovascular endpoint events.

METHOD

The indexes of 243 patients with chest pain were collected.. Binary logistic regression analyses were performed using the dichotomous outcome of high and non-high SYNTAX scores. Receiver operating characteristic curves were employed to comparatively analyze the diagnostic efficiencies of the indices and models. A survival analysis combined with the Cox regression analysis was performed using the Kaplan-Meier method to understand the relationship between the SEVR and the incidence of cardiovascular events within 1 year in patients with coronary heart disease (CHD).

RESULTS

SEVR was significantly lower ( P  < 0.05) in the high-stenosis group than control and low-stenosis groups. The diagnostic efficacy of SEVR [area under the curve (AUC) = 0.861] was better than those of age (AUC = 0.745), ABI (AUC = 0.739), and AIx@HR75 (AUC = 0.659). The cutoff SEVR was 1.105. In patients with confirmed CHD who had been discharged from the hospital for 1 year, only SEVR affected survival outcomes (hazard ratio = 0.010; 95% confidence interval: 0.001-0.418; P  = 0.016).

CONCLUSION

A significant decrease in SEVR predicted severe coronary artery stenosis, with a cutoff value of 1.105 and an accuracy of 0.861. In patients with CHD, the lower the SEVR, the higher was the rate of cardiovascular events at 1 year after hospital discharge.

Non-invasive assessment of stroke volume and cardiovascular parameters based on peripheral pressure waveform

Cardiovascular diseases are the leading cause of death globally, making the development of non-invasive and simple-to-use tools that bring insights into the state of the cardiovascular system of utmost importance. We investigated the possibility of using peripheral pulse wave recordings to estimate stroke volume (SV) and subject-specific parameters describing the selected properties of the cardiovascular system. Peripheral pressure waveforms were recorded in the radial artery using applanation tonometry (SphygmoCor) in 35 hemodialysis (HD) patients and 14 healthy subjects. The pressure waveforms were then used to estimate subject-specific parameters of a mathematical model of pulse wave propagation coupled with the elastance-based model of the left ventricle. Bioimpedance cardiography measurements (PhysioFlow) were performed to validate the model-estimated SV. Mean absolute percentage error between the simulated and measured pressure waveforms was 4.0% and 2.8% for the HD and control group, respectively. We obtained a moderate correlation between the model-estimated and bioimpedance-based SV (r = 0.57, p<0.05, and r = 0.58, p<0.001, for the control group and HD patients, respectively). We also observed a correlation between the estimated end-systolic elastance of the left ventricle and the peripheral systolic pressure in both HD patients (r = 0.84, p<0.001) and the control group (r = 0.70, p<0.01). These preliminary results suggest that, after additional validation and possibly further refinement to increase accuracy, the proposed methodology could support non-invasive assessment of stroke volume and selected heart function parameters and vascular properties. Importantly, the proposed method could be potentially implemented in the existing devices measuring peripheral pressure waveforms.

Structure (Epicardial Stenosis) and Function (Microvascular Dysfunction) That Influence Coronary Fractional Flow Reserve Estimation

Background. The treatment of coronary stenosis is decided by performing high risk invasive surgery to generate the fractional flow reserve diagnostics index, a ratio of distal to proximal pressures in respect of coronary atherosclerotic plaques. Non-invasive methods are a need of the times that necessitate the use of mathematical models of coronary hemodynamic physiology. This study proposes an extensible mathematical description of the coronary vasculature that provides an estimate of coronary fractional flow reserve. Methods. By adapting an existing computational model of human coronary blood flow, the effects of large vessel stenosis and microvascular disease on fractional flow reserve were quantified. Several simulations generated flow and pressure information, which was used to compute fractional flow reserve under several conditions including focal stenosis, diffuse stenosis, and microvascular disease. Sensitivity analysis was used to uncover the influence of model parameters on fractional flow reserve. The model was simulated as coupled non-linear ordinary differential equations and numerically solved using our implicit higher order method. Results. Large vessel stenosis affected fractional flow reserve. The model predicts that the presence, rather than severity, of microvascular disease affects coronary flow deleteriously. Conclusions. The model provides a computationally inexpensive instrument for future in silico coronary blood flow investigations as well as clinical-imaging decision making. A combination of focal and diffuse stenosis appears to be essential to limit coronary flow. In addition to pressure measurements in the large epicardial vessels, diagnosis of microvascular disease is essential. The independence of the index with respect to heart rate suggests that computationally inexpensive steady state simulations may provide sufficient information to reliably compute the index.

Changes in Subendocardial Viability Ratio in Traumatic Brain Injury Patients

Background: Traumatic brain injury (TBI) is often associated with cardiac dysfunction, which is a consequence of the brain-heart cross talk. The subendocardial viability ratio (SEVR) is an estimate of myocardial perfusion. The aim of this study was to analyze changes in the SEVR in patients with severe TBI without previous cardiac diseases. Methods: Adult patients treated for severe TBI with a Glasgow coma score <8 were studied. Pressure waveforms were obtained by a high-fidelity tonometer in the radial artery for SEVR calculation at five time points: immediately after admission to the intensive care unit and 24, 48, 72, and 96 h after admission. SEVRs and other clinically important parameters were analyzed in patients who survived and did not survive after 28 days of treatment, as well as in patients who underwent decompressive craniectomy (DC). Results: A total of 64 patients (16 females and 48 males) aged 18-64 years were included. Fifty patients survived and 14 died. DC was performed in 23 patients. SEVRs decreased 24 h after admission in nonsurvivors (p < 0.05) and after 48 h in survivors (p < 0.01) and its values were significantly lower in nonsurvivors than in survivors at 24, 72, and 96 h from admission (p < 0.05). The SEVR increased following DC (p < 0.05). Conclusions: A decreased SEVR is observed in TBI patients. Surgical decompression increases the SEVR, indicating improvement in coronary microvascular perfusion. The results of our study seem to confirm that brain injury affects myocardium function.

Machine Learning-Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation

BACKGROUND

The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals.

OBJECTIVE

The objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data.

METHODS

Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier.

RESULTS

It was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted.

CONCLUSIONS

We expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status.

Arterial Hypertension and Unusual Ascending Aortic Dilatation in a Neonate With Acute Kidney Injury: Mechanistic Computer Modeling

Background

Neonatal asphyxia caused kidney injury and severe hypertension in a newborn. An unusually dilatated ascending aorta developed. Dialysis and pharmacological treatment led to partial recovery of the ascending aortic diameters. It was hypothesized that the aortic dilatation may be associated with aortic stiffening, peripheral resistance, and cardiovascular changes. Mathematical modeling was used to better understand the potential causes of the hypertension, and to confirm our clinical treatment within the confines of the model's capabilities.

Methods

The patient's systolic arterial blood pressure showed hypertension. Echocardiographic exams showed ascending aorta dilatation during hypertension, which partially normalized upon antihypertensive treatment. To explore the underlying mechanisms of the aortic dilatation and hypertension, an existing lumped parameter hemodynamics model was deployed. Hypertension was simulated using realistic literature informed parameter values. It was also simulated using large parameter perturbations to demonstrate effects. Simulations were designed to permit examination of causal mechanisms. The hypertension inducing effects of aortic stiffnesses, vascular resistances, and cardiac hypertrophy on blood flow and pressure were simulated. Sensitivity analysis was used to stratify causes.

Results

In agreement with our clinical diagnosis, the model showed that an increase of aortic stiffness followed by augmentation of peripheral resistance are the prime causes of realistic hypertension. Increased left ventricular elastance may also cause hypertension. Ascending aortic pressure and flow increased in the simultaneous presence of left ventricle hypertrophy and augmented small vessel resistance, which indicate a plausible condition for ascending aorta dilatation. In case of realistic hypertension, sensitivity analysis showed that the treatment of both the large vessel stiffness and small vessel resistance are more important in comparison to cardiac hypertrophy.

Conclusion and Discussion

Large vessel stiffness was found to be the prime factor in arterial hypertension, which confirmed the clinical treatment. Treatment of cardiac hypertrophy appears to provide significant benefit but may be secondary to treatment of large vessel stiffness. The quantitative grading of pathophysiological mechanisms provided by the modeling may contribute to treatment recommendations. The model was limited due to a lack of data suitable to permit model identification.

A review on low-dimensional physics-based models of systemic arteries: application to estimation of central aortic pressure

The physiological processes and mechanisms of an arterial system are complex and subtle. Physics-based models have been proven to be a very useful tool to simulate actual physiological behavior of the arteries. The current physics-based models include high-dimensional models (2D and 3D models) and low-dimensional models (0D, 1D and tube-load models). High-dimensional models can describe the local hemodynamic information of arteries in detail. With regard to an exact model of the whole arterial system, a high-dimensional model is computationally impracticable since the complex geometry, viscosity or elastic properties and complex vectorial output need to be provided. For low-dimensional models, the structure, centerline and viscosity or elastic properties only need to be provided. Therefore, low-dimensional modeling with lower computational costs might be a more applicable approach to represent hemodynamic properties of the entire arterial system and these three types of low-dimensional models have been extensively used in the study of cardiovascular dynamics. In recent decades, application of physics-based models to estimate central aortic pressure has attracted increasing interest. However, to our best knowledge, there has been few review paper about reconstruction of central aortic pressure using these physics-based models. In this paper, three types of low-dimensional physical models (0D, 1D and tube-load models) of systemic arteries are reviewed, the application of three types of models on estimation of central aortic pressure is taken as an example to discuss their advantages and disadvantages, and the proper choice of models for specific researches and applications are advised.