Adverse Cardiovascular Effects of Nicotine Delivered by Chronic Electronic Cigarettes or Standard Cigarettes Captured by Cardiovascular Intrinsic Frequencies.

BACKGROUND: Electronic cigarettes have gained popularity as a nicotine delivery system, which has been recommended by some as an aid to help people quit traditional smoking. The potential long-term effects of vaping on the cardiovascular system, as well as how their effects compare with those from standard cigarettes, are not well understood. The intrinsic frequency (IF) method is a systems approach for analysis of left ventricle and arterial function. Recent clinical studies have demonstrated the diagnostic and prognostic value of IF. Here, we aim to determine whether the novel IF metrics derived from carotid pressure waveforms can detect effects of nicotine (delivered by chronic exposure to electronic cigarette vapor or traditional cigarette smoke) on the cardiovascular system. METHODS AND RESULTS: One hundred seventeen healthy adult male and female rats were exposed to purified air (control), electronic cigarette vapor without nicotine, electronic cigarette vapor with nicotine, and traditional nicotine-rich cigarette smoke, after which hemodynamics were comprehensively evaluated. IF metrics were computed from invasive carotid pressure waveforms. Standard cigarettes significantly increased the first IF (indicating left ventricle contractile dysfunction). Electronic cigarettes with nicotine significantly reduced the second IF (indicating adverse effects on vascular function). No significant difference was seen in the IF metrics between controls and electronic cigarettes without nicotine. Exposure to electronic cigarettes with nicotine significantly increased the total IF variation (suggesting adverse effects on left ventricle-arterial coupling and its optimal state), when compared with electronic cigarettes without nicotine. CONCLUSIONS: Our IF results suggest that nicotine-containing electronic cigarettes adversely affect vascular function and left ventricle-arterial coupling, whereas standard cigarettes have an adverse effect on left ventricle function.

Detecting elevated left ventricular end diastolic pressure from simultaneously measured femoral pressure waveform and electrocardiogram.

Objective.Instantaneous, non-invasive evaluation of left ventricular end-diastolic pressure (LVEDP) would have significant value in the diagnosis and treatment of heart failure. A new approach called cardiac triangle mapping (CTM) has been recently proposed, which can provide a non-invasive estimate of LVEDP. We hypothesized that a hybrid machine-learning (ML) method based on CTM can instantaneously identify an elevated LVEDP using simultaneously measured femoral pressure waveform and electrocardiogram (ECG).Approach.We studied 46 patients (Age: 39-90 (66.4 ± 9.9), BMI: 20.2-36.8 (27.6 ± 4.1), 12 females) scheduled for clinical left heart catheterizations or coronary angiograms at University of Southern California Keck Medical Center. Exclusion criteria included severe mitral/aortic valve disease; severe carotid stenosis; aortic abnormalities; ventricular paced rhythm; left bundle branch and anterior fascicular blocks; interventricular conduction delay; and atrial fibrillation. Invasive LVEDP and pressure waveforms at the iliac bifurcation were measured using transducer-tipped Millar catheters with simultaneous ECG. LVEDP range was 9.3-40.5 mmHg. LVEDP = 18 mmHg was used as cutoff. Random forest (RF) classifiers were trained using data from 36 patients and blindly tested on 10 patients.Main results.Our proposed ML classifier models accurately predict true LVEDP classes using appropriate physics-based features, where the most accurate demonstrates 100.0% (elevated) and 80.0% (normal) success in predicting true LVEDP classes on blind data.Significance.We demonstrated that physics-based ML models can instantaneously classify LVEDP using information from femoral waveforms and ECGs. Although an invasive validation, the required ML inputs can be potentially obtained non-invasively.

Noninvasive Left Ventricle Pressure-Volume Loop Determination Method With Cardiac Magnetic Resonance Imaging and Carotid Tonometry Using a Physics-Informed Approach.

Left ventricular (LV) pressure-volume loop (PV-loop) is an important tool to quantify intrinsic left ventricular properties and ventricular-arterial coupling. A significant drawback of conventional PV-loop assessment is the need of invasive measurements which limits its widespread application. To tackle this issue, we developed a PV-loop determination method by using non-invasive measurements from arterial tonometry and cardiac magnetic resonance imaging. A physics-based optimization strategy was designed that adaptively identifies the optimal parameters to construct the PV-loop. We conducted comparative analysis in a convenience sample (N = 77) with heart failure (HF) (N = 23) patients and a control (N = 54) group to evaluate the sensitivity our PV-loop estimation algorithm. Significant and coherent differences between cohorts for the parameters derived using the PV-loop were observed. Our method captures the significant elevation of LV end diastolic pressure (p<0.001), and the decrease of the ventricular efficiency (p<0.0001) of the HF patients compared to the Control group. This method further captures the mechanistic changes of the LV by highlighting the significant differences of the smaller stroke work (p<0.0001), mean external power (p<0.05), and contractility (p<0.001) between these groups. The LV performance metrics align well with the previous clinical PV-loop observations of HF patients and our results demonstrate that the proposed PV-loop reconstruction method can be used to assess the ventricular functional changes associated with HF. Using this noninvasive method may significantly impact and facilitate the diagnosis and therapeutic management of HF.

A machine learning approach for computation of cardiovascular intrinsic frequencies.

Analysis of cardiovascular waveforms provides valuable clinical information about the state of health and disease. The intrinsic frequency (IF) method is a recently introduced framework that uses a single arterial pressure waveform to extract physiologically relevant information about the cardiovascular system. The clinical usefulness and physiological accuracy of the IF method have been well-established via several preclinical and clinical studies. However, the computational complexity of the current L2 optimization solver for IF calculations remains a bottleneck for practical deployment of the IF method in real-time settings. In this paper, we propose a machine learning (ML)-based methodology for determination of IF parameters from a single carotid waveform. We use a sequentially-reduced Feedforward Neural Network (FNN) model for mapping carotid waveforms to the output parameters of the IF method, thereby avoiding the non-convex L2 minimization problem arising from the conventional IF approach. Our methodology also includes procedures for data pre-processing, model training, and model evaluation. In our model development, we used both clinical and synthetic waveforms. Our clinical database is composed of carotid waveforms from two different sources: the Huntington Medical Research Institutes (HMRI) iPhone Heart Study and the Framingham Heart Study (FHS). In the HMRI and FHS clinical studies, various device platforms such as piezoelectric tonometry, optical tonometry (Vivio), and an iPhone camera were used to measure arterial waveforms. Our blind clinical test shows very strong correlations between IF parameters computed from the FNN-based method and those computed from the standard L2 optimization-based method (i.e., R≥0.93 and P-value ≤0.005 for each IF parameter). Our results also demonstrate that the performance of the FNN-based IF model introduced in this work is independent of measurement apparatus and of device sampling rate.

Instantaneous detection of acute myocardial infarction and ischaemia from a single carotid pressure waveform in rats.

Aims: Myocardial infarction (MI) is one of the leading causes of death worldwide. It is well accepted that early diagnosis followed by early reperfusion therapy significantly increases the MI survival. Diagnosis of acute MI is traditionally based on the presence of chest pain and electrocardiogram (ECG) criteria. However, around 50% of the MIs are without chest pain, and ECG is neither completely specific nor definitive. Therefore, there is an unmet need for methods that allow detection of acute MI or ischaemia without using ECG. Our hypothesis is that a hybrid physics-based machine learning (ML) method can detect the occurrence of acute MI or ischaemia from a single carotid pressure waveform. Methods and results: We used a standard occlusion/reperfusion rat model. Physics-based ML classifiers were developed using intrinsic frequency parameters extracted from carotid pressure waveforms. ML models were trained, validated, and generalized using data from 32 rats. The final ML models were tested on an external stratified blind dataset from additional 13 rats. When tested on blind data, the best ML model showed specificity = 0.92 and sensitivity = 0.92 for detecting acute MI. The best model's specificity and sensitivity for ischaemia detection were 0.85 and 0.92, respectively. Conclusion: We demonstrated that a hybrid physics-based ML approach can detect the occurrence of acute MI and ischaemia from carotid pressure waveform in rats. Since carotid pressure waveforms can be measured non-invasively, this proof-of-concept pre-clinical study can potentially be expanded in future studies for non-invasive detection of MI or myocardial ischaemia.

Mechanistic insights on age-related changes in heart-aorta-brain hemodynamic coupling using a pulse wave model of the entire circulatory system.

Age-related changes in aortic biomechanics can impact the brain by reducing blood flow and increasing pulsatile energy transmission. Clinical studies have shown that impaired cardiac function in patients with heart failure is associated with cognitive impairment. Although previous studies have attempted to elucidate the complex relationship between age-associated aortic stiffening and pulsatility transmission to the cerebral network, they have not adequately addressed the effect of interactions between aortic stiffness and left ventricle (LV) contractility (neither on energy transmission nor on brain perfusion). In this study, we use a well-established and validated one-dimensional blood flow and pulse wave computational model of the circulatory system to address how age-related changes in cardiac function and vasculature affect the underlying mechanisms involved in the LV-aorta-brain hemodynamic coupling. Our results reveal how LV contractility affects pulsatile energy transmission to the brain, even with preserved cardiac output. Our model demonstrates the existence of an optimal heart rate (near the normal human heart rate) that minimizes pulsatile energy transmission to the brain at different contractility levels. Our findings further suggest that the reduction in cerebral blood flow at low levels of LV contractility is more prominent in the setting of age-related aortic stiffening. Maintaining optimal blood flow to the brain requires either an increase in contractility or an increase in heart rate. The former consistently leads to higher pulsatile power transmission, and the latter can either increase or decrease subsequent pulsatile power transmission to the brain.NEW & NOTEWORTHY We investigated the impact of major aging mechanisms of the arterial system and cardiac function on brain hemodynamics. Our findings suggest that aging has a significant impact on heart-aorta-brain coupling through changes in both arterial stiffening and left ventricle (LV) contractility. Understanding the underlying physical mechanisms involved here can potentially be a key step for developing more effective therapeutic strategies that can mitigate the contributions of abnormal LV-arterial coupling toward neurodegenerative diseases and dementia.

Assessment of Aortic Characteristic Impedance and Arterial Compliance from Non-invasive Carotid Pressure Waveform in The Framingham Heart Study.

The primary goal of this study was to test the hypothesis that a hybrid intrinsic frequency-machine learning (IF-ML) approach can accurately evaluate total arterial compliance (TAC) and aortic characteristic impedance (Zao) from a single noninvasive carotid pressure waveform in both women and men with heart failure (HF). TAC and Zao are cardiovascular biomarkers with established clinical significance. TAC is lower and Zao is higher in women than in men, so women are more susceptible to the consequent deleterious effects of them. Although the principles of TAC and Zao are pertinent to a multitude of cardiovascular diseases, including HF, their routine clinical use is limited because of the requirement for simultaneous measurements of flow and pressure waveforms. For this study, the data were obtained from the Framingham Heart Study (n = 6,201, 53% women). The reference values of Zao and TAC were computed from carotid pressure and aortic flow waveforms. IF parameters of carotid pressure waveform were used in ML models. IF models were developed on n = 5,168 of randomly selected data and blindly tested the remaining data (n = 1,033). The final models were evaluated in patients with HF. Correlations between IF-ML and reference values in all HF and HF with preserved ejection fraction for TAC were 0.88 and 0.90, and for Zao were 0.82 and 0.80, respectively. The classification accuracy in all HF and HF with preserved ejection fraction for TAC were 0.9 and 0.93, and for Zao were 0.81 and 0.89, respectively. In conclusion, the IF-ML method provides an accurate estimation of TAC and Zao in all subjects with HF and in the general population.

Framework Development for Patient-Specific Compliant Aortic Dissection Phantom Model Fabrication: Magnetic Resonance Imaging Validation and Deep-Learning Segmentation.

Type B aortic dissection is a life-threatening medical emergency that can result in rupture of the aorta. Due to the complexity of patient-specific characteristics, only limited information on flow patterns in dissected aortas has been reported in the literature. Leveraging the medical imaging data for patient-specific in vitro modeling can complement the hemodynamic understanding of aortic dissections. We propose a new approach toward fully automated patient-specific type B aortic dissection model fabrication. Our framework uses a novel deep-learning-based segmentation for negative mold manufacturing. Deep-learning architectures were trained on a dataset of 15 unique computed tomography scans of dissection subjects and were blind-tested on 4 sets of scans, which were targeted for fabrication. Following segmentation, the three-dimensional models were created and printed using polyvinyl alcohol. These models were then coated with latex to create compliant patient-specific phantom models. The magnetic resonance imaging (MRI) structural images demonstrate the ability of the introduced manufacturing technique for creating intimal septum walls and tears based on patient-specific anatomy. The in vitro experiments show the fabricated phantoms generate physiologically-accurate pressure results. The deep-learning models also show high similarity metrics between manual segmentation and autosegmentation where Dice metric is as high as 0.86. The proposed deep-learning-based negative mold manufacturing method facilitates an inexpensive, reproducible, and physiologically-accurate patient-specific phantom model fabrication suitable for aortic dissection flow modeling.

Non-Invasive Pressure-Only Aortic Wave Intensity Evaluation Using Hybrid Fourier Decomposition-Machine Learning Approach.

OBJECTIVE: The clinical significance of the wave intensity (WI) analysis for the diagnosis and prognosis of the cardiovascular and cerebrovascular diseases is well-established. However, this method has not been fully translated into clinical practice. From practical point of view, the main limitation of WI method is the need for concurrent measurements of both pressure and flow waveforms. To overcome this limitation, we developed a Fourier-based machine learning (F-ML) approach to evaluate WI using only the pressure waveform measurement. METHODS: Tonometry recordings of the carotid pressure and ultrasound measurements for the aortic flow waveforms from the Framingham Heart Study (2640 individuals; 55% women) were used for developing the F-ML model and the blind testing. RESULTS: Method-derived estimates are significantly correlated for the first and second forward wave peak amplitudes (Wf1, r = 0.88, p 0.05; Wf2, r = 0.84, p 0.05) and the corresponding peak times (Wf1, r = 0.80, p < 0.05; Wf2, r = 0.97, p 0.05). For backward components of WI (Wb1), F-ML estimates correlated strongly for the amplitude (r = 0.71, p 0.05) and moderately for the peak time (r = 0.60, p 0.05). The results show that the pressure-only F-ML model significantly outperforms the analytical pressure-only approach based on the reservoir model. In all cases, the Bland-Altman analysis shows negligible bias in the estimations. CONCLUSION: The proposed pressure-only F-ML approach provides accurate estimates for WI parameters. SIGNIFICANCE: The pressure only F-ML approach introduced in this work expand the clinical usage of WI into inexpensive and non-invasive settings such as wearable telemedicine.

Screening left ventricular systolic dysfunction in children using intrinsic frequencies of carotid pressure waveforms measured by a novel smartphone-based device.

Objective.Children with heart failure have higher rates of emergency department utilization, health care expenditure, and hospitalization. Therefore, a need exists for a simple, non-invasive, and inexpensive method of screening for left ventricular (LV) dysfunction. We recently demonstrated the practicality and reliability of a wireless smartphone-based handheld device in capturing carotid pressure waveforms and deriving cardiovascular intrinsic frequencies (IFs) in children with normal LV function. Our goal in this study was to demonstrate that an IF-based machine learning method (IF-ML) applied to noninvasive carotid pressure waveforms can distinguish between normal and abnormal LV ejection fraction (LVEF) in pediatric patients.Approach. Fifty patients ages 0 to 21 years underwent LVEF measurement by echocardiogram or cardiac magnetic resonance imaging. On the same day, patients had carotid waveforms recorded using Vivio. The exclusion criterion was known vascular disease that would interfere with obtaining a carotid artery pulse. We adopted a hybrid IF- Machine Learning (IF-ML) method by applying physiologically relevant IF parameters as inputs to Decision Tree classifiers. The threshold for low LVEF was chosen as <50%.Main results.The proposed IF-ML method was able to detect an abnormal LVEF with an accuracy of 92% (sensitivity = 100%, specificity = 89%, area under the curve (AUC) = 0.95). Consistent with previous clinical studies, the IF parameterω1was elevated among patients with reduced LVEF.Significance.A hybrid IF-ML method applied on a carotid waveform recorded by a hand-held smartphone-based device can differentiate between normal and abnormal LV systolic function in children with normal cardiac anatomy.

Thermal and Postural Effects on Fluid Mixing and Irrigation Patterns for Intraventricular Hemorrhage Treatment.

Intraventricular hemorrhage is characterized by blood leaking into the cerebral ventricles and mixing with cerebrospinal fluid. A standard treatment method involves inserting a passive drainage catheter, known as an external ventricular drain (EVD), into the ventricle. EVDs have common adverse complications, including the occlusion of the catheter, that may lead to permanent neural damage or even mortality. In order to prevent such complications, a novel dual-lumen catheter (IRRAflow®) utilizing an active fluid exchange mechanism has been recently developed. However, the fluid dynamics of the exchange system have not been investigated. In this study, convective flow in a three-dimensional cerebral lateral ventricle with an inserted catheter is evaluated using an in-house lattice-Boltzmann-based fluid-solid interaction solver. Different treatment conditions are simulated, including injection temperature and patient position. Thermal and gravitational effects on medication distribution are studied using a dye simulator based on a recently-introduced (pseudo)spectral convection-diffusion equation solver. The effects of injection temperature and patient position on catheter performance are presented and discussed in terms of hematoma irrigation, vortical structures, mixing, and medication volume distribution. Results suggest that cold-temperature injections can increase catheter efficacy in terms of dye distribution and irrigation potential, both of which can be further guided by patient positioning.

Direct 0D-3D coupling of a lattice Boltzmann methodology for fluid-structure aortic flow simulations.

This work introduces a numerical approach and implementation for the direct coupling of arbitrary complex ordinary differential equation- (ODE-)governed zero-dimensional (0D) boundary conditions to three-dimensional (3D) lattice Boltzmann-based fluid-structure systems for hemodynamics studies. In particular, a most complex configuration is treated by considering a dynamic left ventricle- (LV-)elastance heart model which is governed by (and applied as) a nonlinear, non-stationary hybrid ODE-Dirichlet system. Other ODE-based boundary conditions, such as lumped parameter Windkessel models for truncated vasculature, are also considered. Performance studies of the complete 0D-3D solver, including its treatment of the lattice Boltzmann fluid equations and elastodynamics equations as well as their interactions, is conducted through a variety of benchmark and convergence studies that demonstrate the ability of the coupled 0D-3D methodology in generating physiological pressure and flow waveforms-ultimately enabling the exploration of various physical and physiological parameters for hemodynamics studies of the coupled LV-arterial system. The methods proposed in this paper can be easily applied to other ODE-based boundary conditions as well as to other fluid problems that are modeled by 3D lattice Boltzmann equations and that require direct coupling of dynamic 0D boundary conditions.

A coupled atrioventricular-aortic setup for in-vitro hemodynamic study of the systemic circulation: Design, fabrication, and physiological relevancy.

In-vitro models of the systemic circulation have gained a lot of interest for fundamental understanding of cardiovascular dynamics and for applied hemodynamic research. In this study, we introduce a physiologically accurate in-vitro hydraulic setup that models the hemodynamics of the coupled atrioventricular-aortic system. This unique experimental simulator has three major components: 1) an arterial system consisting of a human-scale artificial aorta along with the main branches, 2) an artificial left ventricle (LV) sac connected to a programmable piston-in-cylinder pump for simulating cardiac contraction and relaxation, and 3) an artificial left atrium (LA). The setup is designed in such a way that the basal LV is directly connected to the aortic root via an aortic valve, and to the LA via an artificial mitral valve. As a result, two-way hemodynamic couplings can be achieved for studying the effects that the LV, aorta, and LA have on each other. The collected pressure and flow measurements from this setup demonstrate a remarkable correspondence to clinical hemodynamics. We also investigate the physiological relevancies of isolated effects on cardiovascular hemodynamics of various major global parameters found in the circulatory system, including LV contractility, LV preload, heart rate, aortic compliance, and peripheral resistance. Subsequent control over such parameters ultimately captures physiological hemodynamic effects of LV systolic dysfunction, preload (cardiac) diseases, and afterload (arterial) diseases. The detailed design and fabrication of the proposed setup is also provided.

Model-Based Fluid-Structure Interaction Approach for Evaluation of Thoracic Endovascular Aortic Repair Endograft Length in Type B Aortic Dissection.

Thoracic endovascular aortic repair (TEVAR) is a commonly performed operation for patients with type B aortic dissection (TBAD). The goal of TEVAR is to cover the proximal entry tear between the true lumen (TL) and the false lumen (FL) with an endograft to induce FL thrombosis, allow for aortic healing, and decrease the risk of aortic aneurysm and rupture. While TEVAR has shown promising outcomes, it can also result in devastating complications including stroke, spinal cord ischemia resulting in paralysis, as well as long-term heart failure, so treatment remains controversial. Similarly, the biomechanical impact of aortic endograft implantation and the hemodynamic impact of endograft design parameters such as length are not well-understood. In this study, a fluid-structure interaction (FSI) computational fluid dynamics (CFD) approach was used based on the immersed boundary and Lattice-Boltzmann method to investigate the association between the endograft length and hemodynamic variables inside the TL and FL. The physiological accuracy of the model was evaluated by comparing simulation results with the true pressure waveform measurements taken during a live TEVAR operation for TBAD. The results demonstrate a non-linear trend towards increased FL flow reversal as the endograft length increases but also increased left ventricular pulsatile workload. These findings suggest a medium-length endograft may be optimal by achieving FL flow reversal and thus FL thrombosis, while minimizing the extra load on the left ventricle. These results also verify that a reduction in heart rate with medical therapy contributes favorably to FL flow reversal.

Effects of vessel wall mechanics on non-invasive evaluation of cardiovascular intrinsic frequencies.

Intrinsic Frequency (IF) is a systems-based approach that provides valuable information for hemodynamic monitoring of the left ventricle (LV), the arterial system, and their coupling. Recent clinical studies have demonstrated the clinical significance of this method for prognosis and diagnosis of cardiovascular diseases. In IF analysis, two dominant instantaneous frequencies (ω1 and ω2) are extracted from arterial pressure waveforms. The value of ω1 is related to the dynamics of the LV and the value of ω2 is related to the dynamics of vascular function. This work investigates the effects of vessel wall mechanics on the accuracy and applicability of IFs extracted from vessel wall displacement waveforms compared to IFs extracted from pressure waveforms. In this study, we used a computational approach employing a fluid-structure interaction finite element method for various wall mechanics governed by linearly elastic, hyperelastic, and viscoelastic models. Results show that for vessels with elastic wall behavior, the error between displacement-based and pressure-based IFs is negligible. In the presence of stenosis or aneurysm in elastic arteries, the maximum errors associated with displacement-based IFs is less than 2%. For non-linear elastic and viscoelastic arteries, errors are more pronounced (where the former reaches up to 11% and the latter up to 27%). Our results ultimately suggest that displacement-based computations of ω1 and ω2 are accurate in vessels that exhibit elastic behavior (such as carotid arteries) and are suitable surrogates for pressure-based IFs. This is clinically significant because displacement-based IFs can be measured non-invasively.

Accuracy and applicability of non-invasive evaluation of aortic wave intensity using only pressure waveforms in humans.

Background.Wave intensity (WI) analysis is a well-established method for quantifying the energy carried in arterial waves, providing valuable clinical information about cardiovascular function. The primary drawback of this method is the need for concurrent measurements of both pressure and flow waveforms.Objective. We have for the first time investigated the accuracy of a novel methodology for estimating wave intensity employing only single pressure waveform measurements; we studied both carotid- and radial-based estimations in a large heterogeneous cohort.Approach.Tonometry was performed alongside Doppler ultrasound to acquire measurements of both carotid and radial pressure waveforms as well as aortic flow waveforms in 2640 healthy and diseased participants (1439 female) in the Framingham Heart Study. Patterns consisting of two forward waves (Wf1, Wf2) and one backward wave (Wb1) along with reflection metrics were compared with those obtained from exact WI analysis.Main Results. Carotid-based estimates correlated well for forward peak amplitudes (Wf1,r = 0.85,p < 0.05; Wf2,r = 0.72,p < 0.05) and peak time (Wf1,r = 0.94,p < 0.05; Wf2,r = 0.98,p < 0.05), and radial-based estimates correlated fairly to poorly for amplitudes (Wf1,r = 0.62,p < 0.05; Wf2,r = 0.42,p < 0.05) and peak time (Wf1,r = 0.04,p = 0.10; Wf2,r = 0.75,p < 0.05). In all cases, estimated Wb1 measures were not correlated. Reflection metrics were well correlated for healthy patients (r = 0.67,p < 0.05), moderately correlated for valvular disease (r = 0.59,p < 0.05) and fairly correlated for CVD (r = 0.46,p < 0.05) and heart failure (r = 0.49,p < 0.05).Significance. These findings indicate that pressure-only WI produces accurate results only when forward contributions are of primary interest and only for carotid pressure waveforms. The pressure-only WI estimations of this work provide an important opportunity to further the goal of uncovering clinical insights through wave analysis affordably and non-invasively.

Scalability of cardiovascular intrinsic frequencies: Validations in preclinical models and non-invasive clinical studies.

AIMS: Cardiovascular intrinsic frequencies (IFs) are associated with cardiovascular health and disease, separately capturing the systolic and diastolic information contained in a single (uncalibrated) arterial waveform. Previous clinical investigations related to IF have been restricted to studying chronic conditions, and hence its applicability for acute cardiovascular diseases has not been explored. Studies of cardiovascular complications such as acute myocardial infarction are difficult to perform in humans due to the high-risk and invasive nature of such procedures. Although they can be performed in preclinical (animal) models, the corresponding interpretation of IF measures and how they ultimately translate to humans is unknown. Hence, we studied the scalability of IF across species and sensor platforms. MATERIALS AND METHODS: Scaled values of the two intrinsic frequencies ω1 and ω2 (corresponding to systolic and diastolic dynamics, respectively) were extracted from carotid waveforms acquired either non-invasively (via tonometry, Vivio or iPhone) in humans or invasively in rabbits and rats. KEY FINDINGS: The scaled IF parameters for all species were found to fall within the same physiological ranges carrying similar statistical characteristics, even though body sizes and corresponding heart rates of the species were substantially different. Additionally, results demonstrated that all non-invasive sensor platforms were significantly correlated with each other for scaled IFs, suggesting that such analysis is device-agnostic and can be applied to upcoming wearable technologies. SIGNIFICANCE: Ultimately, our results found that IFs are scalable across species, which is particularly valuable for the training of IF-based artificial intelligence systems using both preclinical and clinical data.

The Rheology of the Carotid Sinus: A Path Toward Bioinspired Intervention.

The association between blood viscosity and pathological conditions involving a number of organ systems is well known. However, how the body measures and maintains appropriate blood viscosity is not well-described. The literature endorsing the function of the carotid sinus as a site of baroreception can be traced back to some of the earliest descriptions of digital pressure on the neck producing a drop in blood delivery to the brain. For the last 30 years, improved computational fluid dynamic (CFD) simulations of blood flow within the carotid sinus have demonstrated a more nuanced understanding of the changes in the region as it relates to changes in conventional metrics of cardiovascular function, including blood pressure. We suggest that the unique flow patterns within the carotid sinus may make it an ideal site to transduce flow data that can, in turn, enable real-time measurement of blood viscosity. The recent characterization of the PIEZO receptor family in the sinus vessel wall may provide a biological basis for this characterization. When coupled with other biomarkers of cardiovascular performance and descriptions of the blood rheology unique to the sinus region, this represents a novel venue for bioinspired design that may enable end-users to manipulate and optimize blood flow.

The underlying mechanism of intersite discrepancies in ejection time measurements from arterial waveforms and its validation in the Framingham Heart Study.

Radial applanation tonometry is a well-established method for clinical hemodynamic assessment and is also becoming popular in wrist-worn fitness trackers. The time difference between the foot and the dicrotic notch of the arterial pressure waveform is a well-accepted approximation for the left ventricular ejection time (ET). However, several clinical studies have shown that ET measured from the radial pressure waveform deviates from that measured centrally. In this work, we consider the systolic wave and the dicrotic wave as two independent traveling waves and hypothesize that their wave speed difference leads to the intersite differences of measured ET (ΔET). Accordingly, we derived a mathematical dicrotic wave decomposition model and identified the most influential factors on ΔET via global sensitivity analysis. In our clinical validation on a heterogeneous cohort (N = 5,742) from the Framingham Heart Study (FHS), the local sensitivity analysis results resembled the sensitivity variation patterns of ΔET from model simulations. A regression analysis on FHS data, using morphological features of radial pressure waveforms to estimate the carotid ET, produced a root mean square error of 3.76 ms and R2 of 0.91. The proposed dicrotic wave decomposition model can explain the intersite ET measurement discrepancies observed in the clinical data of FHS and can facilitate the precise identification of ET with radial pressure waveforms. Therefore, the proposed model will improve various physics-based pulse wave analysis methods as well as prospective artificial intelligence methods for tackling the subsequent big data produced from widespread wearable radial pressure monitoring.NEW & NOTEWORTHY Based on a new understanding of pressure wave propagation, we propose a novel dicrotic wave decomposition model considering the dicrotic wave as an independent traveling component. The proposed model can explain the mechanism underlying the intersite discrepancies in ejection time measurement from arterial waveforms and then, in principle, enhance the accuracy of both classical physics-based as well as more contemporary artificial intelligence-based pulse wave analysis methods in clinical and wearable radial blood pressure monitoring applications.

Intrinsic Frequencies of Carotid Pressure Waveforms Predict Heart Failure Events: The Framingham Heart Study.

Intrinsic frequencies (IFs) derived from arterial waveforms are associated with cardiovascular performance, aging, and prevalent cardiovascular disease (CVD). However, prognostic value of these novel measures is unknown. We hypothesized that IFs are associated with incident CVD risk. Our sample was drawn from the Framingham Heart Study Original, Offspring, and Third Generation Cohorts and included participants free of CVD at baseline (N=4700; mean age 52 years, 55% women). We extracted 2 dominant frequencies directly from a series of carotid pressure waves: the IF of the coupled heart and vascular system during systole (ω1) and the IF of the decoupled vasculature during diastole (ω2). Total frequency variation (Δω) was defined as the difference between ω1 and ω2. We used Cox proportional hazards regression models to relate IFs to incident CVD events during a mean follow-up of 10.6 years. In multivariable models adjusted for CVD risk factors, higher ω1 (hazard ratio [HR], 1.14 [95% CI], 1.03-1.26]; P=0.01) and Δω (HR, 1.16 [95% CI, 1.03-1.30]; P=0.02) but lower ω2 (HR, 0.87 [95% CI, 0.77-0.99]; P=0.03) were associated with higher risk for incident composite CVD events. In similarly adjusted models, higher ω1 (HR, 1.23 [95% CI, 1.07-1.42]; P=0.004) and Δω (HR, 1.26 [95% CI, 1.05-1.50]; P=0.01) but lower ω2 (HR, 0.81 [95% CI, 0.66-0.99]; P=0.04) were associated with higher risk for incident heart failure. IFs were not significantly associated with incident myocardial infarction or stroke. Novel IFs may represent valuable markers of heart failure risk in the community.