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Chen Z, Peng B, Zhou Y, Hao Y, Xie X. Interpretable and accurate curve-fitting method for arterial pulse wave modeling and decomposition. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023; 39:e3775. [PMID: 37740645 DOI: 10.1002/cnm.3775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 09/08/2023] [Accepted: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Arterial pulse waveforms contain a wealth of information about the cardiovascular system. There is a lack of physical meaning in the mathematical model of arterial pulse waves, while the physical model fails to offer individuality as too many assumptions are involved. In this article, we focus on promoting the interpretability of the arterial pulse mathematical model. The proposed method is based on newly developed 3-term fitting functions individualized by the physiological parameter assignment, which are the peak times of the reflected and dicrotic waves in a pulse. In this manner, the model allows decomposition of the pulse into sub-signals with clear physiological significance. With nearly 10,000 pulse fitting experiments, it is demonstrated that the proposed method outperforms the standard methods in fitting accuracy while providing parameters linked to hemodynamic characteristics and common clinical indices such as the peripheral augmentation index (pAI). The proposed method innovatively maintains the individuality and accuracy of mathematical models while improving the interpretability of their parameters. The applications of this newly developed method, which explicitly incorporates hemodynamic characteristics, are expected to be particularly valuable in future pulse wave decomposition studies.
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Affiliation(s)
- Zhendong Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Bo Peng
- Department of Musical Instrument Engineering, Xinghai Conservatory of Music, Guangzhou, China
- Sniow Research and Development Laboratory, Foshan, China
| | - Yuqi Zhou
- Department of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yinan Hao
- Department of Musical Instrument Engineering, Xinghai Conservatory of Music, Guangzhou, China
| | - Xiaohua Xie
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
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2
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Karim S, Chahal A, Khanji MY, Petersen SE, Somers V. Autonomic Cardiovascular Control in Health and Disease. Compr Physiol 2023; 13:4493-4511. [PMID: 36994768 PMCID: PMC10406398 DOI: 10.1002/cphy.c210037] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Autonomic neural control of the cardiovascular system is formed of complex and dynamic processes able to adjust rapidly to mitigate perturbations in hemodynamics and maintain homeostasis. Alterations in autonomic control feature in the development or progression of a multitude of diseases with wide-ranging physiological implications given the neural system's responsibility for controlling inotropy, chronotropy, lusitropy, and dromotropy. Imbalances in sympathetic and parasympathetic neural control are also implicated in the development of arrhythmia in several cardiovascular conditions sparking interest in autonomic modulation as a form of treatment. A number of measures of autonomic function have shown prognostic significance in health and in pathological states and have undergone varying degrees of refinement, yet adoption into clinical practice remains extremely limited. The focus of this contemporary narrative review is to summarize the anatomy, physiology, and pathophysiology of the cardiovascular autonomic nervous system and describe the merits and shortfalls of testing modalities available. © 2023 American Physiological Society. Compr Physiol 13:4493-4511, 2023.
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Affiliation(s)
- Shahid Karim
- Mayo Clinic, Rochester, Minnesota, USA
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, UK
| | - Anwar Chahal
- Mayo Clinic, Rochester, Minnesota, USA
- University of Pennsylvania, Pennsylvania, USA
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, UK
| | - Mohammed Y. Khanji
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, London, UK
- Newham University Hospital, Barts Health NHS Trust, London, UK
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, London, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
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3
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Sharifi H, Mann CK, Wenk JF, Campbell KS. A multiscale model of the cardiovascular system that regulates arterial pressure via closed loop baroreflex control of chronotropism, cell-level contractility, and vascular tone. Biomech Model Mechanobiol 2022; 21:1903-1917. [PMID: 36107358 PMCID: PMC10066042 DOI: 10.1007/s10237-022-01628-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 08/11/2022] [Indexed: 11/02/2022]
Abstract
Multiscale models of the cardiovascular system can provide new insights into physiological and pathological processes. PyMyoVent is a computer model that bridges from molecular- to organ-level function and which simulates a left ventricle pumping blood through the systemic circulation. Initial work with PyMyoVent focused on the end-systolic pressure volume relationship and ranked potential therapeutic strategies by their impact on contractility. This manuscript extends the PyMyoVent framework by adding closed-loop feedback control of arterial pressure. The control algorithm mimics important features of the physiological baroreflex and was developed as part of a long-term program that focuses on growth and biological remodeling. Inspired by the underlying biology, the reflex algorithm uses an afferent signal derived from arterial pressure to drive a kinetic model that mimics the net result of neural processing in the medulla and cell-level responses to autonomic drive. The kinetic model outputs control signals that are constrained between limits that represent maximum parasympathetic and maximum sympathetic drive and which modulate heart rate, intracellular Ca2+ dynamics, the molecular-level function of both the thick and the thin myofilaments, and vascular tone. Simulations show that the algorithm can regulate mean arterial pressure at user-defined setpoints as well as maintaining arterial pressure when challenged by changes in blood volume and/or valve resistance. The reflex also regulates arterial pressure when cell-level contractility is modulated to mimic the idealized impact of myotropes. These capabilities will be important for future work that uses computer modeling to investigate clinical conditions and treatments.
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Affiliation(s)
- Hossein Sharifi
- Department of Mechanical Engineering, University of Kentucky, Lexington, KY, USA
| | - Charles K Mann
- Department of Mechanical Engineering, University of Kentucky, Lexington, KY, USA
| | - Jonathan F Wenk
- Department of Mechanical Engineering, University of Kentucky, Lexington, KY, USA
- Department of Surgery, University of Kentucky, Lexington, KY, USA
| | - Kenneth S Campbell
- Division of Cardiovascular Medicine and Department of Physiology, University of Kentucky, Lexington, KY, USA.
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Randall EB, Randolph NZ, Alexanderian A, Olufsen MS. Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model. J Theor Biol 2021; 526:110759. [PMID: 33984355 DOI: 10.1016/j.jtbi.2021.110759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 05/03/2021] [Accepted: 05/04/2021] [Indexed: 12/30/2022]
Abstract
In this study, we develop a methodology for model reduction and selection informed by global sensitivity analysis (GSA) methods. We apply these techniques to a control model that takes systolic blood pressure and thoracic tissue pressure data as inputs and predicts heart rate in response to the Valsalva maneuver (VM). The study compares four GSA methods based on Sobol' indices (SIs) quantifying the parameter influence on the difference between the model output and the heart rate data. The GSA methods include standard scalar SIs determining the average parameter influence over the time interval studied and three time-varying methods analyzing how parameter influence changes over time. The time-varying methods include a new technique, termed limited-memory SIs, predicting parameter influence using a moving window approach. Using the limited-memory SIs, we perform model reduction and selection to analyze the necessity of modeling both the aortic and carotid baroreceptor regions in response to the VM. We compare the original model to systematically reduced models including (i) the aortic and carotid regions, (ii) the aortic region only, and (iii) the carotid region only. Model selection is done quantitatively using the Akaike and Bayesian Information Criteria and qualitatively by comparing the neurological predictions. Results show that it is necessary to incorporate both the aortic and carotid regions to model the VM.
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Affiliation(s)
- E Benjamin Randall
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, United States; Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
| | - Nicholas Z Randolph
- Department of Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
| | - Alen Alexanderian
- Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
| | - Mette S Olufsen
- Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
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Gu F, Randall EB, Whitesall S, Converso-Baran K, Carlson BE, Fink GD, Michele DE, Beard DA. Potential role of intermittent functioning of baroreflexes in the etiology of hypertension in spontaneously hypertensive rats. JCI Insight 2020; 5:139789. [PMID: 33004690 PMCID: PMC7566704 DOI: 10.1172/jci.insight.139789] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 08/31/2020] [Indexed: 12/24/2022] Open
Abstract
The spontaneously hypertensive rat (SHR) is a genetic model of primary hypertension with an etiology that includes sympathetic overdrive. To elucidate the neurogenic mechanisms underlying the pathophysiology of this model, we analyzed the dynamic baroreflex response to spontaneous fluctuations in arterial pressure in conscious SHRs, as well as in the Wistar-Kyoto (WKY), the Dahl salt-sensitive, the Dahl salt-resistant, and the Sprague-Dawley rat. Observations revealed the existence of long intermittent periods (lasting up to several minutes) of engagement and disengagement of baroreflex control of heart rate. Analysis of these intermittent periods revealed a predictive relationship between increased mean arterial pressure and progressive baroreflex disengagement that was present in the SHR and WKY strains but absent in others. This relationship yielded the hypothesis that a lower proportion of engagement versus disengagement of the baroreflex in SHR compared with WKY contributes to the hypertension (or increased blood pressure) in SHR compared with WKY. Results of experiments using sinoaortic baroreceptor denervation were consistent with the hypothesis that dysfunction of the baroreflex contributes to the etiology of hypertension in the SHR. Thus, this study provides experimental evidence for the roles of the baroreflex in long-term arterial pressure regulation and in the etiology of primary hypertension in this animal model. Baroreflex dysfunction contributes to the etiology of hypertension in a genetic model of primary hypertension.
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Affiliation(s)
- Feng Gu
- Department of Vascular Surgery, Second Xiangya Hospital, Central South University, Changsha, China.,Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
| | - E Benjamin Randall
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Steven Whitesall
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kimber Converso-Baran
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Brian E Carlson
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Gregory D Fink
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan, USA
| | - Daniel E Michele
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Daniel A Beard
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
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Campbell KS, Chrisman BS, Campbell SG. Multiscale Modeling of Cardiovascular Function Predicts That the End-Systolic Pressure Volume Relationship Can Be Targeted via Multiple Therapeutic Strategies. Front Physiol 2020; 11:1043. [PMID: 32973561 PMCID: PMC7466769 DOI: 10.3389/fphys.2020.01043] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 07/29/2020] [Indexed: 01/01/2023] Open
Abstract
Most patients who develop heart failure are unable to elevate their cardiac output on demand due to impaired contractility and/or reduced ventricular filling. Despite decades of research, few effective therapies for heart failure have been developed. In part, this may reflect the difficulty of predicting how perturbations to molecular-level mechanisms that are induced by drugs will scale up to modulate system-level properties such as blood pressure. Computer modeling might help with this process and thereby accelerate the development of better therapies for heart failure. This manuscript presents a new multiscale model that uses a single contractile element to drive an idealized ventricle that pumps blood around a closed circulation. The contractile element was formed by linking an existing model of dynamically coupled myofilaments with a well-established model of myocyte electrophysiology. The resulting framework spans from molecular-level events (including opening of ion channels and transitions between different myosin states) to properties such as ejection fraction that can be measured in patients. Initial calculations showed that the model reproduces many aspects of normal cardiovascular physiology including, for example, pressure-volume loops. Subsequent sensitivity tests then quantified how each model parameter influenced a range of system level properties. The first key finding was that the End Systolic Pressure Volume Relationship, a classic index of cardiac contractility, was ∼50% more sensitive to parameter changes than any other system-level property. The second important result was that parameters that primarily affect ventricular filling, such as passive stiffness and Ca2+ reuptake via sarco/endoplasmic reticulum Ca2+-ATPase (SERCA), also have a major impact on systolic properties including stroke work, myosin ATPase, and maximum ventricular pressure. These results reinforce the impact of diastolic function on ventricular performance and identify the End Systolic Pressure Volume Relationship as a particularly sensitive system-level property that can be targeted using multiple therapeutic strategies.
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Affiliation(s)
- Kenneth S Campbell
- Division of Cardiovascular Medicine, Department of Physiology, University of Kentucky, Lexington, KY, United States
| | | | - Stuart G Campbell
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
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Randall EB, Billeschou A, Brinth LS, Mehlsen J, Olufsen MS. A model-based analysis of autonomic nervous function in response to the Valsalva maneuver. J Appl Physiol (1985) 2019; 127:1386-1402. [PMID: 31369335 DOI: 10.1152/japplphysiol.00015.2019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The Valsalva maneuver (VM) is a diagnostic protocol examining sympathetic and parasympathetic activity in patients with autonomic dysfunction (AD) impacting cardiovascular control. Because direct measurement of these signals is costly and invasive, AD is typically assessed indirectly by analyzing heart rate and blood pressure response patterns. This study introduces a mathematical model that can predict sympathetic and parasympathetic dynamics. Our model-based analysis includes two control mechanisms: respiratory sinus arrhythmia (RSA) and the baroreceptor reflex (baroreflex). The RSA submodel integrates an electrocardiogram-derived respiratory signal with intrathoracic pressure, and the baroreflex submodel differentiates aortic and carotid baroreceptor regions. Patient-specific afferent and efferent signals are determined for 34 control subjects and 5 AD patients, estimating parameters fitting the model output to heart rate data. Results show that inclusion of RSA and distinguishing aortic/carotid regions are necessary to model the heart rate response to the VM. Comparing control subjects to patients shows that RSA and baroreflex responses are significantly diminished. This study compares estimated parameter values from the model-based predictions to indices used in clinical practice. Three indices are computed to determine adrenergic function from the slope of the systolic blood pressure in phase II [α (a new index)], the baroreceptor sensitivity (β), and the Valsalva ratio (γ). Results show that these indices can distinguish between normal and abnormal states, but model-based analysis is needed to differentiate pathological signals. In summary, the model simulates various VM responses and, by combining indices and model predictions, we study the pathologies for 5 AD patients.NEW & NOTEWORTHY We introduce a patient-specific model analyzing heart rate and blood pressure during a Valsalva maneuver (VM). The model predicts autonomic function incorporating the baroreflex and respiratory sinus arrhythmia (RSA) control mechanisms. We introduce a novel index (α) characterizing sympathetic activity, which can distinguish control and abnormal patients. However, we assert that modeling and parameter estimation are necessary to explain pathologies. Finally, we show that aortic baroreceptors contribute significantly to the VM and RSA affects early VM.
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Affiliation(s)
- E Benjamin Randall
- Department of Mathematics, North Carolina State University, Raleigh, North Carolina
| | - Anna Billeschou
- Department of Clinical Physiology and Nuclear Medicine, Bispebjerg Frederiksberg Hospital, Frederiksberg, Denmark
| | - Louise S Brinth
- Department of Clinical Physiology and Nuclear Medicine, Bispebjerg Frederiksberg Hospital, Frederiksberg, Denmark
| | - Jesper Mehlsen
- Section of Surgical Pathophysiology, Juliane Marie Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Mette S Olufsen
- Department of Mathematics, North Carolina State University, Raleigh, North Carolina
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Birjiniuk J, Heldt T. Tracking autonomic balance using an open-loop model of the arterial baroreflex. Am J Physiol Regul Integr Comp Physiol 2018; 316:R121-R129. [PMID: 30462526 DOI: 10.1152/ajpregu.00226.2018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Blood pressure control is vital for maintaining adequate perfusion of the brain and other organs in the body across varying physiological demands, and the arterial baroreceptor reflex (baroreflex) is the major short-term blood pressure control loop mediated by the autonomic nervous system (ANS). Accurate quantitative models of the baroreflex would provide physiological insight and could allow for real-time tracking of ANS activity in clinical settings. In this work, we formulate a causal, parametric beat-to-beat model, relating systolic blood pressure (input) to heart rate (output). Model structure and parameterization are explicitly based on prior physiological insights of the response dynamics of the sympathetic and parasympathetic branches of the ANS. We analyze the model's ability to track changes in autonomic balance using data from 14 nonsmoking adult males, without any history of cardiopulmonary disease, subject to both pharmacological blockade and postural changes. Our results show that the model parameters faithfully track expected changes in autonomic balance resulting from changing posture ( P < 0.01) and sympathetic blockade ( P < 0.05), and in many cases, the model parameters are more sensitive to changes in autonomic activity and balance than autonomic indices derived from the power spectral density of heart rate variability. Overall, the contributions of this work further the goal of obtaining real-time quantitative assessment of the ANS.
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Affiliation(s)
- Jonathan Birjiniuk
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Thomas Heldt
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology , Cambridge, Massachusetts.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology , Cambridge, Massachusetts
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