Sensitivity Analysis of Vagus Nerve Stimulation Parameters on Acute Cardiac Autonomic Responses: Chronotropic, Inotropic and Dromotropic Effects
Although the therapeutic effects of Vagus Nerve Stimulation (VNS) have been recognized in pre-clinical and pilot clinical studies, the effect of different stimulation configurations on the cardiovascular response is still an open question, especially in the case of VNS delivered synchronously with c...
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Published in | PloS one Vol. 11; no. 9; p. e0163734 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
30.09.2016
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
ISSN | 1932-6203 1932-6203 |
DOI | 10.1371/journal.pone.0163734 |
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Abstract | Although the therapeutic effects of Vagus Nerve Stimulation (VNS) have been recognized in pre-clinical and pilot clinical studies, the effect of different stimulation configurations on the cardiovascular response is still an open question, especially in the case of VNS delivered synchronously with cardiac activity. In this paper, we propose a formal mathematical methodology to analyze the acute cardiac response to different VNS configurations, jointly considering the chronotropic, dromotropic and inotropic cardiac effects. A latin hypercube sampling method was chosen to design a uniform experimental plan, composed of 75 different VNS configurations, with different values for the main parameters (current amplitude, number of delivered pulses, pulse width, interpulse period and the delay between the detected cardiac event and VNS onset). These VNS configurations were applied to 6 healthy, anesthetized sheep, while acquiring the associated cardiovascular response. Unobserved VNS configurations were estimated using a Gaussian process regression (GPR) model. In order to quantitatively analyze the effect of each parameter and their combinations on the cardiac response, the Sobol sensitivity method was applied to the obtained GPR model and inter-individual sensitivity markers were estimated using a bootstrap approach. Results highlight the dominant effect of pulse current, pulse width and number of pulses, which explain respectively 49.4%, 19.7% and 6.0% of the mean global cardiovascular variability provoked by VNS. More interestingly, results also quantify the effect of the interactions between VNS parameters. In particular, the interactions between current and pulse width provoke higher cardiac effects than the changes on the number of pulses alone (between 6 and 25% of the variability). Although the sensitivity of individual VNS parameters seems similar for chronotropic, dromotropic and inotropic responses, the interacting effects of VNS parameters provoke significantly different cardiac responses, showing the feasibility of a parameter-based functional selectivity. These results are of primary importance for the optimal, subject-specific definition of VNS parameters for a given therapy and may lead to new closed-loop methods allowing for the optimal adaptation of VNS therapy through time. |
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AbstractList | Although the therapeutic effects of Vagus Nerve Stimulation (VNS) have been recognized in pre-clinical and pilot clinical studies, the effect of different stimulation configurations on the cardiovascular response is still an open question, especially in the case of VNS delivered synchronously with cardiac activity. In this paper, we propose a formal mathematical methodology to analyze the acute cardiac response to different VNS configurations, jointly considering the chronotropic, dromotropic and inotropic cardiac effects. A latin hypercube sampling method was chosen to design a uniform experimental plan, composed of 75 different VNS configurations, with different values for the main parameters (current amplitude, number of delivered pulses, pulse width, interpulse period and the delay between the detected cardiac event and VNS onset). These VNS configurations were applied to 6 healthy, anesthetized sheep, while acquiring the associated cardiovascular response. Unobserved VNS configurations were estimated using a Gaussian process regression (GPR) model. In order to quantitatively analyze the effect of each parameter and their combinations on the cardiac response, the Sobol sensitivity method was applied to the obtained GPR model and inter-individual sensitivity markers were estimated using a bootstrap approach. Results highlight the dominant effect of pulse current, pulse width and number of pulses, which explain respectively 49.4%, 19.7% and 6.0% of the mean global cardiovascular variability provoked by VNS. More interestingly, results also quantify the effect of the interactions between VNS parameters. In particular, the interactions between current and pulse width provoke higher cardiac effects than the changes on the number of pulses alone (between 6 and 25% of the variability). Although the sensitivity of individual VNS parameters seems similar for chronotropic, dromotropic and inotropic responses, the interacting effects of VNS parameters provoke significantly different cardiac responses, showing the feasibility of a parameter-based functional selectivity. These results are of primary importance for the optimal, subject-specific definition of VNS parameters for a given therapy and may lead to new closed-loop methods allowing for the optimal adaptation of VNS therapy through time. Although the therapeutic effects of Vagus Nerve Stimulation (VNS) have been recognized in pre-clinical and pilot clinical studies, the effect of different stimulation configurations on the cardiovascular response is still an open question, especially in the case of VNS delivered synchronously with cardiac activity. In this paper, we propose a formal mathematical methodology to analyze the acute cardiac response to different VNS configurations, jointly considering the chronotropic, dromotropic and inotropic cardiac effects. A latin hypercube sampling method was chosen to design a uniform experimental plan, composed of 75 different VNS configurations, with different values for the main parameters (current amplitude, number of delivered pulses, pulse width, interpulse period and the delay between the detected cardiac event and VNS onset). These VNS configurations were applied to 6 healthy, anesthetized sheep, while acquiring the associated cardiovascular response. Unobserved VNS configurations were estimated using a Gaussian process regression (GPR) model. In order to quantitatively analyze the effect of each parameter and their combinations on the cardiac response, the Sobol sensitivity method was applied to the obtained GPR model and inter-individual sensitivity markers were estimated using a bootstrap approach. Results highlight the dominant effect of pulse current, pulse width and number of pulses, which explain respectively 49.4%, 19.7% and 6.0% of the mean global cardiovascular variability provoked by VNS. More interestingly, results also quantify the effect of the interactions between VNS parameters. In particular, the interactions between current and pulse width provoke higher cardiac effects than the changes on the number of pulses alone (between 6 and 25% of the variability). Although the sensitivity of individual VNS parameters seems similar for chronotropic, dromotropic and inotropic responses, the interacting effects of VNS parameters provoke significantly different cardiac responses, showing the feasibility of a parameter-based functional selectivity. These results are of primary importance for the optimal, subject-specific definition of VNS parameters for a given therapy and may lead to new closed-loop methods allowing for the optimal adaptation of VNS therapy through time.Although the therapeutic effects of Vagus Nerve Stimulation (VNS) have been recognized in pre-clinical and pilot clinical studies, the effect of different stimulation configurations on the cardiovascular response is still an open question, especially in the case of VNS delivered synchronously with cardiac activity. In this paper, we propose a formal mathematical methodology to analyze the acute cardiac response to different VNS configurations, jointly considering the chronotropic, dromotropic and inotropic cardiac effects. A latin hypercube sampling method was chosen to design a uniform experimental plan, composed of 75 different VNS configurations, with different values for the main parameters (current amplitude, number of delivered pulses, pulse width, interpulse period and the delay between the detected cardiac event and VNS onset). These VNS configurations were applied to 6 healthy, anesthetized sheep, while acquiring the associated cardiovascular response. Unobserved VNS configurations were estimated using a Gaussian process regression (GPR) model. In order to quantitatively analyze the effect of each parameter and their combinations on the cardiac response, the Sobol sensitivity method was applied to the obtained GPR model and inter-individual sensitivity markers were estimated using a bootstrap approach. Results highlight the dominant effect of pulse current, pulse width and number of pulses, which explain respectively 49.4%, 19.7% and 6.0% of the mean global cardiovascular variability provoked by VNS. More interestingly, results also quantify the effect of the interactions between VNS parameters. In particular, the interactions between current and pulse width provoke higher cardiac effects than the changes on the number of pulses alone (between 6 and 25% of the variability). Although the sensitivity of individual VNS parameters seems similar for chronotropic, dromotropic and inotropic responses, the interacting effects of VNS parameters provoke significantly different cardiac responses, showing the feasibility of a parameter-based functional selectivity. These results are of primary importance for the optimal, subject-specific definition of VNS parameters for a given therapy and may lead to new closed-loop methods allowing for the optimal adaptation of VNS therapy through time. |
Audience | Academic |
Author | Carrault, Guy Le Rolle, Virginie Henry, Christine Mabo, Philippe Bonnet, Jean-Luc Gallet, Clément Romero-Ugalde, Hector M. Ojeda, David Hernández, Alfredo I. Bel, Alain |
AuthorAffiliation | 4 INSERM, UMR970 Paris Cardio-vascular Research Center, Paris, France University of California Los Angeles, UNITED STATES 2 Université de Rennes 1, LTSI, Rennes, France 3 Sorin CRM SAS (a LivaNova company), Clamart, France 5 Assistance Publique-Hôpitaux de Paris, Department of Cardiology, Hôpital Européen Georges Pompidou, Paris, France 6 Paris Descartes University, PRES Paris Sorbonne, Paris, France 1 INSERM, U1099, Rennes, France 7 CHU Rennes, Department of Cardiology, Rennes, France 8 INSERM, CIC-IT 1414, Rennes, France |
AuthorAffiliation_xml | – name: 2 Université de Rennes 1, LTSI, Rennes, France – name: 7 CHU Rennes, Department of Cardiology, Rennes, France – name: University of California Los Angeles, UNITED STATES – name: 6 Paris Descartes University, PRES Paris Sorbonne, Paris, France – name: 4 INSERM, UMR970 Paris Cardio-vascular Research Center, Paris, France – name: 5 Assistance Publique-Hôpitaux de Paris, Department of Cardiology, Hôpital Européen Georges Pompidou, Paris, France – name: 3 Sorin CRM SAS (a LivaNova company), Clamart, France – name: 8 INSERM, CIC-IT 1414, Rennes, France – name: 1 INSERM, U1099, Rennes, France |
Author_xml | – sequence: 1 givenname: David surname: Ojeda fullname: Ojeda, David – sequence: 2 givenname: Virginie surname: Le Rolle fullname: Le Rolle, Virginie – sequence: 3 givenname: Hector M. surname: Romero-Ugalde fullname: Romero-Ugalde, Hector M. – sequence: 4 givenname: Clément surname: Gallet fullname: Gallet, Clément – sequence: 5 givenname: Jean-Luc surname: Bonnet fullname: Bonnet, Jean-Luc – sequence: 6 givenname: Christine surname: Henry fullname: Henry, Christine – sequence: 7 givenname: Alain surname: Bel fullname: Bel, Alain – sequence: 8 givenname: Philippe surname: Mabo fullname: Mabo, Philippe – sequence: 9 givenname: Guy surname: Carrault fullname: Carrault, Guy – sequence: 10 givenname: Alfredo I. surname: Hernández fullname: Hernández, Alfredo I. |
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Keywords | dogs vagal-stimulation fibers heart-failure |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 PMCID: PMC5045213 Conceptualization: AH VLR DO. Formal analysis: AH VLR DO. Funding acquisition: CH GC AH. Investigation: AH DO HR CG JLB AB. Methodology: DO VLR AH. Project administration: AH CH. Resources: AB CH. Software: DO CG HR VLR AH. Supervision: AH CH GC PM. Writing – original draft: AH VLR DO. Writing – review & editing: AH VLR DO JLB CH. Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: CH and JLB are employees of SORIN CRM SAS. PM is consultant for SORIN CRM SAS. No other conflicts are reported. This does not alter our adherence to PLOS ONE policies on sharing data and materials. |
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