Chronic vagal nerve stimulation has no effect on tachycardia‐induced heart failure progression or excitation–contraction coupling

Radcliffe, Emma J.; Pearman, Charles M.; Watkins, Amy; Lawless, Michael; Kirkwood, Graham; Saxton, Sophie; Eisner, David; Trafford, Andrew W.

doi:10.14814/phy2.14321

Cited by 4 publications

(10 citation statements)

References 39 publications

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“…VNS was delivered during closed-loop or open-loop stimulation regimes (see Modes of VNS Delivery for more details). Importantly, these VNS parameters are similar to previous preclinical studies using VNS for cardiovascular control (Sachdeva et al, 2020; Plachta et al, 2013 & 2014; Yamakawa et al, 2014; Shinlapawittayatorn et al, 2013), and fall within clinically relevant stimulation ranges used in previous human trials using VNS for cardiovascular therapy (Anand et al, 2020; Table 1: Radcliffe et al, 2020).…”

Section: Methodssupporting

confidence: 78%

“…5C, Isch. + OL-VNS, magenta; open-loop VNS = 20% duty cycle, with a balanced amount of VNS compared to the closed-loop ANN-VNS paradigm, and parameters similar to previous human open-loop VNS studies: Anand et al, 2020; Table 1: Radcliffe et al, 2020; full 13-element feature vector shown in Supplemental Fig. S6B; experimental schematic: Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, open-loop VNS may simply miss random myocardial ischemia events. We used an open-loop VNS paradigm representative of human cardiovascular studies, near the upper limit of clinically tolerable VNS levels (20% duty cycle at 2-2.5 mA; Anand et al, 2020; Table 1: Radcliffe et al, 2020). From a translational perspective, the closed-loop VNS paradigm used here should deliver significantly less VNS compared to open-loop VNS over time.…”

Section: Discussionmentioning

confidence: 99%

“…S6B). We used a 20% VNS ON / 80% VNS OFF duty cycle for the open-loop VNS condition, to model preprogrammed open-loop VNS duty cycles used in clinical trials for cardiovascular treatments (Anand et al, 2020; Table 1: Radcliffe et al, 2020). Open-loop VNS recordings lasted a total of ∼500 s, with 2-3 recording replicates within an animal (across N=5 animals).…”

Section: Algorithm Performance Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

Detecting and Reversing Myocardial Ischemia Using an Artificially Intelligent Bioelectronic Medicine

Ganzer

Loeian

Roof

et al. 2021

Preprint

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Myocardial ischemia is spontaneous, usually asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, biological neural networks cannot reliably detect and correct myocardial ischemia on their own. Supplementing biological neural networks may enable reliable detection, and potentially even facilitate correction, of myocardial ischemia. In this study, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements biological neural networks enabling reliable detection and correction of myocardial ischemia (preclinical experiments in rats). This responsive bioelectronic medicine uses an ANN with a long short-term memory layer to decode spontaneous myocardial ischemia and autonomously trigger vagus nerve stimulation (VNS) for reducing chronotropy, afterload, and myocardial oxygen demand. We first used injections of cardiovascular stress inducing agents (dobutamine and norepinephrine) that produce a model of spontaneous myocardial ischemia. Next, ANNs were trained to decode spontaneous cardiovascular stress and myocardial ischemia, with an overall accuracy of ~92%. ANN-controlled VNS reversed the major biomarkers of myocardial ischemia with no side effects. In contrast, open-loop VNS or ANN controlled VNS following a caudal vagotomy essentially failed to reverse correlates of myocardial ischemia. Lastly, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations of stress pathophysiology and unsupervised detection of new emerging stress states. Together, this adaptive architecture provides clinically relevant insights as pathophysiology evolves. Overall, these results provide a first-time demonstration that ANNs can supplement deficient biological neural networks to dynamically detect and help bioelectronically reverse cardiovascular pathophysiology.

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Section: Methodssupporting

confidence: 78%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Algorithm Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Detecting and Reversing Myocardial Ischemia Using an Artificially Intelligent Bioelectronic Medicine

Ganzer

Loeian

Roof

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…5C , Isch. + OL-VNS, magenta; open-loop VNS = 20% duty cycle, with a balanced amount of VNS compared to the closed-loop ANN-VNS paradigm, and parameters similar to previous human open-loop VNS studies; Table 1 ( 52 , 53 ); full 13-element feature vector shown in fig. S6B; experimental schematic in Fig.…”

Section: Resultsmentioning

confidence: 99%

Dynamic detection and reversal of myocardial ischemia using an artificially intelligent bioelectronic medicine

et al. 2022

View full text Add to dashboard Cite

Myocardial ischemia is spontaneous, frequently asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, myocardial sensory networks cannot reliably detect and correct myocardial ischemia on their own. Here, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements myocardial sensory networks, enabling reliable detection and correction of myocardial ischemia. ANNs were first trained to decode spontaneous cardiovascular stress and myocardial ischemia with an overall accuracy of ~92%. ANN-controlled vagus nerve stimulation (VNS) significantly mitigated major physiological features of myocardial ischemia, including ST depression and arrhythmias. In contrast, open-loop VNS or ANN-controlled VNS following a caudal vagotomy essentially failed to reverse cardiovascular pathophysiology. Last, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations and unsupervised detection of emerging cardiovascular stress. Overall, these preclinical results suggest that ANNs can potentially supplement deficient myocardial sensory networks via an artificially intelligent bioelectronic medicine system.

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Vagal Nerve Stimulation Reduces Ventricular Arrhythmias and Mitigates Adverse Neural Cardiac Remodeling Post–Myocardial Infarction

Hadaya

Dajani²,

Cha³

et al. 2023

JACC: Basic to Translational Science

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Chronic vagal nerve stimulation has no effect on tachycardia‐induced heart failure progression or excitation–contraction coupling

Cited by 4 publications

References 39 publications

Detecting and Reversing Myocardial Ischemia Using an Artificially Intelligent Bioelectronic Medicine

Detecting and Reversing Myocardial Ischemia Using an Artificially Intelligent Bioelectronic Medicine

Dynamic detection and reversal of myocardial ischemia using an artificially intelligent bioelectronic medicine

Vagal Nerve Stimulation Reduces Ventricular Arrhythmias and Mitigates Adverse Neural Cardiac Remodeling Post–Myocardial Infarction

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