Pre- and postoperative heart rate variability and vagus nerve stimulation in patients with drug-resistant epilepsy – A meta-analysis

Abstract Background Vagus nerve stimulation (VNS) exerts a cortical modulating effect through its diffuse projections, especially involving cerebral structures related to autonomic regulation. The influence of VNS on cardiovascular autonomic function in drug-resistant epilepsy patients is still debated. We aimed to evaluate the impact of VNS on cardiovascular autonomic function in drug-resistant epilepsy patients, after three months of neurostimulation, using the heart rate variability (HRV) analysis. Methodology Multiple Trigonometric Regressive Spectral analysis enables a precise assessment of the autonomic control on the heart rate. We evaluated time and frequency-domain HRV parameters in resting condition and during sympathetic and parasympathetic activation tests in five epilepsy patients who underwent VNS procedure. Results We found appropriate cardiac autonomic responses to sympathetic and parasympathetic activation tests, described by RMSSD, pNN50, HF and LF/HF dynamics after three months of VNS. ON period of the neurostimulation may generate a transient vagal activation reflected on heart rate and RMSSD values, as observed in one of our cases. Conclusion VNS therapy in epilepsy patients seems not to disrupt the cardiac autonomic function. HRV represents a useful tool in evaluating autonomic activity. More extensive studies are needed to further explore cardiac autonomic response after neurostimulation.

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Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

Frontiers in Neurology ◽

10.3389/fneur.2021.691328 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xi Fang ◽

Hong-Yun Liu ◽

Zhi-Yan Wang ◽

Zhao Yang ◽

Tung-Yang Cheng ◽

...

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Nerve Stimulation ◽

Vagus Nerve Stimulation ◽

Outcome Prediction ◽

Healthy Controls ◽

Drug Resistant ◽

Sleep State ◽

Drug Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

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Does non-invasive vagus nerve stimulation affect heart rate variability? A living and interactive Bayesian meta-analysis

10.1101/2021.01.18.426704 ◽

2021 ◽

Cited By ~ 1

Author(s):

Vinzent Wolf ◽

Anne Kühnel ◽

Vanessa Teckentrup ◽

Julian Koenig ◽

Nils B. Kroemer

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Vagus Nerve ◽

Brain Stimulation ◽

Nerve Stimulation ◽

Vagus Nerve Stimulation ◽

Meta Analysis ◽

Current Evidence ◽

Beneficial Effects ◽

Non Invasive

AbstractNon-invasive brain stimulation techniques, such as transcutaneous auricular vagus nerve stimulation (taVNS), have considerable potential for clinical use. Beneficial effects of taVNS have been demonstrated on symptoms in patients with mental or neurological disorders as well as transdiagnostic dimensions, including mood and motivation. However, since taVNS research is still an emerging field, the underlying neurophysiological processes are not yet fully understood, and the replicability of findings on biomarkers of taVNS effects has been questioned. Here, we perform a living Bayesian random effects meta-analysis to synthesize the current evidence concerning the effects of taVNS on heart rate variability (HRV), a candidate biomarker that has, so far, received most attention in the field. To keep the synthesis of evidence transparent and up to date as new studies are being published, we developed a Shiny web app that regularly incorporates new results and enables users to modify study selection criteria to evaluate the robustness of the inference across potential confounds. Our analysis focuses on 17 single-blind studies comparing taVNS versus sham in healthy participants. These newly synthesized results provide strong evidence for the null hypothesis (g = 0.011, CIshortest = [−0.103, 0.125], BF01 = 25.587), indicating that acute taVNS does not alter HRV compared to sham. To conclude, based on a synthesis of the available evidence to date, there is no support for the hypothesis that HRV is a robust biomarker for acute taVNS. By increasing transparency and timeliness, we believe that the concept of living meta-analyses can lead to transformational benefits in emerging fields such as non-invasive brain stimulation.

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