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

Abstract

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.

Clinical perspectives on vagus nerve stimulation: present and future

“…Identifying the precise mechanism of action of VNS in epilepsy may help identify responders vs. non-responders and tailor the treatment to those who will be most likely to benefit. Interestingly, a recent study used a statistical model based on preoperative heart rate variability (HRV) to predict which patients would be suitable for VNS [ 34 ]. Vagal efferents innervate the sinoatrial node and the atrioventricular node to control heart rate [ 35 ].…”

“…HRV is an indirect reflection of the heart’s autonomic function and VNS has been shown to induce acute increases in HRV [ 36 ]. The investigators found that perioperative HRV indices, recorded during sleep, could better predict response to VNS therapy, compared with measurements recorded during the awake state [ 34 ].…”

Early cost–utility analysis of genetically guided therapy for patients with drug‐resistant epilepsy

“…Despite the need for more clinical evidence of the benefits of pharmacogenomic applications in epilepsy medicine, here we present the early case for potential economic efficiency for patients with drug-resistant epilepsy. There is also scope for extending the comparators to include neuromodulation options for drug-resistant epilepsy not amenable to surgical intervention, such as vagus nerve stimulation (VNS), [28][29][30][31] anterior nucleus of the thalamus deep brain stimulation, and responsive neurostimulation. [32][33][34][35] VNS 36 and responsive neurostimulation therapy 37 have been found to be cost-effective.…”

How Is the Norepinephrine System Involved in the Antiepileptic Effects of Vagus Nerve Stimulation?

“…A study used machine learning to build prediction models for evaluating responsiveness to VNS therapy preoperatively based on HRV indices in wake and sleep states ( Fang et al, 2021 ). 52 HRV indices were evaluated in 30 responders and 29 non-responders before the implantation of a VNS device.…”