Deep Brain Stimulation for Refractory Epilepsy: A Meta-Analysis of Stimulation Parameters

doi:10.21203/rs.3.rs-5277762/v1

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Systematic Review

Deep Brain Stimulation for Refractory Epilepsy: A Meta-Analysis of Stimulation Parameters

https://doi.org/10.21203/rs.3.rs-5277762/v1

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Epilepsy affects 50 million people worldwide, with 30% not responding to antiepileptic drugs. Surgical resective intervention may help some patients, and neuromodulation therapies like deep brain stimulation are becoming important treatments for poorly controlled epilepsy. This study conducted a meta analysis following the PRISMA guidelines, searching PubMed, Scopus, and Web of Science databases for studies on deep brain stimulation in drug-resistant epilepsy patients. After screening 568 papers based on their titles and abstracts, we excluded systematic reviews, meta-analyses, animal studies, and other criteria. Two reviewers independently screened titles and abstracts for eligibility, and disagreements were resolved by a third reviewer. Full-text screening and data extraction were then performed for further assessment. Total of 52 studies were analyzed for a meta-analysis. These studies involved 748 patients and looked at various interventions, such as anterior nucleus thalamus, centromedian thalamic, and hypothalamus, with different stimulation parameters, including frequency and type. The results showed moderate variability and indicated that voltage, frequency, and stimulation type significantly influenced the success rates of the interventions.

Surgery

Deep Brain Stimulation

Epilepsia

Anterior Thalamic Nuclei

Epilepsy is a chronic neurological disease that affects approximately 50 million people worldwide. Around 30% of those individuals do not respond to antiepileptic drugs (AEDs) (1, 2). The International League Against Epilepsy (ILAE) has defined this condition as drug-resistant epilepsy, which occurs when seizure control is not achieved or maintained despite adequate trials of properly chosen, timely, and well-tolerated AEDs, whether prescribed alone or in combination (3).

Mortality and morbidity are significant complications of uncontrolled seizures. After surgical resective intervention, a substantial group of medically refractory epilepsy patients (10-50%) may be candidates for that. The seizure-free rate for drug-resistant epilepsy (DRE) is estimated to be around 59% (4, 5). However, surgical removal or resection of the epileptogenic area is not always a preferable choice. Up to 50% of patients with DRE are not suitable candidates for resective surgery, so there is a critical need for alternative treatment options for the millions of patients who are not candidates for surgical resection (6, 7).

In the last ten years, Neuromodulation therapies such as responsive neurostimulation (RNS), deep brain stimulation (DBS), and vagal nerve stimulation (VNS) have become important treatments for individuals with poorly controlled epilepsy (7). DBS is a neuro-interventional modulation therapy that involves the use of electrodes and a device similar to a pacemaker to implant and deliver electrical pulses to specific brain regions. Although the exact mechanism of action is not fully understood, it is believed that DBS works by selectively modulating specific brain functional circuits (8, 9).

Previous research has extensively examined DBS from various perspectives, which has prompted us to delve deeper into the study of seizure outcomes for DBS in drug-resistant epilepsy. We have identified a need for a comprehensive review and meta-analysis that evaluates and compares the effects of different types of DBS target stimulation on patients with DRE. To address this gap in the literature, we conducted a systematic review and meta-analysis encompassing all aspects of DBS stimulation. Our objective is to fill these gaps and analyze the available evidence on efficacy in order to provide guidance for treatment selection for neurologists and neurosurgeons.

2.1. Search strategy

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and recommendations. We conducted a thorough search of PubMed, Scopus, and Web of Science databases from their inception to 2024. We used the following search terms in combination: "Deep brain stimulation", "DBS", "Drug Resistant Epilepsy", "STATUS EPILEPTICUS", and "REFRACTORY EPILEPSY".

2.2. Study selection

We included original retrospective and prospective studies that assessed the primary and secondary effects and outcomes of DBS in patients with DRE. We reviewed the reference lists of retrieved review articles to identify additional relevant studies. At this point, we did not restrict the search by language or publication status. Overall, 814 articles were found, and after removing duplicate papers, 568 papers were left. A total of 568 papers were screened based on their titles and abstracts. Our exclusion criteria included systematic reviews, meta-analyses, animal studies, letters, technical notes, comments, patients under 18 years old, non-English studies, those that did not provide full-text access, and studies with overlapping populations that did not fit our primary and secondary criteria.

2.3. Data extraction

Two reviewers independently screened titles and abstracts for eligibility and Disagreements were resolved by a third reviewer. Full-text screening and data extraction were performed for further assessment, all data were extracted from article texts, tables, and figures and We evaluated each study for primary and secondary outcome, seizure frequency. (The following outcomes were assessed:) to assess the outcomes of DBS, we considered the following data: target, seizure reduction, seizure freedom, of follow-up duration, stimulation parameters, seizure freedom, responder rate (patients with 50% or greater reduction in seizure frequency compared to baseline), duration of epilepsy, seizure frequency at baseline, localization, treatment, seizure type mortality).

2.4. Analysis

In this meta-analysis, we focused on evaluating the success rate as the primary outcome across studies. To perform the statistical analysis, we employed the R programming language, leveraging its robust packages specifically designed for meta-analysis. We initiated the analysis by importing the required packages, including meta and metafor, which are well-suited for handling meta-analytic data. The dataset comprised individual study results reporting the success rates of DBS in reducing more than the 50 percent in seizure frequency. We conducted the meta-analysis using the metaprop function, which is appropriate for pooling proportions or rates. Each study's success rate was transformed and then back-transformed after pooling. The heterogeneity among studies was assessed using the I² statistic, which quantifies the proportion of the total variation due to heterogeneity rather than chance. The results were visualized using a forest plot.

A total of 814 articles were identified. After removing duplicates, abstracts, and titles, 568 papers were screened. Among these, 399 articles were excluded. After screening 169 articles, 55 records were excluded for not meeting the inclusion criteria, including 29 for not meeting the criteria, 4 for being non-English, 12 for being other types of studies, and 5 for involving patients under 18. As a result, 114 articles were included in the review. In the next step, studies with insufficient data for meta-analysis were excluded, leaving 52 studies included in the analysis.

3.1. Stimulation parameters:

Out of the 51 studies included, 748 patients were examined. 33 studies were conducted on ANT (10–40), 6 on CM (16, 25, 31, 41–44), and 10 on HP (37, 45–55). 30 studies utilized DBS at a frequency below 130Hz, 39 studies used pulses at under 90Hz, and 34 studies used intermittent stimulation. Study details summarized in Table 1.

Author	Sample size (female)	Age	Target	Frequency(Hz):	Pulse Width(µ sec):	Volt/ Current:	Timing	Responder Rate
Krishna et al (2016)	16(8)	Mean: 32.3	15 (bilateral ANT) 1 (left sided DBS)	130	90	2.4-7	Intermittent	11/16
Costa-Gertrudes et al (2021)	13	Mean: 31.5	ANT	140–145	90	5	Intermittent	9/13
Miron et al (2022)	10 (5)	Mean: 32.7	ANT	145	90	5	Intermittent	8/11
Herrman et al (2018)	18(11)	Mean:23.7	ANT	150	90	5	Intermittent	4/18
Piacentino et al (2015)	6(2)	Mean: 38	ANT	140	90	4	Intermittent	2/6
Tassigny et al (2020)	5(1)	Mean: 42.8	ATN	140	90	5	Intermittent	2/5
Sitnikov et al (2018)	12(5)	Mean: 28.9	ANT	130	90	4	Intermittent or Continues.	10/12
Alcala-Zermenoa et al (2021)	8 (5)	Mean: 27.75	CM/ CM with ANT	ANT: 5 − 100 CM: 2 − 100	ANT: 90 − 120 CM: 60 − 150	ANT: 2.1–6.3 CM: 1.0–6.3	Intermittent	10/16
Hodaie et al (2002)	5(3)	Mean: 30.4	ANT	100	90	10	Intermittent	3/5
Boex et al (2011)	8 (5)	Median: 31.5	Unilateral /bilat HCP/AMG	130	450	0.5-2	Continues	6/8
Sobstyl et al (2023)	10 (4)	Mean: 38.5	Bilat ANT	130–145	60–90	2	Intermittent	9/10
Freund et al (2023)	7 (4)	Mean: 38.9	ANT	145	90	3.5/4/6	Intermittent	4/7
Son et al (2016)	13 (8)	Mean: 28.2	Unilateral /bilat CMT	129.3 ± 2.7 Hz	124.4 ± 23.0	2.2 ± 0.41	Continues	10/13
Oh et al (2012)	8 (3)	Mean: 35.8	Bilat ANT	100–185	90–150	1.5–3.1	Continues	7/9
Olaciregui Dague et al (2023)	11(7)	Mean: 35.5	ANT	145	90	5	Intermittent	5/11
Vonck et al (2012)	11(3)		Unilateral /bilat HCP/AMG	130	450	1-3.1	Intermittent or continuously	8/11
Schaper et al (2021)	20(3)	Mean: 40.1	ANT	145	90	5	Intermittent	10/20
Boon et al (2007)	10(NR)		Hippocampus	130–200	450	2–3	Continues	7/10
Cheung et al (2023)	5(3)	Mean: 38	Bilat ANT	145	90	5	Intermittent	5/6
Valentin et al (2013)	11(2)	Mean: 37	Bilat CMT	60–130	90	5	Continues	7/11
Guo et al (2020)	19(9)	Mean: 34.8	Bilat ATN	130	90	1.5–3.1	Continues	15/19
Fisher et al (2010)	110(55)	Mean:36.1	Bilat ANT	145	90	5	Intermittent	43/81
Koeppen et al (2019)	10(7)	Mean: 34	Dorsal ANT	145	90	5	Intermittent	7/10
Järvenpää et al (2018)	16(5)	Men: 37.9	ANT	140	90	5	Intermittent	12/16
Xu et al (2023)	18(5)	Mean: 25.9	ANT	130/145	90	2	Intermittent	11/18
Möttönen et al (2023)	16(4)	Mean:36.06	ANT	140	90	5	Intermittent	10/16
Järvenpää et al (2020)	27(9)	Mean: 38	ANT	140	90	5	Intermittent	19/27
Alcala-Zermeno et al (2022)	ANT-DBS = 38,(19) CM-DBS = 19(12)	ANT-DBS = 30 (15–71) CM-DBS = 19 (6–51)	ANT-DBS = 38, CM-DBS = 19	ANT: 40 (Range 7– 145) CM: 60 (Range 2– 100)	ANT: 120 (Range 20–250) CM: 90 (Range 60–210)	ANT: 61 (23) 3.8 (Range 0–7.5) CM: 84 (16) 4 (Range 1–6)	Intermittent	ANT-DBS 21/55 CM-DBS 11/58
Cui et al (2023)	3(1)	Mean: 20.3	subthalamic nucleus STN- DBS	122.5 ± 15.7	58.3 ± 9.4	2.5 ± .7		2/3
Kim et al (2017)	29(10)	Mean: 30.7	BILAT ATN	130	90	1.5–3.1	Continues	22/29
Lee et al (2012)	15(6)	Mean: 34	BILAT ANT	100–185	90–150	1.5–3.1	Continues	13/15
Torres Diaz et al (2021)	10(4)	Mean: 30.8	CM (and PF)	60	90	up to 5		8/10
Saucedo-Alvarado et al (2022)	6(5)	Mean: 29.3	Para hippocampal cortex	130	450	2.5-3	Intermittent	3/6
Lehtimäki et al (2016)	15(5)	Mean: 34.8	Bilat ANT	140	90	5	Intermittent	10/15
Park et al (2019)	7(5)	Mean: 20.8	ANT	133	184	1.9(LEFT), 2.1(RIGHT)	Continues	5/7
Benedetti-Isaac et al (2015)	4(1)	Mean: 24	HP	185	90	2.4-3	Intermittent	4/4
Cukiert et al (2017)	16(11)	Mean: 38.4	Lt hippo-campus (Unilateral /bilat HCP)	130	300	2	Continues	7/8
Fisher et al (1992)	6(4)	Mean: 30	CMN	65	90	0.5–10	Intermittent	3/6
Velasco et al (2007)	8(3)	Mean: 31	HP	130	450	0.3mA	Intermittent	8/8
Herrera et al (2020)	6(1)	Mean: 30.5	ANT	145	90	up to 8.5	Intermittent	2/6
Kaufmann et al (2021)	23(10)	Mean: 35.4	ANT [unilateral DBS 2 pts]	140–145	90	1.5–3.5	Intermittent	17/23
Kerrigan et al (2004)	5(2)	Mean: 36.2	ATN	100	90	1–10	Intermittent	3/5
Salanova et al (2021)	50(35)	Mean: 37.1	Bilat ANT	161	103	6.6	Intermittent	37/50
Voges et al (2015)	9(?)	Mean: 37	ANT	145	90	5	Intermittent	4/9
Miatton et al (2011)	10(3)	Mean: 31	Unilateral / Bilat HCP/AMG	130	450	1-2.5	Continues	7/10
Bondallaz et al (2013)	8(5)	Mean: 31.5	HCP	130	450	0.5-2	Continues	6/8
Cukiert et al (2014)	9(2)	Mean: 37.2	Unilateral /bilat HCP	130	300	1-3.5	Continues	7/9
Wille et al (2011)	5(1)	Median: 32	STN ± Bilat VIM	100–160	60–120	1–4	Continues	4/5
Schmitt et al (2014)	5(2)	Mean: 41.6	Bilat NAC + bilat ANT	125	90	5	Intermittent	2/5
Yang et al (2022)	14(8)	Median: 24	CMN	60–130	90–300	1.0–5.5	Continues or Intermittent	12/14
Tong et al (2022)	6(1)	Mean: 31.3	ANT	142.50	66.67	2.45	Intermittent	4/6

3.2. Meta-Analysis

In the first analysis all studies included. The results showed that the combined success rate for single-arm interventions, measured as proportions, was 0.62 using the common effect model and 0.68 using the random effects model. The studies showed moderate heterogeneity, with an I² value of 45%. These results indicate a high success rate for the interventions, with the random effects model suggesting a slightly higher estimate (Fig. 1).

In the second analysis, we compared three subgroups. The common effect model for the ANT group showed a success rate of 0.62, while the random effects model showed a success rate of 0.64. The HIP group had a consistent success rate of 0.74 in both the common and random effects models. Similarly, the CM group exhibited a success rate of 0.73 across both models. Overall, the common effect model indicated a pooled success rate of 0.64, and the random effects model showed a slightly higher rate of 0.66. The test for subgroup differences was not significant, indicating no substantial variation in success rates among the subgroups (Fig. 2).

In our analysis, we compared different stimulation parameters. For voltage, the group that received a voltage of 5 or more had a common effect model of 0.60 and a random effect model of 0.61, while the group with a voltage of less than 5 showed a common effect model of 0.20 and a random effect model of 0.72. The test for subgroup differences was significant. In another analysis, we compared the frequency of stimulation between studies. The group with a frequency of 130Hz or less had both common and random effect models of 0.74, while the group with a frequency of more than 130Hz had a common effect model of 0.60 and a random effect model of 0.61. The test for subgroup differences was significant. Finally, we compared intermittent and continuous stimulation. The intermittent stimulation group had a common effect model of 0.59 and a random effect model of 0.60, while the continuous stimulation group had a common and random effect model of 0.78. The test for subgroup differences was significant (Fig. 3).

A meta-analysis was conducted to explore the effectiveness of DBS in patients with refractory epilepsy. The analysis included studies that examined the use of DBS for treating these patients. In this analysis, a successful outcome was defined as a reduction of 50% or more in the frequency of seizures.

In the initial analyses of all the studies, we observed moderate heterogeneity, indicating significant differences among the included studies. These variations could be attributed to differences in study design, study populations, and intervention protocols. The studies mainly focused on three regions: the anterior nucleus thalamic, centromedial, and hypothalamus. There was a high degree of variability in the stimulation parameters utilized across the studies, highlighting the need for further investigation into the optimal parameters for stimulation. The analyses on stimulation parameters indicate that both the voltage and frequency of stimulation, as well as the type of stimulation (intermittent vs. continuous), significantly impact the success rates of the interventions. This suggests that carefully selecting the voltage, frequency, and type of stimulation can play a crucial role in the effectiveness of interventions.

The results emphasize the need to customize stimulation parameters to improve the effectiveness of interventions. A deeper understanding of predictors of seizure outcomes can assist neurosurgeons in identifying which surgery candidates are more likely to experience improvement, enabling them to provide appropriate guidance to patients and their families (56).

Despite the higher number of studies on ANT, our analysis shows that HP and CM result in a reduction in more patients. The hippocampus is an essential part of the Papez circuit, which begins with the hippocampus sending signals through the fornix to the mammillary bodies. These bodies then send signals through the mammillothalamic tract to ATN, which in turn project to the anterior cingulate cortex. Finally, the anterior cingulate cortex sends signals back to the hippocampus, completing the circuit (57, 58).

The potential antiepileptic effects of DBS may stem from the long-term modulation of important neural circuits (59). Modulating DBS could lead to significant changes within the neural networks, disrupting seizure propagation or modifying the seizure threshold (60).

In summary, this meta-analysis highlights the effectiveness of DBS in treating patients with refractory epilepsy. It shows significant success rates with a 50% or greater reduction in seizure frequency. The observed moderate heterogeneity suggests variations across studies due to differences in design, populations, and intervention protocols. Notably, the effectiveness of DBS is significantly influenced by stimulation parameters such as voltage, frequency, and type. These findings emphasize the need to customize these parameters to optimize patient outcomes and underscore the potential of DBS to modulate neural circuits, thereby improving seizure control. Understanding predictors of seizure outcomes can help neurosurgeons in selecting suitable candidates for DBS and ultimately improve patient care. While there is a greater focus on the ANT region, analyses reveal that the HP and CM regions are more effective in reducing seizures, indicating a need for further exploration of the mechanisms and optimal stimulation protocols within these regions.

Limitations

The systematic review on the characterization of clinical presentation and etiology of seizures reported several limitations that should be taken into consideration. Firstly, the review only included studies published in English, which might have led to the exclusion of relevant non-English studies, potentially introducing language bias. Additionally, the review highlighted the influence of large relative sample sizes of pivotal trials compared to other included studies, which could have skewed the overall findings and conclusions. Furthermore, the inclusion of observational, nonrandomized, and retrospective studies in the review may have reduced the reliability of the results, as these types of studies are prone to various biases and confounding factors. Another limitation was the clinical heterogeneity of the patients both between and within the groups, as factors such as patient age, baseline seizure frequency, duration of epilepsy, antiseizure medication (ASM) usage, and medical history were not consistently controlled for across the included studies. This lack of control for inter-study heterogeneity may have affected the generalizability and reliability of the review's findings. Overall, while the systematic review provides valuable insights, it is important to interpret its results with caution due to these identified limitations.

Ethics approval and consent to participate

This was a systemic review study using publicly accessible documents as evidence.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests

Funding

The research presented in this manuscript received no external form of funding.

Authors' contributions

Conceptualization: PV, SHL, MER. Database querying: PV, YT, MR. Manuscript drafting: PV, SHL. Tables and figures: SHL, PV, FA. supervision: AZ, MER. All authors reviewed the manuscript.

Availability of data and materials

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

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The authors declare no competing interests.

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Deep Brain Stimulation for Refractory Epilepsy: A Meta-Analysis of Stimulation Parameters

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Abstract

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1. Introduction

2. Methods

2.1. Search strategy

2.2. Study selection

2.3. Data extraction

2.4. Analysis

3. Result

3.1. Stimulation parameters:

3.2. Meta-Analysis

4. Discussion

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References

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