Spectral properties of bursts in therapeutic burst suppression predict successful treatment of refractory status epilepticus

doi:10.21203/rs.3.rs-3307600/v1

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Spectral properties of bursts in therapeutic burst suppression predict successful treatment of refractory status epilepticus

https://doi.org/10.21203/rs.3.rs-3307600/v1

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Burst suppression (BS) on EEG induced by intravenous anesthesia (IVAT) is standard therapy for refractory status epilepticus (RSE). If BS has any independent therapeutic effect on RSE is disputed. We aimed to define EEG characteristics of BS predicting termination or recurrence of status after weaning. All RSE patients treated with IVAT while undergoing continuous EEG monitoring on the neurological intensive care unit between 2014 and 2019 were screened for inclusion. A one hour-period of visually preselected BS-EEG was analyzed. Bursts were segmented by a special thresholding technique and underwent power spectral analysis. Out of 48 enrolled patients, 25 (52.1%) did not develop seizure recurrence (group Non SE) after weaning from IVAT; in 23 patients (47.9%), SE reestablished (group SE). In group Non SE, bursts contained higher amounts of EEG delta power (91.59% vs 80.53%, p < 0.0001), while faster frequencies were more pronounced in bursts in group SE (theta: 11.38% vs 5.41%, p = 0.0008; alpha: 4.89% vs 1.82%, p < 0.0001; beta: 3.23% vs 1.21%, p = 0.0002). Spectral profiles of individual bursts closely resembled preceding seizure patterns in group SE but not in group Non SE. Accordingly, persistence of spectral composition of initial ictal patterns in bursts, suggests ongoing SE, merely interrupted but not altered by BS. Fast oscillations in bursts indicate a high risk of status recurrence after weaning from IVAT. EEG guided individualized sedation regimes might therefore be superior to standardized anesthesia protocols.

Health sciences/Neurology

Health sciences/Neurology/Neurological disorders

Health sciences/Neurology/Neurological disorders/Encephalopathy

Health sciences/Neurology/Neurological disorders/Epilepsy

Status epilepticus (SE) resistant to first line benzodiazepine and second line anticonvulsant treatment is referred to as refractory. ^{1, 2} Refractory status epilepticus (RSE) bears a high risk of mortality and morbidity and therefore frequently requires treatment with intravenous anesthetic therapy (IVAT).³ IVAT is usually titrated until a state of burst suppression (BS) is established in EEG. ^1,3–5 In BS, periods of high voltage electrical activity alternate with phases of isoelectric quiescence thus providing a time window of seizure control while other therapeutic strategies unfold their effects.⁶ BS is also claimed to act as a “reset switch” disrupting dysfunctional states that foster seizure activity.⁷ As IVAT sufficient to induce burst suppression may represent an independent risk factor of complications and poor functional outcome, optimal depth and duration of sedation combining reasonably low risks of side effects and good chances of status termination are a matter of ongoing debate.^8,9 Recent studies found that duration of coma and length of suppression intervals do not predict termination of RSE.^10,11 It was, therefore, suggested to focus on EEG characteristics of bursts such as the presence of epileptiform discharges rather than to aim for strict time frames and burst suppression ratios to guide sedation.¹²

Besides visual inspection aiming for the identification of epileptic discharges, power spectral analysis can be applied to further characterize bursts and quantify spectral contents.¹³ So far, these approaches are mainly used for automated detection of burst suppression based on differentiation between burst and suppression periods.¹⁴

In this study, we tested the hypothesis that spectral composition of EEG bursts during application of IVAT is associated with treatment response in RSE and can predict successful weaning of IVAT without status recurrence. Moreover, we investigated if spectral properties of bursts are related to those of ictal patterns preceding induction of burst suppression.

Patients

All adult patients treated with IVAT for RSE between 2014 and 2019 on the neurological intensive care unit (nICU) of the University Hospital Erlangen were retrospectively screened for inclusion in the study. To be eligible for enrolment, patients had to fulfill the International League Against Epilepsy (ILAE) operational definition of status epilepticus. ² All forms of SE were eligible, including convulsive and nonconvulsive seizures with and without impaired awareness. Prior to IVAT, patients had to be treated with at least one benzodiazepine and one intravenous antiseizure medication (ASM). Patients with hypoxic brain injury were excluded.

Continuous EEG monitoring had to be maintained from the beginning of IVAT until at least one weaning attempt. On visual inspection, EEG had to be classified as BS for at least one period of at least three hours.

The primary outcome was SE recurrence after tapering of IVAT. Therefore, patients were divided in two groups: SE group: SE recurrence on EEG and / or reappearance of clinical seizures within 48 hours after tapering of IVAT; Non SE group: no seizure recurrence on EEG, no clinical seizures within 48 hours after withdrawal of IVAT. Secondary outcomes included mortality and functional disability assessed by the modified Rankin Scale (mRS) at time of discharge.

Clinical data collected included age, history of epilepsy, treatment of epilepsy prior to RSE, etiology of RSE, ASM used to treat RSE, type and doses of anesthetic drug administered for IVAT, other therapeutic measures, such as steroids, ketogenic diet, plasmapheresis, epilepsy surgery, electroconvulsive therapy and complications.

EEG monitoring

Continuous EEG monitoring was started on the first day of IVAT at the latest. 19 Standard scalp electrodes were fixed with collodion according to the International 10-20-System. EEG-data was digitized at a sampling-rate of 250 Hz with a high-pass-filter of 0.05 Hz and a low-pass-filter of 100 Hz (Nicolet v5.92, Natus Medical Inc.). EEG was visually analyzed by a board certified electroencephalographer, and periods of BS were annotated. Burst suppression was defined as paroxysmal bursts of high amplitude activity of different frequencies intermixed with periods of attenuation or suppression (below 10 µV) lasting more than 50% of the record according to guidelines. ^{15 16} For analysis, the first hour of uninterrupted burst-suppression-EEG after start of IVAT was visually selected. During that period, bursts were visually screened for interictal epileptiform discharges (IED), including spikes, polyspikes and sharp waves as previously defined.¹⁵ The percentage of bursts containing IED was captured.

qEEG analysis

For analysis short-time Fourier transformation (STFT) was applied for two multi-channel montages including electrodes on both hemispheres (Fp2-F8, F8-T4, T4-T6, T6-O2, Fp2-F4, F4-C4, C4-P4, P4-O2 and Fp1-F7, F7-T3, T3-T5, T5-O1, Fp1-F3, F3-C3, C3-P3, P3-O1, respectively). The power values in different frequency bands (delta 0.5-3.5Hz, theta 4-7.5Hz, alpha 8-12.5Hz, beta 13-35Hz and 0.5-35Hz) were determined. All analyses were done for the left and right hemisphere separately. An automated artifact rejection was performed that eliminated high amplitude artifacts.¹⁷ For differentiation of bursts from suppression periods we chose an automatic, unsupervised variable-threshold technique, in order to minimize systematic errors due to low amplitude artifacts and individual baseline fluctuations that could arise from fixed thresholding. Therefore, unequivocal suppression epochs were manually labelled during the first minute of the selected hour and the mean spectral power during these epochs was analyzed over the whole frequency spectrum (0.5–35 Hz). The individual threshold for burst detection was defined as mean overall power plus four standard deviations of manually segmented suppression periods. EEG epochs between threshold crossings were regarded as bursts. ^13,18 In bursts, the frequency-distribution was identified by calculating EEG-power-values in four frequency-bands: delta 0.5–3.5 Hz, theta 4–7.5 Hz, alpha 8–12.5 Hz, and beta 13–22 Hz (Fig. 3).

The following parameters were computed: mean burst duration, suppression burst ratio – defined as relative time spent in suppression − ¹⁸, burst frequency per minute, mean total EEG power (0,5–22 Hz), mean delta power, mean theta power, mean alpha power and mean beta power. Also, the percentage of bursts containing epileptiform discharges were captured based on visual inspection.

Whenever EEG recording started before beginning of IVAT, EEG during RSE was also analyzed. Therefore, ictal patterns were visually selected for analysis according to current guidelines. ¹⁹ Thus, eligible patterns had to consist of electrographic seizures for ≥ 10 minutes or for a total of 20% of any 60-minute period. Electrographic seizures were defined as rhythmic or periodic epileptiform discharges with a frequency of ≥ 2.5 Hz or rhythmic patterns with definite evolution in time and space.¹⁹ All analyzed RSE-EEG-epochs were sampled during the first 24 hours of recording and before the first dose of IVAT was administered. We chose EEG epochs of ≥ 10 minutes and ≤ 60 minutes duration depending on the length of recorded ictal patterns fulfilling the above-mentioned criteria. Consistent with BS-analyses we calculated the mean spectral power in all frequency bands.

Statistical analysis

Spearman’s tests were performed to analyze intra-individually the correlation of frequency specific power values between the two hemispheres. As the interhemispheric frequency distribution was highly correlated, the mean power value of both hemispheres was used for further analysis. To test for significant differences in the composition of power spectra of bursts in patients of group SE and Non SE, Mann Whitney tests and receiver operator characteristics (ROC) were performed comparing total power, relative delta, theta, alpha and beta power in both groups. The same methods were applied to test for group differences regarding suppression burst ratio, burst duration, threshold for burst detection and burst frequency per minute. To analyze similarities between spectral properties of ictal patterns during SE and consecutive bursts during BS in SE and non-SE patients, Spearman’s correlation was used. Because of multiple testing in this study Bonferroni correction was applied and significance levels were adjusted accordingly.

Ethics declaration

All study procedures were in concordance with the Declaration of Helsinki, 1975 (revised 2000). Formal ethical approval was not required as the study is based solely on purely clinical retrospective EEG data transferred to an anonymized database for all analyses. Therefore, informed consent and ethics approval were waived by the ethics committee of Friedrich-Alexander University Erlangen-Nuremberg.

Forty-eight patients fulfilled the inclusion criteria and were enrolled in the study. Mean age of patients was 63.3 years (+/- SD 19.2 years), 54.2% were female (Table 1). Epilepsy was pre-existent in 14 patients (29.2%).

Table 1

Patients’ characteristics
	Total cohort (n = 48)	non SE (n = 25)	SE (n = 23)	P value
Gender, female, n (%)	26 (54)	14 (56)	12 (52)
Age, years, mean (SD)	63 (19)	69 (18)	61 (19)	0.11 +
History of epilepsy, n (%)	14 (29)	7 (28)	7 (30)
Etiology of RSE, n (%) Epilepsy /ASM withdrawal Epilepsy / ASM steady Neoplastic Vascular Infectious Autoimmune Malformation Intoxication / withdrawal Posttraumatic Unknown	2 (4) 1 (2) 7 (15) 13 (27) 7 (15) 6 (13) 3 (6) 2 (4) 1 (2) 6 (13)	0 (0) 0 (0) 3 (12) 9 (36) 4 (16) 1 (4) 2 (8) 1 (4) 1 (4) 4 (16)	2 (9) 1 (4) 4 (17) 4 (17) 3 (13) 5 (22) 1 (4) 1 (4) 0 (0) 2 (1)
Anesthetic agent, n (%) Propofol Midazolam Thiopental Combination	7 (15) 16 (33) 1 (2) 24 (50)	6 (24) 8 (32) 0 (0) 11 (44)	1 (4) 8 (35) 1 (4) 13 (57)
Duration of BS, hours, mean (SD)	25 (19)	23 (20)	27 (18)	0.30 +
Functional outcome at discharge In hospital mortality, n (%) Good outcome, n (%) Poor outcome*, n (%)	16 (33) 10 (20) 22 (46)	9 (36) 6 (24) 10 (40)	7 (30) 4 (18) 12 (52)	0.23 ++
BS: burst suppression EEG; RSE: refractory status epilepticus; ASM: antiseizure medication; (mRS 1 or 2 or equals premorbid mRS); *(mRS 3–6 or exceeds premorbid mRS); +Mann Whitney-U-test; ++Chi-square test

On average, BS lasted 25.2 hours (+/- SD 19.2 hours). IVAT was maintained by use of midazolam (33%), propofol (15%), thiopental (2%) or a combination of these (50%).

Twenty-five patients (52.1%) did not develop clinical or electrographic seizure recurrence after weaning from IVAT and were classified as outcome group Non SE accordingly. In 23 patients (47.9%; outcome group SE), SE reestablished after weaning was attempted.

In-hospital mortality rate and functional outcome at time of discharge did not differ significantly in both groups (p = 0.23 Chi-square test; Table 1).

Likewise, SE etiology did not correlate with primary outcome (Spearman’s correlation p = 0.37, r=-0.13; for etiology classification see Table 1).

Total duration of BS maintenance did not affect outcome (p = 0.30, Table 1). Suppression burst ratio (SBR) relating total time of suppression to total time of burst EEG, actually affected outcome (Mann Whitney-U-test p = 0.02), yet counterintuitively. Thus, higher SPR values, marking deeper levels of sedation, were associated with seizure recurrence and vice versa. The spectral power of bursts over the whole frequency band (0.5–35 Hz) did not differ between groups SE and Non SE (p = 0.13).

As frequency specific spectral burst power of both hemispheres were highly correlated intra-individually (delta r = 0.88, p < 0.0001; theta r = 0.89, p < 0.0001; alpha r = 0.88, p < 0.0001; beta r = 0.87, p < 0.0001), the mean power values of the left and right hemisphere were used for further analysis.

The spectral frequency composition of bursts in SE and Non SE patients was significantly different (Fig. 1). The amount of delta power in bursts was higher in Non SE than in SE patients (mean 91.59% +/- SD 4.46% vs mean 80.53% +/- SD 12.47%, p < 0.0001; ROC: p < 0.0001, AUC = 0.78) while faster frequencies were more dominant in SE than Non SE patients (theta: mean 11.38% +/- SD 7.09% vs mean 5.41% +/- SD 2.81%, p = 0.0008; ROC: p = 0.0007, AUC = 0.78; alpha: mean 4.89% +/- SD 3.83% vs mean 1.82% +/- SD 1.19%, p < 0.0001; ROC: p < 0.0001, AUC = 0.84; beta: mean 3.23% +/- SD 2.89% vs mean 1.21% +/- SD 0.93%, p = 0.0002; ROC: p = 0.0002, AUC = 0.82; significance level set at 0.01 after Bonferroni correction).

SBR as a marker of depth of sedation did not correlate with power in the different frequency bands (delta p = 0.20, r=-0.13; theta p = 0.21, r = 0.13, alpha p = 0.30, r = 0.11; beta p = 0.72, r = 0.04).

Bursts containing epileptic discharges were significantly more frequent in SE patients (mean 65.43% +/- SD 31.52%) than Non SE patients (mean 16.88% +/- SD 12.27%; p < 0.0001).

Ictal EEG patterns during SE preceding BS could be analyzed in 34 patients (14 patients in non-SE group, 20 patients in SE group) and had a median duration of 45 minutes (range 17 to 60 minutes). Regarding SE group patients, a high agreement was evident when comparing spectral frequency compounds in ictal patterns to those of bursts in consecutively established BS per patient, (delta p < 0.0001, r = 0.75; theta p < 0.0001, r = 0.74; alpha p < 0.0001, r = 0.75; beta p = 0.0006, r = 0.52). In Non SE group patients, no correlation existed between SE and BS spectral properties (delta p = 1.17, r = 0.27; theta p = 0.46, r = 0.15; alpha p = 0.04, r = 0.39; beta p = 0.07, r = 0.35; significance level 0.01) (Fig. 2).

Intravenous anesthesia aiming for induction of burst suppression on EEG for 24 hours is standard therapy for refractory status epilepticus.^1,20 There is, however, little evidence supporting an optimum depth and duration of anesthesia. ^11,16,21,22 In fact, it is even unclear if establishing and maintaining burst suppression makes any causal contribution to status termination exceeding sedation, at all. ^10,21 Considering potentially harmful side effects of general anesthesia especially in non-convulsive status epilepticus, treatment aggressiveness should be carefully balanced against complications of this treatment.⁸ In this respect, the definition of EEG characteristics predictive of successful status termination versus seizure recurrence after tapering of anesthetic agents could support therapeutic decisions.

In this retrospective study based on quantitative EEG, we found that spectral properties of EEG bursts during burst suppression are indeed highly predictive of success or failure of treatment. Patients were far more likely to recover from the status and develop a normal EEG after lifting of coma, when bursts contained predominantly slow activity in the delta range. Bursts composed of high amounts of fast activity, in contrast, indicated a high risk of seizure recurrence after weaning. Also, high proportions of bursts containing epileptiform discharges were predictive of therapeutic failure. The frequency distribution within bursts was unaffected by depth of sedation, as the amount of delta power did not rise with increasing length of suppression periods. In fact, patients with a higher suppression to burst ratio were more at risk of seizure recurrence after weaning of anesthetics. This seemingly contradictory result might be owed to the treating physician’s decision to aim for deepest levels of sedation in cases rated as most severe based on clinical criteria.

Of note, we also found a high resemblance of spectral frequency distribution in bursts during burst suppression and ictal patterns during the preceding status exclusively in patients with SE recurrence after termination of burst suppression.

In early studies, burst suppression was understood as a global state of inhibition of the majority of cortical neurons with only a subset of thalamo-cortical neurons still firing at quasi-periodic intervals. Thus, induction of burst suppression was assumed to “reset” the EEG, allowing for physiological rhythms to re-establish.²³ Recent studies, however, led to a different interpretation of this EEG pattern. First, burst suppression does not appear to be a global state, instead timing and spectral structure of bursts vary widely in different brain areas. ¹³ In addition, the frequency content of bursts matches the local brainstate preceding burst suppression ²⁴ as in our study. Thus, burst suppression is likely to reflect an impaired ability of cortical neurons to be continuously active causing periodic phases of suppression which disrupts cortical processing. During bursts, being the result of a transient recovery, preceding activities can re-establish.^13,24

The agreement of the frequency spectrum of ictal patterns and bursts in patients with SE recurrence in our cohort are in line with this definition and imply an ongoing status in bursts in these patients. This agreement, however, was not seen in patients without SE recurrence after weaning, meaning successful SE treatment translates into a disintegration of ictal patterns in bursts resulting in a shift in frequency distribution.

Our findings thus support the perception of burst suppression as EEG activity that is interrupted by periods of EEG suppression, but not necessarily altered in its core. This appears to be true also in the setting of iatrogenic burst suppression for the treatment of refractory status epilepticus.

In consequence, our results argue against the frequent treatment strategy to increase sedation up to the point of burst suppression and regard this as causal therapy of status epilepticus per se. Instead, the treatment should aim to stop epileptiform EEG patterns in SE.

Anesthesia is still inevitable in many cases in order to stop convulsive seizures and prevent neuronal damage. ^25,26 Moreover, establishing coma buys time for other anticonvulsant measures to unfold their full effect.

Burst suppression as a set end-point of this approach, however, has to be challenged. Our data speaks in favor of tailored therapy management based on EEG markers instead.

Our study identified fast oscillations and epileptiform discharges in bursts as warning signs of seizure recurrence. Delta-dominated bursts in contrast indicated successful status termination.

The morphology of ictal patterns has been found to allow conclusions on underlying pathologies of seizures, involved epileptogenic networks, seizure duration and therapy response. ^27,28 High frequency ictal patterns, commonly seen at seizure onset, are often associated with highly epileptogenic pathologies, large epileptogenic networks and increased excitability of tissue surrounding the epileptogenic zone. Low frequency seizure patterns, in contrast, often develop over time and mirror response to physiological and iatrogenic measures of seizure termination.^29,30 When recorded as ictal onset patterns they have been described in the context of localized seizure activity embedded in stable surroundings.^31,32 Thus, high frequency bursts seem to reflect ongoing seizure activity in a still highly active epileptogenic network not yet reactive to anticonvulsant treatment.

In practice, lower levels of sedation and early weaning attempts could be aimed for in patients with delta-dominated EEG activity, thus minimizing therapy associated risks. Whereas patients at high risk of seizure recurrence, defined by high frequency and spike activity in the EEG, could benefit from prolonged coma while causal therapy is extended.

The study has several limitations. First and foremost, it is a retrospective analysis. The data is not controlled for medication and status etiology. The cohort is heterogeneous regarding underlying diseases, comorbidities and therapeutic management and the sample size is relatively small due to challenging data acquisition. As a consequence, we were not able to demonstrate a significant difference between patients with and without seizure recurrence in terms of in-hospital mortality and functional outcome at time of discharge.

Status epilepticus is frequently not terminated but merely interrupted by installment of burst suppression. Epileptiform patterns may continue within bursts. Therefore, sedation until establishment of burst suppression is no independent treatment goal of status epilepticus. The aim of treatment should be to terminate seizure patterns in SE independent of their presence before, during or after induction of bursts suppression. Spectral analysis of burst activity can help to evaluate the EEG during burst suppression and its response to treatment, stratify the risk of seizure recurrence and facilitate personalized management of sedation.

Acknowledgements / funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors’ contributions:

S.G., H.H.: Study concept and design

S. G., R.H., S.R., T.W., D.M., J.L., C.R:, J.S., J.K., J.Ku., S.S., H.H., H.H.: Acquisition, analysis, or interpretation of data

H.H., R.H., S.R.,D.M.: Critical revision of the manuscript for important intellectual content

S.G.: Drafting of the manuscript

Data availability statement

The data that support the findings of this study are available on request from the corresponding author.

Conflicts of interest

S. Gollwitzer received personal fees from Desitin, UCB, outside the submitted work.

J.D. Lang served on the speakers’ bureau of Eisai and Destin.

H.M. Hamer has served on the scientific advisory boards of Arvelle, Bial, Corlieve, Eisai, GW, Novartis, Sandoz, UCB Pharma and Zogenix. He has served on the speakers’ bureaus of or received unrestricted grants from Amgen, Ad-Tech, Alnylam, Bracco, Desitin, Eisai, GW, Nihon Kohden, Novartis, Pfizer, and UCB Pharma.

The remaining authors have no conflicts of interest.

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No competing interests reported.

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Spectral properties of bursts in therapeutic burst suppression predict successful treatment of refractory status epilepticus

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Introduction

Materials and methods

Patients

EEG monitoring

qEEG analysis

Statistical analysis

Ethics declaration

Results

Discussion

Conclusion

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Conflicts of interest

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