The number of seizures needed in the EMU

Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data are captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-centre study for model development; two-centre study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using several interictal EEG features derived from recent publications. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. Forty-seven patients (30 female; ages 20-69; 20 left-sided, 10 right-sided and 17 bilateral seizure onsets) were analysed for model development and internal validation. Nineteen patients (10 female; ages 23-73; 5 left-sided, 10 right-sided, 4 bilateral) were analysed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. We replicated the finding that right temporal lobe epilepsy was harder to distinguish in a separate modality of resting-state functional MRI. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome. A potential clinical application of these findings could be to either support or oppose a hypothesis of unilateral temporal lobe epilepsy when deciding to pursue surgical resection or ablation as opposed to device implantation.

The value of ictal scalp EEG in focal epilepsies surgery: a retrospective analysis

OBJECTIVE

To describe the association between preoperative ictal scalp electroencephalogram (EEG) results and surgical outcomes in patients with focal epilepsies.

METHODS

The data of consecutive patients with focal epilepsies who received surgical treatments at our center from January 2012 to December 2021 were retrospectively analyzed.

RESULTS

Our data showed that 44.2% (322/729) of patients had ictal EEG recorded on video EEG monitoring during preoperative evaluation, of which 60.6% (195/322) had a concordant ictal EEG results. No significant difference of surgery outcomes between patients with and without ictal EEG was discovered. Among MRI-negative patients, those with concordant ictal EEG had a significantly better outcome than those without ictal EEG (75.7% vs. 43.8%, p = 0.024). Further logistic regression analysis showed that concordant ictal EEG was an independent predictor for a favorable outcome (OR = 4.430, 95%CI 1.175-16.694, p = 0.028). Among MRI-positive patients, those with extra-temporal lesions and discordant ictal EEG results had a worse outcome compared to those without an ictal EEG result (44.7% vs. 68.8%, p = 0.005). Further logistic regression analysis showed that discordant ictal EEG was an independent predictor of worse outcome (OR = 0.387, 95%CI 0.186-0.807, p = 0.011) in these patients. Furthermore, our data indicated that the number of seizures was not associated with the concordance rates of the ictal EEG, nor the surgical outcomes.

CONCLUSIONS

The value of ictal scalp EEG for epilepsy surgery varies widely among patients. A concordant ictal EEG predicts a good surgical outcome in MRI-negative patients, whereas a discordant ictal EEG predicts a poor postoperative outcome in lesional extratemporal lobe epilepsy.

Impact of antiseizure medication with a very long half-life on long term video-EEG monitoring in focal epilepsy

PURPOSE

To assess the impact of antiseizure medications (ASMs) with a very long half-life on long term video-EEG monitoring (LTM) in people with focal epilepsy (FE).

METHODS

In this retrospective cohort study, we searched our local database for people with FE who underwent ASM reduction during LTM at the University Hospital of 'La Fe', Valencia, from January 2013 to December 2019. Taking into account the half-life of the ASM, people with FE were divided into two groups: Group A contained individuals who were taking at least one ASM with a very long half-life at admission, and Group B consisted of those not taking very long half-life ASMs. Using multivariable analysis to control for important confounders, we compared the following outcomes between both groups: seizure rates per day, time to first seizure, and LTM duration.

RESULTS

Three hundred seventy individuals were included in the study (154 in Group A and 216 in Group B). The median recorded seizure rates (1.3 seizures/day, range 0-15.3 vs.1.3 seizures/day, range 0-9.3, p-value=0.68), median time to the first seizure (24 h, range 2-119 vs. 24 h, range 2-100, p-value=0.92), and median LTM duration (4 days, range 2-5 vs. 4 days, range 2-5, p-value=0.94) were similar in both groups. Multivariable analysis did not reveal any significant differences in the three outcomes between the two groups (all p-values>0.05).

CONCLUSION

ASMs with a very long half-life taken as co-medication do not significantly affect important LTM outcomes, including recorded seizure rates, time to the first seizure, or LTM duration. Therefore, in general, there is no need to discontinue ASMs with a very long half-life prior to LTM.

Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data is captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal (between-seizure) intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-center study for model development; two-center study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using interictal EEG. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. 47 patients (30 women; ages 20-69; 20 left-sided, 10 right-sided, and 17 bilateral seizure onsets) were analyzed for model development and internal validation. 19 patients (10 women; ages 23-73; 5 left-sided, 10 right-sided, 4 bilateral) were analyzed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome.

The impact of the time to last seizure before admission to the epilepsy monitoring unit (EMU) on epilepsy classifications

INTRODUCTION AND BACKGROUND

The impact of the timing of the last seizure (TTLS) prior to admission to the epilepsy monitoring unit (EMU) on epilepsy classification is unclear for which we conducted this study.

METHODS

We reviewed patients with epilepsy admitted to EMU between January 2021 and April 2022 and identified TTLS before EMU admission. We considered EMU yield as whether; it confirmed epilepsy classification, added new knowledge to the classification, or failed to classify epilepsy.

RESULTS

We studied 156 patients. There were 72 (46%) men, with a mean age of 30. TTLS was divided according to a one- or three-month cutoff. We confirmed the pre-EMU epilepsy classification in 52 (33%) patients, learned new findings on epilepsy classification in 80 (51%) patients, and failed to classify epilepsy in 24 (15%) patients. Patients with "confirmed epilepsy classifications" reported seizures sooner to EMU admission than other groups (0.7 vs. 2.3 months, p-value = 0.02, 95% CI; -1.8, -1.3). Also, the odds of confirming epilepsy classification were more than two times in patients with TTLS within a month compared to those with TTLS of more than a month (OR = 2.4, p-value = 0.04, 95% CI; 1.1, 5.9). The odds were also higher when the 3-month TTLS cutoff was considered (OR = 6.2, p-value = 0.002, 95% CI; 1.6, 40.2). Confirming epilepsy classification was also associated with earlier seizures recorded at one- or three-month cutoff (OR = 2.1 and OR = 2.3, respectively, p-value = 0.05). We did not observe similar findings when we modified the classification or failed to reach a classification.

CONCLUSIONS

The timing of the last seizure before EMU admission appeared to influence the yield of EMU and enhanced the confirmation of epilepsy classifications. Such findings can improve the utilization of EMU in the presurgical evaluation of patients with epilepsy.

Multilayer brain networks can identify the epileptogenic zone and seizure dynamics

Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80-200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the EZ as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in the early to mid-seizure. We showed that aging and the duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but possible factors that correlate with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we demonstrate the necessity of collecting sufficient data for identifying epileptogenic networks.

Seizure count forecasting to aid diagnostic testing in epilepsy

OBJECTIVE

Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug-resistant epilepsy but may be nondiagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure-related morbidity. Here, we evaluated the performance of a state-space method that uses prior seizure count data to predict future counts.

METHODS

A Bayesian negative-binomial dynamic linear model (DLM) was developed to forecast daily electrographic seizure counts in 19 patients implanted with a responsive neurostimulation (RNS) device. Holdout validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging 1-7 days ahead.

RESULTS

One-day-ahead prediction of the number of electrographic seizures using a negative-binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained >50% for forecast horizons of up to 7 days. Superior performance (mean error = .99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (integer-valued generalized autoregressive conditional heteroskedasticity model or INGARCH, 1.10; Croston, 1.06; generalized linear autoregressive moving average model or GLARMA, 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day.

SIGNIFICANCE

This study demonstrates that DLMs can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of EMU admissions.

What is the optimal duration of home-video-EEG monitoring for patients with <1 seizure per day? A simulation study

Ambulatory “at home” video-EEG monitoring (HVEM) may offer a more cost-effective and accessible option as compared to traditional inpatient admissions to epilepsy monitoring units. However, home monitoring may not allow for safe tapering of anti-seizure medications (ASM). As a result, longer periods of monitoring may be necessary to capture a sufficient number of the patients' stereotypic seizures. We aimed to quantitatively estimate the necessary length of HVEM corresponding to various diagnostic scenarios in clinical practice. Using available seizure frequency statistics, we estimated the HVEM duration required to capture one, three, or five seizures on different days, by simulating 100,000 annual time-courses of seizure occurrence in adults and children with more than one and <30 seizures per month (89% of adults and 85% of children). We found that the durations of HVEM needed to record 1, 3, or 5 seizures in 80% of children were 2, 5, and 8 weeks (median 2, 12, and 21 days), respectively, and significantly longer in adults −2, 6, and 10 weeks (median 3, 14, and 26 days; p < 10⁻¹⁰ for all comparisons). Thus, longer HVEM than currently used is needed for expanding its clinical value from diagnosis of nonepileptic or very frequent epileptic events to a presurgical tool for patients with drug-resistant epilepsy. Technical developments and further studies are warranted.

Bilateral temporal lobe epilepsy: How many seizures are required in chronic ambulatory electrocorticography to estimate the laterality ratio?

OBJECTIVE

This study was undertaken to measure the duration of chronic electrocorticography (ECoG) needed to attain stable estimates of the seizure laterality ratio in patients with drug-resistant bilateral temporal lobe epilepsy (BTLE).

METHODS

We studied 13 patients with drug-resistant BTLE who were implanted for at least 1 year with a responsive neurostimulation device (RNS System) that provides chronic ambulatory ECoG. Bootstrap analysis and nonlinear regression were applied to model the relationship between chronic ECoG duration and the probability of capturing at least one seizure. Laterality of electrographic seizures in chronic ECoG was compared with the seizure laterality ratio from Phase 1 scalp video-electroencephalographic (vEEG) monitoring. The Kaplan-Meier estimator was used to evaluate time to seizure laterality ratio convergence.

RESULTS

Seizure laterality ratios from Phase 1 scalp vEEG monitoring correlated poorly with those from RNS chronic ECoG (r = .31, p = .30). Across the 13 patients, average electrographic seizure frequencies ranged from 1.4 seizures/month to 5.1 seizures/day. A 50% probability of recording at least one electrographic seizure required 9.1 days of chronic ECoG, and 90% probability required 44.3 days of chronic ECoG. A median recording duration of 150.9 days (5 months), corresponding to a median of 16 seizures, was needed before confidence intervals for the seizure laterality ratio reliably contained the long-term value. The median recording duration before the point estimate of the seizure laterality ratio converged to a stationary value was 236.8 days (7.9 months).

SIGNIFICANCE

RNS chronic ECoG overcomes temporal sampling limitations intrinsic to inpatient Phase 1 vEEG evaluations. In patients with drug-resistant BTLE, approximately 8 months of chronic RNS ECoG are needed to precisely estimate the seizure laterality ratio, with 75% of people with BTLE achieving convergence after 1 year of RNS recording. For individuals who are candidates for unilateral resection based on seizure laterality, optimized recording duration may help avert morbidity associated with delay to definitive treatment.

The safety and efficacy of modifying the admission protocol to the epilepsy monitoring unit in response to the COVID-19 pandemic

PURPOSE

The coronavirus disease 2019 (COVID-19) pandemic has impacted admission to epilepsy monitoring units (EMUs) for classification and presurgical evaluation of patients with refractory epilepsy. We modified the EMU admission protocol via anti-seizure medications (ASM) withdrawal implemented one day before admission; thus, we aimed to evaluate the efficacy and safety of this modified protocol.

METHODS

In January 2021, we initiated ASM tapering 24 h before-rather than on the first day after-EMU admission, contrasting with the previous protocol. We retrospectively reviewed EMU admissions between January and April of 2018, 2019, and 2021, and identified the time required to record the first seizure, and EMU yield to confirm or change the epilepsy classification. We also evaluated the safety of the modified protocol, by monitoring the seizure frequency for up to 5 months after the discharge from the hospital.

RESULTS

One hundred four patients were included (mean age: 30 years, men: 43%); excluding a longer disease duration and abundance of normal routine electro-encephalogram (EEG) in patients admitted before the pandemic, no differences were observed in patients' characteristics. On average, it took 41 h and 21 h to record the first seizure using the standard and modified protocols, respectively (p < 0.001, 95% CI: 10-30). Other characteristics were investigated both before and after the COVID-19 pandemic, and epilepsy classifications were confirmed twice using the modified protocol (OR = 2.4, p = 0.04, 95% CI: 1.1-5.5). Multivariate regression analysis confirmed the shorter time to record the first seizure using the modified admission protocol (23 h less, p < 0.001; 95% CI: 12-34). Finally, 36 (86%) patients admitted during the pandemic exhibited no increase in seizure frequency after the discharge from the hospital.

CONCLUSIONS

Initiating ASM withdrawal one day before EMU admission was deemed to be an efficient and safe way to confirm epilepsy classification and significantly decrease the length of hospital stay. Ultimately, this will shorten the long waiting list for EMU admission created by the COVID-19 pandemic.

Behavioral and Neural Variability of Naturalistic Arm Movements

Motor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed "in the wild." Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous, unstructured arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with considerable neural variability across subjects and events. We modeled interevent variability using 10 behavioral and environmental features; the most important features explaining this variability were reach angle and day of recording. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during unstructured movements and contributes to our understanding of long-term naturalistic behavior.

Drug withdrawal in the epilepsy monitoring unit - The patsalos table

Investigation of possible candidates for epilepsy surgery will usually require inpatient EEG to capture seizures and allow full operative planning. Withdrawal of antiepileptic drugs increases the yield of this valuable diagnostic information and the benefits of this should justify any increase in the risk of harm associated with these seizures This paper outlines our opinion on what would constitute proposed best practice for management of antiepileptic drug (AED) dosing when patients are admitted for monitoring of seizures to an epilepsy monitoring unit (EMU). In the vast majority of cases EMU admissions are safe and, even if seizures occur, will pass off without complication. Previous guidance has concentrated on ensuring practice around technical aspects of EEG monitoring itself and staffing within the unit. In this guidance we aim to outline optimally safe ways of ensuring that EMUs ensure the minimisation of risk to the patients admitted under their care. We propose an algorithm for enhancing the safety of AED withdrawal in VT admissions while ensuring adequate seizure yields. Risk minimisation requires planned management of drug dosing (with reduction if appropriate), provision of adequate rescue medication, and adequate supervision to allow rapid response to generalised seizures. This algorithm is accompanied by a table which uses knowledge of the clinical and pharmacological properties of each AED to ensure dose withdrawal and reduction is timely and safe taking into account the severity and frequency of the individual's seizures.

Unilateral temporal interictal epileptiform discharges correctly predict the epileptogenic zone in lesional temporal lobe epilepsy

OBJECTIVE

To evaluate the necessity of recording ictal electroencephalography (EEG) in patients with temporal lobe epilepsy (TLE) considered for resective surgery who have unilateral temporal interictal epileptiform discharges (IEDs) and concordant ipsitemporal magnetic resonance imaging (MRI) pathology. To calculate the necessary number of recorded EEG seizure patterns (ESPs) to achieve adequate lateralization probability.

METHODS

In a retrospective analysis, the localization and lateralization of interictal and ictal EEG of 304 patients with lesional TLE were analyzed. The probability of further contralateral ESPs was calculated based on a total of 1967 recorded ESPs, using Bayes' theorem.

RESULTS

Two hundred seventy-one patients had unilateral TLE, and in 98% of them (265 of 271), IEDs were recorded during video-EEG monitoring. Purely unilateral temporal IEDs were present in 61% (166 of 271 patients). Ipsilateral temporal MRI pathology was found in 83% (138 of 166). Ictal EEG was concordant with the clinical side of TLE in 99% (136 of 138) of these patients. Two patients had discordant ictal EEG with both ipsilateral and contralateral ESPs. Epilepsy surgery with resection in the lesioned temporal lobe was still performed, and both patients remain seizure-free. Probability calculations demonstrate that at least 6 recorded unilateral ESPs result in a >95% probability for a concordance of >0.9 of any further ESPs.

SIGNIFICANCE

The combination of purely unilateral temporal IED with ipsitemporal MRI pathology is sufficient to identify the epileptogenic zone, and the recording of ictal ESP did not add any surgically relevant information in these 138 patients. Rarely, discordant ESPs might be recorded, but the surgical outcome remains excellent after surgery on the lesioned side.

Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings

The rate of interictal high frequency oscillations (HFOs) is a promising biomarker of the seizure onset zone, though little is known about its consistency over hours to days. Here we test whether the highest HFO-rate channels are consistent across different 10-min segments of EEG during sleep. An automated HFO detector and blind source separation are applied to nearly 3000 total hours of data from 121 subjects, including 12 control subjects without epilepsy. Although interictal HFOs are significantly correlated with the seizure onset zone, the precise localization is consistent in only 22% of patients. The remaining patients either have one intermittent source (16%), different sources varying over time (45%), or insufficient HFOs (17%). Multiple HFO networks are found in patients with both one and multiple seizure foci. These results indicate that robust HFO interpretation requires prolonged analysis in context with other clinical data, rather than isolated review of short data segments.

Safety and efficiency of medication withdrawal at home prior to long-term EEG video-monitoring

PURPOSE

Long-term video-EEG monitoring (LTM) is frequently used for diagnostic purposes and in the workup of epilepsy surgery to determine the seizure onset zone. Different strategies are applied to provoke seizures during LTM, of which withdrawal of anti-epileptic drugs (AED) is most effective. Remarkably, there is no standardized manner of AED withdrawal. For instance, the majority of clinics taper medication during clinical admission, whereas we prefer to taper medication at home prior to admission. Our aim was to study the advantages (efficiency and diagnostic yield) and disadvantages (safety and complication rates) of predominantly tapering of medication at home.

METHOD

We report a retrospective observational cohort of 273 patients who had a LTM at our tertiary epilepsy center from 2005 until 2011. Provocation methods to induce seizures were determined on individual basis. Success rate (duration of admittance, time to first seizure, efficiency and diagnostic yield) and complications and serious adverse events were assessed.

RESULTS

AED were tapered in 180 (66%) patients, in 93 (24%) of these patients with additional (partial) sleep deprivation. In all of these patients tapering started at home one to four weeks prior to admission. In the other patients, only (partial) sleep deprivation or none provocation method at all was applied. Seizure recordings were successful in 79,9% of patients. Complications occurred in 19 patients (10.9%) of which 3 had (1.7%) serious adverse events (status epilepticus (SE)) with AED withdrawal. These complications only occurred during admittance, not at home.

CONCLUSIONS

AED withdrawal at home prior to LTM is an efficient and convenient method to increase the diagnostic yield of LTM and appears relatively safe.

Length of stay for patients undergoing invasive electrode monitoring with stereoelectroencephalography and subdural grids correlates positively with increased institutional profitability

OBJECTIVE

Lowering the length of stay (LOS) is thought to potentially decrease hospital costs and is a metric commonly used to manage capacity. Patients with epilepsy undergoing intracranial electrode monitoring may have longer LOS because the time to seizure is difficult to predict or control. This study investigates the effect of economic implications of increased LOS in patients undergoing invasive electrode monitoring for epilepsy.

METHODS

We retrospectively collected and analyzed patient data for 76 patients who underwent invasive monitoring with either subdural grid (SDG) implantation or stereoelectroencephalography (SEEG) over 2 years at our institution. Data points collected included invasive electrode type, LOS, profit margin, contribution margins, insurance type, and complication rates.

RESULTS

LOS correlated positively with both profit and contribution margins, meaning that as LOS increased, both the profit and contribution margins rose, and there was a low rate of complications in this patient group. This relationship was seen across a variety of insurance providers.

SIGNIFICANCE

These data suggest that LOS may not be the best metric to assess invasive monitoring patients (i.e., SEEG or SDG), and increased LOS does not necessarily equate with lower or negative institutional financial gain. Further research into LOS should focus on specific specialties, as each may differ in terms of financial implications.

A big data approach to the development of mixed-effects models for seizure count data

OBJECTIVE

Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy.

METHODS

Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies.

RESULTS

For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day.

SIGNIFICANCE

The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

Preoperative prediction of temporal lobe epilepsy surgery outcome

PURPOSE

There is controversy about relative contributions of ictal scalp video EEG recording (vEEG), routine scalp outpatient interictal EEG (rEEG), intracranial EEG (iEEG) and MRI for predicting seizure-free outcomes after temporal lobectomy. We reviewed NIH experience to determine contributions at specific time points as well as long-term predictive value of standard pre-surgical investigations.

METHODS

Raw data was obtained via retrospective chart review of 151 patients. After exclusions, 118 remained (median 5 years follow-up). MRI-proven mesial temporal sclerosis (MTSr) was considered a separate category for analysis. Logistic regression estimated odds ratios at 6-months, 1-year, and 2 years; proportional hazard models estimated long-term comparisons. Subset analysis of the proportional hazard model was performed including only patients with commonly encountered situations in each of the modalities, to maximize statistical inference.

RESULTS

Any MRI finding, MRI proven MTS, rEEG, vEEG and iEEG did not predict two-year seizure-free outcome. MTSr was predictive at six months (OR=2.894, p=0. 0466), as were MRI and MTSr at one year (OR=10.4231, p=0. 0144 and OR=3.576, p=0. 0091). Correcting for rEEG and MRI, vEEG failed to predict outcome at 6 months, 1year and 2 years. Proportional hazard analysis including all available follow-up failed to achieve significance for any modality. In the subset analysis of 83 patients with commonly encountered results, vEEG modestly predicted long-term seizure-free outcomes with a proportional hazard ratio of 1.936 (p=0.0304).

CONCLUSIONS

In this study, presurgical tools did not provide unambiguous long-term outcome predictions. Multicenter prospective studies are needed to determine optimal presurgical epilepsy evaluation.