Abstract found on Wiley Online Library

Objective: Choosing candidates for antiseizure medication (ASM) withdrawal in well-controlled epilepsy is challenging. We evaluated 1) the correlation between neurologists’ seizure risk estimation (“clinician predictions”) versus calculated predictions, 2) how viewing calculated predictions influenced recommendations, and 3) barriers to using risk calculation.

Methods: We asked US and European neurologists to predict two-year seizure risk after ASM withdrawal for hypothetical vignettes. We compared ASM withdrawal recommendations before versus after viewing calculated predictions, using generalized linear models.

Results: Three-hundred forty-six neurologists responded. There was moderate correlation between clinician and calculated predictions (Spearman coefficient 0.42). Clinician predictions varied widely, e.g., predictions ranged 5%-100% for a two-year seizure-free adult without epileptiform abnormalities. Mean clinician predictions exceeded calculated predictions for vignettes with epileptiform abnormalities (e.g., childhood absence epilepsy: clinician 65%, 95% confidence interval [CI] 57%-74%; calculated 46%) and surgical vignettes (e.g., focal cortical dysplasia six-months seizure-free mean clinician 56%, 95% CI 52%-60%; calculated 28%). Clinicians overestimated the influence of epileptiform EEG findings on withdrawal risk (26%, 95% CI 24%-28%) compared with calculators (14%, 95% 13%-14%). Viewing calculated predictions slightly reduced willingness to withdraw (-0.8/10 change, 95% CI -1.0 to -0.7), particularly for vignettes without epileptiform abnormalities. The greatest barrier to calculator use was doubting its accuracy (44%).

Significance: Clinicians overestimated the influence of abnormal EEGs particularly for low-risk patients and overestimated risk and the influence of seizure-free duration for surgical patients, compared with calculators. These data may question widespread ordering of EEGs or time-based seizure-free thresholds for surgical patients. Viewing calculated predictions reduced willingness to withdraw particularly without epileptiform abnormalities.

Abstract found on PubMed

Introduction: Inpatient falls within the Epilepsy Monitoring Unit (EMU) are a common, and potentially preventable adverse event contributing to morbidity for patients living with epilepsy. Accurate fall risk screening is important to identify and efficiently allocate proper safety measures to high-risk patients, especially in EMUs with limited resources. We sought to compare existing screening tools for the ability to predict falls in the EMU.

Methods: This is a retrospective, single-center, case-controlled, comparative analysis of 7 nurse-administered fall risk assessment tools (NAFRAT) of patients admitted to the Vanderbilt University Medical Center (VUMC) EMU. Analysis of categorical data was compared using chi-square analysis while quantitative distributions were compared using student’s t-test.

Results: A total of 56 patient records (28 falls and 28 controls) were included in the analysis. Epilepsy Monitoring Unit falls were most common within the first 3 days of admission (p = .0094). Pre-admission documentation of falls was a strong predictor of falls within the EMU (p < .0001). Epilepsy Monitoring Unit falls were associated with documented falls after EMU discharge (p = .011). The John Hopkins fall risk assessment tool (JHFRAT) accurately stratified fall risk in the fall group compared to the control (p = .008), however, none of the 7 NAFRATs demonstrated significant categorical differences among the epilepsy subgroups. There was a significant difference in the distribution of quantitative scores, higher in the fall group according to the Morse Fall Scale (MFS) (p = 0.012), JHFRAT (p = 0.003), Schmid Fall Risk Assessment Scale (p = 0.029) and Hester-Davis Scale (p = 0.049). The modified Conley (p = 0.03) and Morse scale (p = 0.025) demonstrated differences in the distribution of quantitative scores in the epilepsy subgroups.

Conclusion: The findings of this study demonstrate variable accuracy of nurse-administered fall risk assessment tool in assessing fall risk among patients admitted to the epilepsy monitoring unit, particularly among patients with epilepsy. The findings underscore the need for a validated, epilepsy monitoring unit-specific, fall assessment tool that accurately stratifies fall risk and inform efficient use of patient-specific fall prevention resources and protocols.

Abstract found on PubMed

Objective: Seizures have been shown to increase blood-brain barrier (BBB) permeability, yet the role of this phenomenon is not fully understood. Additionally, dysfunction of the BBB leads to initiation and propagation of seizures in animal models. To demonstrate the increased permeability of the BBB in time, we investigated changes of the serum levels of BBB markers in patients with epilepsy after bilateral tonic-clonic seizures. We chose markers that might reflect endothelial activation (ICAM-1, selectins), BBB leakage (MMP-9, S100B) and mechanisms of BBB restoration (TIMP-1, thrombomodulin -TM).

Methods: We enrolled 50 consecutive patients hospitalised after bilateral tonic-clonic seizures who agreed to take part in the study and 50 participants with no history of epilepsy. Serum levels of selected markers were measured by ELISA at 1-3, 24, and 72 hours after seizures and one time in the control group.

Results: We found increased levels of S100B, ICAM-1, MMP-9 and P-selectin at 1-3 and 24 hours after seizures and TIMP-1 and TM at 24 and 72 hours after seizures as compared to the control group. The level of E-selectin was decreased at 72 hours after seizures.

Conclusions: Our findings suggest early activation of endothelium and increased BBB permeability after seizures. While we are aware of the limitations due to the non-specificity of the tested proteins, our results might indicate the presence of prolonged BBB impairment due to seizure activity.

Abstract found on Wiley Online Library

Objective: Guidelines suggest considering antiseizure medication (ASM) discontinuation in patients with epilepsy who become seizure-free. Little is known about how discontinuation decisions are being made in practice. We measured the frequency of, and factors associated with, discussions and decisions surrounding ASM discontinuation.

Methods: We performed a multicenter retrospective cohort study at the University of Michigan (UM) and two Dutch centers: Wilhelmina Children’s Hospital (WCH) and Stichting Epilepsie Instellingen Nederland (SEIN). We screened all children and adults with outpatient epilepsy visits in January 2015 and included those with at least one visit during the subsequent two years where they were seizure-free for at least one year. We recorded whether charts documented 1) a discussion with the patient about possible ASM discontinuation and 2) any planned attempt to discontinue at least one ASM. We conducted multilevel logistic regressions to determine factors associated with each outcome.

Results: We included 1,058 visits from 463 patients. Of all patients who were seizure-free at least one year, 248/463 (53%) had documentation of any discussion and 98/463 (21%) planned to discontinue at least one ASM. Corresponding frequencies for patients who were seizure-free at least two years were 184/285 (65%) and 74/285 (26%). The probability of discussing or discontinuing increased with longer duration of seizure-freedom. Still, even for patients who were ten years seizure-free, our models predicated that in only 49% of visits was a discontinuation discussion documented, and in only 16% of visits was it decided to discontinue all ASMs. Provider-to-provider variation explained 18% of variation in whether patients discontinued any ASM.

Significance: Only approximately half of patients with prolonged seizure-freedom had a documented discussion about antiseizure medication discontinuation. Discontinuation was fairly rare even among low-risk patients. Future work should further explore barriers to and facilitators of counseling and discontinuation attempts.

Abstract found on PubMed

Objective: The evaluation to determine candidacy and treatment for epilepsy surgery in persons with drug-resistant epilepsy (DRE) is not uniform. Many non-invasive and invasive tests are available to ascertain an appropriate treatment strategy. This study examines expert response to clinical vignettes of MRI-positive lesional focal cortical dysplasia in both temporal and extratemporal epilepsy to identify associations in evaluations and treatment choice.

Methods: We analyzed annual report data and a supplemental epilepsy practice survey reported in 2020 from 206 adult and 136 pediatric epilepsy center directors in the United States. Non-invasive and invasive testing and surgical treatment strategies were compiled for the two scenarios. We used chi-square tests to compare testing utilization between the two scenarios. Multivariable logistic regression modeling was performed to assess associations between variables.

Results: The supplemental survey response rate was 100% with 342 responses included in the analyses. Differing testing and treatment approaches were noted between the temporal and extratemporal scenarios such as chronic invasive monitoring selected in 60% of the temporal scenario versus 93% of the extratemporal scenario. Open resection was the most common treatment choice, however overall treatment choices varied significantly (p<0.001). Associations between non-invasive testing, invasive testing, and treatment choices were present in both scenarios. For example, in the temporal scenario SEEG was more commonly associated with FDG-PET (OR 1.85; 95% CI 1.06-3.29; p=0.033), MEG (OR 2.90; 95% CI 1.60-5.28; p = <0.001), HD EEG (OR 2.80; 95% CI 1.27-6.24; p = 0.011), fMRI (OR 2.17; 95% CI 1.19-4.10; p = 0.014) and Wada (OR 2.16; 95% CI 1.28-3.66; p = 0.004). In the extratemporal scenario, choosing SEEG was associated with increased odds of neuromodulation over open resection (OR 3.13; 95% CI 1.24-7.89; p=0.016).

Significance: In clinical vignettes of temporal and extratemporal lesional drug-resistant epilepsy, epilepsy center directors displayed varying patterns of non-invasive testing, invasive testing, and treatment choices. Differences in practice underscore the need for comparative trials for the surgical management of drug-resistant epilepsy.

Abstract found on PubMed

Objective: The aim is to report the performance of an electroencephalogram (EEG) seizure-detector algorithm on data obtained with a wearable device (WD) in patients with focal refractory epilepsy and their experience.

Methods: Patients used a WD, the Sensor Dot (SD), to measure two channels of EEG using dry electrode patches during pre-surgical evaluation and at home for up to eight months. An automated seizure detection algorithm flagged EEG regions with possible seizures, which we reviewed to evaluate the algorithm’s diagnostic yield. In addition, we collected data on usability, side effects and patient satisfaction with an electronic seizure diary application (Helpilepsy®).

Results: Sixteen inpatients used the SD for up to five days and had 21 seizures. Sixteen outpatients used the device for up to eight months and reported 101 focal impaired awareness (FIA) seizures during the periods selected for analysis. Focal seizure detection sensitivity based on behind-the-ear EEG was 52% in inpatients and 23% in outpatients. False detections/hour, positive predictive value (PPV) and F1 scores were 7.13, 0.11%, 0.002for inpatients and 7.77, 0.04% and 0.001 for outpatients. Artefacts and low signal quality contributed to poor performance metrics. The seizure detector identified nineteen non-reported seizures during sleep, when the signal quality was better. Regarding patients’ experience, the likelihood of using the device at six months was 62%, and side effects were the main reason for dropping out. Finally, daily and monthly questionnaire completion rates were 33% and 65%, respectively.

Significance: Focal seizure detection sensitivity based on behind-the-ear EEG was 52% in inpatients and 23% in outpatients with high false alarm rates and low positive predictive value and F1 scores. This unobtrusive wearable seizure detection device was well received but had side effects. The current workflow and low performance limit its implementation in clinical practice. We suggest different steps to improve these performance metrics and patient experience.