Citations

Abstract Objective The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. Methods In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. Results Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. Significance Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care.

AbstractTo date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.

Abstract Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

AbstractObjectiveMost seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.MethodsThis study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective.ResultsFor the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature.SignificanceEnvironmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

Abstract Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor‐quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in‐hospital or in‐home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high‐quality, marginal‐quality, or poor‐quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good‐quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good‐quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good‐, marginal‐, and poor‐quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist‐worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high‐quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.

Abstract Abstract 3949 Acute kidney injury (AKI) is a common but serious sequela of multiple myeloma. Recently, the International Myeloma Working Group (IMWG) introduced a new renal response criteria with complete (CRenal), partial (PRenal) and minimal (MRenal) responses. This study compares the IMWG criteria with other models to determine their correlation with overall survival (OS). Patients with multiple myeloma and AKI were identified from centers around the world. OS was calculated from the day of AKI. Renal function was assessed by estimated glomerular filtration rate (eGFR) via MDRD method. Hematologic response was the best achieved as assessed by International Uniform Response Criteria for Multiple Myeloma. Only patients with peak serum creatinine (Scr) ≥ 2.0 mg/dl were included. Separate analyses were performed on the newly diagnosed (ND) versus previously treated (PT) patients. A total of 158 patients were collected from centers in United States, United Kingdom, France and Austria. Fourteen patients were excluded for having peak Scr < 2.0 mg/dl and one for repeated episodes of AKI. Median age of the 143 patients was 64 (34 – 87) years, 58.7% were male and 115 were newly diagnosed. Median Scr was 5.1 (2.0 to 18.6) mg/dl and median eGFR was 10.6 (2.8 – 36.5) ml/min/1.72 m2. Dialysis was required in 49.3%, and 72.9% were dialysis independent at the end of the study. By IMWG criteria, median OS ranged between 6.0 months (m) in PT patients with no renal response (NRenal) to 23.7m in patients with CRenal, p = 0.34 (Table 1). In ND patients, OS was 31.4m in NRenal vs 73.8m in CRenal (p = 0.08). However, OS was similar between PRenal and CRenal, p = 0.70 (Figure 1). If only patients with a hematologic PR or better were analyzed, the OS range was 6.0m in NRenal to 23.7m in CRenal in PT patients (p = 0.85) and 36.6m in NRenal to 73.8m in CRenal in ND patients, p = 0.73.Table 1.Previously TreatedNMedian SurvivalNMedian Survival (>PR) CRenal323.7 m323.7 m PRenal413.1 m312.8 m MRenal512.0 m119.2 m NRenal166.0 m56.0 m p-value0.340.85Newly Diagnosed CRenal2273.8 m2273.8 m PRenal27Not Reached23Not Reached MRenal2265.6 m1965.6 m NRenal4331.4 m2136.6 m p-value0.080.73Figure 1.Overall survival (OS) of 115 patients evaluated by the IMWG renal response criteria. OS was not significantly different by Log-Rank test between patients with CRenal and NRenal, p = 0.08.Figure 1. Overall survival (OS) of 115 patients evaluated by the IMWG renal response criteria. OS was not significantly different by Log-Rank test between patients with CRenal and NRenal, p = 0.08. The best model tested was one that defined renal response (RR) as regaining or maintaining dialysis independence and no response (NR) as dialysis dependence. RR was associated with a superior survival for both PT patients (13.5 m vs 6.0m (NR), p = 0.03) and ND patients (73.8m vs 20.1m (NR), p < 0.001). RR increased with the depth of hematologic response, p = 0.04. RR occurred in 75% of the PT patients with hematologic PR or better and 66.7% in those with less than PR, p = 0.64. In ND patients, RR occurred in 85.7% of those with PR or better and 54.6% of those with PR or less, p = 0.003. This may explain the differences in OS when hematologic response was considered. In PT patients with hematologic PR or better, renal response did not improve OS (21m vs 6m, p = 0.28) but it remained beneficial in the ND patients (73.8m vs 35.1m, p = 0.04, Figure 2). Renal response was not significantly associated with OS for any patient with hematologic response that was VGPR or better. In a multivariate analysis, RR (p = 0.001), ND (p <0.001) and PR or better hematologic response (p = 0.046) were associated with OS.Figure 2.In a separate model, 83 newly diagnosed patients with hematologic response of PR or better were evaluated. Patients who were dialysis independent achieved a renal response (RR) and those who remained on dialysis had no response (NR). Patients with RR had a superior overall survival than patients with NR. Renal response was independent of a hematologic response of PR or better.Figure 2. In a separate model, 83 newly diagnosed patients with hematologic response of PR or better were evaluated. Patients who were dialysis independent achieved a renal response (RR) and those who remained on dialysis had no response (NR). Patients with RR had a superior overall survival than patients with NR. Renal response was independent of a hematologic response of PR or better. The results of our study suggest renal response is important to OS but the depth of response was not as important. Renal response was most important when the hematologic response was less than a VGPR. This supports renal response as part of the overall response assessment in multiple myeloma patients. Based on our results, reducing the number of response categories and simplifying the current consensus criteria seems justified. Median survival of the 143 patients evaluated by the IMWG renal response criteria. Overall survival was calculated by Kaplan Meier method. Differences in survival were evaluated by Log-Rank test. Disclosures: Ludwig: Mundipharma, Janssen-Cilag: Research Funding, Speakers Bureau. Hutchison:The Binding Site: Honoraria.

Summary To describe the demographics, etiologies, types of status epilepticus ( SE ), and outcomes in people with refractory and super‐refractory SE from around the world, we prospectively collected cases of refractory SE ( RSE ) treated with continuous intravenous anesthetic drugs in an intensive care unit setting through online questionnaires using “active surveillance.” We collected information about 776 cases of RSE in 50 countries over 4 years. Control of SE was achieved in 74% of the cases. Neurologic outcomes were poor in 41% of patients, and 24% died. Good outcome was associated with younger age and a history of epilepsy. Etiology strongly influenced the outcome. Patients from Asia were younger, more frequently presented with convulsive SE , and were more frequently affected by infectious etiologies when compared with patients from Europe and the Americas. Despite these differences, outcomes were similar in all countries. Demographics of patients with RSE in a global audit are similar to those in prior single center series, providing evidence of generalizability of those studies. Important differences exist among patients with RSE from different regions of the world, but these do not seem to significantly influence patient outcomes.