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BackgroundEpileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration of new wearable and mobile technologies to collect electroencephalographic (EEG) and extracerebral signals in a portable system might be the solution to prospectively identify times of seizure occurrence or propensity. The performances of several seizure detection devices have been assessed by validated studies, and patient perspectives on wearables have been explored to better match their needs. Despite this, there is a major gap in the literature on long-term, real-life acceptability and performance of mobile technology essential to managing chronic disorders such as epilepsy. ObjectiveEEG@HOME is an observational, nonrandomized, noninterventional study that aims to develop a new feasible procedure that allows people with epilepsy to independently, continuously, and safely acquire noninvasive variables at home. The data collected will be analyzed to develop a general model to predict periods of increased seizure risk. MethodsA total of 12 adults with a diagnosis of pharmaco-resistant epilepsy and at least 20 seizures per year will be recruited at King’s College Hospital, London. Participants will be asked to self-apply an easy and portable EEG recording system (ANT Neuro) to record scalp EEG at home twice daily. From each serial EEG recording, brain network ictogenicity (BNI), a new biomarker of the propensity of the brain to develop seizures, will be extracted. A noninvasive wrist-worn device (Fitbit Charge 3; Fitbit Inc) will be used to collect non-EEG biosignals (heart rate, sleep quality index, and steps), and a smartphone app (Seer app; Seer Medical) will be used to collect data related to seizure occurrence, medication taken, sleep quality, stress, and mood. All data will be collected continuously for 6 months. Standardized questionnaires (the Post-Study System Usability Questionnaire and System Usability Scale) will be completed to assess the acceptability and feasibility of the procedure. BNI, continuous wrist-worn sensor biosignals, and electronic survey data will be correlated with seizure occurrence as reported in the diary to investigate their potential values as biomarkers of seizure risk. ResultsThe EEG@HOME project received funding from Epilepsy Research UK in 2018 and was approved by the Bromley Research Ethics Committee in March 2020. The first participants were enrolled in October 2020, and we expect to publish the first results by the end of 2022. ConclusionsWith the EEG@HOME study, we aim to take advantage of new advances in remote monitoring technology, including self-applied EEG, to investigate the feasibility of long-term disease self-monitoring. Further, we hope our study will bring new insights into noninvasively collected personalized risk factors of seizure occurrence and seizure propensity that may help to mitigate one of the most difficult aspects of refractory epilepsy: the unpredictability of seizure occurrence. International Registered Report Identifier (IRRID)PRR1-10.2196/25309

BackgroundIn recent years, a growing body of literature has highlighted the role of wearable and mobile remote measurement technology (RMT) applied to seizure detection in hospital settings, whereas more limited evidence has been produced in the community setting. In clinical practice, seizure assessment typically relies on self-report, which is known to be highly unreliable. Moreover, most people with epilepsy self-identify factors that lead to increased seizure likelihood, including mood, behavior, sleep pattern, and cognitive alterations, all of which are amenable to measurement via multiparametric RMT. ObjectiveThe primary aim of this multicenter prospective cohort study is to assess the usability, feasibility, and acceptability of RMT in the community setting. In addition, this study aims to determine whether multiparametric RMT collected in populations with epilepsy can prospectively estimate variations in seizure occurrence and other outcomes, including seizure frequency, quality of life, and comorbidities. MethodsPeople with a diagnosis of pharmacoresistant epilepsy will be recruited in London, United Kingdom, and Freiburg, Germany. Participants will be asked to wear a wrist-worn device and download ad hoc apps developed on their smartphones. The apps will be used to collect data related to sleep, physical activity, stress, mood, social interaction, speech patterns, and cognitive function, both passively from existing smartphone sensors (passive remote measurement technology [pRMT]) and actively via questionnaires, tasks, and assessments (active remote measurement technology [aRMT]). Data will be collected continuously for 6 months and streamed to the Remote Assessment of Disease and Relapse-base (RADAR-base) server. ResultsThe RADAR Central Nervous System project received funding in 2015 from the Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No. 115902. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations. Ethical approval was obtained in London from the Bromley Research Ethics Committee (research ethics committee reference: 19/LO/1884) in January 2020. The first participant was enrolled on September 30, 2020. Data will be collected until September 30, 2021. The results are expected to be published at the beginning of 2022. ConclusionsRADAR Epilepsy aims at developing a framework of continuous data collection intended to identify ictal and preictal states through the use of aRMT and pRMT in the real-life environment. The study was specifically designed to evaluate the clinical usefulness of the data collected via new technologies and compliance, technology acceptability, and usability for patients. These are key aspects to successful adoption and implementation of RMT as a new way to measure and manage long-term disorders. International Registered Report Identifier (IRRID)PRR1-10.2196/21840

Objectives Myocarditis is an overlooked manifestation of anti-synthetase syndrome (ASS). Our study describes the clinical and instrumental features of ASS myocarditis and evaluates the performance of cardiac MRI (CMRI) with mapping techniques in assisting diagnosis of ASS myocarditis. Methods Data from patients with ASS were retrospectively analysed. CMRI data for patients diagnosed with myocarditis, including late gadolinium enhancement (LGE), T2 ratio, T1 mapping, extracellular volume (ECV) and T2 mapping, were reviewed. Myocarditis was defined by the presence of symptoms and/or signs suggestive for heart involvement, including increased high-sensitive troponin T (hs-TnT) and/or N-terminal pro-brain natriuretic peptide (NT-proBNP), and at least an instrumental abnormality. The clinical features of patients with ASS with and without myocarditis were compared. A P -value of <0.05 was considered statistically significant. Results Among a cohort of 43 patients with ASS [median age 58 (48.0–66.0) years; females 74.4%; anti-Jo1 53.5%], 13 (30%) were diagnosed with myocarditis. In 54% of those 13 patients, myocarditis was diagnosed at clinical onset. All patients with ASS with myocarditis had at least one CMRI abnormality: increased ECV in all cases, presence of LGE in 91%, and increased T1 and T2 mapping in 91%. The 2009 Lake Louise criteria (LLC) were satisfied by 6 patients, and the 2018 LLC by 10 patients. With the updated LLC, the sensitivity for myocarditis improved from 54.6% to 91.0%. Patients with ASS with myocarditis were more frequently males (53% vs 13%; P  = 0.009) with fever (69% vs 17%; P  = 0.001), and had higher hs-TnT [88.0 (23.55–311.5) vs 9.80 (5.0–23.0) ng/l; P  < 0.001], NT-proBNP [525.5 (243.5–1575.25) vs 59.0 (32.0–165.5; P  = 0.013) pg/ml; P  = 0.013] and CRP [7.0 (1.7–15.75) vs 1.85 (0.5–2.86) mg/l; P  = 0.011] compared with those without myocarditis. Conclusion In ASS, myocarditis is frequent, even at clinical onset. Patients with ASS with myocarditis frequently presented with fever and increased CRP, suggesting the existence of an inflammatory phenotype. The use of novel CMRI mapping techniques may increase diagnostic sensitivity for myocarditis in ASS. [ABSTRACT FROM AUTHOR]

Objective: Epilepsy management employs self‐reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. Methods: Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%–100%), false alarm rate (FAR; 0–2/day), and device type (external wearable vs. implant) in each scenario. Results: The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. Significance: The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters. [ABSTRACT FROM AUTHOR]

Objective: To determine the diagnostic yield of in‐hospital video–electroencephalography (EEG) monitoring to document seizures in patients with epilepsy. Methods: Retrospective analysis of electronic seizure documentation at the University Hospital Freiburg (UKF) and at King's College London (KCL). Statistical assessment of the role of the duration of monitoring, and subanalyses on presurgical patient groups and patients undergoing reduction of antiseizure medication. Results: Of more than 4800 patients with epilepsy undergoing in‐hospital recordings at the two institutions since 2005, seizures with documented for 43% (KCL) and 73% (UKF).. Duration of monitoring was highly significantly associated with seizure recordings (p <.0001), and presurgical patients as well as patients with drug reduction had a significantly higher diagnostic yield (p <.0001). Recordings with a duration of >5 days lead to additional new seizure documentation in only less than 10% of patients. Significance: There is a need for the development of new ambulatory monitoring strategies to document seizures for diagnostic and monitoring purposes for a relevant subgroup of patients with epilepsy in whom in‐hospital monitoring fails to document seizures. [ABSTRACT FROM AUTHOR]

Introduction: Cardiac injury is commonly reported in COVID‐19 patients, resulting associated to pre‐existing cardiovascular disease, disease severity, and unfavorable outcome. Aim is to report cardiac magnetic resonance (CMR) findings in patients with myocarditis‐like syndrome during the acute phase of SARS‐CoV‐2 infection (AMCovS) and post‐acute phase (cPACS). Methods: Between September 2020 and January 2022, 39 consecutive patients (24 males, 58%) were referred to our department to perform a CMR for the suspicion of myocarditis related to AMCovS (n = 17) and cPACS (n = 22) at multimodality evaluation (clinical, laboratory, ECG, and echocardiography). CMR was performed for the assessment of volume, function, edema and fibrosis with standard sequences and mapping techniques. CMR diagnosis and the extension and amount of CMR alterations were recorded. Results: Patients with suspected myocarditis in acute and post‐COVID settings were mainly men (10 (59%) and 12 (54.5%), respectively) with older age in AMCovS (58 [48–64]) compared to cPACS (38 [26–53]). Myocarditis was confirmed by CMR in most of cases: 53% of AMCovS and 50% of cPACS with negligible LGE burden (3 [IQR, 1–5] % and 2 [IQR, 1–4] %, respectively). Myocardial infarction was identified in 4/17 (24%) patients with AMCovS. Cardiomyopathies were identified in 12% (3/17) and 27% (6/22) of patients with AMCovS and cPACS, including DCM, HCM and mitral valve prolapse. Conclusions: In patients with acute and post‐acute COVID‐19 related suspected myocarditis, CMR improves diagnostic accuracy characterizing ischemic and non‐ischemic injury and unraveling subclinical cardiomyopathies. [ABSTRACT FROM AUTHOR]

Objectives Myocarditis in SSc is associated with a poor prognosis. Cardiac magnetic resonance (CMR) is the non-invasive diagnostic modality of choice for SSc myocarditis. Our study investigates the performance of the mapping techniques included in the revised Lake Louise criteria (LLC) for the identification of SSc myocarditis. Methods CMR data (right and left ventricular function and morphology, early and late gadolinium enhancement [LGE], T2 ratio, and T1 mapping, extracellular volume [ECV] and T2 mapping) of SSc patients diagnosed with myocarditis were reviewed. Myocarditis was defined by the presence of symptoms of SSc heart involvement with increased high-sensitive troponin T (hs-TnT) and/or NT-proBNP and at least an abnormality at 24 h ECG Holter and/or echocardiography and/or CMR. A P -value < 0.05 was considered as statistically significant. Results Nineteen patients (median age 54 [46–70] years; females 78.9%; diffuse SSc 52.6%; anti-Scl70+ 52.6%) were identified: 11 (57.9%) had echocardiographic, and 8 (42.8%) 24 h ECG Holter abnormalities. All patients had at least one CMR abnormality: LGE in 18 (94.7%), increased ECV in 10 (52.6%) and T2 mapping >50 ms in 15 (78.9%). Median T1 and T2 mapping were 1085 [1069–1110] ms and 53.1 [52–54] ms, respectively. T1 mapping directly correlated with NT-proBNP (r  = 0.620; P  = 0.005), ESR (r  = 0.601; P  = 0.008), CRP (r  = 0.685; P  = 0.001) and skin score (r  = 0.507; P  = 0.027); ECV correlated with NT-proBNP serum levels (r  = 0.702; P  = 0.001). No correlations emerged between T2 mapping and other parameters. Ten patients satisfied the 2009 LLC, 17 the 2018 LLC. With the new criteria including T2 mapping, the sensitivity improved from 52.6% to 89.5%. Conclusion The CMR mapping techniques improve the sensitivity to detect myocardial inflammation in patients with SSc heart involvement. The evaluation of T2 mapping increases diagnostic accuracy for the recognition of myocardial inflammation in SSc. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems. [ABSTRACT FROM AUTHOR]

Objective: Movement‐based wearable sensors are used for detection of convulsive seizures. The identification of the absence of motion following a seizure, known as post‐ictal immobility (PI), may represent a potential additional application of wearables. PI has been associated with potentially life‐threatening complications and with sudden unexpected death in epilepsy (SUDEP). We aimed to assess whether wearable accelerometers (ACCs) could be used as a digital marker of PI. Method: Devices with embedded ACCs were worn by patients admitted to an epilepsy monitoring unit. Participants presenting with convulsive seizures were included in the study. PI presence and duration were assessed by experts reviewing video recordings. An algorithm for the automatic detection of post‐ictal ACC silence and its duration was developed and the linear pairwise relationship between the automatically detected duration of post‐ictal ACC silence and the duration of the expert‐labeled PI was analyzed. Results: Twenty‐two convulsive seizures were recorded from 18 study participants. Twenty were followed by PI and two by agitation. The automated estimation of post‐ictal ACC silence identified all the 20 expert‐labeled PI. The regression showed that the duration of the post‐ictal ACC silence was correlated with the duration of PI (Pearson r =.92; P <.001), with the age of study participants (Pearson r =.78; P <.001), and with the duration of post‐ictal generalized electroencephalography suppression (PGES; Pearson r =.4; P =.033). Significance: We highlight a novel application of wearables as a way to record post‐ictal manifestations associated with an increased risk of SUDEP. The occurrence of a fatal seizure is unpredictable and the continuous, non‐invasive, long‐term identification of risk factors associated with each individual seizure may assume a great clinical importance. [ABSTRACT FROM AUTHOR]