Neuropsychiatric complications of coronavirus disease 2019: Mount Sinai Health System cohort study.

OBJECTIVE: To describe the frequency of neuropsychiatric complications among hospitalized patients with coronavirus disease 2019 (COVID-19) and their association with pre-existing comorbidities and clinical outcomes. METHODS: We retrospectively identified all patients hospitalized with COVID-19 within a large multicenter New York City health system between March 15, 2020 and May 17, 2021 and randomly selected a representative cohort for detailed chart review. Clinical data, including the occurrence of neuropsychiatric complications (categorized as either altered mental status [AMS] or other neuropsychiatric complications) and in-hospital mortality, were extracted using an electronic medical record database and individual chart review. Associations between neuropsychiatric complications, comorbidities, laboratory findings, and in-hospital mortality were assessed using multivariate logistic regression. RESULTS: Our study cohort consisted of 974 patients, the majority were admitted during the first wave of the pandemic. Patients were treated with anticoagulation (88.4%), glucocorticoids (24.8%), and remdesivir (10.5%); 18.6% experienced severe COVID-19 pneumonia (evidenced by ventilator requirement). Neuropsychiatric complications occurred in 58.8% of patients; 39.8% experienced AMS; and 19.0% experienced at least one other complication (seizures in 1.4%, ischemic stroke in 1.6%, hemorrhagic stroke in 1.0%) or symptom (headache in 11.4%, anxiety in 6.8%, ataxia in 6.3%). Higher odds of mortality, which occurred in 22.0%, were associated with AMS, ventilator support, increasing age, and higher serum inflammatory marker levels. Anticoagulant therapy was associated with lower odds of mortality and AMS. CONCLUSION: Neuropsychiatric complications of COVID-19, especially AMS, were common, varied, and associated with in-hospital mortality in a diverse multicenter cohort at an epicenter of the COVID-19 pandemic.

Case and Management Considerations of Low-Grade Cystic Duct Stump Dysplasia after Laparoscopic Cholecystectomy.

Cholecystectomies have become one of the more commonly practiced procedures. As a result, there has been a rise in neoplastic changes in excised specimens. Due to this, surgeons must be prepared to manage possible malignancy after resecting what was previously thought to be a benign gallbladder. While management for high-grade dysplasia has been more clearly laid out in literature, data on management of low-grade dysplasia are limited. Here, we report a novel case of a 46-year-old woman with an incidental low-grade dysplasia of the cystic duct stump after a laparoscopic cholecystectomy for biliary colic. The decision was made to excise the remaining stump without further surveillance postoperatively given benign pathology findings. More documented cases and their management and ultimately longitudinal cohort studies will help facilitate the creation of guidelines for managing this particular pathology.

Older adults with epilepsy and COVID-19: Outcomes in a multi-hospital health system.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with high rates of mortality and morbidity in older adults, especially those with pre-existing conditions. There is little work investigating how neurological conditions affect older adults with COVID-19. We aimed to compare in-hospital outcomes, including mortality, in older adults with and without epilepsy. METHODS: This retrospective study in a large multicenter New York health system included consecutive older patients (age ≥65 years) either with or without epilepsy who were admitted with COVID-19 between 3/2020-5/2021. Epilepsy was identified using a validated International Classification of Disease (ICD) and antiseizure medicationbased case definition. Univariate comparisons were calculated using Chi-square, Fisher's exact, Mann-Whitney U, or Student's t-tests. Multivariable logistic regression models were generated to examine factors associated with mortality, discharge disposition and length of stay (LOS). RESULTS: We identified 5384 older adults admitted with COVID-19 of whom 173 (3.21 %) had epilepsy. Mean age was significantly lower in those with (75.44, standard deviation (SD): 7.23) compared to those without epilepsy (77.98, SD: 8.68, p = 0.007). Older adults with epilepsy were more likely to be ventilated (35.84 % vs. 16.18 %, p < 0.001), less likely to be discharged home (21.39 % vs. 43.12 %, p < 0.001), had longer median LOS (13 days vs. 8 days, p < 0.001), and had higher in-hospital death (35.84 % vs. 28.29 %, p = 0.030) compared to those without epilepsy. Epilepsy in older adults was associated with increased odds of in-hospital death (adjusted odds ratio (aOR), 1.55; 95 % CI 1.12-2.14, p = 0.032), non-routine discharge disposition (aOR, 3.34; 95 % CI 2.21-5.03, p < 0.001), and longer LOS (46.46 % 95 % CI 34 %-59 %, p < 0.001). CONCLUSIONS: In models that adjusted for multiple confounders including comorbidity and age, our study found that epilepsy was still associated with higher in-hospital mortality, longer LOS and worse discharge dispositions in older adults with COVID-19 higher in-hospital mortality, longer LOS and worse discharge dispositions in older adults with COVID-19. This work reinforces that epilepsy is a risk factor for worse outcomes in older adults admitted with COVID-19. Timely identification and treatment of COVID-19 in epilepsy may improve outcomes in older people with epilepsy.

In-hospital outcomes in patients with and without epilepsy diagnosed with COVID-19-A cohort study.

OBJECTIVES: Coronavirus disease 2019 (COVID-19) is associated with mortality in persons with comorbidities. The aim of this study was to evaluate in-hospital outcomes in patients with COVID-19 with and without epilepsy. METHODS: We conducted a retrospective study of patients with COVID-19 admitted to a multicenter health system between March 15, 2020, and May 17, 2021. Patients with epilepsy were identified using a validated International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)/ICD-10-CM case definition. Logistic regression models and Kaplan-Meier analyses were conducted for mortality and non-routine discharges (i.e., not discharged home). An ordinary least-squares regression model was fitted for length of stay (LOS). RESULTS: We identified 9833 people with COVID-19 including 334 with epilepsy. On univariate analysis, people with epilepsy had significantly higher ventilator use (37.70% vs 14.30%, p < .001), intensive care unit (ICU) admissions (39.20% vs 17.70%, p < .001) mortality rate (29.60% vs 19.90%, p < .001), and longer LOS (12 days vs 7 days, p < .001). and fewer were discharged home (29.64% vs 57.37%, p < .001). On multivariate analysis, only non-routine discharge (adjusted odds ratio [aOR] 2.70, 95% confidence interval [CI] 2.00-3.70; p < .001) and LOS (32.50% longer, 95% CI 22.20%-43.60%; p < .001) were significantly different. Factors associated with higher odds of mortality in epilepsy were older age (aOR 1.05, 95% CI 1.03-1.08; p < .001), ventilator support (aOR 7.18, 95% CI 3.12-16.48; p < .001), and higher Charlson comorbidity index (CCI) (aOR 1.18, 95% CI 1.04-1.34; p = .010). In epilepsy, admissions between August and December 2020 or January and May 2021 were associated with a lower odds of non-routine discharge and decreased LOS compared to admissions between March and July 2020, but this difference was not statistically significant. SIGNIFICANCE: People with COVID-19 who had epilepsy had a higher odds of non-routine discharge and longer LOS but not higher mortality. Older age (≥65), ventilator use, and higher CCI were associated with COVID-19 mortality in epilepsy. This suggests that older adults with epilepsy and multimorbidity are more vulnerable than those without and should be monitored closely in the setting of COVID-19.

Literacy limitations to psychological evaluation tools: The case of MU.

Introduction to the reader In this case, a survivor of torture presents with symptoms clinically consistent with both major depressive disorder (MDD) and post-traumatic stress disorder (PTSD). During her evaluation, a validated psychological questionnaire for PTSD was administered verbally through a translator and accurately identified this diagnosis. However, a self-administered (read and completed by the client) questionnaire for MDD vastly underestimated the severity of her symptoms and failed to diagnose her with depression. The client had not completed grade school, so it is likely that her literacy level impacted the accuracy of this questionnaire. This highlights one of the many limitations that exist when administering psychological surveys. Through understanding these limitations, forensic evaluators can develop ways to identify, mitigate, and overcome limitations of these useful tools.

Measuring Real-Time Medication Effects From Electroencephalography.

PURPOSE: Evaluating the effects of antiseizure medication (ASM) on patients with epilepsy remains a slow and challenging process. Quantifiable noninvasive markers that are measurable in real-time and provide objective and useful information could guide clinical decision-making. We examined whether the effect of ASM on patients with epilepsy can be quantitatively measured in real-time from EEGs. METHODS: This retrospective analysis was conducted on 67 patients in the long-term monitoring unit at Boston Children's Hospital. Two 30-second EEG segments were selected from each patient premedication and postmedication weaning for analysis. Nonlinear measures including entropy and recurrence quantitative analysis values were computed for each segment and compared before and after medication weaning. RESULTS: Our study found that ASM effects on the brain were measurable by nonlinear recurrence quantitative analysis on EEGs. Highly significant differences (P < 1e-11) were found in several nonlinear measures within the seizure zone in response to antiseizure medication. Moreover, the size of the medication effect correlated with a patient's seizure frequency, seizure localization, number of medications, and reported seizure frequency reduction on medication. CONCLUSIONS: Our findings show the promise of digital biomarkers to measure medication effects and epileptogenicity.

Association between semiologic, autonomic, and electrographic seizure characteristics in children with generalized tonic-clonic seizures.

INTRODUCTION: Generalized tonic-clonic seizures (GTCS) are associated with elevated electrodermal activity (EDA) and postictal generalized electroencephalographic suppression (PGES), markers that may indicate sudden unexpected death in epilepsy (SUDEP) risk. This study investigated the association of GTCS semiology, EDA, and PGES in children with epilepsy. METHODS: Patients admitted to the Boston Children's Hospital long-term video-EEG monitoring unit wore a sensor that records EDA. We selected patients with at least one GTCS and reviewed video-EEGs for semiology, tonic and clonic phase duration, total clinical seizure duration, electrographic onset, offset, and PGES. We grouped patients into three semiology classes: GTCS 1: bilateral symmetric tonic arm extension, GTCS 2: no specific tonic arm extension or flexion, GTCS 3: unilateral or asymmetrical arm extension, tonic arm flexion or posturing that does not fit into GTCS 1 or 2. We analyzed the correlation between semiology, EDA, and PGES, and measured the area under the curve (AUC) of the ictal EDA (seizure onset to one hour after), subtracting baseline EDA (one-hour seizure-free before seizure onset). Using generalized estimating equation (GEE) and linear regression, we analyzed all seizures and single episodes per patient. RESULTS: We included 30 patients (median age 13.8 ±â€¯3.6 years, 46.7% females) and 53 seizures. With GEE, GTCS 1 was associated with longer PGES duration compared to GTCS 2 (Estimate (ß) = -26.32 s, 95% Confidence Interval (CI): -36.46 to -16.18, p < 0.001), and the presence of PGES was associated with greater EDA change (ß = 429604 µS, 95% CI: 3550.96 to 855657.04, p = 0.048). With single-episode analysis, GTCS 1 had greater EDA change than GTCS 2 ((ß = -601339 µS, 95% CI: -1167016.56 to -35661.44, p = 0.047). EDA increased with PGES presence (ß = 637500 µS, 95% CI: 183571.84 to 1091428.16, p = 0.01) and duration (ß = 16794 µS, 95% CI: 5729.8 to 27858.2, p = 0.006). Patients with GTCS 1 had longer PGES duration compared to GTCS 2 (ß = -30.53 s, 95% CI: -44.6 to -16.46, p < 0.001) and GTCS 3 (ß = -22.07 s, 95% CI: -38.95 to -5.19, p = 0.016). CONCLUSION: In children with epilepsy, PGES correlates with greater ictal EDA. GTCS 1 correlated with longer PGES duration and may indirectly correlate with greater ictal EDA. Our study suggests potential applications in monitoring and preventing SUDEP in these patients.

Seizure detection using wearable sensors and machine learning: Setting a benchmark.

OBJECTIVE: Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long-term ambulatory monitoring. This study evaluates the seizure detection performance of custom-developed machine learning (ML) algorithms across a broad spectrum of epileptic seizures utilizing wrist- and ankle-worn multisignal biosensors. METHODS: We enrolled patients admitted to the epilepsy monitoring unit and asked them to wear a wearable sensor on either their wrists or ankles. The sensor recorded body temperature, electrodermal activity, accelerometry (ACC), and photoplethysmography, which provides blood volume pulse (BVP). We used electroencephalographic seizure onset and offset as determined by a board-certified epileptologist as a standard comparison. We trained and validated ML for two different algorithms: Algorithm 1, ML methods for developing seizure type-specific detection models for nine individual seizure types; and Algorithm 2, ML methods for building general seizure type-agnostic detection, lumping together all seizure types. RESULTS: We included 94 patients (57.4% female, median age = 9.9 years) and 548 epileptic seizures (11 066 h of sensor data) for a total of 930 seizures and nine seizure types. Algorithm 1 detected eight of nine seizure types better than chance (area under the receiver operating characteristic curve [AUC-ROC] = .648-.976). Algorithm 2 detected all nine seizure types better than chance (AUC-ROC = .642-.995); a fusion of ACC and BVP modalities achieved the best AUC-ROC (.752) when combining all seizure types together. SIGNIFICANCE: Automatic seizure detection using ML from multimodal wearable sensor data is feasible across a broad spectrum of epileptic seizures. Preliminary results show better than chance seizure detection. The next steps include validation of our results in larger datasets, evaluation of the detection utility tool for additional clinical seizure types, and integration of additional clinical information.

Twenty-four-hour patterns in electrodermal activity recordings of patients with and without epileptic seizures.

OBJECTIVE: Daytime and nighttime patterns affect the dynamic modulation of brain and body functions and influence the autonomic nervous system response to seizures. Therefore, we aimed to evaluate 24-hour patterns of electrodermal activity (EDA) in patients with and without seizures. METHODS: We included pediatric patients with (a) seizures (SZ), including focal impaired awareness seizures (FIAS) or generalized tonic-clonic seizures (GTCS), (b) no seizures and normal electroencephalography (NEEG), or (c) no seizures but epileptiform activity in the EEG (EA) during vEEG monitoring. Patients wore a device that continuously recorded EDA and temperature (TEMP). EDA levels, EDA spectral power, and TEMP levels were analyzed. To investigate 24-hour patterns, we performed a nonlinear mixed-effects model analysis. Relative mean pre-ictal (-30 min to seizure onset) and post-ictal (I: 30 min after seizure offset; II: 30 to 60 min after seizure offset) values were compared for SZ subgroups. RESULTS: We included 119 patients (40 SZ, 17 NEEG, 62 EA). EDA level and power group-specific models (SZ, NEEG, EA) (h = 1; P < .01) were superior to the all-patient cohort model. Fifty-nine seizures were analyzed. Pre-ictal EDA values were lower than respective 24-hour modulated SZ group values. Post hoc comparisons following the period-by-seizure type interaction (EDA level: χ2  = 18.50; P < .001, and power: χ2  = 6.73; P = .035) revealed that EDA levels were higher in the post-ictal period I for FIAS and GTCS and in post-ictal period II for GTCS only compared to the pre-ictal period. SIGNIFICANCE: Continuously monitored EDA shows a pattern of change over 24 hours. Curve amplitudes in patients with recorded seizures were lower as compared to patients who did not exhibit seizures during the recording period. Sympathetic skin responses were greater and more prolonged in GTCS compared to FIAS. EDA recordings from wearable devices offer a noninvasive tool to continuously monitor sympathetic activity with potential applications for seizure detection, prediction, and potentially sudden unexpected death in epilepsy (SUDEP) risk estimation.

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting.

OBJECTIVE: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient-specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head. METHODS: Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way. RESULTS: Using a leave-one-subject-out cross-validation approach, we identified better-than-chance predictability in 43% of the patients. Time-matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future. SIGNIFICANCE: Collectively, these results show that statistically significant seizure risk assessments are feasible from easy-to-use, noninvasive wearable devices without the need of patient-specific training or parameter optimization.

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures.

A better understanding of the early detection of seizures is highly desirable as identification of an impending seizure may afford improved treatments, such as antiepileptic drug chronotherapy, or timely warning to patients. While epileptic seizures are known to often manifest also with autonomic nervous system (ANS) changes, it is not clear whether ANS markers, if recorded from a wearable device, are also informative about an impending seizure with statistically significant sensitivity and specificity. Using statistical testing with seizure surrogate data and a unique dataset of continuously recorded multi-day wristband data including electrodermal activity (EDA), temperature (TEMP) and heart rate (HR) from 66 people with epilepsy (9.9 ± 5.8 years; 27 females; 161 seizures) we investigated differences between inter- and preictal periods in terms of mean, variance, and entropy of these signals. We found that signal mean and variance do not differentiate between inter- and preictal periods in a statistically meaningful way. EDA signal entropy was found to be increased prior to seizures in a small subset of patients. Findings may provide novel insights into the pathophysiology of epileptic seizures with respect to ANS function, and, while further validation and investigation of potential causes of the observed changes are needed, indicate that epilepsy-related state changes may be detectable using peripheral wearable devices. Detection of such changes with wearable devices may be more feasible for everyday monitoring than utilizing an electroencephalogram.

Electrocardiographic changes associated with epilepsy beyond heart rate and their utilization in future seizure detection and forecasting methods.

The ability to assess seizure risk may help provide timely warnings and more personalized treatment plans for people with epilepsy (PWE). ECG changes are commonly observed in epilepsy which make ECG a promising candidate to monitor seizure risk. Most ECG research in this domain has focused on heart rate-related changes. However, several studies have identified a range of other peri-ictal ECG parameter changes that may potentially prove useful for seizure detection and forecasting. Here, we offer a systematic review of ECG changes in epilepsy outside of heart rate. We performed the systematic literature review according to PRISMA guidelines using key words related to ECG, SUDEP and epilepsy. We identified and screened 502 abstracts, read 110 full papers, and included 24 papers in the final review. Our results suggest that PWE may be more prone to cardiac conduction abnormalities than healthy controls. During interictal periods, PWE were more likely to have abnormal QTc intervals, ST segment abnormalities, elevated T Waves, early repolarization (ER), increased P Wave dispersion and PR intervals when compared to controls. Apart from these baseline abnormalities, changes during the pre-ictal and ictal states have been reported, with arrhythmias, QTc prolongation and ST segment changes being the most common. A better understanding of these state-dependent changes may afford less-cumbersome and less-stigmatizing epilepsy monitoring tools in the future.