Kyedhumphrey1553

Objective High-frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high-frequency events in the intracranial EEG that are morphologically distinct from background without requiring assumptions about event amplitude or shape. Method We propose the anomaly detection algorithm (ADA), which uses unsupervised machine learning to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root mean square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared. Result In the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non-SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitude-based method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels (P less then .0045), and the amplitude measurement was more stable over time than the HFO rate, as indicated by a lower coefficient of variation (P less then .0125). Significance ADA does not require human supervision, parameter optimization, or prior assumptions about event shape, amplitude, or duration. Our results suggest that the algorithm's estimate of event amplitude may differentiate SOZ and non-SOZ channels. Further studies will examine the utility of HFO amplitude as a biomarker for epilepsy surgical outcome.Objective We present a model for the outpatient care of patients undergoing continuous electroencephalography (cEEG) monitoring during a hospitalization, named the post-acute symptomatic seizure (PASS) clinic. We investigated whether establishing this clinic led to improved access to epileptologist care. Methods As part of the PASS clinic initiative, electronic health record (EHR) provides an automated alert to the inpatient care team discharging adults on first time antiepileptic drug (AED) after undergoing cEEG monitoring. The alert explains the rationale and facilitates scheduling for a PASS clinic appointment, three-month after discharge, along with a same-day extended (75 minutes) EEG. We compared the initial epilepsy clinic visits by patients undergoing cEEG in 2017, before ("Pre-PASS" period and cohort) and after ("PASS" period and cohort) the alert went live in the EHR. Results Of the 170 patients included, 68 (40%) suffered a seizure during the mean follow-up of 20.9 ± 10 months. AEDs were stopped or reduced in 66 out of 148 (44.6%) patients discharged on AEDs. Pre-PASS cohort included 45 patients compared to 145 patients in the PASS cohort, accounting for 5.8% and 9.9% of patients, respectively, who underwent cEEG during the corresponding periods (odds ratio [OR] = 1.8, 95% CI = 1.26-2.54, P = .001). selleck kinase inhibitor The two cohorts did not differ in terms of electrographic or clinical seizures. The PASS cohort was significantly more likely to be followed up within 1-6 months of discharge (OR = 4.6, 95% CI = 2.1-10.1, P less then .001) and have a pre-clinic EEG (51.2% vs 11.1%; OR = 8.39, 95% CI = 3.1-22.67, P less then .001). Significance PASS clinic, a unique outpatient transition of care model for managing patients at risk of acute symptomatic seizure led to an almost twofold increase in access to an epileptologist. Future research should address the wide knowledge gap about the best post-hospital discharge management practices for these patients.Objective Refractory status epilepticus is a serious condition in which seizure continues despite use of two antiepileptic medications. Retrospective studies have shown that 29%-43% of SE patients progress into RSE despite treatment. Mortality following RSE is high. We aimed to evaluate the predictors of outcome in patients with RSE at a tertiary care center. Methods Sixty-eight consecutive patients with RSE who presented to our hospital between February 2018 and January 2020 were evaluated for outcome. Result In our study 28(41.2%), patients who failed to respond to first- and second-line antiepileptic drug responded to the third-line antiepileptic drug thus avoiding mechanical ventilation and intravenous anesthesia. Low GCS at admission (P less then .001), need for mechanical ventilation and intravenous anesthesia (P = .018), and long duration of RSE before recovery (P = .035) were strongly associated with worse outcome. Duration of RSE before starting treatment (P = .147), previous history of seizure (P = .717), and age of the patient (P = .319) did not influence the outcome. Significance In our study, we prospectively evaluated patients with RSE and followed them for one month after discharge from the hospital. Unlike some of the previous studies, we identified an interesting finding whereby a significant proportion of the patients responded to the third-line antiepileptic drug and thus avoiding the complications related to mechanical ventilation.Objective A 2007 study performed at Montefiore Medical Center (Bronx, NY) identified high prevalence of reduced bone density in an urban population of patients with epilepsy and suggested that bone mineralization screenings should be regularly performed for these patients. We conducted a long-term follow-up study to determine whether bone mineral density (BMD) loss, osteoporosis, and fractures have been successfully treated or prevented. Methods In the current study, patients from the 2007 study who had two dual-energy absorptiometry (DXA) scans performed at least 5 years apart were analyzed. The World Health Organization (WHO) criteria to diagnose patients with osteopenia or osteoporosis were used, and each patient's probability of developing fractures was calculated with the Fracture Risk Assessment Tool (FRAX). Results The median time between the first and second DXA scans for the 81 patients analyzed was 9.4 years (range 5-14.7). The median age at the first DXA scan was 41 years (range 22-77). Based on WHO criteria, 79.