Lancet Neurology: Adult Bureau Of Eclampsia Risk Prediction: A Developmental and Verification Study

Epilepsy is a chronic disease in which neurons in the brain discharge sudden abnormalities, leading to transient brain dysfunction.
the seizure is short-lived, it requires long-term treatment and can lead to severe disability.
people with epilepsy live in an uncertain state, and the constant threat of seizures has implications for personal safety, independence and mental health.
Over the past decade, comprehensive evidence from chronic electro-encephalogram (cEEG) studies has shown that brain activity in people with epilepsy is strongly cyclical, lasting several hours (circadian rhythms) and days (many days and nights).
this study hypothesically used these cycles to estimate the probability of future seizures, and the researchers tested the feasibility of predicting seizures in advance.
In this paper, the authors' team conducted a retrospective analysis of cEEG data recorded using implants in patients with drug-resistant agency epilepsy (age ≥18) who were followed up at 35 U.S. centers between January 19, 2004 and May 18, 2018.
require patients to have 20 or more electrograph seizures (development queues) or self-reported seizures (verification queues).
the device records epileptic activity (IEA; ≥6 months of continuous hourly data) in all patients), where fluctuations help to estimate different seizure risks.
This study developed a point-process statistical model for the initial portion of cEEG data (two queues) for each patient to form a prediction of the probability of seizures, which was tested on subsequently not observed seizure data and evaluated against an alternative time series.
result of this is that the forecast shows the percentage of patients (IoCs) with improved opportunities.
the study screened 72 and 256 patients, respectively, and included 18 and 157 of them in the development and validation queues, respectively.
in 15 patients (83%) in the development queue and 103 (66%) in the validation queue, the model combined information from multiple IEA cycles and generated a seizure prediction for the next date through the IoC.
61 (39%) out of 18 patients (11%) and 157 patients were extended to three days while maintaining the IoC.
predicting a shorter 1-hour range (which may only apply to electrograph seizures in development queues) shows that all 18 (100%) patients have IoCs.
, the results of this paper confirm a new view that seizures are not entirely random events.
Proix, Timothée et al. Forecasting seizure risk in adults with focal epilepsy: a development and validation study. The Lancet Neurology, Volume 20, Issue 2, 127 - 135MedSci Original Source: MedSci Original Copyright Notice: All text, images and audio and video materials on this website that indicate "Source: Met Medical" or "Source: MedSci Original" are owned by Mets Medical and are not authorized to reproduce, and any media, website or individual must indicate "Source: Mays Medicine" when they are reproduced.
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