ArticleEpilepsia2020
Prospective validation study of an epilepsy seizure risk system for outpatient evaluation.
Article in Epilepsia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- Article
- BAYESIAN NON-HOMOGENEOUS HIDDEN MARKOV MODEL WITH VARIABLE SELECTION FOR INVESTIGATING DRIVERS OF SEIZURE RISK CYCLING.The annals of applied statistics · 2023Article
- Seizure forecasting using machine learning models trained by seizure diaries.Physiological measurement · 2022Article
- Seizure count forecasting to aid diagnostic testing in epilepsy.Epilepsia · 2022Article
- Application of Machine Learning Methods for Epilepsy Risk Ranking in Patients with Hematopoietic Malignancies Using.Journal of personalized medicine · 2022Article
- The construction and practice path of safety education mechanism in colleges and universities integrating the psychological characteristics of students in the new era.Frontiers in psychology · 2022Article
- Can Big Data guide prognosis and clinical decisions in epilepsy?Epilepsia · 2021Review
- Epileptic Seizure Cycles: Six Common Clinical Misconceptions.Frontiers in neurology · 2021Article
- Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic.Frontiers in neurology · 2021Review
- Longitudinal clinical decision support for assessing decisions over time: State-of-the-art and future directions.Digital healthReview
- Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation.Brain stimulationArticle
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 1 country.
Funding
Abstract
objectiveWe conducted clinical testing of an automated Bayesian machine learning algorithm (Epilepsy Seizure Assessment Tool [EpiSAT]) for outpatient seizure risk assessment using seizure counting data, and validated performance against specialized epilepsy clinician experts.
methodsWe conducted a prospective longitudinal study of EpiSAT performance against 24 specialized clinician experts at three tertiary referral epilepsy centers in the United States. Accuracy, interrater reliability, and intra-rater reliability of EpiSAT for correctly identifying changes in seizure risk (improvements, worsening, or no change) were evaluated using 120 seizures from four synthetic seizure diaries (seizure risk known) and 120 seizures from four real seizure diaries (seizure risk unknown). The proportion of observed agreement between EpiSAT and clinicians was evaluated to assess compatibility of EpiSAT with clinical decision patterns by epilepsy experts.
resultsEpiSAT exhibited substantial observed agreement (75.4%) with clinicians for assessing seizure risk. The mean accuracy of epilepsy providers for correctly assessing seizure risk was 74.7%. EpiSAT accurately identified seizure risk in 87.5% of seizure diary entries, corresponding to a significant improvement of 17.4% (P = .002). Clinicians exhibited low-to-moderate interrater reliability for seizure risk assessment (Krippendorff's α = 0.46) with good intrarater reliability across a 4- to 12-week evaluation period (Scott's π = 0.89). SIGNIFICANCE: These results validate the ability of EpiSAT to yield objective clinical recommendations on seizure risk which follow decision patterns similar to those from specialized epilepsy providers, but with improved accuracy and reproducibility. This algorithm may serve as a useful clinical decision support system for quantitative analysis of clinical seizure frequency in clinical epilepsy practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.