Does my posterior look big in this?—Bayesian solutions to seizure counting problems

Cook, Blake; Cook, Mark

doi:10.1002/epi4.12328

Cited by 1 publication

(1 citation statement)

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“…However, interpreting changes in raw frequencies of patient‐reported seizures involves a degree of uncertainty . Crude estimates of seizure frequency over the past few months can be misleading; often, apparent changes in seizure frequency can be caused by the simple natural variability of epilepsy or variations in reporting accuracy . If mistakenly interpreted as a change in the underlying propensity toward seizures, this may lead to unnecessary or potentially harmful treatment changes.…”

Section: Introductionmentioning

confidence: 99%

Prospective validation study of an epilepsy seizure risk system for outpatient evaluation

Chiang

Goldenholz

Moss³

et al. 2019

Epilepsia

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Objective: We 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. Methods: We 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. Results: EpiSAT 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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Section: Introductionmentioning

confidence: 99%