Patients' attitude toward AED withdrawal: A survey among individuals who had been seizure-free for over 2 years

Cvetkovska, Emilija; Babunovska, Marija; Kuzmanovski, Igor; Boskovski, Bojan; Sazdova-Burneska, Slobodanka; Aleksovski, Vasko; Isjanovska, Rozalinda

doi:10.1016/j.yebeh.2019.106881

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…Fifth, while we captured many relevant variables (including age, focality, epileptogenic neurological conditions, dementia, comorbidity burden, neurologist care, ASM treatment at least 2 years, etc. ), Medicare lacks potentially important variables such as total duration of ASM treatment, prior failed discontinuation attempts, EEG results, or employment and driving considerations [ 22 , 23 ] (possibly mitigated here by considering a disabled/older population). We also acknowledge that complex interactions may exist between variables that were not studied here.…”

Section: Discussionmentioning

confidence: 99%

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

et al. 2022

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Background For the two-thirds of patients with epilepsy who achieve seizure remission on antiseizure medications (ASMs), patients and clinicians must weigh the pros and cons of long-term ASM treatment. However, little work has evaluated how often ASM discontinuation occurs in practice. We describe the incidence of and predictors for sustained ASM fill gaps to measure discontinuation in individuals potentially eligible for ASM withdrawal. Methods This was a retrospective cohort of Medicare beneficiaries. We included patients with epilepsy by requiring International Classification of Diseases codes for epilepsy/convulsions plus at least one ASM prescription each year 2014–2016, and no acute visit for epilepsy 2014–2015 (i.e., potentially eligible for ASM discontinuation). The main outcome was the first day of a gap in ASM supply (30, 90, 180, or 360 days with no pills) in 2016–2018. We displayed cumulative incidence functions and identified predictors using Cox regressions. Results Among 21,819 beneficiaries, 5191 (24%) had a 30-day gap, 1753 (8%) had a 90-day gap, 803 (4%) had a 180-day gap, and 381 (2%) had a 360-day gap. Predictors increasing the chance of a 180-day gap included number of unique medications in 2015 (hazard ratio [HR] 1.03 per medication, 95% confidence interval [CI] 1.01–1.05) and epileptologist prescribing physician (≥25% of that physician’s visits for epilepsy; HR 2.37, 95% CI 1.39–4.03). Predictors decreasing the chance of a 180-day gap included Medicaid dual eligibility (HR 0.75, 95% CI 0.60–0.95), number of unique ASMs in 2015 (e.g., 2 versus 1: HR 0.37, 95% CI 0.30–0.45), and greater baseline adherence (> 80% versus ≤80% of days in 2015 with ASM pill supply: HR 0.38, 95% CI 0.32–0.44). Conclusions Sustained ASM gaps were rarer than current guidelines may suggest. Future work should further explore barriers and enablers of ASM discontinuation to understand the optimal discontinuation rate.

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Section: Discussionmentioning

confidence: 99%

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

et al. 2022

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show abstract

“…Fifth, while we captured many relevant variables (including age, focality, epileptogenic neurological conditions, dementia, comorbidity burden, neurologist care, ASM treatment at least two years, etc. ), Medicare lacks potentially important variables such as total duration of ASM treatment, prior failed discontinuation attempts, EEG results, or employment and driving considerations (22,23) (possibly mitigated here by considering a disabled/older population).…”

Section: Discussionmentioning

confidence: 99%

“…While many cohorts have estimated post-withdrawal seizure relapse risk,(18) only limited literature (22)(23)(24) has focused on the implementation of discontinuation decisions in practice. (25) For instance, singlecenter work found that only one-third of seizure-free patients reported their physician had even mentioned the possibility of ASM discontinuation suggesting that discontinuation attempts may be too rare.…”

Section: Introductionmentioning

confidence: 99%

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

Terman

Niznik

Slinger

et al. 2022

Preprint

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Background: For the two-thirds of patients with epilepsy who achieve seizure remission on antiseizure medications (ASMs), patients and clinicians must weigh the pros and cons of long-term ASM treatment. However, little work has evaluated how often ASM discontinuation occurs in practice. We describe the incidence of and predictors for sustained ASM fill gaps to measure discontinuation in individuals potentially eligible for ASM withdrawal.Methods: This was a retrospective cohort of Medicare beneficiaries. We included patients with epilepsy by requiring International Classification of Diseases codes for epilepsy/convulsions plus at least one ASM prescription each year 2014-2016, and no acute visit for epilepsy 2014-2015 (i.e., potentially eligible for ASM discontinuation). The main outcome was the first day of a gap in ASM supply (30, 90, 180, or 360 days with no pills) in 2016-2018. We displayed cumulative incidence functions and identified predictors using Cox regressions.Results: Among 21,819 beneficiaries, 5,191 (24%) had a 30-day gap, 1,753 (8%) had a 90-day gap, 803 (4%) had a 180-day gap, and 381 (2%) had a 360-day gap. Predictors increasing the chance of a 180-day gap included number of unique medications in 2015 (hazard ratio [HR] 1.03 per medication, 95% confidence interval [CI] 1.02-1.05) and epileptologist prescribing physician (≥25% of that physician’s visits for epilepsy; HR 2.38, 95% CI 1.42-4.00). Predictors decreasing the chance of a 180-day gap included Medicaid dual eligibility (HR 0.74, 95% CI 0.59-0.93), number of unique ASMs in 2015 (e.g., 2 versus 1: HR 0.37, 95% CI 0.30-0.45), and greater baseline adherence (>80% versus ≤80% of days in 2015 with ASM pill supply: HR 0.38, 95% CI 0.32-0.44).Conclusions: Sustained ASM gaps were rarer than current guidelines may suggest. Future work should further explore barriers and enablers of ASM discontinuation to understand the optimal discontinuation rate.

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“…Additionally, the misdiagnosis rate of epilepsy in children is at least 25% [ 2 ]. Population-based studies have shown that after regular treatment, nearly two-thirds of epileptic children become seizure-free within 3 to 5 years [ 4 ], and almost half of the patients can successfully stop anti-epileptic drugs [ 5 ]. Therefore, timely diagnosis and treatment for children with epilepsy are crucial.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy

Liang

Chen

Zhang

2022

Sensors

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Most of the current complex network studies about epilepsy used the electroencephalogram (EEG) to directly construct the static complex network for analysis and discarded the dynamic characteristics. This study constructed the dynamic complex network on EEG from pediatric epilepsy and pediatric control when they were asleep by the sliding window method. Dynamic features were extracted and incorporated into various machine learning classifiers to explore their classification performances. We compared these performances between the static and dynamic complex network. In the univariate analysis, the initially insignificant topological characteristics in the static complex network can be transformed to be significant in the dynamic complex network. Under most connectivity calculation methods between leads, the accuracy of using dynamic complex network features for discrimination was higher than that of static complex network features. Particularly in the imaginary part of the coherency function (iCOH) method under the full-frequency band, the discrimination accuracies of most machine learning classifiers were higher than 95%, and the discrimination accuracies in the higher-frequency band (beta-frequency band) and the full-frequency band were higher than that of the lower-frequency bands. Our proposed method and framework could efficiently summarize more time-varying features in the EEG and improve the accuracies of the discrimination of the machine learning classifiers more than using static complex network features.

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Patients' attitude toward AED withdrawal: A survey among individuals who had been seizure-free for over 2 years

Cited by 9 publications

References 16 publications

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy

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Patients' attitude toward AED withdrawal: A survey among individuals who had been seizure-free for over 2 years

Cited by 9 publications

References 16 publications

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

Feature Extraction of the Brain’s Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy

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Patients' attitude toward AED withdrawal: A survey among individuals who had been seizure-free for over 2 years