Patient and caregiver preferences for the potential benefits and risks of a seizure forecasting device: A best–worst scaling

Janse, Sarah; Dumanis, Sonya B.; Huwig, Tanya; Hyman, Sarah; Fureman, Brandy E.; Bridges, John F.P.

doi:10.1016/j.yebeh.2019.04.018

Cited by 41 publications

(50 citation statements)

References 34 publications

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“…In one survey, participants reported that missed seizures were considered worse than false alarms, and that perfect accuracy was not considered a requirement for a forecasting device 21 . Recently, Janse et al 23 showed that seizure forecasting devices were acceptable despite the potential for inaccuracy (up to “inaccurate 30% of the time”). Ultimately, post hoc studies and surveys can only provide an indicative measure of the utility and benefits of a seizure forecasting device.…”

Section: Discussionmentioning

confidence: 99%

“…It is now recognized that estimating the probability of someone having a seizure in the near future is more feasible than trying to predict the exact timing of their next seizure 12,17,20 . There are increasing efforts to develop a clinical seizure forecasting device 1 and understand user requirements 21‐23 . Patient surveys have confirmed that probabilistic forecasts are considered useful and that perfect accuracy is not a requirement of such devices 21,23 .…”

Section: Introductionmentioning

confidence: 99%

“…There are increasing efforts to develop a clinical seizure forecasting device 1 and understand user requirements 21‐23 . Patient surveys have confirmed that probabilistic forecasts are considered useful and that perfect accuracy is not a requirement of such devices 21,23 . For practical reasons, externally worn devices are rated more desirable than implanted recording devices in surveys 23 .…”

Section: Introductionmentioning

confidence: 99%

“…Patient surveys have confirmed that probabilistic forecasts are considered useful and that perfect accuracy is not a requirement of such devices 21,23 . For practical reasons, externally worn devices are rated more desirable than implanted recording devices in surveys 23 . There is increasing availability of wearable technology for seizure detection and forecasting 24‐26 .…”

Section: Introductionmentioning

confidence: 99%

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Forecasting cycles of seizure likelihood

et al. 2020

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Objective: Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Methods: Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms. Results: The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low-risk state, with 14.8% of their time in a high-risk warning state. On average, 69.1% of seizures occurred during high-risk states and 10.5% of seizures occurred in low-risk states. Significance: Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities. K E Y W O R D Scircadian rhythms, epilepsy, mobile health, multiday rhythms, seizure cycles, seizure forecasting | 777 KAROLY et AL.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting cycles of seizure likelihood

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are increasing efforts to develop a clinical seizure forecasting device 1 and understand user requirements [21][22][23] . Patient surveys have confirmed that probabilistic forecasts are considered useful and that perfect accuracy is not a requirement of such devices 21,23 . For practical reasons, externally worn devices are rated more desirable than implanted recording devices in surveys 23 .…”

Section: Introductionmentioning

confidence: 99%

Forecasting Cycles of Seizure Likelihood

Karoly

Cook

Maturana³

et al. 2019

Preprint

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Objective: Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Methods: Forecasts based on circadian and multiday seizure cycles were tested pseudo-prospectively using data from 33 app users (mean of 103 seizures per subject). Individual's strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms. Results: The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures. Forecasts using only mobile app diaries allowed users to spend an average of 62.8% of their time in a low-risk state, with 16.6% of their time in a high-risk warning state. On average, 64.5% of seizures occurred during high-risk states and less than 10% of seizures occurred in low-risk states. Significance: Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.

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Development and Validation of Forecasting Next Reported Seizure Using e‐Diaries

Goldenholz

Romero

et al. 2020

Annals of Neurology

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Objective There are no validated methods for predicting the timing of seizures. Using machine learning, we sought to forecast 24‐hour risk of self‐reported seizure from e‐diaries. Methods Data from 5,419 patients on http://SeizureTracker.com (including seizure count, type, and duration) were split into training (3,806 patients/1,665,215 patient‐days) and testing (1,613 patients/549,588 patient‐days) sets with no overlapping patients. An artificial intelligence (AI) program, consisting of recurrent networks followed by a multilayer perceptron (“deep learning” model), was trained to produce risk forecasts. Forecasts were made from a sliding window of 3‐month diary history for each day of each patient's diary. After training, the model parameters were held constant and the testing set was scored. A rate‐matched random (RMR) forecast was compared to the AI. Comparisons were made using the area under the receiver operating characteristic curve (AUC), a measure of binary discrimination performance, and the Brier score, a measure of forecast calibration. The Brier skill score (BSS) measured the improvement of the AI Brier score compared to the benchmark RMR Brier score. Confidence intervals (CIs) on performance statistics were obtained via bootstrapping. Results The AUC was 0.86 (95% CI = 0.85–0.88) for AI and 0.83 (95% CI = 0.81–0.85) for RMR, favoring AI (p < 0.001). Overall (all patients combined), BSS was 0.27 (95% CI = 0.23–0.31), also favoring AI (p < 0.001). Interpretation The AI produced a valid forecast superior to a chance forecaster, and provided meaningful forecasts in the majority of patients. Future studies will be needed to quantify the clinical value of these forecasts for patients. ANN NEUROL 2020;88:588–595

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Patient and caregiver preferences for the potential benefits and risks of a seizure forecasting device: A best–worst scaling

Cited by 41 publications

References 34 publications

Forecasting cycles of seizure likelihood

Forecasting cycles of seizure likelihood

Forecasting Cycles of Seizure Likelihood

Development and Validation of Forecasting Next Reported Seizure Using e‐Diaries

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