Literature DB >> 31150998

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

Sarah A Janse1, Sonya B Dumanis2, Tanya Huwig3, Sarah Hyman3, Brandy E Fureman2, John F P Bridges4.   

Abstract

BACKGROUND: Epilepsy is the 4th most common neurological disorder and is characterized by recurrent, unpredictable seizures. The ability to forecast seizures is a significant unmet need and would have a transformative effect on the lives of people living with epilepsy. In an effort to address this need, the Epilepsy Foundation has committed effort and resources to promote the development of seizure forecasting devices (SFD).
OBJECTIVE: To promote user-centered design of future SFD, we sought to quantify patient and caregiver preferences for the potential benefits and risks of SFD.
METHODS: A community-centered approach was used to develop a survey incorporating a novel best-worst scaling (BWS) to assess preferences for SFD. A main-effect orthogonal array was used to design and generate 18 "prototypes" that systematically varied across six attributes: seizure forecasting probability, seizure forecasting range, inaccuracy of forecasting, amount of time required to use the device, how the device is worn, and cost. The dependent variable was the attributes that respondents selected the best and worst in each profile, and a choice model was estimated using conditional logistic regression, which was also stratified and compared across patients and caregivers. Respondents also indicated that they would accept each of the prototype SFDs if it were real. These acceptance data and net monetary benefits (relative to the least preferred SFD) were explored.
RESULTS: There were 633 eligible respondents; 493 (78%) completed at least one task. Responses indicated that 346 (68%) had epilepsy, and 147 (29%) were primary caregivers or family members of someone with epilepsy. The data show that short forecasting range is the most favored among experimental attributes, followed by mid forecasting range and notification of high chance of seizure. Having the device implanted is the least favorable attribute. Stated preferences differed between patients and caregivers (p < 0.001) for range of forecasting and inaccuracy of device. Caregivers preferred any range of forecasting, regardless of length, more than patients. Patients cared less about inaccuracy of the device compared to caregivers. The groups also differ in impact of fear of having seizures (versus actually having seizures) (p = 0.034) and on device acceptance. The acceptance of devices ranged from 42.3% to 95%, with caregivers being more likely to use a device (p < 0.05) for the majority of device profiles. Acceptance of devices varied with net monetary benefit of the best device being $717.44 more per month relative to the least preferred device.
CONCLUSION: Our finding extends previous calls for seizure forecasting devices by demonstrating the value that they might provide to patients and caregivers affected by epilepsy and the feature that might be most and least desirable. In addition to guiding device development, the data can help inform regulatory decisions makers.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Best–worst scaling; Medical devices; Patient preferences; Seizure forecasting

Mesh:

Year:  2019        PMID: 31150998     DOI: 10.1016/j.yebeh.2019.04.018

Source DB:  PubMed          Journal:  Epilepsy Behav        ISSN: 1525-5050            Impact factor:   2.937


  5 in total

1.  Development and Validation of Forecasting Next Reported Seizure Using e-Diaries.

Authors:  Daniel M Goldenholz; Shira R Goldenholz; Juan Romero; Rob Moss; Haoqi Sun; Brandon Westover
Journal:  Ann Neurol       Date:  2020-07-09       Impact factor: 10.422

2.  Forecasting seizure risk in adults with focal epilepsy: a development and validation study.

Authors:  Timothée Proix; Wilson Truccolo; Marc G Leguia; Thomas K Tcheng; David King-Stephens; Vikram R Rao; Maxime O Baud
Journal:  Lancet Neurol       Date:  2020-12-17       Impact factor: 44.182

3.  Automated seizure detection with noninvasive wearable devices: A systematic review and meta-analysis.

Authors:  Vaidehi Naganur; Shobi Sivathamboo; Zhibin Chen; Shitanshu Kusmakar; Ana Antonic-Baker; Terence J O'Brien; Patrick Kwan
Journal:  Epilepsia       Date:  2022-05-28       Impact factor: 6.740

Review 4.  Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic.

Authors:  Benjamin H Brinkmann; Philippa J Karoly; Ewan S Nurse; Sonya B Dumanis; Mona Nasseri; Pedro F Viana; Andreas Schulze-Bonhage; Dean R Freestone; Greg Worrell; Mark P Richardson; Mark J Cook
Journal:  Front Neurol       Date:  2021-07-13       Impact factor: 4.003

5.  Forecasting Seizure Likelihood With Wearable Technology.

Authors:  Rachel E Stirling; David B Grayden; Wendyl D'Souza; Mark J Cook; Ewan Nurse; Dean R Freestone; Daniel E Payne; Benjamin H Brinkmann; Tal Pal Attia; Pedro F Viana; Mark P Richardson; Philippa J Karoly
Journal:  Front Neurol       Date:  2021-07-15       Impact factor: 4.003

  5 in total

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