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Subarachnoid hemorrhage (SAH) is a devastating neurological injury that can lead to many downstream complications including epilepsy. Predicting who will get epilepsy in order to find ways to prevent it as well as stratify patients for future interventions is a major challenge given the large number of variables not only related to the injury itself, but also to what happens after the injury. Extensive multimodal data are generated during the process of SAH patient care. In parallel, preclinical models are under development that attempt to imitate the variables observed in patients. Computational tools that consider all variables from both human data and animal models are lacking and demand an integrated, time-dependent platform where researchers can aggregate, store, visualize, analyze, and share the extensive integrated multimodal information. We developed a multi-tier web-based application that is secure, extensible, and adaptable to all available data modalities using flask micro-web framework, python, and PostgreSQL database. The system supports data visualization, data sharing and downloading for offline processing. The system is currently hosted inside the institutional private network and holds [Formula: see text] of data from 164 patients and 71 rodents. Clinical Relevance-Our platform supports clinical and preclinical data management. It allows users to comprehensively visualize patient data and perform visual analytics. These utilities can improve research and clinical practice for subarachnoid hemorrhage and other brain injuries.

Purpose: People with epilepsy (PWE) come from a wide variety of social backgrounds and educational skillsets, making self-management (SM) education for improving their condition challenging. Here, we evaluated whether a mobile technology-based personalized epilepsy SM education intervention, PAUSE to Learn Your Epilepsy (PAUSE), improves SM measures such as self-efficacy, epilepsy SM behaviors, epilepsy outcome expectations, quality of life (QOL), and personal impact of epilepsy in adults with epilepsy., Methods: Recruitment for the PAUSE study occurred from October 2015 to March 2019. Ninety-one PWE were educated using an Internet-enabled computer tablet application that downloads custom, patient-specific educational programs from Epilepsy.com. Validated self-reported questionnaires were used for outcome measures. Participants were assessed at baseline (T0), the first follow-up at completion of the PWE-paced 8-12-week SM education intervention (T1), and the second follow-up at least 3 months after the first follow-up (T2). Multiple linear regression was used to assess within-subject significant changes in outcome measures between these time points., Results: The study population was diverse and included individuals with a wide variety of SM educational needs and abilities. The median time for the first follow-up assessment (T1) was approximately 4 months following the baseline (T0) and 8 months following baseline for the second follow-up assessment (T2). Participants showed significant improvement in all SM behaviors, self-efficacy, outcome expectancy, QOL, and personal impact of epilepsy measures from T0 to T1. Participants who scored lower at baseline tended to show greater improvement at T1. Similarly, results showed that participant improvement was sustained in the majority of SM measures from T1 to T2., Conclusion: This study demonstrated that a mobile technology-based personalized SM intervention is feasible to implement. The results provide evidence that epilepsy SM behavior and practices, QOL, outcome expectation for epilepsy treatment and management, self-efficacy, and outcome expectation and impact of epilepsy significantly improve following a personalized SM education intervention. This underscores a greater need for a pragmatic trial to test the effectiveness of personalized SM education, such as PAUSE to Learn Your Epilepsy, in broader settings specifically for the unique needs of the hard-to-reach and hard-to-treat population of PWE., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: People with epilepsy (PWE) from underserved populations face significant barriers to epilepsy management and therefore may lack knowledge about epilepsy and self-management (SM) of epilepsy. This paper evaluates SM practices, self-efficacy, outcome expectancy, quality of life, and personal impact of epilepsy in PWE from underserved populations as compared with all PWE., Methods: Recruitment for the Managing Epilepsy Well (MEW) Network PAUSE to Learn Your Epilepsy study occurred from October 2015 to March 2019. Participants were assessed at baseline; after SM education intervention; and 6-, 9-, and 15-month postbaseline assessment. Baseline data from 112 PWE were analyzed for this report., Results: Study population was diverse: 63% were women, 47.3% were non-Hispanic black, 24.1% were Hispanic, and 57.4% had public healthcare coverage. Participants on average had epilepsy for 14 years, and 49.1% reported at least one seizure within the past month, but only 27% reported having used a seizure diary or calendar for seizure tracking. Self-management practices & behaviors were significantly lower among PWE from underserved populations than all PWE, though self-efficacy among PWE from underserved populations was significantly higher., Conclusion: This study identifies the unique epilepsy SM needs of PWE from underserved populations. We discuss the need for a personalized approach for developing SM skills and behaviors among these PWE., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objective: To assess depressive symptom outcomes in a pooled sample of epilepsy self-management randomized controlled trials (RCTs) from the Managing Epilepsy Well (MEW) Network integrated research database (MEW DB)., Methods: Five prospective RCTs involving 453 adults with epilepsy compared self-management intervention (n = 232) versus treatment as usual or wait-list control outcomes (n = 221). Depression was assessed with the nine-item Patient Health Questionnaire. Other variables included age, gender, race, ethnicity, education, income, marital status, seizure frequency, and quality of life. Follow-up assessments were collapsed into a visit 2 and a visit 3; these were conducted postbaseline., Results: Mean age was 43.5 years (SD = 12.6), nearly two-thirds were women, and nearly one-third were African American. Baseline sample characteristics were mostly similar in the self-management intervention group versus controls. At follow-up, the self-management group had a significantly greater reduction in depression compared to controls at visit 2 (P < .0001) and visit 3 (P = .0002). Quality of life also significantly improved in the self-management group at visit 2 (P = .001) and visit 3 (P = .005)., Significance: Aggregate MEW DB analysis of five RCTs found depressive symptom severity and quality of life significantly improved in individuals randomized to self-management intervention versus controls. Evidence-based epilepsy self-management programs should be made more broadly available in neurology practices., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)