A tool presented as a phone app can be used by non-medical health workers to identify episodes as epileptic or not with good accuracy. It needs to be evaluated more widely but has the potential to play a part in reducing the epilepsy treatment gap.

A B S T R A C TPurpose: Most people with epilepsy live in low-or middle-income countries (LMICs) where there are relatively few doctors. Over 50% of people with epilepsy in these countries are untreated so other models of care are needed. In this report we evaluate a novel model of care. Methods: We trained four residents of Myagdi, a rural district in Nepal as epilepsy field workers (EFWs). They provided epilepsy awareness to their communities. When they identified someone with possible epilepsy they used a smartphone application (app) to determine the probability score for an episode being epileptic and contacted an epilepsy specialist by phone. If the specialist thought treatment was indicated this was arranged by the EFW. We recorded mortality, change of diagnosis at face-to-face consultation and drug-related events as measures of safety. Seizure frequency and general wellbeing were also recorded, and a questionnaire was devised to measure satisfaction. Results: 112 patients with app scores suggesting epileptic seizures were identified and managed in 18 months, of whom 15 had provoked seizures. Forty-three percent of epilepsy patients were untreated. At follow-up one had died of a cause other than epilepsy. Diagnostic agreement at face-to-face assessment was 93%. Overall 5% had side-effects of medication. Seizures were stopped in 33% and reduced in 57%. Ninety-six percent of patients preferred this service to travelling to other doctors. Conclusion: This novel service met all criteria of safety and was effective in reducing frequency of seizures. Patients preferred it to conventional services. It should be transferable to other LMICs. [13]. The sensitivity of this app was 92% and 86% in two previous https://doi.

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Background:Despite optimal medical therapy, a sizeable number of patients continue to have persistent seizures. We evaluated the association of pretreatment and treatment variables with unfavorable seizure outcome.Methods:Patients with follow-up over 12 years in the Nepal Epilepsy Association were evaluated. Patients having seizures for at least a year and already on polytherapy after failure of two monotherapy trials were considered having unfavourable outcome. Variables under study were: age, sex, duration and frequency of seizures prior to treatment, type of seizure, neurological status, Computed Tomography (CT) finding, and failure of first anti-epileptic drug (AED). Bivariate analysis was done with Chi-square and Fisher exact tests. Potential interaction between variables was studied with a logistic regression analysis.Results:Out of a total 529 consecutive patients, 490 were included in the study. Unfavorable seizure outcome was seen in 26.8% of patients. Among 284 patients who remained viable for analysis, bivariate analysis showed significant association of unfavorable outcome with frequency of seizure (p 0.01), abnormal neurological status (p 0.01) and failure of first AED (p 0.00), while no significant association was seen with age at onset (p 0.45), sex (p 0.47), duration of seizure (p 0.43), type of seizure (p 0.12), and presence of CT abnormality (p 0.46). The fitted regression model portended an unfavorable prognosis with failure of first AED and abnormal neurological status, however, failed to show significant association with frequency of seizure.Conclusions:Failure of first AED trial and associated neurological deficits are significant predictors of unfavorable seizure outcome.

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