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The article offers information on epilepsy, which may occur as a result of a genetic disorder and an acquired brain injury and also mentions information on side effects of antiepileptic drugs (AEDs).

We assessed the feasibility of a store-and-forward email teleneurology service between a UK neurologist and a rehabilitation hospital in Bangladesh. Over 12 months, email advice was requested for 12 patients (mean age 43 years, range 15–57 years). Each patient generated an average of 5.2 email messages. Eight cases were considered complicated by the neurologist, who would have preferred a video-link consultation for these. The referring doctor found the neurologist's advice beneficial in 75% of the complex cases and in all of the more straightforward cases. Patient care was changed in 50% of the cases as a result of the specialist advice and one patient transfer out of the country was avoided. Store-and-forward teleneurology is effective for delivering expert neurological advice. [ABSTRACT FROM AUTHOR]

Points out the concept of teleneurology in the practice of neurology in Great Britain. Definition of the modern communication technology; Types of teleneurology; Usability of teleneurology.

Background: Since 1999, the Swinfen Charitable Trust has operated an email referral system between doctors in the developing world and specialists in the industrialized world. Since 2001, it has expanded its operation into the Middle East, in particular Iraq, an area of considerable conflict. Objectives: The aim was to compare referral patterns to the Trust from the Middle East with those received from the rest of the developing world and to look for qualitative evidence of health gain. Methods: We analyzed referrals to the Swinfen Charitable Trust between July 2004 and June 2007 and compared these by speciality with those received from elsewhere during the same 3-year period. We asked two referring doctors for their views of the process, and we analyzed the total Middle Eastern referrals made to a single specialty (neurology). Results: Between July 2004 and June 2007, 283 referrals were received from four countries in the Middle East (Iraq, Afghanistan, Pakistan, Kuwait) and 500 cases were received from 22 other countries. The 283 cases resulted in 522 separate queries to specialists. The median time to specialist reply for the queries relating to the 283 Middle Eastern cases was 24.3 hours (interquartile range 6.1-63.3). There was a significant difference in case mix between the Middle East and the rest of the world (P < .001), with more obstetric referrals and fewer referrals in medical specialties and radiology. The referring doctors were helped greatly by the service. The neurologist was confident of the diagnosis in 20 of 26 referrals received (77%). Both referring doctors and the specialist were able to cite referred cases where management was improved as a result of the service. [ABSTRACT FROM AUTHOR]

Telemedicine (TM) is the use of telecommunications' technologies to provide medical information and services. Telehealth (TH) permits broader and psychosocial support for patients and their families. We aimed to highlight the importance of the use of TH for all aspects of epilepsy, either for the scientific aspects (e.g., research, education, care, management, etc.) or for the social matters (e.g., education, sensitization, association support, etc.). There is a deep gap in knowledge and use of TH in the developing and developed countries. Epilepsy is a condition responsible for 1% of the global burden of disease. More than 50 million people have epilepsy, and barriers to care include shortage of human resources, medical facilities, and resources. Eighty (80) percent of people with epilepsy (PWE) live in low- and middle-income countries. Telehealth has the potential of addressing limited resources and improving access to PWE across the globe. • WHO, in the overall policy and strategy for the attainment of health for all in the 21st century, recommends integrating the appropriate use of health telematics. • The development of information technologies offers an opportunity to improve and disseminate knowledge and awareness about epilepsy. • TM has the capacity to improve the quality of epilepsy management, and increase patient and caregiver satisfaction. • TM addresses access to care in remote areas where health professionals and facilities are not readily available. • Beneficiaries of TM include medical experts, neurologists of 1st, 2nd, 3rd levels of health systems and also other medical & paramedical staff. [ABSTRACT FROM AUTHOR]

• 79% patients had epilepsy, 21% had non-epilepsy diagnosis. • There were 33 variables with LRs either >3 or <0.33. • Eleven variables form the basis of the diagnostic aid. • The pre-test odds and algorithm combined as a Children's Epilepsy Diagnostic Aid. • There is a good separation of probability scores between epilepsy and non-epilepsy groups. The diagnosis of epilepsy in children is difficult and misdiagnosis rates can be as much as 36%. Diagnosis in all countries is essentially clinical, based on asking a series of questions and interpreting the answers. Doctors experienced enough to do this are either scarce or absent in very many parts of the world so there is a need to develop a diagnostic aid to help less-experienced doctors or non-physician health workers (NPHWs) do this. We used a Bayesian approach to determine the most useful questions to ask based on their likelihood ratios (LR), and incorporated these into a Children's Epilepsy Diagnosis Aid (CEDA). Ninety-six consecutive new referrals with possible epilepsy aged under 10 years attending a pediatric neurology clinic in Khartoum were included. Initially, their caregivers were asked 65 yes/no questions by a medical officer, then seen by pediatric neurologist and the diagnosis of epilepsy (E), not epilepsy (N), or uncertain (U) was made. The LR was calculated and then we selected the variables with the highest and lowest LRs which are the most informative at differentiating epilepsy from non-epilepsy. An algorithm, (CEDA), based on the most informative questions was constructed and tested on a new sample of 47 consecutive patients with a first attendance of possible epilepsy. We calculated the sensitivity and specificity for CEDA in the diagnosis of epilepsy. Sixty-nine (79%) had epilepsy and 18 (21%) non-epilepsy giving pre-test odds of having epilepsy of 3.83. Eleven variables with the most informative LRs formed the diagnostic aid (CEDA). The pre-test odds and algorithm were used to determine the probability of epilepsy diagnosis in a subsequent sample of 47 patients. There were 36 patients with epilepsy and 11 with nonepileptic conditions. The sensitivity of CEDA was 100% with specificity of 97% and misdiagnosis 8.3%. Children's Epilepsy Diagnosis Aid has the potential to improve pediatric epilepsy diagnosis and therefore management and is particularly likely to be useful in the many situations where access to epilepsy specialists is limited. The algorithm can be presented as a smartphone application or used as a spreadsheet on a computer. [ABSTRACT FROM AUTHOR]

• In low-income countries Non-Physician Health Workers should diagnose epilepsy. • A smartphone app for epilepsy has been developed for Non-Physician Health Workers. • We validated the app in a low-income country and a high-income country. • The app showed a very good level of sensitivity and specificity for epilepsy. • The sensitivity of the app was higher for epilepsy with tonic-clonic seizures. In low- and middle-income countries (LMIC), the diagnosis of epilepsy should be made by Non-Physician Health Workers (NPHW) who are widely available in these settings. Recently a smartphone app (Epilepsy Diagnosis Aid) has been developed and validated to be used by NPHW, in order to confirm the diagnosis of epilepsy. The aim of our study was to perform a validation of the app in two different contexts: a hospital-based setting of a high-income country (HIC) and a population-based setting of the rural communities of a LMIC. For the hospital-based setting, the app was administered to a sample of patients with epilepsy (PWE) and to a sample of subjects affected by syncope attending the epilepsy center of the University of Catania. For the population-based setting, performed in the rural communities of the Gran Chaco region in Bolivia, the app was administered by NPHW to a sample of PWE previously identified. Sensitivity and specificity were calculated for the diagnosis of epilepsy. In the hospital-setting, the app was administered to 100 PWE and 20 syncopes. A probability score > 80 showed a sensitivity of 76% (95%CI 66.4–84) and a specificity of 100% (95%CI 83.2–100) for the diagnosis of epilepsy; higher values were found for active epilepsy with tonic-clonic seizures. In the rural-setting, the app was administered to 38 PWE, giving a sensitivity of 92.1% (95%CI 78.6–98.3). The app for epilepsy could represent a valuable instrument, which can be easily employed by trained NPHW to diagnose epilepsy in primary health-care settings of LMIC. [ABSTRACT FROM AUTHOR]

Purpose: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods., Methods: We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa., Results: The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use., Conclusion: A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [VP is the co-developer of smartphone applications for epilepsy diagnosis and management.]., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose: The effects of epilepsy are worse in lower- and middle-income countries (LMICs) where most people with epilepsy live, and where most are untreated. Correct treatment depends on determining whether focal or generalised epilepsy is present. EEG and MRI are usually not available to help so an entirely clinical method is required. We applied an eight-variable algorithm, which had been derived from 503 patients from India using naïve-Bayesian methods, to an adult Sudanese cohort with epilepsy., Methods: There were 150 consecutive adult patients with known epilepsy type as defined by two neurologists who had access to clinical information, EEG and neuroimaging ("the gold standard"). We used seven of the eight variables, together with their likelihood ratios, to calculate the probability of focal as opposed to generalised epilepsy in each patient and compared that to the "gold standard". Sensitivity, specificity, accuracy, and Cohen's kappa statistic were calculated., Results: Mean age was 28 years (range 17-49) and 53% were female. The accuracy of an algorithm comprising seven of the eight variables was 92%, with sensitivity of 99% and specificity of 72% for focal epilepsy. Cohen's kappa was 0.773, indicating substantial agreement. Ninety-four percent of patients had probability scores either less than 0.1 (generalised) or greater than 0.9 (focal)., Conclusion: The results confirm the high accuracy of this algorithm in determining epilepsy type in Sudan. They suggest that, in a clinical condition like epilepsy, where a history is crucial, results in one continent can be applied to another. This is especially important as untreated epilepsy and the epilepsy treatment gap are so widespread. The algorithm can be applied to patients giving an individual probability score which can help determine the appropriate anti-seizure medication. It should give epilepsy-inexperienced doctors confidence in managing patients with epilepsy., Competing Interests: Declaration of Competing Interest VP is the co-developer of smartphone applications for epilepsy diagnosis and management. The other authors have no interests to declare., (Copyright © 2023 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Purpose: Epilepsy is treatable but in low- and middle-income countries (LMICs) it goes untreated with dire consequences for people with it and their families. There are not enough available doctors to treat it so it has been suggested that non-physician health workers (NPHWs) take a role in diagnosis and management. Tools will be essential to help them. A smartphone application (app) for episode diagnosis has proved safe and effective; this paper describes an app for epilepsy management., Methods: Questions were devised which captured temporal characteristics of episodes, diagnosis of episodes, seizure types, and epilepsy type, together with information on previous investigations, treatment, drug reactions, and current treatment. For untreated patients a management plan was suggested. The app generated a summary which could be sent to a remote specialist for advice. The finished app was evaluated in 23 people presenting with possible epilepsy by four doctors in training and one NPHW; its summary was compared to face-to-face evaluation by a neurologist., Results: The app was correct in 22 of 23 (96 %) patients for episode diagnosis, 2 of 2 for symptomatic seizures,18/20 (90 %) for epilepsy type and 9/10 (90 %) for treatment suggestion in untreated patients. The app took less than 15 min to complete., Conclusion: The initial results suggest that this management app is a worthwhile tool to help inexpert doctors or NPHWs manage suspected epilepsy. Its accuracy is well within reported inter-observer agreement. In its present form it requires input from a remote epilepsy specialist. This combination is a potential solution to managing epilepsy in LMICs., Competing Interests: Declaration of Competing Interest The author jointly holds the Intellectual Property of the app described., (Copyright © 2020 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Purpose: To compare long-term treatment outcomes in epilepsy patients from a single-visit outreach clinic on the Lifeline Express (LLE) with a conventional hospital (AIIMS) based epilepsy clinic in India., Methods: Using a cross-sectional observational study design, consecutive epilepsy patients from fifteen LLE clinics conducted from 2009 to 2014 were compared to epilepsy patients registered in the same duration at the AIIMS epilepsy clinic. The primary outcome was to determine if patients were still taking AEDs. To determine current AED status, patients from the LLE clinic were contacted telephonically. For the AIIMS patients, hospital records were reviewed and phone calls made to those patients who had not followed-up for more than a year., Results: In the 5 years under review, 1923 and 1257 patients had consulted at the LLE and AIIMS clinics respectively. Long-term outcomes were available for analysis in 688 AIIMS and 531 LLE clinic patients. Of the AIIMS patients, 581(87%) were continuing AEDs, 49(7%) had discontinued AEDs after being seizure-free for at least 5 years, 39(6%) had discontinued AEDs without medical advice and 19(2.8%) were dead. Outcomes in 531 LLE patients revealed that 351(72%) continued to be on AEDs, 34(7%) had discontinued AEDs on advice, 106 (22%) had discontinued AEDs without any medical advice and 40 (7.5%) were dead. The treatment gap in the LLE patients was reduced from 49% at first contact to 22% at follow-up 2-8 years later., Conclusions: Even single-visit epilepsy clinics may be an effective option for reducing treatment gap in limited-resource regions of the world., (Copyright © 2019 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)