Usefulness of a smartphone application for the diagnosis of epilepsy: Validation study in high-income and rural low-income countries

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches

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.

Personalized antiseizure medication therapy in critically ill adult patients

Precision medicine has the potential to have a significant impact on both drug development and patient care. It is crucial to not only provide prompt effective antiseizure treatment for critically ill patients after seizures start but also have a proactive mindset and concentrate on epileptogenesis and the underlying cause of the seizures or seizure disorders. Critical illness presents different treatment issues compared with the ambulatory population, which makes it challenging to choose the best antiseizure medications and to administer them at the right time and at the right dose. Since there is a paucity of information available on antiseizure medication dosing in critically ill patients, therapeutic drug monitoring is a useful tool for defining each patient's personal therapeutic range and assisting clinicians in decision-making. Use of pharmacogenomic information relating to pharmacokinetics, hepatic metabolism, and seizure etiology may improve safety and efficacy by individualizing therapy. Studies evaluating the clinical implementation of pharmacogenomic information at the point-of-care and identification of biomarkers are also needed. These studies may make it possible to avoid adverse drug reactions, maximize drug efficacy, reduce drug-drug interactions, and optimize medications for each individual patient. This review will discuss the available literature and provide future insights on precision medicine use with antiseizure therapy in critically ill adult patients.

An epilepsy type algorithm developed in India is accurate in Sudan: A prospective validation study

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.

Adult epilepsy

Epilepsy is a common medical condition that affects people of all ages, races, social classes, and geographical regions. Diagnosis of epilepsy remains clinical, and ancillary investigations (electroencephalography, imaging, etc) are of aid to determine the type, cause, and prognosis. Antiseizure medications represent the mainstay of epilepsy treatment: they aim to suppress seizures without adverse events, but they do not affect the underlying predisposition to generate seizures. Currently available antiseizure medications are effective in around two-thirds of patients with epilepsy. Neurosurgical resection is an effective strategy to reach seizure control in selected individuals with drug-resistant focal epilepsy. Non-pharmacological treatments such as palliative surgery (eg, corpus callosotomy), neuromodulation techniques (eg, vagus nerve stimulation), and dietary interventions represent therapeutic options for patients with drug-resistant epilepsy who are not suitable for resective brain surgery.

Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

BACKGROUND

Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa.

METHODS

In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region.

FINDINGS

We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91-0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92-0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93-0·96, sensitivity 88·2%, specificity 95·3%).

INTERPRETATION

On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy.

FUNDING

The Wellcome Trust, the UK National Institute of Health Research, and the Oxford NIHR Biomedical Research Centre.

Prevalence of epilepsy in the rural area of the Bolivian Gran Chaco: Usefulness of telemedicine and impact of awareness campaigns

OBJECTIVE

The objective of this study is to estimate the prevalence of epilepsy with Tonic-Clonic (TC) seizures in rural areas of the Bolivian Gran Chaco and to evaluate the usefulness of telemedicine in this context.

METHODS

The study was carried out in the Isozo Area, southern-eastern Bolivia. Twenty-five rural communities with a population of 8258 inhabitants were included in the survey. Trained community-health workers administered a validated single screening question to the householders (stage I). A second face-to-face questionnaire was administered to each positive subject (stage II). At stage II subjects were also screened using the smartphone app "Epilepsy Diagnosis Aid". Subjects screened positive at stage II underwent a complete neurological examination to confirm the diagnosis (stage III). Due to the COVID-19 lockdown, some subjects have been evaluated through a digital platform (Zoom®).

RESULTS

One-thousand two-hundred and thirteen interviews were performed at stage I, corresponding to a total screened population of 6692 inhabitants. Thirty-eight screened positive were identified at stage I and II and of these, 28 people with epilepsy were identified, giving an overall prevalence of 4.2/1000 (95% CI 2.6-5.7). Prevalence rate steeply increased with age reaching a peak of 7.9/1000 in the population aged 20-29 years without significant differences between women and men. For almost 50% of the screened positive subjects, confirmation of epilepsy by a neurologist at stage III was achieved through simple videoconsultation. After a simultaneous awareness campaign, 22 self-reported PWE requested a consultation and, among them, 11 had a diagnosis of epilepsy confirmed.

SIGNIFICANCE

This study shows a prevalence estimate close to those reported for LMIC. Simple videoconsultation and specific apps may be valuable tools in epidemiological research. Awareness campaigns are important allies for a full case identification, particularly in contexts where higher rates of stigma are recorded.

The pharmacological treatment of epilepsy: recent advances and future perspectives

The pharmacological armamentarium against epilepsy has expanded considerably over the last three decades, and currently includes over 30 different antiseizure medications. Despite this large armamentarium, about one third of people with epilepsy fail to achieve sustained seizure freedom with currently available medications. This sobering fact, however, is mitigated by evidence that clinical outcomes for many people with epilepsy have improved over the years. In particular, physicians now have unprecedented opportunities to tailor treatment choices to the characteristics of the individual, in order to maximize efficacy and tolerability. The present article discusses advances in the drug treatment of epilepsy in the last 5 years, focusing in particular on comparative effectiveness trials of second-generation drugs, the introduction of new pharmaceutical formulations for emergency use, and the results achieved with the newest medications. The article also includes a discussion of potential future developments, including those derived from advances in information technology, the development of novel precision treatments, the introduction of disease modifying agents, and the discovery of biomarkers to facilitate conduction of clinical trials as well as routine clinical management.

Epidemiology of Epilepsy in Low- and Middle-Income Countries: Experience of a Standardized Questionnaire over the Past Two Decades

INTRODUCTION

Epilepsy affects >50 million people worldwide, with 80% of them living in low- and middle-income countries (LMICs). Studies with a standardized methodology are required to obtain comparable data on epilepsy and implement health policies in order to reduce the treatment gap and improve the diagnosis and management of epilepsy. In 2000, following the guidelines of the International League Against Epilepsy (ILAE), the "questionnaire for investigation of epilepsy in tropical countries" (IENT questionnaire) was developed to promote epidemiological surveys on epilepsy using a standard methodology. This study aims to describe how, when, where, and why the IENT questionnaire has been used through epidemiological studies on epilepsy over the last 2 decades and to acquire users' opinions about the tool.

METHODS

Studies that used the IENT questionnaire were searched through international and local bibliographic databases, including the gray literature. An online survey was carried out, including a snowball effect. Original research studies were included. Characteristics of the studies and populations and general information on the instrument and its use were collected.

RESULTS

Eighty-two documents were selected referring to 61 studies that were mostly carried out on the African continent (n = 54). Most of them aimed to determine the prevalence (n = 31) and associated factors (n = 28) of epilepsy in LMICs. Among the 61 studies, 35 were population-based, and 30 included both adults and children. A methodological heterogeneity was found between studies, and in cases where the IENT questionnaire alone did not ensure complete data collection, other tools were used concomitantly (n = 40).

DISCUSSION/CONCLUSION

Over the last 2 decades, the IENT questionnaire has been continuously used in different LMICs. This result favors its promotion and updating, with the inclusion of new topics related to epilepsy (e.g., comorbidities, quality of life, and stigma), current ILAE guidelines, and digital versions.