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.

Evaluating the Triage of Suspected COVID-19 Cases in Sudan's Emergency Settings: A Clinical Audit.

Background: The inevitable coronavirus disease 2019 global pandemic has severely affected Sudan's fragile healthcare system. The authors share the experience of COVID-19 triage in the emergency departments of five public hospitals in Khartoum state, Sudan. Methods: A clinical audit was conducted in December 2020 using the Centers for Disease Control and Prevention Checklist and Monitoring Tool for Triage of Suspected COVID-19 Cases. The tool was categorised into 5 domains and 38 indicators. Results: Only three hospitals had hand hygiene stations in their triage areas: Ibrahim Malik, Omdurman, and Al-Nau. Omdurman Teaching Hospital was the sole hospital with a designated respiratory waiting area. At Al-Nau and Omdurman Hospitals, all respiratory symptomatic patients wore a facemask or alternative. Ibrahim Malik and Bahri Teaching Hospitals had 60% and 50% compliance, respectively, while none at El-Tamayouz Hospital did. No posters or job aids were present in donning and doffing areas. Heavy duty gloves were worn only at Ibrahim Malik (50%) and Omdurman (20%). 100% of staff wore closed-toe footwear at Ibrahim Malik and Omdurman, 75% at El-Tamayouz, 63% at Bahri, and none at Al-Nau. Conclusion: The healthcare facilities displayed significant shortcomings in preparedness and response to COVID-19, with variations across hospitals in infrastructure, human resources, and procedures. To better combat future outbreaks, systemic improvements and a focused approach on consistent staff training, standard triage algorithms, and adequate PPE availability are imperative.

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.

Guillain-Barre syndrome associated with COVID-19 infection: A case series.

In this communication, we reported a series of six patients presented with Guillain-Barré syndrome that associated with COVID-19 infection, which was confirmed with RT-PCR. Here we discuss the laboratory investigation and case management, as well as clinical presentation and outcome of each case. The current report demonstrated the first case series of COVID-19-associated GBS-cases in Sudan.

Knowledge, attitudes, and practices of caregivers of children with epilepsy in Sudan.

OBJECTIVE: To explore the knowledge, attitudes, and practices of caregivers of children with epilepsy. METHODS: This cross-sectional study included 107 consecutive caregivers of children diagnosed with epilepsy, attending the neurology outpatient clinic in a Pediatric tertiary university hospital, in Khartoum, Sudan for regular check-up. Data were obtained by an interviewer through a structured questionnaire, while clinical parameters of children with epilepsy were taken from medical records. RESULTS: Most respondents knew that epilepsy is not an infectious disease (92.5%), while the least proportion of caregivers (41.1%) knew that epilepsy did not emanate from a supernatural origin. The majority of caregivers were with the idea that epilepsy caused lost opportunities for study (66.4%) and that epilepsy was looked upon negatively by others (64.5%). Less than half (39.7%) of these caregivers rolled their child on to the side during a seizure, and more than a third (37.2%) admitted to sprinkling water over the child during his/her seizure. Caregivers' of children with Generalized Tonic-Clonic (GTC) convulsions, reported significantly lower knowledge scores than caregivers whose children did not experience GTC seizures (p = 0.016). Caregivers of children with GTC seizures were more likely to believe epilepsy was for the most part hereditary (p = 0.006), and that children with epilepsy lost consciousness during a seizure (p < 0.001). CONCLUSION: Our findings suggest that caregivers require periodic epilepsy-related educational programs. This study is an invaluable tool for tailoring the delivery of information and support resources for caregivers in our region.