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

Sarah Misbah El-Sadig, Rahba El-Amin, Inaam Mohamed, Shambhu Kumar, Mamta Singh, David H. Glass, Victor Patterson

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)187-190
Number of pages4
JournalSeizure - European Journal of Epilepsy
Early online date1 Sept 2023
Publication statusPublished (in print/issue) - 31 Oct 2023

Bibliographical note

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  • Algorithm
  • Epilepsy
  • Epilepsy treatment gap
  • Focal epilepsy
  • Naive bayes


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