Abstract, published in Epilepsy & Behavior
Introduction: 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).
Methods: 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.
Results: 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%.
Conclusion: 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.