Neurology Division, Department of Paediatric and Child Health, Faculty of Medicine, University of Khartoum, Sudan.
Soba University Hospital, Khartoum, Sudan.
Epilepsy Behav. 2021 Aug;121(Pt A):108062. doi: 10.1016/j.yebeh.2021.108062. Epub 2021 Jun 4.
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.
儿童癫痫的诊断较为困难，误诊率可达 36%。在所有国家，癫痫的诊断基本上都是临床诊断，通过询问一系列问题并解释答案。有经验的医生在世界上许多地方都很稀缺或根本不存在，因此需要开发一种诊断辅助工具，帮助经验较少的医生或非医师卫生工作者（NPHW）进行诊断。我们使用贝叶斯方法来确定最有用的问题，根据其似然比（LR）进行询问，并将这些问题纳入儿童癫痫诊断辅助工具（CEDA）中。
96 例连续新转诊的 10 岁以下疑似癫痫患者在喀土穆儿科神经科诊所就诊。最初，由一名医疗官向他们的照顾者询问 65 个是/否问题，然后由儿科神经科医生进行检查，并做出癫痫（E）、非癫痫（N）或不确定（U）的诊断。计算了 LR，然后选择具有最高和最低 LR 的变量，这些变量在区分癫痫与非癫痫方面最具信息性。基于最具信息性的问题构建了一个算法（CEDA），并在 47 例首次就诊疑似癫痫的连续患者新样本中进行了测试。我们计算了 CEDA 诊断癫痫的敏感性和特异性。
69 例（79%）为癫痫，18 例（21%）为非癫痫，癫痫的先验概率为 3.83。具有最具信息性 LR 的 11 个变量构成了诊断辅助工具（CEDA）。使用先验概率和算法来确定在随后的 47 例患者样本中癫痫诊断的概率。其中 36 例为癫痫患者，11 例为非癫痫患者。CEDA 的敏感性为 100%，特异性为 97%，误诊率为 8.3%。
儿童癫痫诊断辅助工具有可能改善儿科癫痫的诊断，从而改善管理，特别是在癫痫专科医生资源有限的许多情况下，该工具可能非常有用。该算法可以呈现为智能手机应用程序，也可以在计算机上作为电子表格使用。
