Abstract：
OBJECTIVE: 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).
CONCLUSIONS: 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.
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).
CONCLUSIONS: 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.
摘要：
目标：在大多数癫痫患者生活的中低收入国家（LMICs），癫痫的影响更为严重，大多数人都没有得到治疗。正确的治疗取决于确定是否存在局灶性或全身性癫痫。EEG和MRI通常无法提供帮助，因此需要完全临床的方法。我们应用了一种八变量算法，这是使用朴素贝叶斯方法从503名来自印度的患者中提取的，苏丹成年癫痫患者队列。
方法：有150名连续的成人患者，其已知癫痫类型由两名神经科医师定义，他们可以获得临床信息，脑电图和神经成像(“黄金标准”)。我们使用了八个变量中的七个，连同它们的似然比，计算每位患者发生局灶性癫痫的概率，并将其与“黄金标准”进行比较。灵敏度,特异性，准确度,并计算了科恩的卡帕统计量。
结果：平均年龄28岁（17-49岁），53%为女性。包含八个变量中的七个的算法的准确度为92%，对局灶性癫痫的敏感性为99%，特异性为72%。科恩的卡帕为0.773，表明达成了实质性协议。94％的患者的概率得分小于0.1（一般）或大于0.9（局灶性）。
结论：结果证实了该算法在确定苏丹癫痫类型方面的高准确性。他们建议,在像癫痫这样的临床疾病中，历史至关重要的地方，一个大陆的结果可以应用于另一个大陆。这一点尤其重要，因为未经治疗的癫痫和癫痫治疗差距如此普遍。该算法可以应用于给出个体概率评分的患者,其可以帮助确定适当的抗癫痫药物。它应该让没有癫痫经验的医生对治疗癫痫患者充满信心。
方法：有150名连续的成人患者，其已知癫痫类型由两名神经科医师定义，他们可以获得临床信息，脑电图和神经成像(“黄金标准”)。我们使用了八个变量中的七个，连同它们的似然比，计算每位患者发生局灶性癫痫的概率，并将其与“黄金标准”进行比较。灵敏度,特异性，准确度,并计算了科恩的卡帕统计量。
结果：平均年龄28岁（17-49岁），53%为女性。包含八个变量中的七个的算法的准确度为92%，对局灶性癫痫的敏感性为99%，特异性为72%。科恩的卡帕为0.773，表明达成了实质性协议。94％的患者的概率得分小于0.1（一般）或大于0.9（局灶性）。
结论：结果证实了该算法在确定苏丹癫痫类型方面的高准确性。他们建议,在像癫痫这样的临床疾病中，历史至关重要的地方，一个大陆的结果可以应用于另一个大陆。这一点尤其重要，因为未经治疗的癫痫和癫痫治疗差距如此普遍。该算法可以应用于给出个体概率评分的患者,其可以帮助确定适当的抗癫痫药物。它应该让没有癫痫经验的医生对治疗癫痫患者充满信心。
