目的：本研究旨在评估罕见癫痫的发生率，并与文献进行比较。
方法：我们使用电子健康记录文本搜索来识别纽约市28例罕见癫痫患儿（2010-2014年）。我们估计了累积发生率并与文献进行了比较。
结果：28例罕见癫痫患者中有8例先前有5个或更多的估计，我们的测量结果都在公布的范围内。最常见的是婴儿癫痫性痉挛综合征（2920名活产儿中有1名），Lennox-Gastaut综合征（1/9690），和与结节性硬化症相关的癫痫发作（1/14300）。28人中有15人的先前估计少于5人，其中，我们提供了早期婴儿发育性脑病和癫痫性脑病的额外估计（1/32700），癫痫伴肌阵挛性-失超性癫痫发作（1/34100），Sturge-Weber综合征加癫痫发作/癫痫（1/40900），婴儿期癫痫伴转移性局灶性癫痫发作（1/54500），Aicardi综合征加癫痫发作/癫痫（1/71600），下丘脑错构瘤伴癫痫发作（1/225000），和拉斯穆森综合征(450000人中有1人)。28例罕见癫痫中有5例没有事先估计，其中，我们对睡眠中出现尖峰波激活和/或睡眠中出现连续尖峰波的发育性/癫痫性脑病(1/34100)提供了新的估计.其余12例罕见癫痫的数据有限，都是遗传性癫痫，包括PCDH19，CDKL5，阿尔伯斯病，SCN8A,KCNQ2,SCN2A,GLUT1缺乏,Phelan-McDermid综合征,肌阵挛性癫痫与参差不齐的红色纤维，dup15q综合征,14号环状染色体和20号环状染色体。
结论：我们使用基于人群的电子健康记录数据和文献综述来估计罕见癫痫的发病率。需要更多的研究来更好地估计具有非特异性临床特征的遗传性癫痫的发病率。电子健康记录可能是研究罕见癫痫和其他罕见疾病的有价值的数据来源，特别是随着基因检测越来越广泛的采用。
OBJECTIVE: This study was undertaken to estimate incidence of rare epilepsies and compare with literature.
METHODS: We used electronic health record text search to identify children with 28 rare epilepsies in New York City (2010-2014). We estimated cumulative incidence and compared with literature.
RESULTS: Eight of 28 rare epilepsies had five or more prior estimates, and our measurements were within the published range for all. The most common were infantile epileptic spasms syndrome (1 in 2920 live births), Lennox-Gastaut syndrome (1 in 9690), and seizures associated with tuberous sclerosis complex (1 in 14 300). Fifteen of 28 had fewer than five prior estimates, and of these, we provided additional estimates for early infantile developmental and epileptic encephalopathy (1 in 32 700), epilepsy with myoclonic-atonic seizures (1 in 34 100), Sturge-Weber syndrome plus seizures/epilepsy (1 in 40 900), epilepsy in infancy with migrating focal seizures (1 in 54 500), Aicardi syndrome plus seizures/epilepsy (1 in 71 600), hypothalamic hamartoma with seizures (1 in 225 000), and Rasmussen syndrome (1 in 450 000). Five of 28 rare epilepsies had no prior estimates, and of these, we provided a new estimate for developmental/epileptic encephalopathy with spike-and-wave activation in sleep and/or continuous spikes and waves during sleep (1 in 34 100). Data were limited for the remaining 12 rare epilepsies, which were all genetic epilepsies, including PCDH19, CDKL5, Alpers disease, SCN8A, KCNQ2, SCN2A, GLUT1 deficiency, Phelan-McDermid syndrome, myoclonic epilepsy with ragged-red fibers, dup15q syndrome, ring chromosome 14, and ring chromosome 20.
CONCLUSIONS: We estimated the incidence of rare epilepsies using population-based electronic health record data and literature review. More research is needed to better estimate the incidence of genetic epilepsies with nonspecific clinical features. Electronic health records may be a valuable data source for studying rare epilepsies and other rare diseases, particularly as genetic testing becomes more widely adopted.
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