家族性偏瘫性偏头痛
光环
医学
先兆偏头痛
队列
儿科
偏头痛
人口
内科学
环境卫生
作者
Giuseppe Donato Mangano,Maria Rita Capizzi,Elide Mantuano,Liana Veneziano,G Santangelo,Giuseppe Quatrosi,Rosaria Nardello,Vincenzo Raieli
出处
期刊:Headache
[Wiley]
日期:2023-06-16
卷期号:63 (7): 889-898
被引量:1
摘要
The aim of this study was to describe a cohort of pediatric patients with genetically confirmed familial hemiplegic migraine (FHM). The knowledge of genotype-phenotype correlations may suggest prognostic factors associated with severe phenotypes.Hemiplegic migraine is a rare disease and data concerning the pediatric population are even more rare as they are often extrapolated from mixed cohorts.We selected patients who met International Classification of Headache Disorders, third edition criteria for FHM, who had a molecular diagnosis, and whose first attack occurred under the age of 18 years.We enrolled nine patients (seven males and two females) first referred to our three centers. Three of the nine (33%) patients had calcium voltage-gated channel subunit alpha1 A (CACNA1A) mutations, five (55%) had ATPase Na+/K+ transporting subunit alpha 2 (ATP1A2) mutations, and one had both genetic mutations. The patients experienced at least one aura feature other than hemiplegia during the first attack. The mean (SD) duration of HM attacks in the sample was 11.3 (17.1) h; 3.8 (6.1) h in the ATP1A2 group, and 24.3 (23.5) h in the CACNA1A group. The mean (SD, range) duration of follow-up was 7.4 (2.2, 3-10) years. During the first year from the disorder's onset, only four patients had additional attacks. Over the course of follow-up, the attack frequency overall was 0.4 attacks/year without a difference between the two groups (CACNA1A and ATP1A2).The study data show that most of our patients with early-onset FHM experienced infrequent and non-severe attacks, which improved over time. Furthermore, the clinical course revealed neither the appearance of novel neurological disorders or a deterioration of basic neurological or cognitive functioning.
科研通智能强力驱动
Strongly Powered by AbleSci AI