Extended Phenotyping of Migraine in Children: A Cross-Sectional Study in a Specialist Children's Headache Clinic
Abstract
BACKGROUND: The pediatric migraine phenotype may exhibit differences to adults, leading to diagnostic challenges. We aimed to perform a cross-sectional systematic study to characterize the extended phenotype of pediatric migraine.
METHODS: New migraine patients presenting to the Children's Headache Clinic were included (n = 105). Data were collected via a detailed symptom questionnaire at the first clinical encounter and were analyzed using descriptive statistics, Cohen kappa (k), Spearman correlation (ρ), and Poisson and binomial logistic regression models within SPSS.
RESULTS: Patients were 65% female and aged five to 17 years (median 14, interquartile range [IQR] 11 to 15), with a mean disease duration of 4.7 years (S.D. 2.8). Monthly headache frequency was 1 to 30 days (median 30, IQR 12 to 30). Attack duration varied between 2 and 168 hours (median 12, IQR 5 to 72). The majority (81%) experienced bilateral headache. Premonitory symptoms (PS) were reported by 93% (range 0 to 7; mood change and tiredness most commonly), cranial autonomic symptoms (CAS) by 58% (range 0 to 6; pallor and lacrimation most commonly), and premonitory CAS by 23%. Vertigo (53%) and allodynia (16%) were present. The laterality of headache and CAS showed agreement (k = 0.5, P < 0.001). For every year of disease duration, 1.07 times more PS were reported (95% confidence interval [CI] 1.03 to 1.12, P < 0.001). The number of CAS (odds ratio 2.13, 95% CI 1.2 to 3.8, P = 0.01) significantly predicted…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 105 participants or records.
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.