Neuroimaging findings in headache with normal neurologic examination: Systematic review and meta-analysis
Abstract
OBJECTIVE: To determine if pooled estimates of the prevalence of unexpected findings in patients with headache and normal neurologic examination support current expert opinion-based neuroimaging guidelines.
METHODS: We searched PubMed and EMBASE for studies reporting neuroimaging findings in patients with headache and normal neurologic examination up to September 30, 2017. The overall and disease-specific prevalence of unexpected findings were pooled through random-effects meta-analysis. This study is registered with PROSPERO, registration number CRD42017079714.
RESULTS: In forty-one studies including 15,760 participants, the overall prevalence of unexpected findings and normal variants was 17.5% (95% CI: 13.1-22.3). The prevalence was 26.6% (95% CI: 15.5-39.4) in studies using MRI only. The prevalence of vascular, neoplastic, and non-neoplastic findings was 6.6%, 1.4%, and 9.6%. The pooled disease-specific prevalence was 2.0% for stroke, 1.8% for aneurysms, 0.8% for subdural hematoma, 0.7% for hydrocephalus, 0.2% for glioma, and 0.1% for meningioma. In secondary analysis, there was 0.4% increase in the prevalence of vascular unexpected findings with each 1% increase in the proportion of migraine with aura (p-value for meta-regression = 0.005).
CONCLUSIONS: In patients with headache and normal neurologic examination, important vascular and neoplastic unexpected findings are rare and better detected with MRI. This supports current American College of Radiology and European…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 15,760 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.