Cluster headache suicidality: a systematic review with a meta-analysis
Abstract
BACKGROUND: Suicide is considered as common in patients with cluster headache (CH) and is defined as 'suicidal headache'. However, the exact level of suicidality is not known and is intuitively assumed to be correlated with the CH severity.
METHODS: This work is a systematic review of data accessible through PubMed and published up to July 25, focusing on the suicidality of CH according to the rates of suicidal ideation and suicide attempts. This meta-analysis was carried out with all selected studies then by considering studies with specialized recruitment and studies with non-specialized recruitment. A qualitative analysis was performed to identify determinants of CH suicidality.
RESULTS: Among the 53 publications identified, 12 were selected corresponding to 10 studies. These selected studies included a total of 34180 subjects (range 75-24131). Eight were performed in a specialized field (headache tertiary center, neurology clinic or via CH patients' association) and 2 in a non-specialized field (via heath registry). The overall rate of suicidal ideation in CH was estimated at 8.0% (95%CI [7.7; 8.3] and overall rate of suicide attempts in CH was estimated at 1.2% (95%CI [1.1; 1.3]). In a non-specialized field, these rates were estimated at 5.2% (95%CI [4.9; 5.4]) and 1.1% (95%CI [1.0; 1.2]) respectively. In specialized field, these rates were estimated at 44.6% (95%CI [42.7; 46.6]) and 5.1% (95%CI [3.9; 6.7]) respectively. The qualitative analysis showed that few…
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: 34,180 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.