Evidence map›Paper›PMID 40129868›Full record

ArticleFrontiers in neurology2025

Profiling chronic migraine patients according to clinical characteristics: a cluster analysis approach.

Masahito Katsuki, Yasuhiko Matsumori, Shin Kawamura, Kenta Kashiwagi, Akihito Koh, Tetsuya Goto, Kazuma Kaneko, Naomichi Wada, Fuminori Yamagishi

Abstract read
In one paragraph

Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Masahito KatsukiInsight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland.
Yasuhiko MatsumoriSendai Headache and Neurology Clinic, Sendai, Japan.
Shin KawamuraDepartment of Neurosurgery, Itoigawa General Hospital, Itoigawa, Japan.
Kenta KashiwagiDepartment of Neurology, Itoigawa General Hospital, Itoigawa, Japan.
Akihito KohDepartment of Neurosurgery, Itoigawa General Hospital, Itoigawa, Japan.
Tetsuya GotoDepartment of Neurosurgery, Suwa Red Cross Hospital, Suwa, Japan.
Kazuma KanekoHeadache Outpatient, Suwa Red Cross Hospital, Suwa, Japan.
Naomichi WadaDepartment of Neurosurgery, Suwa Red Cross Hospital, Suwa, Japan.
Fuminori YamagishiDepartment of Surgery, Itoigawa General Hospital, Itoigawa, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To group the characteristics of chronic migraine (CM) by headache characteristics. Methods: We performed a retrospective analysis of the medical records of 821 adult CM patients who visited a specialized outpatient clinic for headaches. Using the headache characteristics, we performed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering to group CM patients. The burdens to their lives, monthly headache days (MHD), monthly acute medication intake days (AMD), and treatment outcomes were evaluated among the clusters. Results: Through a cluster analysis based on headache characteristics, our findings indicated the potential existence of three distinct types of CM: cluster 1 (predominantly female with CM resembling migraine), cluster 2 (higher age, higher BMI, smoker), and cluster 3 (mostly female with CM that have fewer migraine characteristics). The impact on quality of life was significant in cluster 1 compared to cluster 3. However, there were no differences in treatment outcomes, initial MHD, AMD, the years of migraine, or treatment sensitivity among these three clusters. Conclusion: Cluster analysis mathematically divided CM patients into three groups, with predominant differences in the degree of disruption to their lives and their characteristics; further research is needed on the diagnostic criteria for CM and its characteristics.

Indexed as

chronic migrainecluster analysis (clustering analysis)data-driven medicinemedication-overuse headachetension-type headache

Identifiers

PMID40129868
PMCPMC11932020

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