| Literature DB >> 31247699 |
Ye Eun Jang1, Eun Young Cho2, Hee Yea Choi1, Sun Mi Kim3, Hye Youn Park1.
Abstract
OBJECTIVE: Neuroimaging in headache patients identifies clinically significant neurological abnormalities and plays an important role in excluding secondary headache diagnoses. We performed a systematic review and meta-analysis of the existing guidelines and studies surrounding neuroimaging in headache patients.Entities:
Keywords: Headache; Magnetic resonance imaging; Neuroimaging; X-ray computed tomography
Year: 2019 PMID: 31247699 PMCID: PMC6603699 DOI: 10.30773/pi.2019.04.11
Source DB: PubMed Journal: Psychiatry Investig ISSN: 1738-3684 Impact factor: 2.505
Figure 1.PRISMA flowchart of the study selection process. Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) flow chart.
Main characteristics of the included studies-research question 1
| Author (year) | Study type | Sample size | Age | Country | Hospital setting | Headache subtype | N with including insignificant abnormality | N with only significant abnormality | Type of detection measurement | RoB score (0–10) |
|---|---|---|---|---|---|---|---|---|---|---|
| Alons et al. (2015) [ | Retro | 70 | 17–80 y (mean: 45 y) | Nether-lands | ER in tertiary hospital | Acute headache | 13 | 13 | CTA | 6 low risk |
| Carstairs et al. (2006) [ | Pro | 116 | 18 y or older (mean: 38.8 y) | US | ER in tertiary hospital | Acute headache | 30 | 23 | CTA | 7 low risk |
| Clarke et al. (2010) [ | Pro | <CT> | Mean age 42 y | UK | The Birmingham headache service in tertiary hospital | Non-acute headache | <CT> | <CT> | CT | 6 low risk |
| 226 | 65 | 2 | MRI | |||||||
| <MRI> | <MRI> | <MRI> | ||||||||
| 304 | 149 | 9 | ||||||||
| Cooper et al. (2016) [ | Retro | 510 | 16–89 y (mean: 39.5 y) | UK | Tertiary hospitals | Acute headache | 27 | 27 | CT | 8 low risk |
| Rizk et al. (2013) [ | Pro | 74 | 16–82 y (mean: 42 y) | Switzerland | ER in tertiary hospital | Acute headache | 15 | 11 | CT | 5 moderate risk |
| Tsushima and Endo (2005) [ | Retro | 306 | 19–91 y (mean: 54.2 y) | Japan | Tertiary hospitals | Non-acute headache | 137 | 2 | MRI | 6 low risk |
| Ziegler et al. (1991) [ | Pro | 18 | 27–64 y | USA | Migraine clinic of tertiary hospital | Non-acute headache | 4 | 4 | MRI | 7 low risk |
| Cooney et al. (1996) [ | Retro | 185 | 15–83 y (median: 38 y) | US | Tertiary hospitals | Non-acute headache | 30 | 30 | MRI | 6 low risk |
| Han et al. (2013) [ | Retro | 512 | 16–86 y (mean: 46.2 y) | Korea | Secondary care hospital | Acute headache | 34 | 34 | CTA | 6 low risk |
| Chen et al. (2006) [ | Pro | 56 | 22–76 y (mean: 49.6) | Taiwan | Tertiary hospital | Acute headache | 22 | 22 | MRA | 6 low risk |
| Total | NA | 2,377 | NA | NA | NA | NA | 526 (22.06%) | 146 (6.12%) | NA | NA |
RoB: risk of bias, Pro: prospective study, Retro: retrospective study, ER: emergency room, NA: not applicable
Figure 2.Forest plot and funnel plot for the Research Question. Forest plot (A) showing the prevalence of detecting clinically significant abnormalities. The events refer to cases in which abnormal findings were observed when neuroimaging was performed on the patients in each study. The total refers to the number of patients participating in each study, and the x-axis represents the confidence interval. When the confidence interval does not include zero, the incidence is not zero. Publication bias was tested using funnel plots. Funnel plot (B) showing the log-transformed proportion of detecting clinically significant abnormalities. To calculate the pooled proportion, the data were log-transformed and evaluated by the Clopper-Pearson method to calculate 95% confidence intervals (CIs). Asymmetrical points indicate the presence of publication bias.
Figure 3.Forest plots for the subgroup analyses. Forest plot (A) showing the subgroup analysis by detection method. Forest plot (B) showing the subgroup analysis by headache type. Forest plot (C) showing the subgroup analysis by study type. Forest plot (D) showing the subgroup analysis by study region.