Evidence map›Paper›PMID 40366499›Full record

ArticleEmergency radiology2025

Investigating clinical indicators for neuroimaging abnormalities in acute headache cases: insights from a retrospective study.

Felipe Mejía-Herrera, Roger Figueroa-Paz, Jaime Quintero-Ramirez, Luis Alfonso Bustamante-Cristancho

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Article in Emergency radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Felipe Mejía-HerreraFacultad de Ciencias de la Salud, Departamento de Posgrados, Medicina de Emergencias, Universidad Icesi, Calle 18 # 122 -135, Cali, Colombia.
Roger Figueroa-PazCentro de Investigaciones Clínicas (CIC), Fundación Valle del Lili, Carrera 98 # 18-49, Cali, 760032, Colombia. roger.figueroa@fvl.org.co.ORCID http://orcid.org/0000-0002-9265-5420
Jaime Quintero-RamirezCentro de Investigaciones Clínicas (CIC), Fundación Valle del Lili, Carrera 98 # 18-49, Cali, 760032, Colombia.ORCID http://orcid.org/0000-0002-3605-1761
Luis Alfonso Bustamante-CristanchoServicio de Emergencias, Fundación Valle del Lili, Carrera 98 # 18-49, Cali, 760032, Colombia.ORCID http://orcid.org/0000-0001-5587-810X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeHeadache is common at emergency services and neuroimaging can help in timely diagnosis of life-threatening pathologies. We evaluated clinical indicators associated with abnormal neuroimaging in patients with acute headache, aiming to develop a scoring system with reliable diagnostic performance.

methodsThis analytical and retrospective study was conducted at a teaching tertiary care hospital in Cali, Colombia, from January 2011 to December 2019. Patients aged 18 years or older with non-traumatic headaches who attended the emergency department and underwent neuroimaging were included. Demographic and clinical data were recorded, including headache associated signs and symptoms, imaging diagnosis and disposition. Statistically significant variables and clinically relevant variables were selected. Data was analyzed using a combination of logistic regression and Receiver Operator Characteristic (ROC) curves, leading to the derivation of three models.

results626 patients were included, 15.5% with abnormal neuroimaging. The variables with the highest odds ratio (OR) were: age > 40 years (OR 3.2 CI 1.86-5.56), motor deficit (OR 5.4 CI 2.62-11.18), visual deficit (OR 3.2 CI 1.56-6.63) and gait disturbance (OR 2.27 CI 0.87-5.96). Three abnormal neuroimaging prediction logistic regression models have been derived. The better scale is performed with model 1, which is validated internally and a cut-off point of 0.179, the Area Under the Curve (AUC) of 0.757 is obtained with a diagnostic accuracy of 0.79 (0.73-0.85).

conclusionOur straightforward scale incorporates clinical factors associated with abnormal neuroimaging, with the aim of improving diagnostic performance and predictive capacity to distinguish patients who require neuroimaging.

Indexed as

HeadacheNeuroimagingAcute DiseaseAdultAgedColombiaEmergency Service, HospitalFemaleHumansMaleMiddle AgedRetrospective StudiesDiagnostic imagingEmergency serviceHeadache disordersHospitalNeuroimaging

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.