Literature DB >> 33246274

Yield of head CT for acute findings in patients presenting to the emergency department.

Dafna Nesselroth1, Eyal Klang2, Shelly Soffer3, Evgeni Druskin4, Yiftah Barash2, Chen Hoffmann5, Eli Konen5, Eyal Zimlichman6.   

Abstract

OBJECTIVES: The aim of our study was to evaluate the yield of head CT in the ED in different age groups and different referral indications. PATIENTS AND METHODS: Records of one large academic tertiary care ED were retrospectively reviewed for consecutive adult patients who underwent a head CT between January 1st 2017 and February 10th 2017. CT referral forms and interpretations were obtained and evaluated for demographics, referral indications, and findings. Scans were divided into three groups: acute findings, chronic findings, and normal. The cohort was divided into three age groups. Associations between referral indications and acute findings were calculated.
RESULTS: Overall, 1536 of adult patients with ED head CT were included. Acute findings were found in 239/1536 (15.5%) of the CTs. The frequency of acute findings increased with age (p = 0.027). The most common acute findings were brain hemorrhage (32.6%), infarct (27.6%), and mass (23%). The top three referral indications were focal neurologic deficit (28%), trauma (24.7%), and headache (17.5%). The rates of positive acute findings for different referral indications were seizure 27%, confusion 20%, syncope 19%, focal neurologic deficit 16%, head injury 15%, headache 12%, and dizziness 8%.
CONCLUSION: This study shows the yield of ED head CT for acute findings for different age groups and for different referral indications. The frequency of acute findings increased with age. Suspected seizure had the highest association with an acute finding, whereas dizziness had the lowest association.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Age groups; Emergency departments; Referral and consultation; Spiral computed tomography

Mesh:

Year:  2020        PMID: 33246274     DOI: 10.1016/j.clinimag.2020.11.025

Source DB:  PubMed          Journal:  Clin Imaging        ISSN: 0899-7071            Impact factor:   1.605


  1 in total

1.  Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis.

Authors:  Kai Guo; Xiongfei Li; Xiaohan Hu; Jichen Liu; Tiehu Fan
Journal:  BMC Med Imaging       Date:  2021-07-14       Impact factor: 1.930

  1 in total

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