Abstract
Introduction
Computed tomography (CT) of the chest (CTC), abdomen, and pelvis (CTAP) is common
when assessing trauma patients in the emergency department. However, unnecessary imaging
can expose patients to unneeded radiation and increase healthcare costs. Here, we
characterize the use of torso CT imaging for the evaluation of ground level falls
(GLF) at a single level 1 trauma center.
Patients and methods
We conducted a retrospective review of all patients ≥18 years old presenting to a
single level 1 trauma center with a GLF (1m or less) in 2015-2019. Data were obtained
through chart review. Descriptive statistics were used to summarize patient characteristics.
Multivariable logistic regression was used to assess factors leading to patients obtaining
torso CT imaging. The utility of CT imaging in identifying injuries that changed management
was also evaluated.
Results
Of the 1,195 patients captured during the study period, 492 patients had a positive
torso physical exam (PE), and 703 had a negative torso PE. Of patients with a negative
torso PE, 127 CTC and 142 CTAP were obtained, with only 5.5% CTC identifying traumatic
injuries not previously diagnosed on chest radiograph (CXR), and only 0.7% CTAP identifying
new injuries not identified on pelvic radiograph (PXR). Multivariable logistic regression
demonstrated that only a positive PE was significantly associated with the identification
of abnormal imaging findings on torso CT. A negative PE, CXR, and PXR have a negative
predictive value of 98%.
Discussion
These data suggest that patients with a negative PE, even if intoxicated, intubated,
or with a decreased GCS, are highly unlikely to have new, clinically relevant findings
on torso CT imaging.
Conclusion
Using PE, CXR, and PXR as a screening tool in patients sustaining GLF, which if negative
close to obviates the need for torso CT, may reduce healthcare costs and radiation
exposure without compromising patient care.
Keywords
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Article info
Publication history
Published online: November 28, 2022
Accepted:
November 23,
2022
Identification
Copyright
© 2022 Elsevier Ltd. All rights reserved.
