Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
April 20, 2026 · via Europe PMC
Redundant Computed Tomography in Maxillofacial Trauma: A National Analysis of Frequency and Predictors Following Interfacility Transfer.
Wang TT, Liang L, Wilken N, Peacock Z, Warburton G, Caccamese J, Lee C.
AI summary — for doctors, generated from the abstract only
About one in six adults transferred for isolated facial fractures received redundant maxillofacial CT, driven mainly by trauma pan-scan protocols and hospital characteristics.
What was studied
A retrospective NTDB cohort (2017-2024) of 72,766 adults transferred for isolated facial fractures, assessing repeat maxillofacial CT within 24 hours.
Key findings
- Redundant imaging occurred in 16.8%
- Trauma pan-scan was the strongest predictor (OR 11.0)
- Larger hospitals were less likely to duplicate scans (OR 0.59 for the largest category)
Clinical relevance
Highlights a target for reducing unnecessary imaging and radiation; registry data cannot assess image quality reasons for repeats.
Who should care
Limitations
Registry data without information on adequacy of outside imaging.
Repeat facial CT after transfer is common and largely protocol-driven.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Duplication of computed tomography (CT) imaging following interfacility transfer for trauma is a well-established systems level inefficiency. However, the incidence and drivers of repeat imaging have not been examined within the maxillofacial trauma demographic.
Purpose
The purpose was to estimate the frequency and factors associated with redundant maxillofacial CT imaging among adults transferred for isolated facial fractures.
Study design, setting sample
This was a retrospective cohort study using the American College of Surgeons National Trauma Data Bank from 2017 to 2024. Patients at least 18 years of age, transferred for management of isolated facial fractures, were included. Patients with missing data were excluded.
Predictor variable
The predictor variable was a set of factors categorized as demographic (age, sex), clinical (Charlson comorbidity index), injury-related (fracture location), and hospital-level characteristics (bed size, teaching status).
Main outcome variable
The primary outcome was duplicated imaging, defined as the receipt of a dedicated maxillofacial CT scan at the receiving trauma center within 24 hours of arrival.
Covariates
None.
Analyses
Descriptive, bivariate, and multivariable logistic regression statistics were computed to measure the associations between study variables and duplicated imaging. An alpha threshold of P < .001 was considered statistically significant.
Results
The cohort consisted of 72,766 subjects with a mean age of 47.0 ± 20.2 years and was 69.8% male (n = 50,817). Redundant imaging occurred in 16.8% (n = 12,206) of the cohort over the study period. In the fully adjusted model, the strongest predictor of redundancy was the receipt of a trauma pan-scan (odds ratio [OR] = 11.0, 95% CI 10.2 to 11.9, P 600 beds were less likely to duplicate scans (OR = 0.59, 95% CI, 0.52 to 0.67, P < .001). Repeated imaging was associated with a 2 to 3% increase in operative intervention for fractures of the mandible, maxilla, and orbit (all P < .001).
Conclusions and relevance
Imaging redundancy affects approximately one in 6 transferred facial trauma patients and is driven primarily by trauma imaging protocols and hospital characteristics.
DOI
10.1016/j.joms.2026.04.007
PMID
42097550
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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