Feasibility of preventing massive contrast media extravasation using a sensor device in contrast-enhanced CT: an observational study.

Computed tomography contrast media extravasation risk factors sensor device

Journal

Acta radiologica (Stockholm, Sweden : 1987)
ISSN: 1600-0455
Titre abrégé: Acta Radiol
Pays: England
ID NLM: 8706123

Informations de publication

Date de publication:
16 Oct 2024
Historique:
medline: 16 10 2024
pubmed: 16 10 2024
entrez: 16 10 2024
Statut: aheadofprint

Résumé

Recent guidelines recommend direct patient observation, pressure monitoring, and sensor devices to prevent extravasation during contrast media (CM) injection. However, it is impractical in terms of time and cost to install sensors for all patients. To identify risk factors for CM extravasations during contrast-enhanced computed tomography (CECT) in a large population and to establish criteria for placing the sensor device on patients. This retrospective study included 143,556 patients who underwent CECT at our hospital between April 2012 and July 2022. We performed multivariable logistic regression analysis between patients with (n = 350) and randomly selected patients without CM extravasation (n = 350). We investigated the percentage of patients with sensor devices and their sensitivity for detecting extravasation using receiver operating characteristic curve analysis. The extravasation rate was 0.27%. Multivariable logistic regression analysis showed that the injection rate (adjusted odds ratio [AOR] = 1.61, 95% confidence interval [CI] = 1.33-1.95: Sensitivity analysis established criteria for effective placing sensor devices.

Sections du résumé

BACKGROUND BACKGROUND
Recent guidelines recommend direct patient observation, pressure monitoring, and sensor devices to prevent extravasation during contrast media (CM) injection. However, it is impractical in terms of time and cost to install sensors for all patients.
PURPOSE OBJECTIVE
To identify risk factors for CM extravasations during contrast-enhanced computed tomography (CECT) in a large population and to establish criteria for placing the sensor device on patients.
MATERIAL AND METHODS METHODS
This retrospective study included 143,556 patients who underwent CECT at our hospital between April 2012 and July 2022. We performed multivariable logistic regression analysis between patients with (n = 350) and randomly selected patients without CM extravasation (n = 350). We investigated the percentage of patients with sensor devices and their sensitivity for detecting extravasation using receiver operating characteristic curve analysis.
RESULTS RESULTS
The extravasation rate was 0.27%. Multivariable logistic regression analysis showed that the injection rate (adjusted odds ratio [AOR] = 1.61, 95% confidence interval [CI] = 1.33-1.95:
CONCLUSION CONCLUSIONS
Sensitivity analysis established criteria for effective placing sensor devices.

Identifiants

pubmed: 39410914
doi: 10.1177/02841851241287314
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2841851241287314

Déclaration de conflit d'intérêts

Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Auteurs

Yoriaki Matsumoto (Y)

Department of Radiology, Hiroshima University Hospital, Hiroshima, Japan.

Ayaka Chikasue (A)

Department of Nursing, Hiroshima University Hospital, Hiroshima, Japan.

Miho Kondo (M)

Department of Nursing, Hiroshima University Hospital, Hiroshima, Japan.

Tomoyuki Akita (T)

Department of Epidemiology, Infectious Disease Control and Prevention, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

Masao Kiguchi (M)

Department of Radiology, Hiroshima University Hospital, Hiroshima, Japan.

Yuko Nakamura (Y)

Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

Kazuo Awai (K)

Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

Classifications MeSH