326 - Clinician Characteristics Associated with Computed Tomography Use in Children with Blunt Abdominal Trauma

Saturday, April 29, 2023

3:30 PM – 6:00 PM ET

Poster Number: 326
Publication Number: 326.208

Pradip P.. Chaudhari, Children's Hospital Los Angeles, Los Angeles, CA, United States; Irma Ugalde, McGovern Medical School at UTHealth Houston, Houston, TX, United States; Kenneth Yen, UT Southwestern Medical Center, Dallas, TX, United States; Kevan A. McCarten-Gibbs, UCSF Benioff Children's Hospital Oakland, Oakland, CA, United States; Mohamed Badawy, University of Texas Southwestern Medical School, Dallas, TX, United States; Paul Ishimine, UC San Diego, San Diego, CA, United States; Nisa atigapramoj, UCSF Benioff Children's Hospital Oakland, San Francisco, CA, United States; Donovan Nielsen, UC Davis Health EM Research, Sacramento, CA, United States; Daniel J. Tancredi, University of California, Davis, School of Medicine, Sacramento, CA, United States; Jeffrey S.. Upperman, Vanderbilt University Medical Center, Nashville, TN, United States; Nathan Kuppermann, UC Davis School of Medicine, Sacrametno, CA, United States; James Holmes, University of California, Davis, School of Medicine, Sacramento, CA, United States

Presenting Author(s)

Pradip P. Chaudhari, MD (he/him/his)

Attending Physician
Children's Hospital Los Angeles
Los Angeles, California, United States

Background:

Evidence-based clinical prediction rules improve care delivery to children in the emergency department (ED), however clinician-level factors may impact rule implementation.

Objective:

We aimed to investigate the association of clinician perceptions, demographic characteristics, and risk tolerance with abdominal/pelvic computed tomography (CT) imaging ordering in children with blunt abdominal trauma at very low risk for intra-abdominal injuries undergoing acute intervention (IAI-AI) based on validated PECARN prediction rules.

Design/Methods:

< 18 years with blunt abdominal trauma evaluated in 6 EDs from December 2016 to December 2021. The clinician survey consisted of questions on clinician demographics, clinical experience, self-reported risk tolerance based on a validated scale, and perceptions/self-reported use of clinical prediction rules. Children were considered very low risk for IAI-AI if they had no PECARN IAI-AI risk variables. IAI-AI was defined as therapeutic laparotomy, angiographic embolization, blood transfusion, or intravenous fluid for ≥2 days for pancreatic/gastrointestinal injuries. We performed multivariable logistic regression analyses to identify variables associated with CT ordering in children at very low risk of IAI-AI.

Results:

Of the 550 ED clinicians surveyed, 537 (97.6%, 95% CI 96.0, 98.7%) completed some or all of the survey. Of the 7542 children enrolled in the study, 3488 (46.2%, 95% CI 45.1, 47.4%) were very low risk for IAI-AI, with a mean age of 9.1 ± 5.2 years. Of these very low risk children, 360 (10.3%, 95% CI 9.3, 11.4%) underwent CT. Mean clinician age was 37.6 ± 8.0 years and years of experience were 6.6 ± 7.4 years. Of the 537 respondents, 217 (41%) were Pediatric Emergency Medicine (EM), 174 (33%) were General EM, and 83 (16%) were Pediatrics/Primary Care. Among the 461 (83.8%) respondents with complete survey responses, clinician characteristics associated with ordering CTs in children very low risk for IAI-AI included: caring for < 50% pediatric patients (OR=2.37, 95% CI 1.38, 4.07) and disagreeing with use of prediction rules (OR=1.54, 95% CI 1.00, 2.36).

Conclusion(s):

Clinicians uncommonly ordered CTs in children at very low risk for IAI-AI. Clinicians who care for a smaller proportion of children and those disagreeing with use of prediction rules were associated with more frequent CT use. This information can be used to target education to certain clinician characteristics to further safely lower CT use in very low risk children.