Citation

Pennell C, Polet C, Arthur LG, Grewal H, Aronoff S. J. Trauma Acute Care Surg. 2020; ePub(ePub): ePub.

Affiliation

St. Christopher's Hospital for Children, Section of Pediatric Infectious Diseases, 160 East Erie Avenue, Philadelphia, PA 19134.

Copyright

(Copyright © 2020, Lippincott Williams and Wilkins)

DOI

10.1097/TA.0000000000002717

PMID

32282753

Abstract

BACKGROUND: Computed tomography (CT) is the gold standard for diagnosing intra-abdominal injury (IAI) but is expensive and risks radiation exposure. The Pediatric Emergency Care Applied Research Network (PECARN) model identifies children at low risk of IAI requiring intervention (IAI-I) in whom CT may be omitted, but does not provide an individualized risk assessment to positively predict IAI-I. We sought to apply machine learning algorithms to the PECARN blunt abdominal trauma (BAT) dataset experimentally to create models for predicting both the presence and absence of IAI-I for pediatric BAT victims.

METHODS: Using the PECARN dataset we derived and validated predictive models for IAI-I. The dataset was divided into derivation (n=7940) and validation (n=4089) subsets. Six algorithms were tested to create two models using 19 clinical variables including emesis, dyspnea, GCS<15, visible thoracic or abdominal trauma, seatbelt sign, abdominal distension, tenderness or rectal bleeding, peritoneal signs, absent bowel sounds, flank pain, pelvic pain or instability, gender, age, HR, and RR. Five algorithms were fitted to predict the absence (low-risk model) or presence (high-risk model) of IAI-I. Models were validated using the test subset.

RESULTS: For the low risk model, 4 algorithms were significantly better than the baseline rate (2.28%) when validated using the test set. The random forest model identified 73% of children as low-risk, having a predicted IAI-I rate of 0.54%. For the high risk model, all 6 algorithms had added predictive power compared to the baseline rate with the highest reportable risk being 39.0%. By incorporating both models into a web-application child-specific risks of IAI-I can be estimated ranging from 0.28% to 39.0% CONCLUSIONS: We developed a tool that provides a child-specific risk estimate for IAI-I after BAT. This publically available model provides a powerful tool for clinicians triaging pediatric victims of blunt abdominal trauma. LEVEL OF EVIDENCE: II, Diagnostic Tests or Criteria.

Language: en