Risk assessment for intra-abdominal injury following blunt trauma in children: Derivation and validation of a machine learning model. Issue 1 (July 2020)

• Record Type: Journal Article
• Authors: Pennell, Christopher; Polet, Conner; Arthur, L. Grier; Grewal, Harsh; Aronoff, Stephen
• Is Part Of: Journal of trauma and acute care surgery. Volume 89:Issue 1(2020)
• Publication Date: 2020-07
• Subjects: Pediatric trauma -- blunt abdominal trauma -- PECARN -- intra-abdominal injury -- machine learning; Surgical intensive care -- Periodicals; Surgical emergencies -- Periodicals; Wounds and injuries -- Surgery -- Periodicals; 617.026
• DOI: 10.1097/TA.0000000000002717
• Languages: English
• ISSNs: 2163-0755

Abstract

Abstract : BACKGROUND: Computed tomography is the criterion 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 computed tomography 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) data set experimentally to create models for predicting both the presence and absence of IAI-I for pediatric BAT victims. METHODS: Using the PECARN data set, we derived and validated predictive models for IAI-I. The data set was divided into derivation (n = 7, 940) and validation (n = 4, 089) subsets. Six algorithms were tested to create 2 models using 19 clinical variables including emesis, dyspnea, Glasgow Coma Scale score of <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, sex, age, heart rate, and respiratory rate (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, four 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 six algorithms had added predictive power compared with 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% CONCLUSION: 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: Prognostic, Level II.