Development and validation of machine learning models for the prediction of blunt cerebrovascular injury in children

Cyrus A Farzaneh; John Schomberg; Brittany G Sullivan; Yigit S Guner; Michael L Nance; David Gibbs; Peter T Yu

doi:10.1016/j.jpedsurg.2021.11.008

Development and validation of machine learning models for the prediction of blunt cerebrovascular injury in children

J Pediatr Surg. 2022 Apr;57(4):732-738. doi: 10.1016/j.jpedsurg.2021.11.008. Epub 2021 Nov 20.

Authors

Cyrus A Farzaneh¹, John Schomberg², Brittany G Sullivan³, Yigit S Guner⁴, Michael L Nance⁵, David Gibbs², Peter T Yu⁴

Affiliations

¹ Department of Surgery, University of California, Irvine, 101 The City Drive South, Orange, CA 92868, United States.
² Division of Pediatric Surgery, Children's Hospital of Orange County, Orange, CA, United States.
³ Department of Surgery, University of California, Irvine, 101 The City Drive South, Orange, CA 92868, United States. Electronic address: bgsulliv@hs.uci.edu.
⁴ Department of Surgery, University of California, Irvine, 101 The City Drive South, Orange, CA 92868, United States; Division of Pediatric Surgery, Children's Hospital of Orange County, Orange, CA, United States.
⁵ Division of Pediatric Surgery, Division of Pediatric Trauma, Children's Hospital of Philadelphia, Philadelphia, PA, United States.

PMID: 34872731
DOI: 10.1016/j.jpedsurg.2021.11.008

Abstract

Background: Blunt cerebrovascular injury (BCVI) is a rare finding in trauma patients. The previously validated BCVI (Denver and Memphis) prediction model in adult patients was shown to be inadequate as a screening option in injured children. We sought to improve the detection of BCVI by developing a prediction model specific to the pediatric population.

Methods: The National Trauma Databank (NTDB) was queried from 2007 to 2015. Test and training datasets of the total number of patients (885,100) with complete ICD data were used to build a random forest model predicting BCVI. All ICD features not used to define BCVI (2268) were included within the random forest model, a machine learning method. A random forest model of 1000 decision trees trying 7 variables at each node was applied to training data (50% of the dataset, 442,600 patients) and validated with test data in the remaining 50% of the dataset. In addition, Denver and Memphis model variables were re-validated and compared to our new model.

Results: A total of 885,100 pediatric patients were identified in the NTDB to have experienced blunt pediatric trauma, with 1,998 (0.2%) having a diagnosis of BCVI. Skull fractures (OR 1.004, 95% CI 1.003-1.004), extremity fractures (OR 1.001, 95% 1.0006-1.002), and vertebral injuries (OR 1.004, 95% CI 1.003-1.004) were associated with increased risk for BCVI. The BCVI prediction model identified 94.4% of BCVI patients and 76.1% of non-BCVI patients within the NTDB. This study identified ICD9/ICD10 codes with strong association to BCVI. The Denver and Memphis criteria were re-applied to NTDB data to compare validity and only correctly identified 13.4% of total BCVI patients and 99.1% of non BCVI patients.

Conclusion: The prediction model developed in this study is able to better identify pediatric patients who should be screened with further imaging to identify BCVI.

Level of evidence: Retrospective diagnostic study-level III evidence.

Keywords: Denver; Memphis model; Pediatric blunt cerebrovascular injury.

Publication types

Review

MeSH terms

Adult
Cerebrovascular Trauma* / diagnosis
Cerebrovascular Trauma* / epidemiology
Child
Humans
Machine Learning
Retrospective Studies
Skull Fractures*
Wounds, Nonpenetrating* / complications
Wounds, Nonpenetrating* / diagnosis
Wounds, Nonpenetrating* / epidemiology