Enhancing utility of interfacility triage guidelines using machine learning: Development of the Geriatric Interfacility Trauma Triage score

J Trauma Acute Care Surg. 2023 Apr 1;94(4):546-553. doi: 10.1097/TA.0000000000003846. Epub 2022 Nov 21.

Abstract

Background: Undertriage of injured older adults to tertiary trauma centers (TTCs) has been demonstrated by many studies. In predominantly rural regions, a majority of trauma patients are initially transported to nontertiary trauma centers (NTCs). Current interfacility triage guidelines do not highlight the hierarchical importance of risk factors nor do they allow for individual risk prediction. We sought to develop a transfer risk score that may simplify secondary triage of injured older adults to TTCs.

Methods: This was a retrospective prognostic study of injured adults 55 years or older initially transported to an NTC from the scene of injury. The study used data reported to the Oklahoma State Trauma Registry between 2009 and 2019. The outcome of interest was either mortality or serious injury (Injury Severity Score, ≥16) requiring an interventional procedure at the receiving facility. In developing the model, machine-learning techniques including random forests were used to reduce the number of candidate variables recorded at the initial facility.

Results: Of the 5,913 injured older adults initially transported to an NTC before subsequent transfer to a TTC, 32.7% (1,696) had the outcome of interest at the TTC. The final prognostic model (area under the curve, 75.4%; 95% confidence interval, 74-76%) included the following top four predictors and weighted scores: airway intervention (10), traffic-related femur fracture (6), spinal cord injury (5), emergency department Glasgow Coma Scale score of ≤13 (5), and hemodynamic support (4). Bias-corrected and sample validation areas under the curve were 74% and 72%, respectively. A risk score of 7 yields a sensitivity of 78% and specificity of 56%.

Conclusion: Secondary triage of injured older adults to TTCs could be enhanced by use of a risk score. Our study is the first to develop a risk stratification tool for injured older adults requiring transfer to a higher level of care.

Level of evidence: Prognostic and Epidemiolgical; Level III.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Emergency Medical Services* / methods
  • Emergency Service, Hospital
  • Humans
  • Injury Severity Score
  • Machine Learning
  • Retrospective Studies
  • Trauma Centers
  • Triage* / methods
  • Wounds and Injuries / diagnosis
  • Wounds and Injuries / therapy