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Predicting severe injury using vehicle telemetry data.

Patricia Ayoung-Chee1, Christopher D Mack, Robert Kaufman

  • 1Harborview Injury Prevention and Research Center, and Department of Surgery, University of Washington, Seattle, Washington 98104, USA. ayoungp@uw.edu

The Journal of Trauma and Acute Care Surgery
|December 29, 2012
PubMed
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A new model predicts severe injury using only vehicle collision data, improving emergency medical service (EMS) response. This approach enhances automatic collision notification systems for better triage.

Area of Science:

  • Trauma research
  • Emergency medical services
  • Automotive safety engineering

Background:

  • Standardized collision data collection by event data recorders aids emergency medical service (EMS) response.
  • Existing severe injury prediction models rely on occupant data, which is not always available.
  • Automatic collision notification systems face challenges with unanswered calls, necessitating improved triage methods.

Purpose of the Study:

  • To develop a predictive model for severe injury using solely vehicle collision data.
  • To enhance the effectiveness of automatic collision notification for emergency medical service (EMS) triage.

Main Methods:

  • Utilized the National Automotive Sampling System Crashworthiness Data System (2000-2010).
  • Included telematic and non-telematic collision variables for front-seat occupants in non-rollover and rollover crashes.

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  • Tested the University of Washington model for severe injury prediction using a 20% probability threshold, aligning with CDC Field Triage Guidelines.

    • Main Results:

    • The study analyzed 28,633 crashes involving 52,033 occupants, with 9.9% experiencing severe injury.
    • The University of Washington model demonstrated 40.0% sensitivity and 20.7% positive predictive value (PPV) at Step 0.
    • Excluding non-telematic variables reduced model sensitivity and PPV; the re-created General Motors model showed lower performance.

    Conclusions:

    • A novel model effectively predicts severe injury using only vehicle collision data.
    • This model is comparable to existing methods and shows potential for advanced automatic collision notification systems.
    • The findings support the use of collision data for improved EMS response planning and field triage.