Crash Telemetry-Based Injury Severity Prediction is Equivalent to or Out-Performs Field Protocols in Triage of Planar Vehicle Collisions

Prehosp Disaster Med. 2019 Aug;34(4):356-362. doi: 10.1017/S1049023X19004515. Epub 2019 Jul 19.


Introduction: With the increasing availability of vehicle telemetry technology, there is great potential for Advanced Automatic Collision Notification (AACN) systems to improve trauma outcomes by detecting patients at-risk for severe injury and facilitating early transport to trauma centers.

Methods: National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data from 1999-2013 were used to construct a logistic regression model (injury severity prediction [ISP] model) predicting the probability that one or more occupants in planar, non-rollover motor vehicle collisions (MVCs) would have Injury Severity Score (ISS) 15+ injuries. Variables included principal direction of force (PDOF), change in velocity (Delta-V), multiple impacts, presence of any older occupant (≥55 years old), presence of any female occupant, presence of right-sided passenger, belt use, and vehicle type. The model was validated using medical records and 2008-2011 crash data from AACN-enabled Michigan (USA) vehicles identified from OnStar (OnStar Corporation; General Motors; Detroit, Michigan USA) records. To compare the ISP to previously established protocols, a literature search was performed to determine the sensitivity and specificity of first responder identification of ISS 15+ for MVC occupants.

Results: The study population included 924 occupants in 836 crash events. The ISP model had a sensitivity of 72.7% (95% Confidence Interval [CI] 41%-91%) and specificity of 93% (95% CI 92%-95%) for identifying ISS 15+ occupants injured in planar MVCs. The current standard 2006 Field Triage Decision Scheme (FTDS) was 56%-66% sensitive and 75%-88% specific in identifying ISS 15+ patients.

Conclusions: The ISP algorithm comparably is more sensitive and more specific than current field triage in identifying MVC patients at-risk for ISS 15+ injuries. This real-world field study shows telemetry data transmitted before dispatch of emergency medical systems can be helpful to quickly identify patients who require urgent transfer to trauma centers.

Keywords: AACN: Advanced Automotive Collision Notification; ACS COT: American College of Surgeons Committee on Trauma; Abdomen; CDC: Centers for Disease Control and Prevention; CRAMS: Circulation; FTDS: Field Triage Decision Scheme; GPS: Global Positioning System; ISP: injury severity prediction; ISS: Injury Severity Score; MVC: motor vehicle collision; Motor; NASS-CDS: National Automotive Sampling System Crashworthiness Data System; PDOF: principal direction of force; PHI: Prehospital Index; Respiration; SMUR: Service Mobile d’Urgence et Reanimation; T-RTS: Revised Trauma Score for Triage; TS: Trauma Score; and Speech Criteria; Emergency Medical Services; field triage; injury severity; trauma center; vehicle telemetry.

MeSH terms

  • Accidents, Traffic / statistics & numerical data*
  • Adult
  • Algorithms
  • Databases, Factual
  • Emergency Medical Service Communication Systems / organization & administration*
  • Female
  • Humans
  • Injury Severity Score
  • Logistic Models
  • Male
  • Michigan
  • Middle Aged
  • Predictive Value of Tests
  • Retrospective Studies
  • Risk Assessment
  • Survival Analysis
  • Telemetry / methods*
  • Time-to-Treatment
  • Transportation of Patients / organization & administration
  • Trauma Centers / organization & administration
  • Treatment Outcome
  • Triage / methods*
  • Wounds and Injuries / diagnosis*
  • Wounds and Injuries / mortality
  • Wounds and Injuries / therapy