In this study, we aimed to evaluate the correlation between the trauma score of individuals wounded in the Lushan earthquake and emergency workload for treatment. We further created a trauma score-emergency workload calculation model.

We included data from patients wounded in the Lushan earthquake and treated at West China Hospital, Sichuan University. We calculated scores per the following models separately: Revised Trauma Score (RTS), Prehospital Index (PHI), Circulation Respiration Abdominal Movement Speech (CRAMS), Therapeutic Intervention Scoring System (TISS-28), and Nursing Activities Score (NAS). We assessed the association between values for CRAMS, PHI, and RTS and those for TISS-28 and NAS. Subsequently, we built a trauma score-emergency workload calculation model to quantitative workload estimation.

Significant correlations were observed for all pairs of trauma scoring models with emergency workload scoring models. TISS-28 score was significantly associated with PHI score and RTS; however, no significant correlation was observed between the TISS-28 score and CRAMS score.

CRAMS, PHI, and RTS were consistent in evaluating the injury condition of wounded individuals; TISS-28 and NAS scores were consistent in evaluating the required treatment workload. Dynamic changes in emergency workload in unit time were closely associated with wounded patient visits.

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.

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.

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.

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.