{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T17:16:34Z","timestamp":1791047794894,"version":"4.1.0"},"reference-count":35,"publisher":"Cambridge University Press (CUP)","issue":"04","license":[{"start":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T00:00:00Z","timestamp":1563494400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Prehosp. Disaster med."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S1049023X19004515_as1\">\n                    <jats:title>Introduction:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1049023X19004515_as2\">\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>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 (\u226555 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1049023X19004515_as3\">\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1049023X19004515_as4\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/s1049023x19004515","type":"journal-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T05:45:12Z","timestamp":1563515112000},"page":"356-362","source":"Crossref","is-referenced-by-count":16,"title":["Crash Telemetry-Based Injury Severity Prediction is Equivalent to or Out-Performs Field Protocols in Triage of Planar Vehicle 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