title: Predicting severe head injury after light motor vehicle crashes: implications for automatic crash notification systems.
abstract: Motor vehicle crashes (MVC) are a leading public health problem. Improving notification times and the ability to predict which crashes will involve severe injuries may improve trauma system utilization. This study was undertaken to develop and validate a model to predict severe head injury following MVC using information readily incorporated into an automatic crash notification system. A cross-sectional study with derivation and validation sets was performed. The cohort was drawn from drivers of vehicles involved in MVC obtained from the National Automotive Sampling System (NASS). Independent multivariable predictors of severe head injury were identified. The model was able to stratify drivers according to their risk of severe head injury indicating its validity. The areas under the receiver-operating characteristic (ROC) curves were 0.7928 in the derivation set and 0.7940 in the validation set. We have developed a prediction model for head injury in MVC. As the development of automatic crash notification systems improves, models such as this one will be necessary to permit triage of what would be an overwhelming increase in crash notifications to pre-hospital responders.
authors.affiliation: Department of Anesthesia and Critical Care, Beth Israel Deaconess Medical Center and Harvard Medical School, 1 Deaconess Rd., CC470, Boston, MA 02215, USA. dtalmor@bidmc.harvard.edu
authors.forename: Daniel
authors.initials: D
authors.lastname: Talmor
authors.forename: Kimberly M
authors.initials: KM
authors.lastname: Thompson
authors.forename: Anna T R
authors.initials: AT
authors.lastname: Legedza
authors.forename: Ram
authors.initials: R
authors.lastname: Nirula
mesh_terms: D000063:Accidents, Traffic; D000328:Adult; D006259:Craniocerebral Trauma / Q000145:classification / Q000453:epidemiology* / Q000517:prevention & control; D003430:Cross-Sectional Studies; D004632:Emergency Medical Services; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D012622:Seat Belts; D014218:Triage