Objective Demonstrate the effectiveness of automatic notification of injury severity information in improving the survivability of motor vehicle related

Objective

Demonstrate the effectiveness of automatic notification of injury severity information in improving the survivability of motor vehicle related trauma victims

Potential Benefits of an Automatic Injury Notification System in Reducing Motor Vehicle Crash FatalitiesDipan Bose, Jeff Crandall, Gerald McGwin, Jay Goldman, Jeff Foster, Russ Fine

Contact : [email protected]

Overview

Pre-Crash Crash

Post-Crash

Crash Prevention And SeverityReduction

Technologies

RestraintsInterior Changes

Structural Changes

NotificationEMS

Medical Care

Crash event

Simulation & control:Delta-v, PDOF, posture, belt useairbag

Statistical predictors

Phone communication

Prediction of injury severity by body region

Automatic notificationEMS/Fire/rescue:• Appropriate emergency

response• Proper deployment of

equipment and personnel• Avoid over/under

estimation of necessary response

Hospital:• Early awareness of

severity: ER and OR needs• Decision on level of

trauma center

Traffic:• Prediction based on delay• Broadcast information for

better route guidance• Input to EMS/rescue/utilities



Motivation

• Automatic crash notification systems (AACN) can significantly reduce notification times for motor vehicle crashes (MVC) and potentially reduce related morbidity and mortality1

• Additionally, predictions of injury severity and subsequent automatic transfer of severity data to EMS and trauma care providers is expected to improve the timeliness, appropriateness, and efficacy of emergency care provided2



Method

• EMS data from sampled MVCs (weighted sample, n ~200,000 adult occupants involved in serious crashes) in the year 2002-2003 were obtained from the National Automotive Sampling System – Crash Worthiness Data System (NASS-CDS)

• Crash survivability, as a function of EMS interval times was estimated using multivariate regression models while controlling for the effects of entrapment at scene, trauma care, and transportation type.

• Comparison was done to estimate survivability as a function of EMS times for known injury severity to specific body regions (head, thorax, abdomen)



Dataset

• Cases selected from years 2002 and 2003 (n=2,589)

• Case occupant must be an adult (> 16y)

• Time of crash (as estimated) & Fatality status known

• Notification time less than 6 hours

• Case occupant must sustain a severe injury (AIS 3+)



Descriptive Analysis

Variable Units mean (95% CI)

Age (y) 40 (38, 43)

Sex=male (prop.) 61 (56, 67)

Basic life support=yes

(prop.) 67 (54,80)

Air transport= yes

(prop.) 4 (<1, 7)

Trauma care=yes

(prop.) 67 (54, 80)

Entrapment=y (prop.) 23 (16, 27)

Variable Units mean (95% CI)

MAIS =4+ (prop.) 33 (23, 42)

Fatality=yes (prop.) 21 (18, 23)

Fatality at scene=yes

(prop.) 11 (9, 13)

MAIS in head (prop.) 37 (31, 43)

MAIS in chest (prop.) 34 (29, 38)

MAIS in abdomen

(prop.) 14 (10, 17)



Descriptive Analysis: EMS times

0

20

40

60

80

100

120

140

160

180

Not fatal Fatal Not fatal Fatal Not fatal Fatal Not fatal Fatal

Tim

e (m

inut

es)

EMS time categories

First notification time EMS arrival time

EMS departure timeMedical facility arrival time



Results

Pearson’s partial correlation coefficients(controlling for fatality, EMS by air, entrapment, trauma care)

Notification time EMS arrival time EMS depart time Hospital Arrival timeNotification time 1.00 0.96 0.74 0.67EMS arrival time 0.96 1.00 0.76 0.72EMS depart time 0.74 0.76 1.00 0.95Hospital Arrival time 0.67 0.72 0.95 1.00

Univariate Poisson Regression Model

Outcome=fatality Unadj. OR (p<0.05)

Occupant age 1.006Ambulance transport 0.321Entrapment rescue 1.99Trauma care 0.180Notification time 1.009EMS arrival time 1.003EMS departure time 1.004Hospital arrival time 1.004



Results

Predicted survival for a male victim not entrapped, ground transported via ambulance taking 40 minutes to a trauma care center

Cox’s Proportional Hazard Survival model



Results

Predicted probability of surviving 24 hours as a function of hospital arrival time





Conclusions

• Compared to first notification time and EMS arrival time, the time to arrive at the hospital was a better predictor to estimate survival probability

• Severe injury (AIS 3+) to specific body regions (e.g., thorax, abdomen) may affect the survival rate as a function of EMS times

• Automatic prediction and notification of injury severity is necessary to improve overall survival of victims involved in severe motor vehicle crashes.

References:1. Clark, DE, and Cushing, BM, 2002. Predicted Effect of Automatic Crash Notification on Traffic Mortality. Accident Analysis and Prevention, 34 (2002), 507-5132. Champion, H. R., Augenstein, J. S., Blatt, A. J., Cushing, B., Digges, K. H., Flanigan, M. C., et al. (2005). New tools to reduce deaths and disabilities by improving

emergency care: urgency software, occult injury warnings, and air medical services database. The 19th Enhanced Safety of Vehicles (ESV) conference, Washington, DC

Documents

Objective Demonstrate the effectiveness of automatic notification of injury severity information in improving the survivability of motor vehicle related