Physiologic, demographic and mechanistic factors predicting New Injury Severity Score (NISS) in motor vehicle accident victims.

BACKGROUND

Current literature on motor vehicle accidents (MVAs) has few reports regarding field factors that predict the degree of injury. Also, studies of mechanistic factors rarely consider concurrent predictive effects of on-scene patient physiology. The New Injury Severity Score (NISS) has previously been found to correlate with mortality, need for ICU admission, length of hospital stay, and functional recovery after trauma. To potentially increase future precision of trauma triage, we assessed how the NISS is associated with physiologic, demographic and mechanistic variables from the accident site.

METHODS

Using mixed-model linear regression analyses, we explored the association between NISS and pre-hospital Glasgow Coma Scale (GCS) score, Revised Trauma Score (RTS) categories of respiratory rate (RR) and systolic blood pressure (SBP), gender, age, subject position in the vehicle, seatbelt use, airbag deployment, and the estimated squared change in vehicle velocity on impact ((Δv)(2)). Missing values were handled with multiple imputation.

RESULTS

We included 190 accidents with 353 dead or injured subjects (mean NISS 17, median NISS 8, IQR 1-27). For the 307 subjects in front-impact MVAs, the mean increase in NISS was -2.58 per GCS point, -2.52 per RR category level, -2.77 per SBP category level, -1.08 for male gender, 0.18 per year of age, 4.98 for driver vs. rear passengers, 4.83 for no seatbelt use, 13.52 for indeterminable seatbelt use, 5.07 for no airbag deployment, and 0.0003 per (km/h)(2) velocity change (all p<0.002).

CONCLUSION

This study in victims of MVAs demonstrated that injury severity (NISS) was concurrently and independently predicted by poor pre-hospital physiologic status, increasing age and female gender, and several mechanistic measures of localised and generalised trauma energy. Our findings underscore the need for precise information from the site of trauma, to reduce undertriage, target diagnostic efforts, and anticipate need for high-level care and rehabilitative resources.