A chi-square automatic interaction detection (CHAID) analysis of factors determining trauma outcomes.

OBJECTIVE Analysis of variables predictive of trauma outcome by the CHAID (chi-square automatic interaction detection) statistical program. DESIGN Retrospective analysis of a prospectively maintained trauma database. METHODS The study group consisted of 607 primary ambulance retrievals to Royal Prince Alfred Hospital, Sydney, for the period of 6 fiscal years (1990-1996) with major injury (Injury Severity Score > 15). MAIN RESULTS The overall mortality fell from 26.6 to 16% (chi 2 test = 14.7, p = 0.01) during the study period. The emergency room Glasgow Coma Scale (GCS) score (preresuscitation) was the strongest predictor of death or survival. CHAID segmented the study group into three categories based on GCS scores (¿3¿, ¿4-12¿, and ¿13-15¿), each with significantly different outcome predictability. The mortality rate in those with a GCS score of 3 (n = 89) was 67%. Systolic blood pressure was the strongest predictor of outcome in this subset. The mortality in those with GCS score of 4-12 (n = 160) was 18%. Injury Severity Score was the strongest predictor in this subset. The mortality rate in those with GCS score of 13-15 was 5%. Age was the strongest predictor in this group. CONCLUSION The CHAID-generated flowchart has proved useful in this pilot study to analyze the interrelation between variables predictive of outcome in an Australian urban trauma population.

[1]  W. Haddon,et al.  The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. , 1974, The Journal of trauma.

[2]  J. G. West,et al.  Impact of regionalization. The Orange County experience. , 1983, Archives of surgery.

[3]  W. Copes,et al.  Evaluating trauma care: the TRISS method. Trauma Score and the Injury Severity Score. , 1987, The Journal of trauma.

[4]  Royal Pd.  The value of trauma centres: a methodologic review. , 1987 .

[5]  S. Shackford,et al.  Base deficit as a guide to volume resuscitation. , 1988, The Journal of trauma.

[6]  T A Gennarelli,et al.  A revision of the Trauma Score. , 1989, The Journal of trauma.

[7]  W. Baxt,et al.  The impact of a regionalized trauma system on trauma care in San Diego County. , 1989, Annals of emergency medicine.

[8]  J H Siegel,et al.  Early physiologic predictors of injury severity and death in blunt multiple trauma. , 1990, Archives of surgery.

[9]  H. Champion,et al.  The Major Trauma Outcome Study: establishing national norms for trauma care. , 1990, The Journal of trauma.

[10]  Ellen J. MacKenzie,et al.  The Effect of Preexisting Conditions on Mortality in Trauma Patients , 1990 .

[11]  J. Morris,et al.  Base deficit stratifies mortality and determines therapy. , 1992, The Journal of trauma.

[12]  G. Kane,et al.  Impact of the Los Angeles County Trauma System on the survival of seriously injured patients. , 1992, The Journal of trauma.

[13]  D. Hill.  A model to teach trauma care to medical students. , 1993, Medical teacher.

[14]  W. Shoemaker,et al.  Unreliability of blood pressure and heart rate to evaluate cardiac output in emergency resuscitation and critical illness , 1993, Critical care medicine.

[15]  M A Foulkes,et al.  The role of secondary brain injury in determining outcome from severe head injury. , 1993, The Journal of trauma.

[16]  T. Scalea,et al.  Lactate clearance and survival following injury. , 1993, The Journal of trauma.

[17]  J. Morris,et al.  Mortality factors in geriatric blunt trauma patients. , 1994, Archives of surgery.

[18]  R H West,et al.  Outcome of patients with haemorrhagic shock: an indicator of performance in a trauma centre. , 1995, Journal of the Royal College of Surgeons of Edinburgh.

[19]  D. Hill,et al.  A population-based study of outcome after injury to car occupants and to pedestrians. , 1996, The Journal of trauma.