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.
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.
Authors: Kaveh Hajifathalian; Reem Z Sharaiha; Sonal Kumar; Tibor Krisko; Daniel Skaf; Bryan Ang; Walker D Redd; Joyce C Zhou; Kelly E Hathorn; Thomas R McCarty; Ahmad Najdat Bazarbashi; Cheikh Njie; Danny Wong; Lin Shen; Evan Sholle; David E Cohen; Robert S Brown; Walter W Chan; Brett E Fortune Journal: PLoS One Date: 2020-09-30 Impact factor: 3.240