Table 2.
Logistic regression modeling for developing traumatic aortic injuries
| OR (95% CI) | p-value | |
|---|---|---|
| Low elevation | 1.0 (Ref.) | Ref. |
| High elevationa | 2.4 (1.6, 3.7) | < 0.0001 |
| GCS (Abnormal vs. Normal) | 4.7 (3.0, 7.1) | < 0.0001 |
| SBP (Abnormal vs. Normal) | 0.3 (0.1, 0.6) | 0.003 |
| Cause of Injury (Fall vs. Other) | 0.3 (0.1, 0.6) | 0.0002 |
| Cause of Injury (MCC/MVC vs. Other) | 1.4 (0.8, 2.4) | 0.02 |
| Cause of Injury (Sports vs. Other) | 1.7 (0.5, 5.4) | 0.16 |
| Caucasian (No vs. Yes) | 1.5 (1.0, 2.4) | 0.047 |
| Alcoholism (Yes vs. No) | 1.4 (0.7, 2.7) | 0.30 |
| Drug Use (Yes vs. No) | 1.7 (0.9, 3.2) | 0.09 |
TAI Traumatic aortic injury, OR Odds ratio, CI Confidence interval, GCS Glasgow Coma Scale, SBP Systolic Blood Pressure, MCC/MVC Motorcycle collision / motor vehicle collision. a = Per Hosmer-Lemeshow Rule on confounding, up to eight variables other than elevation could be included in the model. Model fit: AUROC = 0.80. Bold values denote p < 0.05