Trauma severity associated with stress index in emergency settings: an observational prediction-and-validation study

Early judgments for treating severe trauma patients are essential for life-saving. Stress index (SI), obtained from a division of blood glucose level by serum potassium at arrival, might be useful for early prediction. However, the efficacy of SI was unknown. The purpose of this study was to identify and validate prediction models of severe trauma (ST) and the need for damage control operation (DCOP) and massive transfusion (MT) by using SI among trauma patients.

This study was a retrospective and prospective observational study. The prediction models were created by 1-year retrospective data of 167 trauma patients. The prediction models were validated by 6 months of prospective data of 87 trauma patients.

The prediction model for ST contained respiratory rate and SI as significant factors. The prediction model for DCOP contained SI. The prediction model for MT contained systolic blood pressure and SI. The correlation of probability of MT, ST, and DCOP was r = 0.70 (P < 0.001), r = 0.46 (P < 0.001), and r = 0.15 (P = 0.196), respectively. The predicted probability of MT, ST, and DCOP showed 0.93 (95% confidence interval [CI], 0.88-0.90) and 0.80 (95% CI, 0.74-0.86), and 0.79 (95% CI, 0.70-0.88).

We identified and validated our prediction models for ST and the need for DCOP and MT among trauma patients using SI as a main predictor. Our models indicated that fewer variables in an early phase of the treatment process can inform clinicians regarding how severe a patient is and which intervention is needed.