Predictive model to identify trauma patients with blood alcohol concentrations > or = 50 mg/dl

Abstract

Objective: To develop a simple model for identification of trauma patients who are likely to have a blood alcohol concentration > or = 50 mg/dL (BAC + 50).

Methods: Demographic, clinical, and BAC data were collected from the clinical trauma registry and toxicology data base at a Level I trauma center. Logistic regression was used to analyze data from 11,206 patients to develop a predictive model, which was validated using a subsequent cohort of 3,523 patients.

Results: In the model development cohort, alcohol was detected in the blood of 3,180 BAC-tested patients (28.7%), of whom 91.2% had a BAC + 50 status. Preliminary analysis revealed associations between a BAC + 50 status and sex, age, race, injury type (intentional vs. unintentional), and time of injury (night vs. day and weekend vs. weekday). A predictive model using four attributes (sex and injury type) identified patients at low, medium, and high risk for being BAC + 50. The model was validated using the second group of patients.

Conclusion: Injured patients with a high probability of being alcohol positive can be identified using a simple scoring system based on readily available demographic and clinical information.