A clinical decision model identifies patients at risk for delayed diagnosed injuries after high-energy trauma

Abstract

Objective: Tertiary trauma survey is widely implemented in trauma care to identify all injuries in trauma patients. However, various studies consistently show that some trauma patients have missed injuries. In this study, we developed a clinical decision model to identify patients who are at risk for delayed diagnosed injuries.

Methods: During a period of 18 months, we collected the medical records of all the adult patients who presented after a high-energy trauma at the emergency department of a Dutch trauma centre. The type of trauma, patient characteristics, the radiology studies performed, Glasgow Coma Scale, Revised Trauma Score, and Injury Severity Score (ISS) were registered. We thoroughly screened all medical records for delayed diagnosed injuries. Stepwise logistic regression analysis was used to identify the variables associated with the outcome delayed diagnosed injuries and to develop a clinical prediction model.

Results: We included 475 patients. Thirteen (2.7%) patients with delayed diagnosed injuries were identified. Stepwise logistic regression analysis revealed several models with the ISS, ICU admittance, and CT-head as predictive variables. The model we proposed with the ISS could identify patients who are at a risk for delayed diagnosed injuries with a sensitivity of 92.3% and a specificity of 86.4%.

Conclusion: Our newly developed clinical decision model can identify patients who are at a risk for delayed diagnosed injuries and who should undergo an intensified search for potential unidentified injuries.