Construction and verification of an early warning model for hypothermia in emergency trauma patients based on the random forest method.

objectiveTo construct an early warning model for hypothermia in emergency trauma patients using Random Forest (RF) and compare its predictive performance with Logistic regression.

methodsA total of 400 trauma patients were included. Hypothermia was defined as any temperature < 36.0°C within 3 hours after admission. Patients were randomly divided into training and test sets (7:3). Based on the data of the modeling set, the least absolute shrinkage and selection operator (LASSO) regression was used to identify the characteristic variables of hypothermia in patients. Logistic regression model and RF model are constructed based on the identified characteristic variables.

resultsAmong 400 patients, 92 (23.00%) developed hypothermia. The LASSO regression identified six non-zero coefficient indicators, including RTS score, ISS, SI index, ambient temperature at the time of injury, wet clothing, and shock upon entering the room. Based on these, a Logistic regression model and a RF model were constructed. The RF model achieved an AUC of 0.985 (95% CI: 0.972-0.997) in the training set and 0.956 (95% CI: 0.921-0.990) in the test set; the Logistic model achieved 0.963 (95% CI: 0.939-0.988) and 0.956 (95% CI: 0.923-0.989), respectively. Calibration curves demonstrated good agreement between predicted and observed outcomes. DCA revealed that the RF model provided higher standardized net benefit across a wider range of threshold probabilities.

conclusionThe RF-based early warning model demonstrates high predictive efficacy for hypothermia in emergency trauma patients and outperforms traditional Logistic regression, supporting early identification and preventive intervention.