Which criteria is a better predictor of ICU admission in trauma patients? An artificial neural network approach

Abstract

PurposeOne of the most critical concerns in the intensive care unit (ICU) section is identifying the best criteria for entering patients to this part. This study aimed to predict the best compatible criteria for entering trauma patients in the ICU section. MethodThe present study was a historical cohort study. The data were collected from 2448 trauma patients referring to Shahid Rajaee Hospital between January 2015 and January 2017 in Shiraz, Iran. The artificial neural network (ANN) models with cross-validation and logistic regression (LR) with a backward method was used for data analysis. The final analysis was performed on a total of 958 patients who were transferred to the ICU section. ResultsBased on the present results, the motor component of the GCS score at each cutoff point had the highest importance. The results also showed better performance for the AUC and accuracy rate for ANN compared with LR. ConclusionThe most critical indicators in predicting the optimal use of ICU services in this study were the Motor component of the GCS. Results revealed that the ANN had a better performance than the LR in predicting the main outcomes of the traumatic patients in both the accuracy and AUC index. Trauma section surgeons and ICU specialists will benefit from this study's results and can assist them in making decisions to predict the patient outcomes before entering the ICU.