Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran; Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
Research Center for Health Sciences, Institute of Health, Non-communicable Diseases Research Center, Epidemiology Department, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
Surgeon. 2022 Oct;20(5):e175-e186. doi: 10.1016/j.surge.2021.08.003. Epub 2021 Sep 23.
One 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.
The 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.
Based 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.
The 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.
重症监护病房（ICU）的一个关键问题是确定将患者送入该病房的最佳标准。本研究旨在预测创伤患者进入 ICU 的最佳兼容标准。
本研究为回顾性队列研究。数据来自 2015 年 1 月至 2017 年 1 月期间伊朗设拉子沙阿里耶医院的 2448 名创伤患者。采用交叉验证人工神经网络（ANN）模型和向后法逻辑回归（LR）进行数据分析。最终分析了总共 958 名转入 ICU 病房的患者。
根据本研究结果，GCS 评分的运动成分在每个截止点的重要性最高。结果还表明，与 LR 相比，ANN 的 AUC 和准确率性能更好。
在本研究中，预测 ICU 服务最佳使用的最重要指标是 GCS 的运动成分。结果表明，ANN 在预测创伤患者的主要结局方面的准确性和 AUC 指数均优于 LR。创伤科医生和 ICU 专家将从这项研究的结果中受益，并可以帮助他们在进入 ICU 之前做出预测患者预后的决策。
