Zhang Jin, Jin Zhichao, Tang Bihan, Huang Xiangtong, Wang Zongyu, Chen Qi, He Jia
School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai 200000, China.
Department of Health Statistics, Naval Medical University, Shanghai 200433, China.
Bioengineering (Basel). 2024 Jul 30;11(8):768. doi: 10.3390/bioengineering11080768.
Trauma is a leading cause of death worldwide, with many incidents resulting in hemorrhage before the patient reaches the hospital. Despite advances in trauma care, the majority of deaths occur within the first three hours of hospital admission, offering a very limited window for effective intervention. Unfortunately, a significant increase in mortality from hemorrhagic trauma is primarily due to delays in hemorrhage control. Therefore, we propose a machine learning model to predict the need for urgent hemorrhage intervention.
This study developed and validated an XGBoost-based machine learning model using data from the National Trauma Data Bank (NTDB) from 2017 to 2019. It focuses on demographic and clinical data from the initial hours following trauma for model training and validation, aiming to predict whether trauma patients require urgent hemorrhage intervention.
The XGBoost model demonstrated superior performance across multiple datasets, achieving an AUROC of 0.872 on the training set, 0.869 on the internal validation set, and 0.875 on the external validation set. The model also showed high sensitivity (77.8% on the external validation set) and specificity (82.1% on the external validation set), with an accuracy exceeding 81% across all datasets, highlighting its high reliability for clinical applications.
Our study shows that the XGBoost model effectively predicts urgent hemorrhage interventions using data from the National Trauma Data Bank (NTDB). It outperforms other machine learning algorithms in accuracy and robustness across various datasets. These results highlight machine learning's potential to improve emergency responses and decision-making in trauma care.
创伤是全球主要的死亡原因之一，许多事故在患者到达医院之前就已导致出血。尽管创伤护理取得了进展，但大多数死亡发生在入院后的头三个小时内，有效干预的窗口期非常有限。不幸的是，出血性创伤死亡率的显著增加主要是由于出血控制的延迟。因此，我们提出了一种机器学习模型来预测紧急出血干预的需求。
本研究使用2017年至2019年国家创伤数据库（NTDB）的数据开发并验证了一种基于XGBoost的机器学习模型。它专注于创伤后最初几小时的人口统计学和临床数据用于模型训练和验证，旨在预测创伤患者是否需要紧急出血干预。
XGBoost模型在多个数据集上表现出卓越的性能，在训练集上的AUROC为0.872，内部验证集上为0.869，外部验证集上为0.875。该模型还显示出高敏感性（外部验证集上为77.8%）和特异性（外部验证集上为82.1%），所有数据集的准确率均超过81%，突出了其在临床应用中的高可靠性。
我们的研究表明，XGBoost模型利用国家创伤数据库（NTDB）的数据有效地预测了紧急出血干预。在各种数据集的准确性和稳健性方面，它优于其他机器学习算法。这些结果凸显了机器学习在改善创伤护理中的应急响应和决策方面的潜力。
