Lammers Daniel, Marenco Christopher, Morte Kaitlin, Conner Jeffrey, Williams James, Bax Tim, Martin Matthew, Eckert Matthew, Bingham Jason
Department Of General Surgery, Madigan Army Medical Center, Tacoma, Washington.
Department Of General Surgery, Madigan Army Medical Center, Tacoma, Washington.
J Surg Res. 2022 Feb;270:369-375. doi: 10.1016/j.jss.2021.09.017. Epub 2021 Nov 1.
Damage control resuscitation has become the standard of care in military and civilian trauma. Early identification of blood product requirements may aid in optimizing the clinical decision-making process while improving trauma related outcomes. This study aimed to assess and compare multiple machine learning models for predicting patients at highest risk for massive transfusion on the battlefield.
Supervised machine learning approaches using logistic regression, support vector machine, neural network, and random forest techniques were used to create predictive models for massive transfusion using standard prehospital and arrival data points from the Department of Defense Trauma Registry, 2008-2016. Seventy percent of the population was used for model development and performance was validated using the remaining 30%. Models were tested for accuracy and compared by standard performance statistics.
A total of 22,158 patients (97% male, 58% penetrating injury, median age 25-29 y/o, average Injury Severity Score 9, with an overall mortality of 3%) were included in the analysis. Massive transfusion was required by 7.4% of patients. Overall accuracy was found to be above 90% in all models tested. Following cross validation and model training, the random forest model outperformed the alternatively tested models for precision, recall, and area under the curve.
Machine learning techniques may allow for more optimal and rapid identification of combat trauma patients at highest risk for massive transfusion. These powerful approaches may uncover novel correlations and help improve triage, activation of massive transfusion resources, and trauma-related outcomes. Further research seeking to optimize and apply these algorithms to trauma-centered research should be pursued.
损伤控制复苏已成为军事和民用创伤治疗的标准。早期确定血液制品需求可能有助于优化临床决策过程，同时改善创伤相关结局。本研究旨在评估和比较多种机器学习模型，以预测战场上最有可能大量输血的患者。
使用逻辑回归、支持向量机、神经网络和随机森林技术等监督机器学习方法，利用2008 - 2016年国防部创伤登记处的标准院前和入院数据点，创建大量输血的预测模型。70%的人群用于模型开发，其余30%用于验证模型性能。对模型进行准确性测试，并通过标准性能统计进行比较。
共有22158例患者（97%为男性，58%为穿透伤，中位年龄25 - 29岁，平均损伤严重度评分9分，总死亡率3%）纳入分析。7.4%的患者需要大量输血。在所有测试模型中，总体准确率均高于90%。经过交叉验证和模型训练，随机森林模型在精度、召回率和曲线下面积方面优于其他测试模型。
机器学习技术可能有助于更优化、快速地识别最有可能大量输血的战斗创伤患者。这些强大的方法可能揭示新的相关性，并有助于改善分诊、激活大量输血资源以及创伤相关结局。应进一步开展研究，寻求优化并将这些算法应用于以创伤为中心的研究。
