TIME OUT：时间特异性机器学习评估以优化超大容量输血。
TiME OUT: Time-specific machine-learning evaluation to optimize ultramassive transfusion.
机构信息
From the Department of Trauma and Surgical Critical Care (C.H.M., J.N., T.S., J.G., J.D.S., J.S., C.D., C.N., R.N.S.), Grady Health System; Department of Surgery (C.H.M., T.S., J.G., J.D.S., J.S., C.D., J.L., C.M.C., R.N.S.), Emory University School of Medicine; Department of Behavioral, Social and Health Sciences (C.H.M., R.N.S.), Rollins School of Public Health, Emory University; Department of Surgery (J.N.), Morehouse School of Medicine; Department of Operations Research (A.E.), Georgia Institute of Technology, Atlanta, Georgia; Department of Biomedical Engineering (N.V.), University of Texas at Austin, Austin, Texas; and Department of Surgery and Emory Critical Care Center (J.L., C.M.C.), Emory University School of Medicine, Atlanta, Georgia.
出版信息
J Trauma Acute Care Surg. 2024 Mar 1;96(3):443-454. doi: 10.1097/TA.0000000000004187. Epub 2023 Nov 13.
BACKGROUND
Ultramassive transfusion (UMT) is a resource-demanding intervention for trauma patients in hemorrhagic shock, and associated mortality rates remains high. Current research has been unable to identify a transfusion ceiling or point where UMT transitions from lifesaving to futility. Furthermore, little consideration has been given to how time-specific patient data points impact decisions with ongoing high-volume resuscitation. Therefore, this study sought to use time-specific machine learning modeling to predict mortality and identify parameters associated with survivability in trauma patients undergoing UMT.
METHODS
A retrospective review was conducted at a Level I trauma (2018-2021) and included trauma patients meeting criteria for UMT, defined as ≥20 red blood cell products within 24 hours of admission. Cross-sectional data were obtained from the blood bank and trauma registries, and time-specific data were obtained from the electronic medical record. Time-specific decision-tree models predicating mortality were generated and evaluated using area under the curve.
RESULTS
In the 180 patients included, mortality rate was 40.5% at 48 hours and 52.2% overall. The deceased received significantly more blood products with a median of 71.5 total units compared with 55.5 in the survivors ( p < 0.001) and significantly greater rates of packed red blood cells and fresh frozen plasma at each time interval. Time-specific decision-tree models predicted mortality with an accuracy as high as 81%. In the early time intervals, hemodynamic stability, undergoing an emergency department thoracotomy, and injury severity were most predictive of survival, while, in the later intervals, markers of adequate resuscitation such as arterial pH and lactate level became more prominent.
CONCLUSION
This study supports that the decision of "when to stop" in UMT resuscitation is not based exclusively on the number of units transfused but rather the complex integration of patient and time-specific data. Machine learning is an effective tool to investigate this concept, and further research is needed to refine and validate these time-specific decision-tree models.
LEVEL OF EVIDENCE
Prognostic and Epidemiological; Level IV.
背景
对于失血性休克的创伤患者，超大容量输血（UMT）是一项需要大量资源的干预措施，其相关死亡率仍然很高。目前的研究还无法确定一个输血上限或转折点，即 UMT 从拯救生命转变为无效。此外，对于如何考虑特定时间的患者数据点对持续大容量复苏中的决策的影响，研究关注甚少。因此，本研究旨在使用特定时间的机器学习建模来预测死亡率，并确定与接受 UMT 的创伤患者存活率相关的参数。
方法
在一级创伤中心（2018-2021 年）进行回顾性研究，纳入符合 UMT 标准的创伤患者，定义为入院 24 小时内输注≥20 个红细胞制品。从血库和创伤登记处获取横断面数据，从电子病历获取特定时间的数据。使用曲线下面积生成并评估预测死亡率的特定时间决策树模型。
结果
在纳入的 180 名患者中，48 小时和总体死亡率分别为 40.5%和 52.2%。死亡患者接受的血液制品明显更多，中位数为 71.5 个全血单位，而幸存者为 55.5 个（p<0.001），且每个时间间隔的浓缩红细胞和新鲜冷冻血浆的比例明显更高。特定时间的决策树模型预测死亡率的准确率高达 81%。在早期时间间隔，血流动力学稳定性、急诊开胸术和损伤严重程度是生存的最主要预测因素，而在后期间隔，动脉 pH 值和乳酸水平等充分复苏的标志物变得更加突出。
结论
本研究支持 UMT 复苏中“何时停止”的决定不仅仅基于输注的单位数，而是患者和特定时间数据的复杂综合。机器学习是研究这一概念的有效工具，需要进一步研究来改进和验证这些特定时间的决策树模型。
证据水平
预后和流行病学；IV 级。
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