院前连续生命体征预测需要进行主动脉复苏性血管内球囊阻断术和复苏性开胸术。
Prehospital continuous vital signs predict need for resuscitative endovascular balloon occlusion of the aorta and resuscitative thoracotomy prehospital continuous vital signs predict resuscitative endovascular balloon occlusion of the aorta.
机构信息
From the Shock, Trauma and Anesthesiology Research (STAR) Center (A.Z., P.H., S.Y., C.-Y.L., R.A.K.), Shock Trauma Center (T.M.S., R.A.K.), University of Maryland School of Medicine; and Maryland Institute for Emergency Medical Services Systems (D.F.), Baltimore, Maryland.
出版信息
J Trauma Acute Care Surg. 2021 Nov 1;91(5):798-802. doi: 10.1097/TA.0000000000003171.
BACKGROUND
Rapid triage and intervention to control hemorrhage are key to survival following traumatic injury. Patients presenting in hemorrhagic shock may undergo resuscitative thoracotomy (RT) or resuscitative endovascular balloon occlusion of the aorta (REBOA) as adjuncts to rapidly control bleeding. We hypothesized that machine learning along with automated calculation of continuously measured vital signs in the prehospital setting would accurately predict need for REBOA/RT and inform rapid lifesaving decisions.
METHODS
Prehospital and admission data from 1,396 patients transported from the scene of injury to a Level I trauma center via helicopter were analyzed. Utilizing machine learning and prehospital autonomous vital signs, a Bleeding Risk Index (BRI) based on features from pulse oximetry and electrocardiography waveforms and blood pressure (BP) trends was calculated. Demographics, Injury Severity Score and BRI were compared using Mann-Whitney-Wilcox test. Area under the receiver operating characteristic curve (AUC) was calculated and AUC of different scores compared using DeLong's method.
RESULTS
Of the 1,396 patients, median age was 45 years and 68% were men. Patients who underwent REBOA/RT were more likely to have a penetrating injury (24% vs. 7%, p < 0.001), higher Injury Severity Score (25 vs. 10, p < 0.001) and higher mortality (44% vs. 7%, p < 0.001). Prehospital they had lower BP (96 [70-130] vs. 134 [117-152], p < 0.001) and higher heart rate (106 [82-118] vs. 90 [76-106], p < 0.001). Bleeding risk index calculated using the entire prehospital period was 10× higher in patients undergoing REBOA/RT (0.5 [0.42-0.63] vs. 0.05 [0.02-0.21], p < 0.001) with an AUC of 0.93 (95% confidence interval [95% CI], 0.90-0.97). This was similarly predictive when calculated from shorter periods of transport: BRI initial 10 minutes prehospital AUC of 0.89 (95% CI, 0.83-0.94) and initial 5 minutes AUC of 0.90 (95% CI, 0.85-0.94).
CONCLUSION
Automated prehospital calculations based on vital sign features and trends accurately predict the need for the emergent REBOA/RT. This information can provide essential time for team preparedness and guide trauma triage and disaster management.
LEVEL OF EVIDENCE
Therapeutic/care management, Level IV.
背景
在创伤后，快速分诊和干预以控制出血是生存的关键。出现出血性休克的患者可能会接受抢救性开胸术（RT）或抢救性主动脉内球囊阻断（REBOA），以迅速控制出血。我们假设，在院前环境中使用机器学习以及对连续测量的生命体征进行自动计算，可以准确预测需要 REBOA/RT，并为快速救生决策提供信息。
方法
分析了 1396 名通过直升机从受伤现场运送到一级创伤中心的患者的院前和入院数据。利用机器学习和院前自主生命体征，根据脉搏血氧饱和度和心电图波形以及血压（BP）趋势的特征计算出血风险指数（BRI）。使用曼-惠特尼-威尔科克检验比较人口统计学、损伤严重程度评分和 BRI。计算受试者工作特征曲线下的面积（AUC），并使用德隆方法比较不同评分的 AUC。
结果
在 1396 名患者中，中位年龄为 45 岁，68%为男性。接受 REBOA/RT 的患者更有可能患有穿透性损伤（24%比 7%，p<0.001）、更高的损伤严重程度评分（25 比 10，p<0.001）和更高的死亡率（44%比 7%，p<0.001）。院前时，他们的血压（96[70-130]比 134[117-152]，p<0.001）较低，心率（106[82-118]比 90[76-106]，p<0.001）较高。在接受 REBOA/RT 的患者中，使用整个院前期间计算的出血风险指数高出 10 倍（0.5[0.42-0.63]比 0.05[0.02-0.21]，p<0.001），AUC 为 0.93（95%置信区间[95%CI]，0.90-0.97）。当从较短的运输时间计算时，这同样具有预测性：BRI 初始 10 分钟院前 AUC 为 0.89（95%CI，0.83-0.94），初始 5 分钟 AUC 为 0.90（95%CI，0.85-0.94）。
结论
基于生命体征特征和趋势的自动院前计算可以准确预测紧急 REBOA/RT 的需求。这些信息可以为团队准备提供重要时间，并指导创伤分诊和灾害管理。
证据水平
治疗/护理管理，IV 级。
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