Zadorozny Eva V, Weigel Tyler, Galvagno Samuel M, Martin-Gill Christian, Brown Joshua B, Guyette Francis X
University of Pittsburgh, Graduate School of Public Health, 4420 Bayard Street, Suite 616-12, Pittsburgh, PA, 15213, USA.
University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Int J Emerg Med. 2024 Jun 19;17(1):76. doi: 10.1186/s12245-024-00650-0.
Traumatic shock is the leading cause of preventable death with most patients dying within the first six hours from arriving to the hospital. This underscores the importance of prehospital interventions, and growing evidence suggests prehospital transfusion improves survival. Optimizing transfusion triggers in the prehospital setting is key to improving outcomes for patients in hemorrhagic shock. Our objective was to identify factors associated with early in-hospital transfusion requirements available to prehospital clinicians in the field to develop a simple algorithm for prehospital transfusion, particularly for patients with occult shock.
We included trauma patients transported by a single critical care transport service to a level I trauma center between 2012 and 2019. We used logistic regression, Fast and Frugal Trees (FFTs), and Bayesian analysis to identify factors associated with early in-hospital blood transfusion as a potential trigger for prehospital transfusion.
We included 2,157 patients transported from the scene or emergency department (ED) of whom 207 (9.60%) required blood transfusion within four hours of admission. The mean age was 47 (IQR = 28 - 62) and 1,480 (68.6%) patients were male. From 13 clinically relevant factors for early hospital transfusions, four were incorporated into the FFT in following order: 1) SBP, 2) prehospital lactate concentration, 3) Shock Index, 4) AIS of chest (sensitivity = 0.81, specificity = 0.71). The chosen thresholds were similar to conventional ones. Using conventional thresholds resulted in lower model sensitivity. Consistently, prehospital lactate was among most decisive factors of hospital transfusions identified by Bayesian analysis (OR = 2.31; 95% CI 1.55 - 3.37).
Using an ensemble of frequentist statistics, Bayesian analysis and machine learning, we developed a simple, clinically relevant prehospital algorithm to help identify patients requiring transfusion within 4 h of hospital arrival.
创伤性休克是可预防死亡的主要原因，大多数患者在抵达医院后的头六个小时内死亡。这凸显了院前干预的重要性，越来越多的证据表明院前输血可提高生存率。优化院前环境中的输血触发因素是改善失血性休克患者预后的关键。我们的目标是确定现场院前临床医生可获得的与早期院内输血需求相关的因素，以制定一种简单的院前输血算法，特别是针对隐匿性休克患者。
我们纳入了2012年至2019年间由单一重症监护转运服务转运至一级创伤中心的创伤患者。我们使用逻辑回归、快速节俭树（FFT）和贝叶斯分析来确定与早期院内输血相关的因素，作为院前输血的潜在触发因素。
我们纳入了2157例从现场或急诊科转运来的患者，其中207例（9.60%）在入院后4小时内需要输血。平均年龄为47岁（四分位间距=28-62岁），1480例（68.6%）患者为男性。从13个与早期院内输血相关的临床因素中，以下四个因素按顺序纳入FFT：1）收缩压，2）院前乳酸浓度，3）休克指数，4）胸部简明损伤定级（灵敏度=0.81，特异性=0.71）。所选阈值与传统阈值相似。使用传统阈值会导致模型灵敏度较低。同样，院前乳酸是贝叶斯分析确定的院内输血最具决定性的因素之一（比值比=2.31；95%置信区间1.55-3.37）。
通过结合频率统计、贝叶斯分析和机器学习，我们开发了一种简单的、与临床相关的院前算法，以帮助识别入院后4小时内需要输血的患者。
