Hemorrhage Prediction Models in Surgical Intensive Care: Bedside Monitoring Data Adds Information to Lab Values
Hemorrhage Prediction Models in Surgical Intensive Care: Bedside Monitoring Data Adds Information to Lab Values
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DOI：
10.1109/jbhi.2017.2653849
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发表时间：
2017-11-01
影响因子：
7.7
通讯作者：
Ferrario, Manuela
中科院分区：
文献类型：
--
作者：
De Pasquale, Marco;Moss, Travis J.;Ferrario, Manuela
出血是外科手术患者常见的并发症，其识别和管理作为重症监护室（ICU）患者护理质量改进的目标越来越受到关注。这项工作的目的是：1）通过探索目前可用的数据挖掘方法的范围，找到出血的早期检测模型; 2）比较利用床旁监护仪连续测量的生理数据的预测模型与使用常用实验室检查的预测模型。我们研究了3766例弗吉尼亚大学卫生系统外科创伤烧伤ICU的患者。出血定义为24小时内输注3个或3个以上单位的红细胞，前24小时内未输注红细胞。222例患者（5.9%）发生了出血，基于生命体征及其趋势的多变量模型显示了良好的结果（AUC = 76.1%）。毫不奇怪，红细胞压积具有优异的性能（AUC = 87.7%）。包括连续监测和实验室测试的模型具有最佳性能（AUC = 92.2%）。结果表明，连续监测和间歇性实验室检查相结合的策略是外科ICU早期发现出血的合理临床方法。
Hemorrhage is a frequent complication in surgery patients; its identification and management have received increasing attention as a target for quality improvement in patient care in the Intensive Care Unit (ICU). The purposes of this work were 1) to find an early detection model for hemorrhage by exploring the range of data mining methods that are currently available, and 2) to compare prediction models utilizing continuously measured physiological data from bedside monitors to those using commonly obtained laboratory tests. We studied 3766 patients admitted to the University of Virginia Health System Surgical Trauma Burn ICU. Hemorrhage was defined as three or more units of red blood cells transfused within 24 h without red blood cell transfusion in the preceding 24 h. 222 patients (5.9%) experienced a hemorrhage, and multivariate models based on vital signs and their trends showed good results (AUC = 76.1%). The hematocrit, not surprisingly, had excellent performance (AUC = 87.7%). Models that included both continuous monitoring and laboratory tests had the best performance (AUC = 92.2%). The results point to a combined strategy of continuous monitoring and intermittent lab tests as a reasonable clinical approach to the early detection of hemorrhage in the surgical ICU.