Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data
Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data
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DOI：
10.1186/1472-6947-13-28
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发表时间：
2013-02-27
影响因子：
3.5
通讯作者：
Amarasingham, Ruben
中科院分区：
文献类型：
--
作者：
Alvarez, Carlos A.;Clark, Christopher A.;Amarasingham, Ruben
背景：利用电子病历（EMR）中现成的临床信息，准确、及时和自动地识别有严重临床恶化高风险的患者，可以告知卫生系统针对稀缺资源并挽救生命。方法：我们选取了2009年5月至2010年3月在一家大型公立城市学术医院住院的7,466例患者。使用多变量逻辑回归，在衍生样本（从总队列中随机选择50%）中创建了重症监护病房（ICU）外心肺骤停和意外死亡的自动临床预测模型。然后在整个队列中剩余的50%（验证样本）中验证自动化模型。主要结局是复苏事件和死亡（RED）的综合结果。RED包括心肺骤停、急性呼吸衰竭和意外死亡。预测因子是使用前24小时的数据来测量的。候选变量包括生命体征、实验室数据、医嘱、药物、楼层分配、修正早期预警评分（MEWS）以及其他治疗变量。结果：在整个队列中，RED率为1.2%。14个变量是RED的独立预测因子，包括年龄、氧合、舒张压、动脉血气和实验室值、紧急医嘱和高风险底线分配。自动模型具有良好的判别性（c-statistic=0.85）和校准性，灵敏度（51.6%和42.2%）和特异性（94.3%和91.3%）均高于单独的MEWS。自动化模型在事件发生前15.9小时预测RED，比快速反应小组（RRT）激活早（事件发生前5.7小时，p=0.003）。结论：利用EMR数据的自动化模型在识别RED方面具有很大的潜力，优于先验风险模型和人为判断驱动的RRT。
Background: Accurate, timely and automated identification of patients at high risk for severe clinical deterioration using readily available clinical information in the electronic medical record (EMR) could inform health systems to target scarce resources and save lives.Methods: We identified 7,466 patients admitted to a large, public, urban academic hospital between May 2009 and March 2010. An automated clinical prediction model for out of intensive care unit (ICU) cardiopulmonary arrest and unexpected death was created in the derivation sample (50% randomly selected from total cohort) using multivariable logistic regression. The automated model was then validated in the remaining 50% from the total cohort (validation sample). The primary outcome was a composite of resuscitation events, and death (RED). RED included cardiopulmonary arrest, acute respiratory compromise and unexpected death. Predictors were measured using data from the previous 24 hours. Candidate variables included vital signs, laboratory data, physician orders, medications, floor assignment, and the Modified Early Warning Score (MEWS), among other treatment variables.Results: RED rates were 1.2% of patient-days for the total cohort. Fourteen variables were independent predictors of RED and included age, oxygenation, diastolic blood pressure, arterial blood gas and laboratory values, emergent orders, and assignment to a high risk floor. The automated model had excellent discrimination (c-statistic=0.85) and calibration and was more sensitive (51.6% and 42.2%) and specific (94.3% and 91.3%) than the MEWS alone. The automated model predicted RED 15.9 hours before they occurred and earlier than Rapid Response Team (RRT) activation (5.7 hours prior to an event, p=0.003)Conclusion: An automated model harnessing EMR data offers great potential for identifying RED and was superior to both a prior risk model and the human judgment-driven RRT.