Department of Anesthesiology, University of Utah, Salt Lake City, 84132, USA.
J Clin Monit Comput. 2010 Jun;24(3):223-35. doi: 10.1007/s10877-010-9238-0. Epub 2010 Jun 18.
Vasoactive drug infusion rates are titrated to achieve a desired effect, e.g., mean arterial blood pressure (MAP), rather than using infusion rates based on body weight. The purpose of this study is to evaluate a method to automatically identify a patient's sensitivity to sodium-nitroprusside, dobutamine or dopamine and to evaluate, whether an advisory system that predicts MAP 5 min in the future enhances a clinician's ability to titrate sodium-nitroprusside infusions.
We used published models implemented in MATLAB to simulate the response of 100 individual patients to infusions of sodium-nitroprusside, dopamine and dobutamine. The simulated patient's sensitivity to the three drugs was identified using an adaptive filter approach, where MAP was altered in a binary stepwise fashion. Next, 9 nurses were asked to control the MAP of 6 of the simulated patients. For half of the patients, we used the identified sensitivity to predict and display MAP 5 min into the future.
Identifying each individual patient's sensitivity improved the accuracy of the MAP prediction by 75% for sodium-nitroprusside, 82% for dopamine and 52% for dobutamine over the MAP prediction based on an "average" patient's sensitivity. The advisory system shortened the median time to reach the desired MAP from 10.2 to 4.1 min, decreased the median number of infusion rate changes from 6 to 4, and resulted in a significant reduction of mental workload and effort.
Patient-specific drug sensitivity identifi- cation significantly improved the prediction of future MAP. By predicting and displaying the expected MAP 5 min in the future, the advisory system helped nurses titrate faster, reduced their perceived workload and might improve patient safety.
血管活性药物的输注率是根据所需的效果来调整的,例如平均动脉血压(MAP),而不是根据体重来调整输注率。本研究的目的是评估一种自动识别患者对硝普钠、多巴酚丁胺或多巴胺敏感性的方法,并评估预测未来 5 分钟 MAP 的咨询系统是否能提高临床医生调整硝普钠输注的能力。
我们使用发表的模型,用 MATLAB 来模拟 100 名个体患者对硝普钠、多巴胺和多巴酚丁胺输注的反应。使用自适应滤波器方法识别模拟患者对三种药物的敏感性,其中 MAP 以二进制的方式逐步改变。然后,我们要求 9 名护士来控制 6 名模拟患者的 MAP。对于一半的患者,我们使用识别的敏感性来预测和显示未来 5 分钟的 MAP。
确定每个个体患者的敏感性,使硝普钠的 MAP 预测准确性提高了 75%,多巴胺提高了 82%,多巴酚丁胺提高了 52%,而基于“平均”患者敏感性的 MAP 预测。咨询系统将达到所需 MAP 的中位数时间从 10.2 分钟缩短到 4.1 分钟,将输注率变化的中位数从 6 次减少到 4 次,并且显著降低了心理工作量和努力。
患者特异性药物敏感性识别显著提高了未来 MAP 的预测准确性。通过预测和显示未来 5 分钟的预期 MAP,咨询系统帮助护士更快地调整剂量,降低了他们的感知工作量,并可能提高患者的安全性。