IEEE Trans Biomed Eng. 2021 Jan;68(1):181-191. doi: 10.1109/TBME.2020.2997929. Epub 2020 Dec 21.
Septic shock is a life-threatening manifestation of infection with a mortality of 20-50% [1]. A catecholamine vasopressor, norepinephrine (NE), is widely used to treat septic shock primarily by increasing blood pressure. For this reason, future blood pressure knowledge is invaluable for properly controlling NE infusion rates in septic patients. However, recent machine learning and data-driven methods often treat the physiological effects of NE as a black box. In this paper, a real-time, physiology-informed human mean arterial blood pressure model for septic shock patients undergoing NE infusion is studied.
Our methods combine learning theory, adaptive filter theory, and physiology. We learn least mean square adaptive filters to predict three physiological parameters (heart rate, pulse pressure, and the product of total arterial compliance and arterial resistance) from previous data and previous NE infusion rate. These predictions are combined according to a physiology model to predict future mean arterial blood pressure.
Our model successfully forecasts mean arterial blood pressure on 30 septic patients from two databases. Specifically, we predict mean arterial blood pressure 3.33 minutes to 20 minutes into the future with a root mean square error from 3.56 mmHg to 6.22 mmHg. Additionally, we compare the computational cost of different models and discover a correlation between learned NE response models and a patient's SOFA score.
Our approach advances our capability to predict the effects of changing NE infusion rates in septic patients.
More accurately predicted MAP can lessen clinicians' workload and reduce error in NE titration.
感染性休克是一种危及生命的感染表现，死亡率为 20-50%[1]。去甲肾上腺素（NE）是一种儿茶酚胺升压药，广泛用于治疗感染性休克，主要通过增加血压。出于这个原因，未来的血压知识对于正确控制感染性休克患者的 NE 输注率是非常宝贵的。然而，最近的机器学习和数据驱动方法经常将 NE 的生理效应视为黑箱。在本文中，研究了一种用于接受 NE 输注的感染性休克患者的实时、生理学知情的人类平均动脉血压模型。
我们的方法结合了学习理论、自适应滤波理论和生理学。我们学习最小均方自适应滤波器，以便根据以前的数据和以前的 NE 输注率预测三个生理参数（心率、脉压和总动脉顺应性与动脉阻力的乘积）。根据生理学模型对这些预测值进行组合，以预测未来的平均动脉血压。
我们的模型成功地对来自两个数据库的 30 名感染患者的平均动脉血压进行了预测。具体来说，我们可以预测平均动脉血压 3.33 分钟到 20 分钟以后的情况，均方根误差在 3.56mmHg 到 6.22mmHg 之间。此外，我们比较了不同模型的计算成本，并发现学习的 NE 响应模型与患者的 SOFA 评分之间存在相关性。
我们的方法提高了预测感染性休克患者改变 NE 输注率的效果的能力。
更准确地预测 MAP 可以减轻临床医生的工作量，并减少 NE 滴定的误差。
