Jin Xin, Bighamian Ramin, Hahn Jin-Oh
IEEE Trans Biomed Eng. 2018 Nov 19. doi: 10.1109/TBME.2018.2880927.
To develop and evaluate in silico a model-based closed-loop fluid resuscitation control algorithm via blood volume feedback.
Model-based adaptive control algorithm for fluid resuscitation was developed by leveraging a low-order lumped-parameter blood volume dynamics model, and then in silico evaluated based on a detailed mechanistic model of circulatory physiology. The algorithm operates in two steps: (1) the blood volume dynamics model is individualized based on the patient's fractional blood volume response to an initial fluid bolus via system identification; and (2) an adaptive control law built on the individualized blood volume dynamics model regulates the blood volume of the patient.
The algorithm was able to track the blood volume set point as well as accurately estimate and monitor the patient's absolute blood volume level. The algorithm significantly outperformed a population-based proportional-integral-derivative control.
Model-based development of closed-loop fluid resuscitation control algorithm may enable regulation of blood volume and monitoring of absolute blood volume level.
Model-based closed-loop fluid resuscitation algorithm may offer opportunities for standardized and patient-tailored therapy and reduction of clinician workload.
通过血容量反馈在计算机上开发并评估基于模型的闭环液体复苏控制算法。
利用低阶集总参数血容量动力学模型开发用于液体复苏的基于模型的自适应控制算法，然后基于循环生理学的详细机理模型在计算机上进行评估。该算法分两步运行：（1）通过系统辨识，根据患者对初始液体推注的血容量分数反应对血容量动力学模型进行个体化；（2）基于个体化血容量动力学模型构建的自适应控制律调节患者的血容量。
该算法能够跟踪血容量设定点，并准确估计和监测患者的绝对血容量水平。该算法显著优于基于群体的比例积分微分控制。
基于模型开发闭环液体复苏控制算法可实现血容量调节和绝对血容量水平监测。
基于模型的闭环液体复苏算法可能为标准化和个体化治疗以及减轻临床医生工作量提供机会。
