Research Group of Dynamical Systems and Control, Department of Electromechanical, Systems and Metal Engineering, Ghent University, 9052 Ghent, Belgium.
Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
J Adv Res. 2021 May 21;32:27-36. doi: 10.1016/j.jare.2021.04.004. eCollection 2021 Sep.
In long-term induced general anesthesia cases such as those uniquely defined by the ongoing Covid-19 pandemic context, the clearance of hypnotic and analgesic drugs from the body follows anomalous diffusion with afferent drug trapping and escape rates in heterogeneous tissues. Evidence exists that drug molecules have a preference to accumulate in slow acting compartments such as muscle and fat mass volumes. Currently used patient dependent pharmacokinetic models do not take into account anomalous diffusion resulted from heterogeneous drug distribution in the body with time varying clearance rates.
This paper proposes a mathematical framework for drug trapping estimation in PK models for estimating optimal drug infusion rates to maintain long-term anesthesia in Covid-19 patients. We also propose a protocol for measuring and calibrating PK models, along with a methodology to minimize blood sample collection.
We propose a framework enabling calibration of the models during the follow up of Covid-19 patients undergoing anesthesia during their treatment and recovery period in ICU. The proposed model can be easily updated with incoming information from clinical protocols on blood plasma drug concentration profiles. Already available pharmacokinetic and pharmacodynamic models can be then calibrated based on blood plasma concentration measurements.
The proposed calibration methodology allow to minimize risk for potential over-dosing as clearance rates are updated based on direct measurements from the patient.
The proposed methodology will reduce the adverse effects related to over-dosing, which allow further increase of the success rate during the recovery period.
在长期诱导全身麻醉的情况下，例如在持续的 COVID-19 大流行背景下定义的那些情况，催眠药和镇痛药从体内清除遵循异常扩散，伴有传入药物捕获和异质组织中的逃逸率。有证据表明，药物分子有优先积聚在缓慢作用的部位，如肌肉和脂肪质量体积。目前使用的基于患者的药代动力学模型没有考虑到由于药物在体内分布不均而导致的异常扩散，以及随时间变化的清除率。
本文提出了一种药物捕获估计的数学框架，用于在 PK 模型中估计最佳药物输注率，以维持 COVID-19 患者的长期麻醉。我们还提出了一种测量和校准 PK 模型的方案，以及一种最小化血液样本采集的方法。
我们提出了一个框架，使模型能够在 COVID-19 患者接受麻醉治疗和在 ICU 恢复期间进行随访时进行校准。该模型可以通过临床方案中关于血浆药物浓度曲线的信息进行轻松更新。然后可以根据血浆浓度测量值对现有的药代动力学和药效学模型进行校准。
所提出的校准方法可以最大限度地降低潜在过度用药的风险，因为清除率是根据患者的直接测量值进行更新的。
所提出的方法将减少与过度用药相关的不良反应，从而进一步提高恢复期的成功率。
