Article describes controlled infusion of intravenous cardiac drugs

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The objective of the study is to develop an automatic drug infusion control system during cardiovascular surgery” Sowparnika et al (2019).

Abstract:

OBJECTIVES: The objective of the study is to develop an automatic drug infusion control system during cardiovascular surgery.

MATERIALS AND METHODS: Based on the clinical drug dosage analysis, the modeling of cardiovascular system with baroreceptor model is mathematically modeled using compartmental approach, considering the relationship between the volume and flow rate of blood during each heartbeat. This model is then combined with drug modeling of noradrenaline and nitroglycerine by deriving the volume and drug mass concentration equations, based on pharmacokinetics and pharmacodynamics of the drugs. The closed-loop patient models are derived from the open-loop data obtained from the physiology-drug model with covariate as age. The proportional-integral controller is designed based on optimal values obtained from bacterial foraging-oriented particle swarm optimization algorithm. The controllers are implemented individually for each control variable such as aortic pressure and cardiac output (CO), irrespective of varying weights based on the relative gain array analysis which depicts the maximum influence of cardiac drugs on control variables.

RESULTS: The physiology-drug model output responses are simulated using MATLAB. The controlled responses of aortic pressure and CO with infusion rate of cardiac drugs are obtained. The robustness of the controller is checked by introducing variations in cardiovascular model parameters. The efficiency of the controller during normal and abnormal conditions is compared using time domain analysis.

CONCLUSIONS: The controller design was efficient and can be further improved by designing switching-based controllers.

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Reference:

Sowparnika, G.C., Thirumarimurugan, M., Sivakumar, V.M. and Vinoth, N. (2019) Controlled infusion of intravenous cardiac drugs using global optimization. Indian Journal of Pharmacology. 51(1), p.61-71. doi: 10.4103/ijp.IJP_612_18.

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