Computer Control of Drug Delivery by Continuous Intravenous Infusion

Parker, Michael; Lovich, Mark A.; Tsao, Amy C.; Wei, Abraham E.; Wakim, Matthew G.; Maslov, Mikhail Y.; Tsukada, Hisashi; Peterfreund, Robert A.

doi:10.1097/aln.0000000000000519

Cited by 17 publications

(4 citation statements)

References 8 publications

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“…(ii) Another application of the method presented in this paper could be actual computer control of an infusion system. It has been shown that a computer-controlled pump with “knowledge” about the dead volume and the mixing effect within the dead volume can be useful in preventing overshoot [16]. Moreover, a control system with a feedback approach has also been attempted, where the mean arterial blood pressure was used to control the administration of a fast-acting vasodilator [22, 23].…”

Section: Discussionmentioning

confidence: 99%

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Analytical method for calculation of deviations from intended dosages during multi-infusion

et al. 2017

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BackgroundIn this paper, a new method is presented that combines mechanical compliance effects with Poiseuille flow and push-out effects (“dead volume”) in one single mathematical framework for calculating dosing errors in multi-infusion set-ups. In contrast to existing numerical methods, our method produces explicit expressions that illustrate the mathematical dependencies of the dosing errors on hardware parameters and pump flow rate settings.MethodsOur new approach uses the Z-transform to model the contents of the catheter, and after implementation in Mathematica (Wolfram), explicit expressions are produced automatically. Consistency of the resulting analytical expressions has been examined for limiting cases, and three types of in-vitro measurements have been performed to obtain a first experimental test of the validity of the theoretical results.ResultsThe relative contribution of various factors affecting the dosing errors, such as the Poiseuille flow profile, resistance and internal volume of the catheter, mechanical compliance of the syringes and the various pump flow rate settings, can now be discerned clearly in the structure of the expressions generated by our method. The in-vitro experiments showed a standard deviation between theory and experiment of 14% for the delay time in the catheter, and of 13% for the time duration of the dosing error bolus.ConclusionsOur method provides insight and predictability in a large range of possible situations involving many variables and dependencies, which is potentially very useful for e.g. the development of a fast, bed-side tool (“calculator”) that provides the clinician with a precise prediction of dosing errors and delay times interactively for many scenario’s. The interactive nature of such a device has now been made feasible by the fact that, using our method, explicit expressions are available for these situations, as opposed to conventional time-consuming numerical simulations.

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Section: Discussionmentioning

confidence: 99%

“…The third major factor, the mixing effect due to the Poiseuille profile [16], can be described as a convolution, as will be explained in more detail in “Appendix”. It causes a spreading out of the dosing error in time, in which the first arrival of the dosing error occurs sooner then it would have without Poiseuille mixing effect.…”

Section: Introductionmentioning

confidence: 99%

Analytical method for calculation of deviations from intended dosages during multi-infusion

et al. 2017

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“…These pumps are designed with tubing, drive mechanisms with flow controllers, interfaces, and IV catheters for single and multiple infusion devices [12]. Even though the design and implementation are done very accurately, in practical situations, when the drug flow towards the infusion systems, unforeseen delays based on carrier flow, the flow rate of the drug can lead to autonomic dysfunction in the administration of drugs to the patient [13].…”

Section: Infusion Techniquesmentioning

confidence: 99%

Review of Medical Cyber Physical System based Infusion Pump-perception on control strategy

Alamelu¹,

Mythili²

2018

EAI Endorsed Transactions on Energy Web

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The revolutionary change in recent technologies has dominated the Medical field. Huge medical devices are deployed in hospitals and in-home environment. The innovations of smart medical devices are prominent which can be used in the fully automated and semi-automated environment. For several decades, numerous medical devices are designed and utilized effectively. These devices are designed for the past few years, manufactured in micro and nanotechnologies with smart sensing techniques. Such devices are network-enabled and integrated into the cyber world in physical means, leading to a medical cyber physical system. The design, working, and optimal control of smart medical devices are essential. In this paper, the emphasis on the review of infusion pump control mechanism with essential parameters such as drug delivery, flow rate, pump control has been considered.

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“…Clinical studies on infusion pumps have been transformed for several decades by using different designs with manual, semi-automated, and automated systems. In smart infusion pumps, the drug dosage to be infused, flow rate settings are controlled manually or automatically the closed loop drug infusion concepts are reported [4], [5]. The dosing errors are more in manually programmed pumps as well as in syringe pumps and misleading of drug delivery, flow rate, miscalculations have been discussed [6].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

Alamelu

Mythili

2022

International Journal of Biology and Biomedical Engineering

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In the current scenario, usage of the smart medical pump is predominant in the medical field. The precise drug dosage, flow accuracy should be maintained to increase the performance of an infusion pump. In this work, an attempt has been made to predict and control the speed of the infusion pump for suitable infusion flowrate using machine learning technique and Linear Quadratic Gaussian (LQG) controller. The data for this study is considered from the publicly available online database, electronic Medicines Compendium (eMC). The speed of the infusion pump has been calculated using the drug dosage and flow rate for two different drugs. The prediction of infusion pump speed is achieved using Linear regression with Principal Component analysis (PCR) and Support Vector Machine Regression (SVR). The performance of the prediction schemes is evaluated using standard metrics. To validate the optimal control of the predicted speed, two different medical graded motors are considered. Further, the optimal control of the pump speed is investigated using Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), and LQG controllers for its stability criteria. The prediction of the pump speed using regression models PCR, SVR has been verified and then the transient response analysis with rise time, settling time for both the motors have been examined. Results demonstrate that the LQG optimal control strategy achieves fast rise time, settling time of motor1 with 0.653s, 1.15s, and 0.22, 0.392s for motor2 respectively.

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Computer Control of Drug Delivery by Continuous Intravenous Infusion

Cited by 17 publications

References 8 publications

Analytical method for calculation of deviations from intended dosages during multi-infusion

Analytical method for calculation of deviations from intended dosages during multi-infusion

Review of Medical Cyber Physical System based Infusion Pump-perception on control strategy

Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

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