Novel Pump Control Technology Accelerates Drug Delivery Onset in a Model of Pediatric Drug Infusion

Parker, Michael; Lovich, Mark A.; Tsao, Amy C.; Deng, Hao; Houle, Timothy T.; Peterfreund, Robert A.

doi:10.1213/ane.0000000000001706

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…20 Another approach involving the use of two syringe pumps at different flow rates, whose respective outflows are joined at a vented manifold controlled by a computer-executed algorithm, has been proposed to achieve an accelerated start-up. 21 However, clinical applicability remains moot. Another countermeasure is the use of an in-line filter, increasing driving pressure, which helps reduce the delays and flow irregularities described above at very low infusion rates.…”

Section: Discussionmentioning

confidence: 99%

Performance of modern syringe infusion pump assemblies at low infusion rates in the perioperative setting

Baeckert

Batliner

Grass

et al. 2020

British Journal of Anaesthesia

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Section: Discussionmentioning

confidence: 99%

Performance of modern syringe infusion pump assemblies at low infusion rates in the perioperative setting

Baeckert

Batliner

Grass

et al. 2020

British Journal of Anaesthesia

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“…However, there is much utility in refined models of drug infusion. Forward-difference computational methodology can be used to precisely predict drug movement inside the common-volume of infusion systems [28,29]. In these techniques, the common-volume is modeled as a series of small discrete nodes each having a single drug concentration (c).…”

Section: Computational Simulationsmentioning

confidence: 99%

“…Finally, they can be used to help develop novel technologies for mitigating the risks associated with the common-volume of drug infusion. Algorithms for coordination and control of both the drug and carrier pump have allowed significantly faster achievement of steady state drug delivery in vitro, and biologic effect in vivo than conventional drug administration through infusion systems with clinically relevant common-volumes [28,29].…”

Section: Computational Simulationsmentioning

confidence: 99%

Drug Flow Through Clinical Infusion Systems: How Modeling of the Common-volume Helps Explain Clinical Events

Lovich

Peterfreund

2017

Pharmaceutical Technology in Hospital Pharmacy

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AbstractThis review aims to describe analytic models of drug infusion that demonstrate the impact of the infusion system common-volume on drug delivery. The common-volume of a drug infusion system is defined as the volume residing between the point where drug and inert carrier streams meet and the patient’s blood. We describe 3 sets of models. The first is quantitative modeling which includes algebraic mathematical constructs and forward-difference computational simulation. The second set of models is with in vitro benchtop simulation of clinical infusion system architecture. This modeling employs devices including pumps, manifolds, tubing and catheters used in patient care. The final set of models confirms in vitro findings with pharmacodynamic endpoints in living large mammals. Such modeling reveals subtle but important issues inherent in drug infusion therapy that can potentially lead to patient instability and morbidity. The common-volume is an often overlooked reservoir of drugs, especially when infusions flows are slowed or stopped. Even with medications and carriers flowing, some mass of drug always resides within this common-volume. This reservoir of drug can be inadvertently delivered into patients. When infusions are initiated, or when dose rate or carrier flow is altered, there can be a significant lag between intended and actual drug delivery. In the case of vasoactive and inotropic drug infusions, these unappreciated time delays between intended and actual drug delivery can lead to iatrogenic hemodynamic instability. When a drug infusion is discontinued, drug delivery continues until the common-volume is fully cleared of residual drug by the carrier. The findings from all 3 sets of models described in this review indicate that minimizing the common-volume of drug infusion systems may enhance patient safety. The presented models may also be configured into teaching tools and possibly point to technological solutions that might mitigate sources of iatrogenic patient lability.

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“…The influencing parameters are dead volume, carrier infusion, and lag time. (4) Specifically, the prediction of lag time for a specific drug from the dataset needs attention. (5) The performance and functionality of these medical pumps are critical because precision and accuracy in fluid and drug delivery are critical in a real-time situation.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach to predict delay in smart infusion pump

Alamelu

Sivanantham

2022

Salud, Ciencia y Tecnología

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Wireless smart infusion pumps are currently under development. It is critical to ensure that the patient receives the correct drug concentration. Practically, the performance of the pump has relied on the minimum startup delay. The minimization of the startup delay is prominent in open-type infusion pumps and rarely in closed types. The emphasis on reducing startup delay puts practitioners and caregivers at ease while ensuring patient safety. The startup delay of the infusion pump is based on the flow rate and the lag time. The prediction of the flow rate and lag time for an infusion pump is necessitated to ensure a safe drug dosage for the patient. Currently, machine learning methods and computational methods to predict the desired parameter are widely used in healthcare applications and medical device performance. The reduction of start-up delay can be achieved by predicting its associated parameters lag time and flow rate. The flow rate is dependent on the speed of the infusion pump, which has to be calculated based on the number of gears and revolutions. The speed of the pump has to be predicted for accurate flow delivery. Our present research attempts to predict the lag time of an infusion pump using different kernel functions of support vector regression (SVR). The performance of the SVR for each kernel function is compared with R2, RMSE, MAE, and prediction accuracy. The prediction accuracy of 99,7 % has been obtained in optimized SVM.

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Novel Pump Control Technology Accelerates Drug Delivery Onset in a Model of Pediatric Drug Infusion

Cited by 10 publications

References 24 publications

Performance of modern syringe infusion pump assemblies at low infusion rates in the perioperative setting

Performance of modern syringe infusion pump assemblies at low infusion rates in the perioperative setting

Drug Flow Through Clinical Infusion Systems: How Modeling of the Common-volume Helps Explain Clinical Events

Machine learning approach to predict delay in smart infusion pump

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