Infusion Pump Control via Predictive Flow Modeling

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Solution Overview

Problem

Infusion systems face challenges in accurately delivering drugs due to 'dead volume' in the fluid path, leading to delays and discordance between intended and actual drug delivery profiles.

Innovation Solution

The system employs predictive algorithms that model the flow of drugs and carrier fluids, considering parameters like radial diffusion, axial diffusion, and laminar flow, to control the delivery profile by adjusting the flow rates of multiple pumps, ensuring accurate and timely drug delivery.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple infusion pumps are used to deliver drugs and carrier fluid, then the drug delivery capacity and flexibility are improved, but the complexity of controlling the delivery profile and predicting actual delivery increases due to dead volume effects

Engineering Contradiction:

Improvedrug delivery capacityVSAvoidcontrol system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by calculating and storing the impulse response function (IRF) of the fluid path before drug delivery. The IRF characterizes the dead volume effects and flow dynamics in advance, allowing the control system to predict and compensate for delivery delays and discordances without adding real-time computational complexity during actual drug administration.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback by using the predicted delivery profile (convolution of pump output with IRF) to adjust pump control signals. The control system continuously monitors the difference between intended and predicted actual delivery, and adjusts pump rates to compensate for dead volume effects, ensuring accurate drug delivery despite system complexity.

Inventive Principle:

Principle #23Feedback

2Loss of time

If the flow rates are adjusted to compensate for dead volume delays, then the timing accuracy of drug delivery is improved, but the precision required for flow rate control increases

Engineering Contradiction:

Improvedelivery onset delayVSAvoidflow rate control precision

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The system introduces an intermediary mathematical model (the impulse response function and convolution operation) between the pump control and the actual drug delivery. This intermediary allows the system to account for dead volume effects through computational prediction rather than requiring extremely precise manual flow rate adjustments, reducing the burden on measurement and control precision.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Device complexity

If empirical models are used to predict drug delivery, then the system simplicity is maintained, but the prediction accuracy decreases compared to physics-based models

Engineering Contradiction:

Improvemodel complexityVSAvoiddelivery prediction accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system changes the parameters of the predictive model from simple empirical constants to physics-based parameters characterizing radial diffusion, axial diffusion, and laminar flow. By incorporating these physical parameters into the impulse response function, the system achieves higher prediction accuracy while maintaining computational efficiency through pre-calculated models.

Inventive Principle:

Principle #35Parameter changes

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach significantly reduces drug delivery onset delays, enhances the accuracy of drug infusion, and provides reliable and safe drug delivery by maintaining precise control over drug profiles, even in critical care settings.

Implementation Method 1

Predictive models can take into consideration multiple physical parameters including radial diffusion (molecules moving toward the walls of the tubing or catheter)

Methodology Applied

Scientific EffectRadial diffusion: Diffusion

Implementation Method 2

axial diffusion (molecules moving along the axis of flow)

Methodology Applied

Scientific EffectAxial diffusion: Diffusion

Implementation Method 3

laminar flow (smooth bulk fluid flow)

Methodology Applied

Scientific EffectLaminar flow: Laminar Flow

Data Source

PatentUS10758672B2Prediction, visualization, and control of drug delivery by multiple infusion pumps

Publication Date: 2020.09.01 STEWARD ST ELIZABETHS MEDICAL CENT OF BOSTON

AI summary

The subject technology is embodied in a method for predicting a delivery rate of a plurality of drugs dispensed by multiple infusion pumps at a delivery point. The method includes receiving one or more operating parameters related to multiple drug pumps and a carrier fluid pump, wherein each of the drug pumps dispenses a drug, and the carrier fluid pump dispenses a carrier fluid. The method also includes determining a delivery rate of a first drug at the delivery point. This can be done by predicting time variation of a concentration of the first drug at the delivery point based on a mathematical model of a mixed flow through a fluid path that terminates at the delivery point. The mixed flow includes the drugs and the carrier fluid. The model includes the operating parameters and a plurality of flow-parameters related to the mathematical model of the mixed flow.