Hierarchical Adaptive Closed-Loop Fluid Resuscitation
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Solution Overview
Problem
Current fluid resuscitation methods in perioperative and ICU settings face challenges due to large intra-patient and inter-patient variability in physiological parameters, leading to under-resuscitation and over-resuscitation, which can result in hypovolemia, organ failure, and fluid overload with associated morbidity and mortality.
Innovation Solution
A closed-loop fluid resuscitation system using a hierarchical control architecture with adaptive and logic-based controllers to continuously monitor hemodynamic data, adjust infusion rates, and provide clinical decision support, ensuring precise fluid and cardiovascular drug administration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fluid resuscitation methods are used, then fluid administration can be initiated, but large variability in physiological parameters leads to under-resuscitation or over-resuscitation
Solution Approach 1:
The system implements closed-loop feedback control by continuously monitoring hemodynamic parameters (stroke volume variation, pulse pressure variation, mean arterial pressure) and automatically adjusting infusion rates based on real-time physiological response. This feedback mechanism eliminates the variability problem by dynamically adapting fluid administration to individual patient needs rather than using fixed protocols
Solution Approach 2:
The control system autonomously manages fluid resuscitation by automatically calculating target infusion rates, adjusting pump settings, and modifying therapy based on patient response without requiring continuous manual intervention. The system serves itself by integrating sensor data processing, control algorithm execution, and pump control into an autonomous closed-loop that self-regulates fluid administration precision
2Productivity
If manual fluid resuscitation control is used, then clinical judgment can be applied, but time-consuming adjustments and human error lead to inconsistent outcomes
Solution Approach 1:
The system autonomously manages fluid resuscitation by automatically calculating target infusion rates, adjusting pump settings, and modifying therapy based on patient response without requiring continuous manual intervention. The system serves itself by integrating sensor data processing, control algorithm execution, and pump control into an autonomous closed-loop that self-regulates fluid administration
Solution Approach 2:
The system replaces manual clinical judgment and manual pump adjustment with an automated control system that uses computational algorithms to determine infusion rates. The mechanical action of manual pump adjustment is substituted with electronic control signals that automatically set pump parameters, eliminating human error and increasing both speed and consistency
3Speed
If aggressive fluid resuscitation is administered, then blood volume can be rapidly restored, but fluid overload and organ failure risks increase
Solution Approach 1:
The system implements closed-loop feedback control by continuously monitoring hemodynamic parameters and automatically adjusting infusion rates based on real-time physiological response. This feedback mechanism prevents fluid overload by detecting when the patient has reached adequate resuscitation targets and automatically reducing or stopping infusion, thereby eliminating the need to choose between aggressive resuscitation and conservative approaches
Solution Approach 2:
The system dynamically adjusts infusion rates based on real-time patient response rather than using fixed aggressive or conservative protocols. The control algorithm continuously modifies therapy intensity according to measured hemodynamic parameters, allowing the resuscitation strategy to adapt its aggressiveness level moment-by-moment based on patient needs
Data Source
AI summary
The present disclosure describes a closed-loop fluid resuscitation and/or cardiovascular drug administration system that uses continuous measurements and adaptive control architecture. The adaptive control architecture uses a function approximator to identify unknown dynamics and physiological parameters of a patient to compute appropriate infusion rates and to regulate the endpoint of resuscitation.