Ventilator Resistance Monitoring via Segmented Flow Sensors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for monitoring respiratory resistance and work of breathing in ventilator-dependent patients are invasive, impractical, and fail to distinguish between physiologic airway resistance and endotracheal tube resistance, leading to inappropriate bronchodilator administration and inefficient ventilatory therapy.
Innovation Solution
A system and method for non-invasively estimating respiratory resistance and work of breathing components, including physiologic airway resistance, endotracheal tube resistance, and their contributions to total respiratory resistance and work of breathing, using mathematical models and sensors integrated into ventilator systems, allowing for real-time monitoring and optimization of ventilator settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional methods are used to measure total respiratory resistance, then RTOT can be obtained, but the individual contributions of RAW and RETT cannot be distinguished
Solution Approach 1:
The patent segments the total respiratory resistance measurement into two distinct components: physiologic airway resistance (RAW) and endotracheal tube resistance (RETT). This is achieved by placing flow sensors at multiple locations in the breathing circuit to independently measure flows through different segments, allowing calculation of each resistance component separately rather than as a combined value.
Solution Approach 2:
The patent introduces flow sensors as intermediary measurement devices that capture intermediate flow data at specific points in the breathing circuit. These intermediate measurements serve as mediators that enable the decomposition of RTOT into RAW and RETT components by providing the necessary flow differential data for separate resistance calculations.
2Reliability
If bronchodilator therapy is administered based on increased RTOT alone, then treatment may be initiated, but unnecessary bronchodilator use occurs when increased RTOT is due to RETT rather than RAW
Solution Approach 1:
The patent segments the resistance measurement to distinguish between RAW and RETT components. This segmentation allows clinicians to identify whether increased RTOT is due to actual airway obstruction (increased RAW indicating need for bronchodilators) or endotracheal tube issues (increased RETT indicating need for tube assessment or replacement), thereby preventing unnecessary bronchodilator administration.
Solution Approach 2:
The patent implements feedback by continuously monitoring and displaying separate RAW and RETT values to guide clinical decision-making. This feedback mechanism enables real-time differentiation between the causes of increased resistance, allowing appropriate therapeutic intervention only when RAW is elevated, thus avoiding unnecessary bronchodilator use.
3Measurement precision
If end inspiratory pause is applied to measure RTOT, then resistance measurement can be obtained, but patient breathing is interrupted and the method is impractical for spontaneously breathing patients
Solution Approach 1:
The patent enables continuous resistance measurement without interrupting the patient's breathing. By using multiple flow sensors to continuously monitor flows through different circuit segments, the system calculates RAW and RETT in real-time during spontaneous breathing, eliminating the need for end inspiratory pauses while maintaining measurement capability.
Solution Approach 2:
The patent replaces the mechanical intervention of end inspiratory pause with a sensor-based measurement system. Instead of mechanically interrupting breaths to measure resistance, the system uses flow sensors and computational algorithms to continuously derive resistance values during normal spontaneous breathing, substituting mechanical disruption with electronic sensing and calculation.
Data Source
AI summary
Methods for non-invasively and accurately estimating and monitoring resistance and work of breathing parameters from airway pressure and flow sensors attached to the ventilator-dependent patient using an adaptive mathematical model are provided. These methods are based on calculations using multiple parameters derived from the above-mentioned sensors. The resistance and work of breathing parameters are important for: assessing patient status and diagnosis, appropriately selecting treatment, assessing efficacy of treatment, and properly adjusting ventilatory support.


