Personalized Ventilator Model Predicts Transient Cardiorespiratory States
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Solution Overview
Problem
Current mechanical ventilation systems lack the ability to accurately predict and manage transient cardiorespiratory states in patients, particularly during changes in ventilator settings, leading to potential lung damage, discomfort, and increased risk of respiratory muscle injury.
Innovation Solution
A medical ventilator system that uses a dynamic modeling component to predict both transient and steady-state cardiorespiratory responses by simulating new ventilator settings, incorporating sensitivity analysis and personalized patient models to provide comprehensive cardiorespiratory data through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mechanical ventilation is applied to assist or replace pulmonary function in COPD patients, then respiratory support is provided, but ventilator-induced lung injury and respiratory muscle fatigue may occur due to improper settings
Solution Approach 1:
The system performs preliminary simulation of ventilator settings before actual application to patients. The virtual patient model allows clinicians to test different ventilation parameters and predict their effects on cardiorespiratory variables, enabling selection of safe and effective settings before implementing them on real patients, thus preventing ventilator-induced lung injury while ensuring respiratory support effectiveness
Solution Approach 2:
The invention creates a virtual copy of the patient's cardiorespiratory system through physiological models. This digital twin allows clinicians to observe and analyze the effects of various ventilator settings on simulated cardiorespiratory variables without exposing the actual patient to potential harm, enabling safe exploration of treatment options
2Adaptability or versatility
If ventilator settings are adjusted based on trial-and-error observation, then clinical expertise is utilized, but patient discomfort and prolonged ICU stay increase due to delayed optimization
Solution Approach 1:
The system allows preliminary testing of multiple ventilator settings scenarios before implementation. Clinicians can simulate different ventilation strategies and predict their effects on patient comfort and physiological parameters, enabling faster optimization of ventilator settings and reducing the time required to reach optimal steady-state conditions
Solution Approach 2:
The system provides predictive feedback about the effects of proposed ventilator settings changes. By simulating and displaying predicted cardiorespiratory responses, the system guides clinicians in making informed decisions about setting adjustments, accelerating the optimization process and reducing the time to achieve effective ventilation
3Manufacturing precision
If advanced ventilatory modes are used to provide precise control, then ventilation effectiveness improves, but system complexity and difficulty of operation increase
Solution Approach 1:
The virtual patient model serves as a training and exploration tool that replicates real patient physiology. Clinicians can practice with advanced ventilatory modes and observe their effects in a risk-free environment, building competence with complex settings before applying them to actual patients, thereby reducing the perceived complexity while maintaining precision
Solution Approach 2:
The system enables preliminary exploration and understanding of advanced ventilatory modes through simulation before clinical application. By allowing clinicians to preview and understand the effects of complex settings in advance, the system reduces the operational burden while maintaining the precision benefits of advanced modes
4Reliability
If high oxygen flow and high mechanical energy are applied to ensure adequate ventilation, then oxygenation is improved, but lung-damaging effects and oxygen toxicity occur
Solution Approach 1:
The system performs preliminary simulation of oxygen delivery settings to predict their effects on oxygenation and potential toxic effects. By testing different oxygen flow and mechanical energy parameters in the virtual model before actual application, clinicians can identify settings that achieve adequate oxygenation while avoiding lung-damaging levels, thus preventing oxygen toxicity
Solution Approach 2:
The physiological model creates a safe virtual environment to test oxygen delivery parameters. Clinicians can observe the predicted effects of high oxygen flow settings on simulated cardiorespiratory variables without exposing the actual patient to oxygen toxicity, enabling selection of safe and effective oxygenation strategies
Data Source
AI summary
A medical ventilator system and a method for predicting transient states of a ventilated patient upon changes in mechanical ventilator settings are provided. The system includes a mechanical ventilator connected to a ventilated patient; and a modeling component to receive clinical and/or physiological variables of the ventilated patient and ventilator settings. The modeling component predicts a cardiorespiratory transient state of the ventilated patient by: using physiological submodels that are linked by having one or more clinical and/or physiological variables (101, 102) in common and comprising one or more parameters that define features and behaviors of the clinical and/or physiological variables (101, 102); identifying and selecting the most sensitive parameters; generating a personalized ventilated patient model (125) by adjusting some values of the selected most sensitive parameters; and simulating the generated personalized ventilated patient model (125) based on one or more new ventilator settings (131) requested by an operator (130).