Personalized Virtual Patient Model for Precision Mechanical Ventilation
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Solution Overview
Problem
Current methods for determining optimal mechanical ventilation settings, particularly positive end-expiratory pressure (PEEP), are inadequate due to significant inter- and intra-individual variability, leading to ventilator-induced lung injury (VILI) and increased mortality and cost.
Innovation Solution
An automated digital cloning method and personalized virtual patient model using nonlinear hysteresis analysis (HLA) and a nonlinear hysteresis loop model (HLM) to create a patient-specific lung mechanics model, allowing for real-time prediction of lung response to ventilator settings and adjustment to optimize care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If lower tidal volumes and peak pressures are used to implement lung-protective strategy, then patient safety is improved, but sufficient PEEP is needed to provide alveolar recruitment and adequate gas exchange which creates confusion in setting selection
Solution Approach 1:
The system dynamically adjusts PEEP settings based on real-time monitoring of lung mechanics parameters (compliance, resistance, elastance) and patient response, transitioning from static protocol-based PEEP selection to adaptive, patient-specific PEEP titration that evolves with changing lung condition
Solution Approach 2:
The system implements continuous feedback loops where lung mechanics measurements (pressure-volume loops, compliance, resistance) are monitored and fed back to automatically adjust PEEP settings, creating a closed-loop control system that optimizes alveolar recruitment while preventing over-distension
2Measurement precision
If statistical and machine learning models are used to interpret large amounts of data for predictive relationships, then prediction accuracy is improved, but understanding of the underlying mechanics is poor to non-existent
Solution Approach 1:
The system introduces an intermediary layer of physiologically-based computational lung models that translate raw data into mechanically meaningful parameters (compliance, resistance, elastance, recruitment curves), serving as a bridge between data processing and clinical interpretation while maintaining mechanical understanding
Solution Approach 2:
The system replaces pure statistical/black-box machine learning models with physics-based mechanical models of lung behavior (pressure-volume relationships, elastance curves, recruitment characteristics) that provide interpretable mechanical insights while maintaining predictive accuracy
3Loss of information
If deterministic computational models are used to represent lung mechanics, then physical and physiological meaning is improved, but model complexity is either too great to identify or too simple for accurate use
Solution Approach 1:
The system segments the lung into functional compartments (recruited vs. non-recruited alveoli, different lung regions) and models each with appropriate mechanical parameters, allowing complex heterogeneous lung mechanics to be represented through multiple simpler compartmental models rather than a single overly complex model
Solution Approach 2:
The system uses parameter identification techniques to estimate key mechanical parameters (compliance, resistance, elastance, recruitment pressure) from routine ventilator measurements, transforming complex model structures into simplified parameter sets that can be clinically identified and used
4Ease of operation
If current PEEP determination methods are used, then ease of operation is maintained, but measurement precision of lung response and prediction capability are insufficient
Solution Approach 1:
The system enables the ventilator to automatically perform lung mechanics assessments and PEEP optimization using its own built-in sensors and actuators, eliminating the need for separate complex measurement equipment while maintaining ease of operation through integrated automated functionality
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 enables personalized precision mechanical ventilation care, reducing the risk of VILI, shortening the length of mechanical ventilation, and improving patient outcomes by accurately predicting lung mechanics and response to ventilator settings.
Implementation Method 1
using nonlinear hysteresis analysis (HLA) to find compliances (1/stiffness values) and resistances for use in a nonlinear hysteresis loop model (HLM)
Data Source
AI summary
A method and device for developing an automated digital cloning method to create an accurate, predictive and personalized virtual patient model enabling personalized precision mechanical ventilation care.