Multivariate Predictive Model for Tissue Metabolic Adeacy
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Solution Overview
Problem
Current techniques lack a universal set of optimal setpoints or ranges for managing cardiorespiratory compensatory mechanisms, as these mechanisms are patient-specific and vary in response to stimuli, making it challenging to maintain tissue metabolic adequacy effectively.
Innovation Solution
A system utilizing a multivariate predictive model and open-loop feedback control, integrated with machine learning algorithms, to classify responses to stimuli, predict interventions, and adjust cardiovascular parameters such as vasomotor tone and circulatory volume, ensuring tissue metabolic adequacy and cardiovascular stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If universal setpoints or ranges are used for managing cardiorespiratory compensatory mechanisms, then the control system is simple and easy to operate, but the management precision and effectiveness deteriorate because patient-specific variations are not accounted for
Solution Approach 1:
The system dynamically adjusts control parameters (setpoints for cardiorespiratory compensatory mechanisms) based on patient-specific responses to stimuli. Instead of using fixed universal setpoints, the system changes parameters individually for each patient based on their unique physiological response patterns, thereby improving management precision while maintaining operational simplicity through automated parameter adaptation.
2Manufacturing precision
If patient-specific operating ranges are determined for each subject, then the management precision and effectiveness improve, but the device complexity and computational requirements increase
Solution Approach 1:
The system employs feedback mechanisms where patient responses to stimuli are continuously monitored and used to refine individual operating ranges. The multivariate predictive model processes feedback data about patient responses and automatically adjusts control parameters, reducing the need for complex manual calibration while maintaining high management precision through automated feedback-driven personalization.
Solution Approach 2:
The system creates virtual copies or models of patient-specific physiological responses using multivariate predictive models trained on individual patient data. These digital twins allow the system to simulate and predict patient responses without requiring complex real-time computational adjustments, thereby reducing device complexity while maintaining precision through modeled patient-specific behavior patterns.
3Reliability
If real-time adjustment of cardiovascular parameters is performed, then the reliability of maintaining tissue metabolic adequacy improves, but the computational load and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing patient-specific response patterns and operating ranges before actual control is needed. The multivariate predictive model is trained in advance on patient response data, allowing the system to quickly retrieve and apply pre-computed guidance during real-time control, thereby improving reliability through personalized predictions while minimizing processing time by avoiding complex real-time calculations.
Data Source
AI summary
A system includes a processor comprising a multivariate predictive model. The multivariate predictive model can be configured to classify responses of a plurality of subjects to a stimulus, predict at least one of a response or an intervention of a target subject to the stimulus based on the classified responses, and adjust a tissue metabolic adequacy of the target subject based on at least one of the predicted response or the predicted intervention of the target subject. The stimulus can include a physical stress, a disease-related stress, a severity of stress, a severity of insult, an external environment condition, or combinations thereof. The tissue metabolic adequacy can be calculated based on a vasomotor tone, a ventricular pump function, an effective circulatory volume, or combinations thereof.