Cardiovascular Parameter Calculation Using Multivariate Models
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Solution Overview
Problem
Current methods for monitoring cardiovascular parameters in patients fail to accurately distinguish between normal and hyperdynamic conditions, leading to inaccurate calculations and inappropriate treatments, as they do not account for the decoupling of peripheral arterial pressure from central aortic pressure.
Innovation Solution
The development of multivariate statistical and Boolean models that analyze arterial pressure waveform data to determine if a subject is experiencing abnormal or hyperdynamic conditions, applying appropriate models to calculate accurate cardiovascular parameters such as arterial tone and cardiac output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single multivariate statistical model is used for all patients, then the device complexity is reduced, but the measurement precision deteriorates because it cannot distinguish between normal and hyperdynamic conditions
Solution Approach 1:
The patent segments the patient population into distinct groups (normal vs. hyperdynamic conditions) and applies different multivariate statistical models to each group. This segmentation allows the system to maintain measurement precision for each specific condition while managing overall system complexity through structured model selection based on waveform characteristics.
2Measurement precision
If separate multivariate statistical models are developed for normal and hyperdynamic patients, then the measurement precision improves, but the device complexity increases due to multiple models and differentiation requirements
Solution Approach 1:
The system performs preliminary analysis of arterial pressure waveform characteristics before selecting and applying the appropriate multivariate statistical model. By pre-establishing differentiation criteria and model selection rules based on waveform features, the system prepares the decision framework in advance, reducing the complexity of real-time model differentiation while maintaining high measurement precision.
3Ease of operation
If the decoupling of peripheral arterial pressure from central aortic pressure is not accounted for, then the ease of operation is maintained, but the measurement precision deteriorates in hyperdynamic patients
Solution Approach 1:
The system automatically detects whether a patient is in hyperdynamic or normal conditions by analyzing arterial pressure waveform characteristics and self-selects the appropriate multivariate statistical model without requiring manual intervention. This self-service approach maintains ease of operation while improving measurement precision by ensuring the correct model is applied based on the patient's actual physiological state.
Data Source
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AI summary
Methods for measuring a cardiovascular parameter in a subject regardless of whether the subject is experiencing normal hemodynamic or abnormal hemodynamic conditions are described. These methods involve the determination of whether a subject is experiencing normal hemodynamic conditions or abnormal hemodynamic conditions, then applying an appropriate model to subject data to determine a cardiovascular parameter for the subject.. Multivariate Boolean models are used to establish if the subject is experiencing normal hemodynamic or abnormal hemodynamic conditions, then multivariate statistical models are used to calculate the appropriate cardiovascular parameter. Having correct cardiovascular parameters for a subject experiencing abnormal hemodynamic conditions, for example, enables the calculation of accurate values for treatment relevant parameters, such as, cardiac output and stroke volume.