Personalized Digital Heart Model for Real-Time CRT Adaptation
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Solution Overview
Problem
Current heart function monitoring systems are limited in their ability to provide real-time, personalized tracking of heart function and dyssynchrony, particularly due to the non-response ratio in cardiac resynchronization therapy, which is influenced by weak predictive value of ECG signals and lack of personalization in stimulation probe placement and resynchronization strategies.
Innovation Solution
A real-time heart function tracking system that combines non-real-time information from imaging and diagnosis systems with real-time data from patient-worn devices, using a personalized digital heart model to estimate electrical, mechanical, and hemodynamic functions, and adjust cardiac resynchronization therapy accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a personalized digital heart model is used to estimate heart function in real-time, then the accuracy and personalization of heart function assessment is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing imaging data (MRI, CT, echocardiography) to create a personalized digital heart model before real-time monitoring begins. The model includes pre-defined anatomical structures, tissue properties, and electromechanical parameters that are prepared in advance based on patient-specific imaging data, enabling real-time operation without complex calculations during actual monitoring.
Solution Approach 2:
The heart model is segmented into distinct functional regions (atria, ventricles, valves, conduction system) that can be independently monitored and updated. This segmentation allows the system to focus computational resources on specific regions of interest rather than processing the entire heart model simultaneously, reducing real-time computational complexity while maintaining assessment accuracy.
2Reliability
If continuous real-time monitoring data is integrated with imaging data, then the personalization and continuity of heart function tracking is improved, but the data processing time and system resource requirements increase
Solution Approach 1:
The system maintains continuous monitoring of heart function parameters by continuously acquiring data from implantable devices (pacemakers, ICDs, CRT devices) and seamlessly integrating it with the digital heart model. The model continuously updates electromechanical parameters without interruption, ensuring uninterrupted tracking of heart function evolution and therapy response.
Solution Approach 2:
The digital heart model acts as an intermediary that processes and integrates data from multiple sources (imaging systems, implantable devices, ECG signals) before presenting the synthesized heart function assessment. This intermediary role allows asynchronous data integration where imaging data and continuous monitoring data are combined through the model without requiring simultaneous processing, reducing data processing time.
3Adaptability or versatility
If the heart model parameters are continuously updated with new measurements, then the adaptability and current accuracy of the model is improved, but the computational load and energy consumption increase
Solution Approach 1:
The system updates heart model parameters periodically based on the availability of new imaging data or significant changes in heart function, rather than continuously updating with every measurement. The digital heart model is re-calibrated at scheduled intervals or when triggered by clinically significant events, balancing model adaptability with reduced computational energy consumption compared to continuous updates.
Solution Approach 2:
The system performs partial updates of the heart model by selectively updating only those parameters that have changed or require refinement, rather than re-processing the entire model with all parameters. This partial update approach maintains model adaptability while significantly reducing the computational energy load compared to complete model re-generation.
4Loss of information
If multiple data sources (imaging, therapy systems, patient database) are combined, then the comprehensiveness of heart function estimation is improved, but the system complexity and integration requirements increase
Solution Approach 1:
The system merges multiple data sources (imaging data from MRI/CT/echocardiography, therapy data from pacemakers/ICD/CRT devices, and patient database information) into a unified digital heart model. This consolidation integrates anatomical, functional, and therapeutic information into a single comprehensive model that provides holistic heart function assessment without requiring separate analysis systems.
Solution Approach 2:
The digital heart model serves multiple functions simultaneously: it acts as an anatomical representation, an electromechanical simulator, a therapy optimization tool, and a prognostic indicator. This multi-functionality allows the same integrated model to process diverse data types (imaging, electrical signals, hemodynamic parameters) without requiring separate specialized systems for each data source.
Data Source
AI summary
This invention relates to a method and system for determining and/or tracking a heart function of a patient. wherein the heart function is estimated by combining information representative of an electrical, mechanical and/or hemodynamic heart function received from imaging, therapy and/or diagnosis systems or a patient database with real-time information representative of an electrical, mechanical and/or hemodynamic heart function received from a measuring device attached or implanted to the patient. The information and the real-time information from can be associated to update an evaluation of the heart function of the patient in real-time and thereby improve the heart function and possibly optimize its efficiency.