Adaptive CRT Timing Optimization via Conduction Time Equations
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Solution Overview
Problem
Current methods for optimizing cardiac resynchronization therapy (CRT) control parameters, such as atrioventricular (AV) and ventricular-ventricular (VV) delays, are time-consuming and require technical expertise, and may not be patient-specific, leading to suboptimal hemodynamic performance.
Innovation Solution
A medical device and method that use updatable equations to compute optimized CRT control parameters based on measured cardiac conduction times, with coefficients and intercepts stored in the device to adjust pacing parameters dynamically, allowing for patient-specific optimization without continuous hemodynamic measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians use Doppler echocardiography to select optimal AV or VV delays, then the accuracy of parameter selection is improved, but the time required and technical expertise needed increase significantly
Solution Approach 1:
The system performs self-optimization by automatically adjusting CRT parameters based on equations that relate conduction times to optimal delays. The implantable device autonomously computes and updates AV and VV delays using stored patient-specific equations and measured conduction times, eliminating the need for continuous expert intervention and reducing optimization time while maintaining accuracy.
Solution Approach 2:
The patent introduces mathematical equations as intermediaries that link conduction time measurements to optimal pacing delays. These equations, stored in the device memory, serve as a mediator that translates simple conduction time measurements into accurate parameter recommendations without requiring complex real-time echocardiographic analysis.
2Device complexity
If fixed equations are used to compute CRT control parameters, then device complexity is reduced, but adaptability to changing patient conditions deteriorates
Solution Approach 1:
The system implements dynamic adaptability by allowing the equations themselves to be updated over time. The implantable device can revise the coefficients and intercepts of stored equations based on new conduction time measurements and hemodynamic feedback, enabling the parameter computation mechanism to adapt to changing patient conditions while maintaining a relatively simple overall device architecture.
Solution Approach 2:
The patent pre-stores multiple equations and coefficient sets in device memory that can be selectively applied. This preliminary preparation of computational models allows the device to quickly adapt to different clinical scenarios without requiring complex real-time analysis, balancing simplicity with adaptability.
3Measurement precision
If hemodynamic measurements are performed frequently to optimize parameters, then parameter optimization accuracy is improved, but the burden of expert intervention and measurement time increases
Solution Approach 1:
The system extracts and utilizes simple conduction time measurements that can be obtained automatically from intracardiac electrograms, separating this essential function from complex hemodynamic measurements. By focusing on conduction time as the primary input parameter, the system achieves adequate optimization accuracy without requiring frequent expert-performed echocardiographic studies.
Solution Approach 2:
The system implements a feedback mechanism where conduction time measurements are continuously monitored and fed into stored equations to automatically adjust CRT parameters. This closed-loop feedback enables ongoing optimization using simple automated measurements rather than frequent complex hemodynamic assessments.
Data Source
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AI summary
A medical device and associated method for controlling a cardiac pacing therapy sense a first cardiac signal including events corresponding to cardiac electrical events and a second cardiac signal including events corresponding to cardiac hemodynamic events. A processor is enabled to measure a cardiac conduction time interval using the first cardiac signal and control a signal generator to deliver a pacing therapy. A pacing control parameter is adjusted to a plurality of settings during the pacing therapy delivery. A hemodynamic parameter value is measured from the second cardiac signal during application of each of the control parameter settings. The processor identifies an optimal setting from the plurality of settings and solves for a patient-specific equation defining the pacing control parameter as a function of the cardiac conduction time interval.