Pacing Vector Prioritization in Implantable Cardiac Devices
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Solution Overview
Problem
Current implantable medical devices face challenges in efficiently selecting the optimal pacing vector for cardiac pacing, which can be time-consuming and requires manual testing of multiple electrode combinations, often leading to suboptimal results due to factors like phrenic nerve stimulation and battery longevity.
Innovation Solution
A system and method that prioritize pacing vectors based on user-selected criteria such as CRT efficacy, capture thresholds, R-wave amplitudes, phrenic nerve stimulation, and impedance, using a processor to automatically assess and rank these criteria, facilitating quick selection of the most suitable electrode combinations for pacing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual testing of multiple electrode combinations is performed to select the optimal pacing vector, then the selection can be made with clinical judgment, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary automated assessment of multiple pacing vectors by evaluating predetermined criteria (capture threshold, R-wave amplitude, impedance, phrenic nerve stimulation) before clinical selection. This preliminary testing of multiple electrode combinations provides pre-ranked options to the clinician, reducing the time required for manual evaluation while maintaining selection accuracy through comprehensive criterion assessment.
2Reliability
If comprehensive criteria are evaluated for each pacing vector, then the selection quality improves, but the complexity of the selection process increases
Solution Approach 1:
The system segments the comprehensive evaluation process into distinct, independently measurable criteria: capture threshold, R-wave amplitude, impedance, and phrenic nerve stimulation. Each criterion is assessed separately through automated testing, and results are compiled into a prioritized ranking. This segmentation allows thorough evaluation of multiple factors without overwhelming the clinician, as the complex multi-criteria assessment is handled systematically by the device.
Solution Approach 2:
The system provides feedback to the clinician in the form of an automatically generated prioritized ranking of pacing vectors based on measured criterion values. This feedback mechanism translates complex multi-criteria evaluation results into an intuitive ranked list, maintaining high selection quality while reducing perceived process complexity by presenting synthesized rather than raw data.
3Reliability
If multiple pacing vectors are tested to ensure optimal selection, then the risk of suboptimal pacing is reduced, but the time and resources required increase
Solution Approach 1:
The system performs self-service automated testing and evaluation of multiple pacing vectors without requiring extensive clinician involvement in the measurement process. The device autonomously tests predetermined criteria for each vector, processes the data, and generates prioritized rankings. This self-service capability ensures thorough optimization through multiple vector testing while preserving clinician time and resources, as the automated system handles the labor-intensive evaluation work.
Data Source
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AI summary
Various techniques are disclosed for facilitating selection of at least one vector from among a plurality of vectors for pacing a chamber of a heart. In one example, a method includes presenting, by a computing device, a plurality of criteria by which each of the plurality of vectors may be prioritized, selecting at least one criterion from among a plurality of criteria by which each of the plurality of vectors may be prioritized, measuring the at least one selected criterion for each of the plurality of vectors, and automatically prioritizing, by the computing device, the plurality of vectors based on the measurement of the at least one selected criterion.