Ambulatory CRT Programming to Improve Cardiac Capture
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Solution Overview
Problem
Current cardiac resynchronization therapy (CRT) devices often suffer from suboptimal programming, resulting in less than 98% cardiac capture in many patients, leading to inefficient use of device resources and potential harm to patients.
Innovation Solution
Utilizing machine learning models to analyze parameter settings of CRT devices, identify suboptimal combinations, and generate reprogramming recommendations to improve cardiac capture rates by optimizing parameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional programming methods are used for CRT devices, then device complexity is reduced, but cardiac capture efficiency deteriorates (less than 98% capture)
Solution Approach 1:
The system enables self-service by automatically analyzing device parameters and generating optimization recommendations without requiring expert intervention. The machine learning model processes device data autonomously to identify suboptimal settings and suggest improvements, allowing the system to self-optimize therapy delivery while maintaining high cardiac capture rates
Solution Approach 2:
The patent replaces manual programming mechanics with an automated machine learning-based system. Instead of clinicians manually adjusting parameters based on experience, the system uses algorithms to analyze device data, compare against optimal settings, and automatically generate reprogramming recommendations, substituting human expertise with computational intelligence
2Manufacturing precision
If manual programming optimization is performed, then time consumption increases, but programming precision improves
Solution Approach 1:
The system performs preliminary action by pre-calculating optimal parameter settings using machine learning models trained on extensive clinical data. Before actual device programming, the system analyzes patient-specific factors and pre-determines optimal settings, so that when programming is needed, precise recommendations are already available immediately without requiring time-consuming manual optimization
Solution Approach 2:
The machine learning model acts as an intermediary between raw device data and optimal programming decisions. Instead of direct manual programming or automated blind optimization, the intermediary model processes device parameters, patient data, and clinical guidelines to generate informed recommendations, bridging the gap between data and actionable insights with high precision and speed
3Productivity
If device parameters are not optimized, then resource consumption remains constant, but therapy effectiveness deteriorates
Solution Approach 1:
The system implements continuous feedback by monitoring device performance data, comparing actual therapy delivery against optimal parameters, and generating reprogramming recommendations when suboptimal settings are detected. This closed-loop feedback mechanism ensures therapy effectiveness is maintained while identifying opportunities to reduce resource wastage from ineffective pacing, such as failed captures or unnecessary high-energy deliveries
Solution Approach 2:
The system optimizes therapy effectiveness through parameter changes by identifying specific device settings that are suboptimal and recommending precise adjustments. Rather than constant high-energy delivery, the system modifies parameters such as pacing amplitude, pulse width, and timing to achieve effective therapy at lower energy consumption, converting resource wastage into efficient therapy delivery
Data Source
AI summary
Systems and methods to improve programming of medical devices and delivery of cardiac pacing are disclosed, including receiving parameter settings of an ambulatory medical device, processing the received parameter settings by inputting the received parameter settings into one or more pre-trained machine learning models to identify one or more differences between the parameter settings of the ambulatory medical device and the model parameter settings of one or more other ambulatory medical devices, and generating a programming recommendation for the ambulatory medical device to improve cardiac capture for the patient based on the identified one or more differences.