Renal Denervation Response Index Using Multi-Parameter Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack the ability to accurately predict patient responsiveness to renal denervation procedures, leading to a significant portion of the population showing no response or negative response due to physiological factors and inadequate therapy administration.
Innovation Solution
A renal denervation suitability prediction system utilizing a collection of multiple physiological parameters, including patient data and real-time or point-of-care measurements, to generate a single index that stratifies patient suitability and confirms therapy completeness, employing machine learning algorithms to determine the effectiveness of renal denervation procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple physiological parameters are collected and processed through machine learning algorithms to generate a predictive index, then prediction accuracy of patient responsiveness is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the prediction task into distinct functional modules: data acquisition from multiple physiological parameters, machine learning model processing, and output generation of predictive indices. This modular architecture manages complexity by organizing the system into independent, manageable components that can be developed and validated separately.
Solution Approach 2:
The patent introduces an intermediary processing layer (machine learning algorithms and computational models) that transforms raw physiological parameter data into clinically actionable predictive indices. This intermediary layer bridges the gap between complex multi-parameter data and simple binary or graded predictions of patient responsiveness.
2Reliability
If real-time physiological measurements are implemented during the procedure, then patient responsiveness prediction is improved, but procedure time and resource requirements increase
Solution Approach 1:
The system performs preliminary data collection and processing by gathering physiological parameters continuously during the renal denervation procedure itself, rather than requiring separate pre-procedure or post-procedure testing. This allows prediction to be integrated into the procedural workflow, minimizing additional time requirements.
Solution Approach 2:
The patent implements continuous monitoring and processing of physiological parameters throughout the procedure, enabling real-time prediction updates as the treatment progresses. This continuous action allows the system to provide ongoing feedback without interrupting the procedural flow, maintaining treatment effectiveness while managing time efficiently.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An apparatus includes a processor configured to obtain values for a plurality of physiological metrics for a patient from at least one of an electronic health record database, a non-invasive measurement device, or an invasive measurement device. The processor is also configured to calculate a value of a renal denervation index using the physiological metrics such that the renal denervation index is itself not measured from the patient, where the value of the renal denervation index is representative of at least one of a suitability of the patient for a renal denervation treatment or an expected responsiveness of the patient to the renal denervation treatment. The processor is also configured to output, to a display, a screen display based on the value of the renal denervation index.