Ambulatory Blood Pressure Assessment for Renal Denervation Suitability
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Solution Overview
Problem
Current methods lack a reliable parameter, algorithm, or method to predict patient responsiveness to renal denervation (RDN) procedures, leading to a significant portion of patients showing no response or negative response due to physiological factors and inadequate therapy administration.
Innovation Solution
An ambulatory blood pressure monitor with network connectivity, combined with electronic health record data and medication compliance tracking, uses machine learning to predict patient suitability and response to RDN treatment, providing evidence-based guidance for clinicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If renal denervation procedure is performed on all hypertensive patients, then treatment coverage is maximized, but treatment effectiveness decreases due to non-responders
Solution Approach 1:
The system performs preliminary analysis of ambulatory blood pressure data, EHR data, and medication compliance before the RDN procedure to predict patient responsiveness. This preliminary action identifies suitable candidates in advance, allowing clinicians to target only those likely to respond, thereby maximizing both treatment coverage and effectiveness.
Solution Approach 2:
The system uses machine learning algorithms that learn from outcomes of previous RDN procedures to improve prediction accuracy over time. By incorporating feedback from treatment results, the system refines its ability to identify responders, progressively improving treatment effectiveness while maintaining broad coverage.
2Ease of operation
If traditional blood pressure measurement methods are used, then measurement simplicity is maintained, but measurement accuracy decreases due to white coat hypertension and fluctuations
Solution Approach 1:
The system employs ambulatory blood pressure monitoring that continuously measures blood pressure throughout the patient's daily activities over multiple days. This continuous measurement approach captures blood pressure variations and eliminates the white coat effect, providing accurate baseline data for prediction while remaining simple for patients to wear and use.
3Measurement precision
If comprehensive data collection from multiple sources is implemented, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system integrates multiple data sources (ambulatory blood pressure monitor, EHR database, medication compliance tracking) into a single unified prediction platform. This multi-functional system handles diverse data types through standardized interfaces and processing pipelines, improving prediction accuracy while managing complexity through integration rather than separate systems.
Solution Approach 2:
The system employs an intermediary processing layer that standardizes and harmonizes data from different sources before feeding it to the machine learning model. This intermediary layer handles data formatting, validation, and integration, allowing comprehensive data collection without proportionally increasing system complexity.
Data Source
AI summary
An apparatus includes a processor configured to receive, via an ambulatory blood pressure monitor, ambulatory blood pressure data for a patient; retrieve, from an electronic health record database, patient data including at least one of demographic data, diagnostic data, or treatment data for the patient; and receive, via an electronic medication diary, medication compliance data for the patient. The processor is also configured to determine a suitability of the patient for a renal denervation treatment, wherein the determination is based on the patient data. The processor is also configured to output, to a display, a screen display based on the determination, wherein the screen display comprises at least one of a first indication of whether the patient is expected to respond to the renal denervation treatment; or a second indication of an expected level of responsiveness of the patient to the renal denervation treatment.