Ambulatory BP Monitoring for Renal Denervation Response Prediction
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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, and there is no easy way to combine biometric, electronic health record, and behavioral data for treatment planning.
Innovation Solution
An ambulatory blood pressure monitor with network connectivity is used to collect and analyze blood pressure data, combined with electronic health record and medication compliance data, employing machine learning algorithms to predict patient suitability and response to RDN treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If renal denervation procedure is performed on all hypertensive patients, then more patients may benefit from blood pressure reduction, but a significant portion (approximately 30%) will show no response or negative response, leading to wasted procedures and resources
Solution Approach 1:
The system performs preliminary analysis of ambulatory blood pressure data, electronic health records, and medication compliance information before the RDN procedure to predict patient responsiveness. This preliminary assessment allows clinicians to identify suitable candidates in advance, preventing unnecessary procedures on patients unlikely to respond, thereby improving overall treatment effectiveness and success rates
Solution Approach 2:
The system incorporates feedback loops where ambulatory blood pressure monitoring data is continuously collected and analyzed to refine predictions of patient responsiveness. By integrating real-world outcome data back into the prediction model, the system continuously improves its ability to identify responsive patients, reducing the proportion of non-responders over time
2Measurement precision
If multiple data sources (ambulatory BP monitor, EHR, medication diary) are integrated for comprehensive analysis, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system employs a multi-functional platform that can process and integrate diverse data types including ambulatory blood pressure measurements, electronic health record data, and medication compliance information through a single unified interface. This universal system handles multiple data sources simultaneously, achieving high prediction accuracy without requiring separate complex systems for each data type
Solution Approach 2:
The system uses an intermediary processing layer that standardizes and harmonizes data from different sources before analysis. This intermediary component translates diverse data formats into a unified structure, simplifying the integration process and reducing system complexity while maintaining comprehensive data analysis capabilities
3Measurement precision
If ambulatory blood pressure monitoring is used instead of office measurements, then white coat hypertension is avoided and more accurate baseline data is obtained, but monitoring time and patient burden increase
Solution Approach 1:
The system implements a balanced monitoring approach that collects sufficient ambulatory blood pressure data to achieve high measurement accuracy and eliminate white coat hypertension effects, while optimizing the monitoring duration to minimize patient burden. By analyzing the temporal patterns in the collected data, the system identifies the minimum necessary monitoring period required to generate reliable predictions, avoiding excessive monitoring time
Data Source
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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.