Implantable Device Parameter Adaptation Using Between-Patient Stratification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current implantable medical devices (IMDs) face challenges in efficiently adjusting parameters for predictive disease risk assessments, particularly in distinguishing between short-term and long-term physiological changes, and in effectively communicating and adapting to clinician feedback for improved patient monitoring and therapy optimization.
Innovation Solution
The system and method involve a communication module in IMDs that obtains results from both within-patient and between-patient assessments, using predictive calculations to adjust parameters, including thresholds, probabilistic models, and weighting factors, to enhance performance measures such as specificity and sensitivity, and to adaptively adjust settings based on clinician approval and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses fixed parameters for within-patient decompensation detection, then the algorithm is simple to implement, but the detection accuracy and adaptability to individual patient variations deteriorate
Solution Approach 1:
The system performs a between-patient assessment before the within-patient assessment to pre-determine optimized parameters. This preliminary action allows the system to establish patient-specific thresholds, timeframes, and sensor weighting factors in advance, which are then used to configure the within-patient decompensation detection algorithm, thereby improving detection accuracy without requiring complex real-time adjustments
Solution Approach 2:
The system dynamically adjusts parameters based on patient-specific characteristics identified through between-patient assessment. The algorithm adapts thresholds, timeframes, and sensor weighting factors according to individual patient variations, enabling the detection system to optimize its sensitivity and specificity for each patient while maintaining manageable complexity through structured adaptation
2Reliability
If the system performs comprehensive within-patient assessments continuously, then the detection sensitivity improves, but the number of false positives increases
Solution Approach 1:
The system applies different assessment parameters and thresholds tailored to specific patient characteristics and risk profiles. By customizing the detection criteria locally for each patient based on between-patient assessment results, the system improves detection reliability for genuine decompensation events while reducing false alarms that would result from overly sensitive universal thresholds
Solution Approach 2:
The system uses results from between-patient assessments to inform and adjust within-patient assessment parameters. This feedback mechanism allows the system to learn from population-level patterns and apply them to individual patient monitoring, optimizing the balance between sensitivity and false positive rates through adaptive parameter selection
3Measurement precision
If the system uses multiple sensors and complex probabilistic models, then the assessment accuracy improves, but the computational resources and processing time increase
Solution Approach 1:
The system pre-determines which sensors to use and what weighting factors to apply based on between-patient assessment results. This preliminary configuration allows the within-patient assessment to use optimized, patient-specific sensor combinations and simplified probabilistic models, reducing computational energy requirements while maintaining assessment accuracy through targeted use of multiple sensors
Solution Approach 2:
The system changes parameters such as sensor weighting factors, timeframes, and model complexity based on patient-specific characteristics identified through between-patient assessment. By adapting these parameters to individual patient needs, the system achieves high assessment accuracy with optimized computational resource usage, avoiding unnecessary processing of redundant or low-value data
4Reliability
If the system requires clinician approval for parameter adjustments, then the safety and control improve, but the response time and operational efficiency deteriorate
Solution Approach 1:
The system performs comprehensive between-patient assessments and pre-determines optimized parameters before they are needed for within-patient monitoring. This preliminary action allows the system to have adjustment proposals ready in advance, reducing the need for frequent clinician approvals and improving operational efficiency while maintaining safety through pre-validated parameter selections
Solution Approach 2:
The system automatically performs between-patient assessments and generates parameter adjustment recommendations based on population-level patterns and individual patient data. This self-service capability reduces the burden on clinicians by handling routine parameter optimization automatically, improving operational efficiency while maintaining safety through structured, evidence-based adjustment protocols
Data Source
AI summary
This document discusses, among other things, systems and methods for using one or more inter-relations between within-patient detection methods and between-patient stratifier methods. A method comprises obtaining results of a first physiological assessment, wherein the first physiological assessment includes a between-patient assessment; and using the results to adjust one or more parameters of a second physiological assessment, wherein the second physiological assessment includes a within-patient assessment.