Phenotyping Sensor Patterns for Heart Failure Detection
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Solution Overview
Problem
Current medical technologies lack an efficient method to accurately detect and predict heart failure decompensation events, leading to potential worsening of heart failure and increased healthcare costs.
Innovation Solution
The development of a system that monitors physiologic parameters in patients and generates a heart failure status, which is then classified into predetermined phenotype clusters based on collected data from a group of patients, allowing for personalized medical treatment determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current medical technologies are used to detect heart failure decompensation events, then detection capability is limited, but processing and memory requirements remain high
Solution Approach 1:
The patent segments the complex heart failure detection problem into distinct phenotype clusters (e.g., pulmonary congestion, systemic congestion, cardiogenic shock). Each cluster represents a specific pattern of physiological deterioration, allowing the system to identify and treat specific types of decompensation events rather than treating all heart failure cases uniformly. This segmentation improves detection accuracy by focusing on specific failure modes while reducing overall system complexity through pattern recognition.
Solution Approach 2:
The system monitors multiple physiological parameters (heart rate, blood pressure, respiratory rate, oxygen saturation, fluid balance) and detects decompensation events by identifying significant changes in these parameters over time. By tracking parameter trends and patterns rather than relying on single threshold values, the system achieves high detection accuracy while using straightforward computational methods that reduce processing requirements.
2Measurement precision
If comprehensive patient monitoring is implemented to improve detection accuracy, then measurement precision increases, but device complexity and resource requirements increase
Solution Approach 1:
The patent extracts key distinguishing features from comprehensive patient data to define phenotype clusters. Instead of storing and analyzing all raw physiological data, the system identifies and extracts critical patterns (e.g., specific combinations of parameter changes that indicate pulmonary vs. systemic congestion). This extraction approach maintains high detection accuracy by focusing on discriminative features while significantly reducing memory requirements by eliminating redundant data storage.
Solution Approach 2:
The system performs preliminary classification of patient data into phenotype clusters based on established criteria derived from clinical knowledge. By pre-defining cluster characteristics and matching rules, the system can rapidly classify new patient data without requiring complex real-time analysis, thereby improving detection accuracy while minimizing computational and memory resources needed during active monitoring.
Data Source
AI summary
This document discusses, among other things, systems and methods to receive physiologic information from a patient and to generate a heart failure status using the received physiologic information. Based on the received physiologic information, the generated heart failure status can be classified into at least one of a set of predetermined phenotype clusters.