Composite Alert Score System for Heart Failure Decompensation Prediction
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Solution Overview
Problem
Current implantable medical devices (IMDs) lack effective methods for accurately predicting heart failure decompensation and managing patient data in real-time, relying on manual monitoring and limited diagnostic capabilities.
Innovation Solution
A system and method that utilizes a composite alert score calculated from multiple alert scores, derived from various sensors, to predict heart failure decompensation by combining weighted functions of detected alerts over time, with dynamic threshold adjustment for improved performance and reduced false positives/negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors and alert scores are combined to improve prediction accuracy, then the reliability of heart failure decompensation detection is improved, but the device complexity increases
Solution Approach 1:
The patent combines multiple sensor data streams (weight, activity, heart rate, respiratory rate) and integrates them through a composite alert score calculation system. This merging of multiple detection mechanisms improves prediction reliability by considering multiple physiological parameters simultaneously, while the systematic integration approach manages the inherent complexity through structured data fusion.
Solution Approach 2:
The IMD device is designed to perform multiple functions: it monitors various physiological parameters, calculates individual alert scores for each parameter, combines them into a composite alert score, and provides predictions for heart failure decompensation. This multi-functionality approach allows a single device to handle complex prediction tasks without requiring separate specialized devices for each function.
2Measurement precision
If dynamic threshold adjustment is implemented to reduce false positives and negatives, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent implements dynamic threshold adjustment where the alert thresholds are not fixed but adapt based on individual patient baseline characteristics and historical data. The system continuously learns and adjusts thresholds to optimize detection accuracy for each patient, improving measurement precision while managing complexity through adaptive algorithms that evolve with patient-specific patterns.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and patient responses are used to continuously refine threshold settings. The composite alert score system provides feedback loops that adjust detection parameters based on actual patient outcomes, improving precision over time while the feedback structure organizes the complexity into manageable iterative refinement cycles.
3Productivity
If real-time monitoring and data processing are implemented, then the productivity of patient management is improved, but the use of energy increases
Solution Approach 1:
The patent implements periodic monitoring and batch processing approaches where data is collected and analyzed at scheduled intervals rather than continuously. The system processes sensor data periodically to calculate alert scores and update predictions, improving patient management productivity through timely updates while reducing energy consumption by avoiding constant real-time processing of all sensor streams.
Data Source
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AI summary
This document discusses, among other things, systems and methods for predicting heart failure decompensation using within-patient diagnostics. A system comprises a patient device comprising: a communication module adapted to detect an alert status of each of one or more sensors; an analysis module adapted to: calculate an alert score by combining the detected alerts; and calculate a composite alert score, the composite alert score being indicative of a physiological condition and comprising a combination of two or more alert scores.