Heart Failure Decompensation Prediction Using Multi-Parameter Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting decompensation in heart failure patients are inadequate, particularly in predicting worsening heart failure symptoms due to low sensitivity of weight gain as a predictive indicator.
Innovation Solution
A system and method utilizing hardware processors to receive and analyze weight, blood pressure, and heart rate information from sensors, determining parameters associated with changes in these vital signs to predict decompensation in heart failure patients, employing machine-readable instructions and a Naïve Bayes classifier for risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weight gain is used as the sole indicator for detecting decompensation in heart failure patients, then the detection method is simple, but the sensitivity is poor and prediction accuracy is low
Solution Approach 1:
The patent combines multiple monitoring parameters (weight, blood pressure, heart rate) into a unified prediction system. Instead of relying on weight alone, the system integrates data from different physiological measurements to improve prediction accuracy while maintaining operational simplicity through automated processing.
Solution Approach 2:
The prediction system serves multiple functions: it monitors weight changes, tracks blood pressure variations, analyzes heart rate patterns, and generates decompensation predictions. This multi-functional approach allows the system to address limitations of single-parameter monitoring while providing comprehensive patient assessment.
2Reliability
If multiple parameters (weight, blood pressure, heart rate) are monitored and analyzed, then prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The system automatically collects, processes, and analyzes data from multiple sensors without requiring manual intervention. The automated processing of weight, blood pressure, and heart rate data reduces the operational burden on users while maintaining high prediction reliability through consistent multi-parameter monitoring.
Solution Approach 2:
The patent replaces manual clinical assessment with an automated electronic prediction system. Instead of relying on manual measurement and interpretation by healthcare providers, the system uses electronic sensors and algorithms to automatically detect decompensation patterns, reducing human effort while improving consistency and reliability.
3Reliability
If early prediction of decompensation is achieved through multi-parameter monitoring, then patient outcomes improve and hospitalization rates reduce, but the cost of implementation increases
Solution Approach 1:
The system performs preliminary detection of decompensation by continuously monitoring physiological parameters and identifying early warning signs before clinical deterioration occurs. This early intervention capability allows for timely treatment adjustments, preventing full-blown decompensation events and reducing the need for expensive emergency hospitalizations.
Data Source
AI summary
The present disclosure pertains to a system configured to predict decompensation in a subject with heart failure. The system comprises one or more hardware processors configured by machine-readable instructions to receive weight information, blood pressure information, and heart rate information about the subject; determine one or more weight parameters, one or more blood pressure parameters, and one or more heart rate parameters based on the received information; and predict decompensation in the subject based on the one or more weight parameters, the one or more blood pressure parameters, and the one or more heart rate parameters. Prior art systems use weight parameters alone for such prediction. However, weight parameters alone are often not predictive of decompensation.