Respiratory Pattern Variability for Heart Failure Risk Stratification
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Solution Overview
Problem
Current heart failure monitoring systems face challenges in accurately and reliably identifying patients at elevated risk of worsening heart failure due to the non-specificity of respiratory measurements, which can be affected by confounding diseases and environmental interferences, leading to inaccurate WHF event detection and risk stratification.
Innovation Solution
A patient management system that utilizes respiratory pattern variability indicators, such as the rapid-shallow breathing index (RSBI) variability, to assess the risk of worsening heart failure by analyzing respiratory rate, tidal volume, and respiration timing, and generates a WHF risk indicator to guide event detection and therapy delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If respiratory measurements are used to detect worsening heart failure events, then patient monitoring capability is improved, but measurement precision deteriorates due to non-specificity and confounding factors
Solution Approach 1:
The patent segments the respiratory measurement into multiple independent parameters (respiratory rate, tidal volume, inspiratory time, expiratory time) and analyzes their individual trends and combinations. This segmentation allows the system to identify specific patterns associated with WHF while filtering out non-specific variations, thereby improving detection reliability without sacrificing measurement precision.
Solution Approach 2:
The patent transforms raw respiratory measurements into derived parameters such as respiratory rate trend, tidal volume trend, and respiratory pattern variability. By changing the parameters from absolute values to trends and variability metrics, the system enhances its ability to detect WHF events while compensating for the non-specificity of individual respiratory measurements.
2Measurement precision
If multiple respiratory parameters are monitored to improve WHF risk stratification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional monitoring system where a single IMD device performs multiple functions: sensing respiratory parameters, tracking trends over time, calculating variability metrics, and generating WHF risk indicators. This universal approach allows the system to achieve high measurement precision through multiple parameters without proportionally increasing device complexity, as all functions are integrated into one platform.
Solution Approach 2:
The system performs preliminary processing of respiratory signals by continuously tracking trends and variability before WHF events occur. By pre-calculating these metrics and establishing baseline patterns, the system reduces the complexity of real-time WHF detection, as the heavy analytical work is done in advance rather than during critical decision-making moments.
3Reliability
If respiratory pattern variability is used to assess WHF risk, then WHF event detection accuracy is improved, but loss of information increases due to data processing requirements
Solution Approach 1:
The patent extracts specific meaningful patterns (trends and variability) from the complex respiratory data while discarding redundant information. By focusing only on the essential features that predict WHF events, the system improves detection accuracy while minimizing information loss. The extraction process identifies and retains only the critical signal components necessary for WHF risk assessment.
Solution Approach 2:
The system creates simplified representations (copies) of the complex respiratory patterns by generating trend indicators and variability metrics. These copied representations capture the essential WHF-predictive information in a condensed format, maintaining detection accuracy while reducing the information processing burden compared to analyzing raw respiratory data in full detail.
Data Source
AI summary
Systems and methods for monitoring patients for risk of worsening heart failure (WHF) are discussed. A patient management system includes a receiver to receive patient respiration measurement. A respiratory pattern analyzer circuit measures respiratory pattern indicative of rapid-shallow breathing pattern from the received respiration measurement, and determine a respiratory pattern variability indicator. A risk analyzer circuit determines patient WHF risk using the respiratory pattern variability indicator. The system may use the WHF risk to guide WHF event detection, or to deliver or adjust a heart failure therapy.