Heart Failure Risk Score Differentiation via Temporal Segmentation
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Solution Overview
Problem
Current medical devices struggle to efficiently differentiate between varying levels of heart failure risk in patients, leading to challenges in timely intervention and resource allocation, as they often rely on binary alerts without nuanced differentiation of risk status.
Innovation Solution
Implementing a system that utilizes implantable medical devices to collect and analyze multiple patient metrics, such as thoracic impedance, heart rate variability, and atrial fibrillation burden, to generate detailed heart failure risk scores and differentiate alerts as high/medium or high/medium ongoing, enabling more precise patient prioritization and therapy assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binary alerts are used for heart failure monitoring, then the system is simple to operate, but the measurement precision of risk status is insufficient
Solution Approach 1:
The patent segments the binary alert system into multiple risk categories (high new, high ongoing, medium new, medium ongoing) by dividing the monitoring parameters into different time windows and comparing current values with historical values. This segmentation allows precise differentiation of risk status while maintaining systematic organization of the monitoring process.
Solution Approach 2:
The patent adds a temporal dimension to the monitoring system by comparing current parameter values with historical values from previous time windows. This transforms a static binary alert into a dynamic multi-level risk assessment that considers both current and historical data, enabling differentiation between new and ongoing events.
2Measurement precision
If multiple patient metrics are analyzed to generate detailed risk scores, then the measurement precision of heart failure risk is improved, but the device complexity increases
Solution Approach 1:
The patent segments the analysis into distinct time windows (current window vs. historical window) and processes different parameters (thoracic impedance, heart rate, arrhythmia burden) independently within each window. This segmentation simplifies the complex multi-parameter analysis by organizing it into manageable comparative units.
Solution Approach 2:
The patent applies partial action by selectively comparing only the most critical parameters (thoracic impedance, heart rate, arrhythmia burden) rather than analyzing all possible metrics. This selective approach achieves sufficient measurement precision for heart failure risk assessment while limiting the complexity of the data processing system.
3Productivity
If nuanced risk differentiation is implemented, then the productivity of patient prioritization is improved, but the loss of time in processing and analyzing data increases
Solution Approach 1:
The patent performs preliminary action by pre-defining time windows and parameter thresholds before monitoring begins. Current values are compared against pre-established historical baselines, allowing rapid risk assessment without requiring complex real-time calculations. This preliminary structuring enables efficient patient prioritization while minimizing processing time.
Data Source
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AI summary
A method for differentiating heart failure risk scores that includes receiving a current data transmission and acquiring patient metrics from a remote device, determining a daily heart failure risk score for each day occurring during a time period from a previous received data transmission to the current received data transmission based on the acquired patient metrics, determining a maximum daily heart failure risk score of the determined daily heart failure risk scores during a lookback window prior to the current received data transmission, determining a heart failure risk status alert for the received data transmission based on the temporal proximity of the determined maximum heart failure risk score and receipt of the current data transmission, selecting a type of notification based on the heart failure risk status differentiation, and indicating the transmission heart failure risk status and the heart failure risk status differentiation via the selected type of notification.