Heart Failure Predictor Circuit Forecasting Sensor Trends
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Solution Overview
Problem
Current medical devices struggle to accurately forecast the future heart failure status of patients, leading to delayed or inappropriate treatment, which can worsen the condition and increase healthcare costs due to unnecessary interventions and hospitalizations.
Innovation Solution
A system that includes a heart failure predictor circuit capable of generating a projected sensor trend using monitored sensor data from patients, combined with historical data from similar patients, to forecast future heart failure status, utilizing non-parametric or parametric models and displaying the trajectory for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent patient monitoring is performed to detect worsening heart failure, then patient outcomes improve and hospitalization is reduced, but device battery life is depleted and healthcare costs increase due to unnecessary interventions
Solution Approach 1:
The system performs preliminary action by predicting future heart failure status before actual worsening occurs. The predictor circuit analyzes sensor trends and generates forecasts indicating the likelihood of worsening heart failure in the future, allowing clinicians to intervene proactively rather than reactively after battery life is consumed or costs are incurred from unnecessary monitoring.
Solution Approach 2:
The system implements feedback by continuously monitoring sensor data, comparing it against predicted trends, and adjusting monitoring intensity based on risk stratification. Patients with low predicted risk receive reduced monitoring (conserving battery life), while those with high predicted risk receive intensified monitoring and intervention, creating a closed-loop system that optimizes both patient outcomes and energy consumption.
2Productivity
If continuous monitoring and prediction are performed to enable early intervention, then healthcare costs are reduced by avoiding unnecessary hospitalizations, but device complexity increases
Solution Approach 1:
The system applies segmentation by dividing the prediction function into distinct modular components: sensor data collection module, trend analysis module, prediction generation module, and risk stratification module. Each module processes specific aspects of the data independently, reducing overall system complexity while maintaining comprehensive prediction capability. The HF predictor circuit is segmented into functional blocks that can be implemented separately and combined as needed.
Solution Approach 2:
The system uses an intermediary approach by introducing a trend analysis layer between raw sensor data and final predictions. The trend analysis module processes sensor data into meaningful patterns and characteristics before the prediction model generates forecasts. This intermediary layer simplifies the prediction process by pre-processing and contextualizing the data, making the overall system more manageable and less complex.
3Loss of time
If sensor data is collected and analyzed to generate predictions, then timely intervention is enabled and patient outcomes improve, but measurement and detection difficulty increases
Solution Approach 1:
The system performs preliminary analysis of sensor data trends before generating predictions. The trend analysis module continuously processes sensor measurements to identify patterns and characteristics that predict future heart failure status. This preliminary action enables timely intervention by preparing and contextualizing the data in advance, reducing the complexity of real-time detection and measurement requirements.
Solution Approach 2:
The trend analysis module serves as an intermediary between raw sensor data and prediction generation. It processes and contextualizes sensor measurements, transforming them into meaningful trends and patterns. This intermediary layer simplifies the detection and measurement process by pre-processing the data into actionable insights, reducing the difficulty of identifying prediction-relevant features in real-time.
Data Source
AI summary
Systems and methods for monitoring a heart failure (HF) patient and forecasting the patient's future HF status are discussed. A system includes a HF predictor circuit to generate a monitored sensor trend using sensor data collected up to a prediction time from the patient. The HF predictor circuit can generate a projected sensor trend for the patient over a forecast period of time in future beyond the prediction time using the monitored sensor trend of the patient and sensor trends collected from a plurality of patients. The projection can be based on a non-parametric predictor or a parametric model. A trajectory of the projected sensor trend may be displayed as a visual forecast of the patient's HF status.