Heart Failure Risk Stratification via Environmental Data Integration
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Solution Overview
Problem
Current medical devices struggle to accurately predict and prevent hospitalizations due to heart failure by relying solely on internal patient data, lacking integration of environmental factors that significantly impact heart health, such as particulate matter and temperature, which can trigger worsening heart conditions.
Innovation Solution
A system that combines internal patient data from implantable medical devices with environmental factor information, using a predictive cardiovascular disease software tool to generate more accurate heart failure risk scores and provide timely alerts or therapy adjustments, thereby improving patient outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IMDs use only internal patient data for heart failure risk prediction, then device complexity is minimized, but prediction accuracy is insufficient
Solution Approach 1:
The patent combines internal patient data from IMDs with external environmental factors (particulate matter, temperature, humidity) into a unified risk prediction model. This merging of data sources improves prediction accuracy by capturing both physiological and environmental triggers of heart failure events.
Solution Approach 2:
The patent introduces a remote server as an intermediary that receives data from the IMD, integrates it with environmental data from external sources, and generates risk predictions. This intermediary approach allows complex data integration without increasing the complexity of the implanted device itself.
2Reliability
If environmental factors are integrated into risk prediction, then hospitalization prevention capability is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary data processing and risk assessment on a remote server before clinical intervention is needed. Environmental data is continuously monitored and integrated with patient data in advance, allowing the system to predict heart failure risks before hospitalization occurs, enabling timely preventive actions.
Solution Approach 2:
The system implements feedback mechanisms where risk predictions and alerts are communicated back to patients and clinicians. When environmental conditions combined with patient data indicate elevated risk, the system generates alerts that trigger preventive interventions, creating a closed-loop feedback system that improves hospitalization prevention.
3Difficulty of detecting and measuring
If particulate matter data is used as input, then environmental trigger detection is enhanced, but model interpretability challenges increase
Solution Approach 1:
The patent transforms environmental parameters (particulate matter concentration, temperature, humidity) into standardized risk scores that can be integrated with physiological parameters. By converting diverse environmental data into a unified risk metric framework, the system enhances environmental trigger detection while maintaining model interpretability through consistent parameter transformations.
Data Source
AI summary
A method uses internal patient data from an implantable medical device (IMD) and environmental factor information associated with cardiovascular diseases as input to a predictive cardiovascular disease software tool for enabling alerts, e.g., generated by a computing system configured to receive data from the IMD. A computing system may receive diagnostic metric data from the IMID and time correlated location data of the patient, e.g., from a smartphone, smartwatch, or other computing device of the user. The computing system may use the patient's location, such as from a user's device such a programmer or patient's computing device, to determine a particulate matter exposure level corresponding to the diagnostic metric data that may then be used as an input to a predictive cardiovascular disease software tool to refine the risk score or risk stratification.