Adverse Event Detection from Cardiac Compass Data
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Solution Overview
Problem
Implantable blood pumps, such as heart ventricular assist devices (HVAD), often experience adverse events like thrombus, stroke, and ventricular arrhythmia, which existing detection methods fail to predict early, leading to potential harm or death in patients who also have cardiac implanted electronic devices (CIED).
Innovation Solution
A system and method that utilize an implantable medical device with processing circuitry to measure patient metrics like heart rate, pacing percentages, heart rate variability, and intrathoracic impedance, comparing these to predetermined thresholds to generate an adverse event high-risk alert, triggering medical interventions to prevent or reduce the occurrence of adverse events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing detection methods (waveform analysis and log file analysis) are used to detect adverse events, then adverse events can be detected, but early warning of upcoming adverse events is not provided
Solution Approach 1:
The system performs preliminary analysis of CIED data (heart rate, pacing percentages, heart rate variability, intrathoracic impedance) to identify trends and patterns that precede adverse events. By analyzing these parameters over time and comparing them to baseline values, the system generates early warnings before actual adverse events occur, enabling preventive medical interventions.
Solution Approach 2:
The system continuously monitors CIED data and provides feedback by comparing current values to baseline values and predetermined thresholds. When deviations indicate increased risk, the system generates alerts that feed back to clinicians, enabling them to adjust treatment or intervene before adverse events occur. This closed-loop feedback mechanism transforms reactive detection into proactive prevention.
2Loss of time
If multiple patient metrics are monitored and analyzed, then early warning capability is improved, but device complexity increases
Solution Approach 1:
The system leverages the existing CIED device to perform multiple functions: it continues its primary cardiac monitoring and pacing functions while simultaneously collecting and analyzing additional parameters (heart rate variability, intrathoracic impedance, pacing percentages) for adverse event prediction. This multi-functionality approach avoids adding separate dedicated hardware while achieving comprehensive monitoring.
Solution Approach 2:
The system uses the CIED device as an intermediary to collect and process data that would otherwise require separate monitoring equipment. By utilizing the already-implanted CIED's sensors and processing capabilities, the system avoids the complexity of adding new implantable sensors while still achieving multi-parameter monitoring for early adverse event detection.
Data Source
AI summary
An example system includes an implantable medical device configured to obtain measurement values of one or more patient metrics; and processing circuitry configured to: determine a baseline value for each of the respective one or more patient metrics based on measurement values of the one or more patient metrics over a first period of time; determine a short-term value for each of the one or more patient metrics based on measurement values of the one or more patient metrics over a second period of time, determine a difference between each of the short-term values and the respective baseline value for each of the one or more patient metrics; determine that a risk of an adverse event occurring in the patient is high in response to the determined difference meeting a respective adverse event risk threshold; and generate for output an adverse event high risk alert.