HVAD Pulsatility Tracking for Early Adverse Event Alerts
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Solution Overview
Problem
Existing implantable blood pumps lack the ability to analyze changes in pump parameters over time, which are crucial for identifying shifts in a patient's health condition and predicting adverse events.
Innovation Solution
A method and system that correlate pulsatility values with flow trough values to determine flow peak values, then use moving averages to track these values and generate alerts when deviations occur, thereby predicting adverse events and determining risk factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time pump parameters are monitored, then adverse events can be detected, but the ability to analyze changes in parameters over time is lacking
Solution Approach 1:
The system performs preliminary actions by calculating moving averages and establishing baseline ranges before adverse events occur. The processor continuously computes moving averages of pump parameters over predetermined time periods and establishes normal ranges, enabling proactive detection of deviations before they become critical adverse events.
Solution Approach 2:
The system implements feedback by comparing real-time pump parameters against the established moving average ranges. When parameters deviate from the normal range, the system generates alerts and can automatically adjust pump operation, creating a closed-loop control system that continuously monitors and responds to parameter changes.
2Reliability
If multiple pump parameters are tracked continuously, then patient health changes can be identified, but system complexity increases
Solution Approach 1:
The processor performs multiple functions using a unified approach: it calculates moving averages, establishes baseline ranges, compares real-time parameters, generates alerts, and controls pump operation. This multi-functional design consolidates what could be separate complex systems into a single integrated processor, improving reliability without proportionally increasing complexity.
Solution Approach 2:
The system monitors changes in pump parameters (flow, power, current, speed) and their derivatives over time. By focusing on parameter changes and trends rather than absolute values, the system can detect adverse events with high reliability while using computationally efficient methods that don't excessively increase system complexity.
3Speed
If fast moving averages are used for real-time alerts, then responsiveness improves, but stability decreases
Solution Approach 1:
The system segments the parameter tracking into multiple time scales by calculating moving averages over different predetermined time periods. Fast moving averages provide responsive alerts for immediate actions, while slow moving averages establish stable baseline ranges. This segmentation allows the system to simultaneously achieve both responsiveness and stability without compromise.
Data Source
AI summary
A method of predicting an adverse event in a patient having an implantable blood pump including correlating a pulsatility value to a flow trough value associated with the blood pump to determine a flow peak value; dividing the determined flow peak value by a pump current to determine a pulsatility peak value; tracking a first moving average of the pulsatility peak value, the first moving average defining a threshold range; tracking a second moving average of the pulsatility peak value, the second moving average being faster than the first moving average; and generating an alert when the second moving average deviates from the threshold range.