Cerebral Autoregulation Monitoring With Real-Time Limit Updates
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Solution Overview
Problem
Existing systems for monitoring cerebral autoregulation are inaccurate due to noise, sensor motion, operator error, and rapid changes in patient state, failing to reliably determine and update the limits of autoregulation, leading to potentially incorrect clinical decisions.
Innovation Solution
A regional oximetry device with processing circuitry that determines a relationship between blood pressure and physiological parameters, calculates expected and actual values, and updates these relationships in real-time to accurately identify shifts in cerebral autoregulation limits, using near-infrared spectroscopy and correlation indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing monitoring systems are used to track cerebral autoregulation, then the monitoring process can be performed, but the accuracy and reliability of determining autoregulation limits are insufficient due to noise, sensor motion, and rapid patient state changes
Solution Approach 1:
The system performs preliminary actions by continuously monitoring physiological parameters and pre-calculating expected values based on stored relationships between blood pressure and physiological parameters. This allows the system to detect changes in autoregulation limits proactively rather than reactively, improving both accuracy and reliability by having baseline data ready for comparison.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual physiological parameter values against expected values derived from stored blood pressure-physiological parameter relationships. When deviations exceed thresholds, the system updates the relationships and recalculates expected values, creating a closed-loop feedback system that adapts to rapid patient state changes and maintains accurate autoregulation monitoring.
2Productivity
If real-time monitoring of cerebral autoregulation is performed, then current patient status can be assessed, but rapid changes in patient state cause inaccuracies in determining autoregulation limits
Solution Approach 1:
The system maintains continuous monitoring and updating of physiological parameter relationships throughout the patient's condition. Rather than performing discrete measurements, the system continuously tracks blood pressure and physiological parameters, continuously updating the stored relationships to reflect current patient state. This continuous action enables both rapid detection of changes and maintained accuracy by ensuring the reference data is always current.
Solution Approach 2:
The system dynamically adapts the stored relationships between blood pressure and physiological parameters based on real-time patient condition changes. The relationships are not fixed but are continuously adjusted as patient state changes, allowing the system to maintain accurate autoregulation limit determination even during rapid transitions while preserving the ability to detect changes quickly.
3Device complexity
If traditional monitoring methods are used, then the monitoring system remains simple, but noise and sensor motion artifacts lead to incorrect clinical decisions
Solution Approach 1:
The system introduces intermediary processing layers that filter and interpret raw physiological data before clinical decisions are made. The stored relationships act as intermediaries that translate raw blood pressure and physiological parameter measurements into meaningful autoregulation status assessments. This intermediary processing layer filters out noise and sensor motion artifacts while maintaining system operational simplicity, thereby improving the reliability of clinical decision support without significantly increasing device complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables faster and more accurate detection of changes in cerebral autoregulation limits, providing clinicians with real-time, reliable information for improved patient care.
Implementation Method 1
using near-infrared spectroscopy and correlation indices
Data Source
AI summary
In some examples, a device includes a memory configured to store a first relationship between a blood pressure and another physiological parameter of a patient, the first relationship being indicative of cerebral autoregulation of the patient. The device also includes processing circuitry configured to receive first and second signals indicative of the blood pressure and the physiological parameter, respectively, of a patient. The processing circuitry is also configured to determine an expected value and an actual value of the physiological parameter at a particular blood pressure value of the first physiological signal. The processing circuitry is configured to determine, based on a difference between the actual value and the expected value, and store, in the memory, a second relationship between the blood pressure and the physiological parameter, the second relationship being indicative of a change in the cerebral autoregulation of the patient from the first relationship.