Hemodynamic Sensor Predicts Post-Induction Hypotension
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Solution Overview
Problem
Current monitoring systems for hypotension in patients under anesthesia lack the ability to predict post-induction hypotensive events before they occur, leading to delayed remedial measures and potentially devastating consequences.
Innovation Solution
A hemodynamic monitoring system that collects and analyzes arterial pressure waveforms prior to anesthesia administration, using predictive risk models and machine learning to calculate a post-induction score indicating the likelihood of a hypotensive event, thereby providing early warning to medical professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional continuous or periodic blood pressure measurement is used, then real-time assessment of hypotension is provided, but prediction of hypotensive events before they occur is not possible
Solution Approach 1:
The system performs preliminary analysis of arterial pressure waveform features before hypotension occurs by continuously monitoring and storing baseline hemodynamic data. The machine learning model processes these pre-event waveform characteristics to predict the likelihood of future hypotensive events, enabling intervention before the actual hypotension begins.
Solution Approach 2:
The system provides continuous feedback by analyzing arterial pressure waveform features in real-time and updating the risk prediction score. The feedback loop compares current waveform characteristics against the trained model to dynamically adjust the hypotension risk assessment, allowing timely detection and intervention.
2Object-affected harmful factors
If hypotension is detected only after it begins to occur, then remedial measures can be taken, but adverse effects such as organ injury and mortality have already occurred
Solution Approach 1:
The system identifies preliminary waveform features that predict future hypotension before it occurs. By analyzing characteristics of the arterial pressure waveform in advance and using machine learning to forecast hypotensive events, the system enables preventive intervention that avoids or reduces adverse effects like organ injury and mortality.
Solution Approach 2:
The system applies preliminary anti-action by identifying patients at high risk for hypotension before the event occurs and alerting clinicians to take preventive measures. This counteracts the harmful effect of hypotension before it can cause organ injury or other adverse outcomes.
3Reliability
If arterial pressure waveform analysis and machine learning prediction models are implemented, then prediction of hypotensive events is enabled, but device complexity increases
Solution Approach 1:
The system extracts and analyzes specific waveform features from the arterial pressure signal, such as dicrotic notch timing, systolic upstroke rate, and other characteristic parameters. By isolating and focusing on these specific predictive features rather than processing the entire complex waveform, the system achieves accurate prediction while managing computational complexity.
Solution Approach 2:
The system transforms the arterial pressure waveform into a set of standardized waveform features and parameters that can be processed by the machine learning model. This parameter transformation simplifies the input data structure and enables more efficient analysis while maintaining prediction accuracy.
Data Source
AI summary
A method for determining a post-induction score that represents a prediction that a patient will experience a hypotensive event after beginning an administration of anesthesia is disclosed herein that includes receiving, by a hemodynamic monitor prior to the administration of anesthesia on the patient, sensed hemodynamic data representative of an arterial pressure waveform of the patient. The method further includes extracting, by the hemodynamic monitor, at least one waveform feature from the sensed hemodynamic data. Additionally, the method includes determining, by the hemodynamic monitor based on the at least one waveform feature, the post-induction score that represents the likelihood that the patient will experience a hypotensive event after beginning the administration of anesthesia. Finally, the post-induction score is displayed.