PVP Waveform Monitoring for Early Blood Loss and Anesthetic Prediction
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Solution Overview
Problem
Existing methods fail to accurately detect ongoing blood loss until the onset of shock, leading to poor patient management, particularly in pediatric patients, and there is a need for a minimally invasive method to predict the effect of inhaled and infused anesthetics.
Innovation Solution
A device using peripheral venous pressure waveform analysis with a minimally invasive technology, comprising a peripheral intravenous line and a pressure-monitoring transducer, employs machine learning algorithms to predict hemodynamic states and anesthetic depth by analyzing PVP waveforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clinical signs (hypertension, tachycardia, lacrimation) are used to assess anesthesia depth, then the assessment can be performed without additional equipment, but the indicators are unreliable and cannot provide accurate real-time monitoring
Solution Approach 1:
The patent replaces traditional mechanical/clinical assessment methods (manual observation of hypertension, tachycardia, lacrimation) with electronic signal processing of the PVP waveform. The machine learning model processes the PVP signal to generate an anesthesia depth index, substituting unreliable clinical signs with automated electronic monitoring that provides both accuracy and reliability simultaneously.
2Device complexity
If minimally invasive technology (peripheral intravenous line and pressure transducer) is used to monitor PVP waveforms, then the device complexity and invasiveness are reduced, but the ability to accurately detect blood loss and anesthetic effects must be maintained
Solution Approach 1:
The patent uses the PVP waveform as an intermediary signal that indirectly reflects both blood volume status and anesthetic depth. Instead of directly measuring blood loss or anesthetic concentration, the system monitors changes in peripheral venous pressure wave characteristics, which serve as a mediator that correlates with these physiological parameters, enabling accurate detection through a minimally invasive approach.
Solution Approach 2:
The system detects blood loss and anesthetic effects by monitoring changes in PVP waveform parameters (amplitude, frequency, morphology) rather than requiring direct measurement of blood volume or anesthetic concentration. The machine learning model identifies subtle parameter changes in the PVP signal that correlate with physiological states, maintaining measurement precision while using simple instrumentation.
3Loss of time
If machine learning algorithms are implemented to analyze PVP waveforms and predict hemodynamic states, then early detection of blood loss and anesthetic depth can be achieved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The machine learning model is pre-trained on extensive datasets of PVP waveforms correlated with known hemodynamic states and anesthetic depths. This preliminary training allows the system to quickly classify new PVP signals without requiring complex real-time calculations, enabling rapid detection while keeping the operational computational complexity manageable through use of pre-computed classification rules.
Data Source
AI summary
Disclosed herein are devices and systems for analyzing one or more conditions of a patient. The system may comprise a device comprising a tubular body having a first lumen operable to deliver a fluid, at least one sensor at near tip of the tubular body, configured to measure a peripheral venous pressure within a vein of the patient, and at least one processor configured to receive a peripheral venous pressure (PVP) waveform from the at least one sensor, process the PVP waveform, and determine one or more conditions of the patient based on the processed PVP waveform.