Right Ventricular Dysfunction Detection via Hemodynamic Waveform Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Right ventricular dysfunction in critically ill patients is challenging to identify due to its association with various illnesses and differing hemodynamic indicia, often leading to delayed or inappropriate intervention.
Innovation Solution
A hemodynamic monitoring system that uses machine learning to extract features from right ventricular and pulmonary artery pressure waveforms, tissue oxygen saturation, and mixed venous oxygen saturation to produce risk scores, alerting medical personnel to current or future dysfunction, enabling timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hemodynamic monitoring methods are used to identify right ventricular dysfunction, then the monitoring system is simple, but the detection precision is low and dysfunction events are missed
Solution Approach 1:
The patent segments the detection process into multiple independent waveform feature extraction components (systolic features, diastolic features, pulse contour analysis) that can be individually processed and combined. This allows complex detection to be broken down into manageable analytical modules, improving detection precision without overwhelming system complexity
Solution Approach 2:
The patent transitions from traditional single-parameter hemodynamic monitoring to multi-dimensional waveform analysis by extracting numerous features from pressure waveforms (amplitude, slope, curvature, timing parameters). This dimensional expansion enables more precise detection of right ventricular dysfunction patterns that single parameters cannot capture
2Measurement precision
If comprehensive hemodynamic parameters are monitored to improve detection accuracy, then the measurement precision improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent introduces automated computational algorithms as intermediaries between raw hemodynamic data and clinical interpretation. These algorithms process complex waveform data, extract relevant features, and generate standardized risk scores, eliminating the need for clinicians to manually analyze complex hemodynamic patterns and reducing measurement difficulty
Solution Approach 2:
The patent replaces manual clinical assessment of hemodynamic parameters with automated computer-based waveform analysis systems. The mechanical/manual process of examining pressure waveforms is substituted with electronic signal processing and automated feature extraction, significantly reducing the difficulty of detecting and measuring dysfunction indicators
3Productivity
If early prediction of right ventricular dysfunction is implemented, then the productivity of intervention improves, but the device complexity increases due to machine learning requirements
Solution Approach 1:
The patent implements preliminary action by continuously analyzing hemodynamic waveforms in real-time to predict right ventricular dysfunction before clinical symptoms manifest. The system proactively identifies at-risk patients and generates early warnings, enabling preventive intervention rather than reactive treatment, thus improving intervention timeliness
Solution Approach 2:
The patent enables the monitoring system to self-assess patient risk by automatically processing hemodynamic data through embedded algorithms and generating risk scores without external intervention. The system serves itself by autonomously detecting patterns, evaluating dysfunction risk, and alerting clinicians, reducing the need for complex external analytical infrastructure
Data Source
AI summary
A system for monitoring hemodynamic data of a patient and providing a risk score representative of a likelihood of a right ventricular dysfunction event includes at least two hemodynamic sensors, a system memory, a user interface display, and a hardware processor. The hardware processor executes a right ventricular prediction software code stored within the system memory to receive hemodynamic data representative of a right ventricular pressure waveform of the patient and at least one of a pulmonary artery pressure waveform, a tissue oxygen saturation, a mixed venous oxygen saturation, and a cardiac output of the patient. Based on the hemodynamic data, the system determines and outputs the risk score to the display.