Pattern Recognition for Pulmonary Hypertension Classification
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Solution Overview
Problem
Current methods for diagnosing chronic diseases such as pulmonary hypertension are inefficient, often requiring invasive procedures and are unable to accurately differentiate between types of pulmonary hypertension or detect the disease at an early stage, leading to delayed diagnosis and increased risk of vascular damage.
Innovation Solution
A system and method using a cardiopulmonary exercise gas exchange analyzer to collect breath-by-breath data during a gas exchange test, which includes a rest phase, exercise phase, and recovery period, to determine contribution values for various physiological conditions contributing to dyspnea, allowing for more accurate classification and early detection of pulmonary hypertension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If right heart catheterization is performed to differentiate types of pulmonary hypertension, then diagnostic accuracy is improved, but patient risk and discomfort increase
Solution Approach 1:
The patent uses gas exchange measurements and pattern recognition algorithms as an intermediary diagnostic tool that provides accurate differentiation of pulmonary hypertension types without requiring invasive catheterization. The system analyzes breath-by-breath gas exchange data during exercise to classify PH types, serving as a non-invasive mediator between clinical suspicion and definitive diagnosis.
Solution Approach 2:
The patent replaces the mechanical invasive catheterization procedure with a non-invasive gas exchange analysis system. Instead of physically inserting catheters into the heart and pulmonary arteries, the system uses sensors to measure respiratory gas exchange and computational algorithms to diagnose pulmonary hypertension types, eliminating mechanical intrusion into the patient's body.
2Reliability
If right heart catheterization is used for pulmonary hypertension diagnosis, then hemodynamic data is obtained, but the procedure is limited to rest conditions without exercise capability
Solution Approach 1:
The patent transitions from static rest-based catheterization to dynamic exercise-based gas exchange measurement. The system captures breath-by-breath data during progressive exercise, allowing diagnosis under physiologically relevant conditions that reflect real-world patient symptoms and disease progression, thereby improving both reliability and adaptability.
Solution Approach 2:
The gas exchange analysis system serves multiple functions: it differentiates pulmonary hypertension types, assesses functional capacity, monitors disease progression, and evaluates treatment response—all within a single non-invasive testing framework that works across various patient conditions and exercise intensities.
3Measurement precision
If traditional diagnostic methods are used for chronic disease detection, then diagnosis is made, but detection is delayed until disease is relatively advanced
Solution Approach 1:
The patent enables preliminary detection of pulmonary hypertension and differentiation of disease types before symptoms become severe or before invasive procedures are needed. By analyzing subtle gas exchange patterns during exercise, the system identifies disease presence and classification early in the diagnostic process, preventing delays associated with traditional stepwise investigation.
Data Source
AI summary
Systems and methods for quantifying the likelihood of the contribution of multiple possible forms of chronic disease to patient reported dyspnea can include the testing protocol having a flow/volume loop, performed at rest, flowed by the measurement of cardiopulmonary exercise gas exchange variables during rest, exercise and recovery as unique data sets. The data sets are analyzed using feature extraction steps to produce a pictorial image consisting of disease silos displaying the likelihood of the contribution of various chronic diseases to patient reported dyspnea. In some embodiments, the silos are split into subclass silos. In some embodiments, multiple chronic disease indexes are used to differentiate between sub-types of a particular chronic disease (e.g., differentiating WHO 1 PH from WHO 2 or WHO 3 PH). Test results are plotted serially to asses to provide feedback to the physician on the efficacy of therapy provided to the patient.