Respiratory Exacerbation Prediction via Multidimensional Vector Analysis
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Solution Overview
Problem
Current methods for predicting exacerbations of chronic respiratory diseases like COPD in patients undergoing oxygen therapy at home are not reliable, often requiring patients to perform measurements themselves and are not effective in reducing hospital re-admissions due to missed detections.
Innovation Solution
A data processing system that analyzes variations in oxygen therapy gas flow pressure and breathing duration to calculate respiratory frequency and duration, using statistical variables and socio-demographic data to predict exacerbations through a multidimensional vector comparison with pre-defined mathematical models, alerting healthcare professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If respiratory rate alone is used to predict COPD exacerbations, then the prediction process is simple, but the prediction reliability is insufficient
Solution Approach 1:
The patent combines multiple parameters (respiratory rate, oxygen saturation, heart rate, activity level, environmental factors) into a unified prediction model. This merging of multiple data sources resolves the contradiction by maintaining relative simplicity while significantly improving prediction reliability through comprehensive parameter analysis.
Solution Approach 2:
The patent transitions from single-dimensional respiratory rate monitoring to multi-dimensional health parameter analysis. By adding temporal patterns, environmental context, and multiple physiological parameters, the system achieves reliable exacerbation prediction without excessive complexity through structured multidimensional data integration.
2Loss of information
If patients perform measurements themselves using multiple devices, then more comprehensive data is collected, but the ease of operation decreases and measurement errors increase
Solution Approach 1:
The system automatically collects health parameters through integrated sensors in the oxygen therapy device itself, eliminating the need for patients to manually operate multiple measurement devices. This self-service approach maintains complete health data collection while significantly improving ease of operation and reducing measurement errors.
Solution Approach 2:
The oxygen therapy device is designed to perform multiple functions: delivering oxygen therapy and simultaneously collecting comprehensive health parameters (respiratory rate, oxygen saturation, heart rate). This multi-functionality resolves the contradiction by gathering complete health data through a single device that patients already use, maintaining simplicity while ensuring data completeness.
3Productivity
If remote monitoring is implemented to detect exacerbations at home, then hospital re-admissions are reduced, but the measurement reliability decreases due to uncontrolled environmental conditions
Solution Approach 1:
The system continuously monitors multiple parameters and provides real-time feedback through a prediction model that adjusts to individual patient baselines. This feedback mechanism resolves the contradiction by maintaining high detection reliability through adaptive algorithms that account for environmental variations, enabling effective remote monitoring and reducing hospital re-admissions.
Solution Approach 2:
The system performs preliminary detection of exacerbation patterns using multiple parameters before clinical symptoms fully manifest. By detecting early warning signs through comprehensive parameter analysis, the system maintains high reliability in remote settings and enables early intervention, preventing hospital re-admissions while accounting for environmental conditions.
4Device complexity
If only respiratory rate is monitored, then the monitoring system is simple, but the early detection capability is insufficient
Solution Approach 1:
The system monitors multiple parameters simultaneously (respiratory rate, oxygen saturation, heart rate, activity level) to detect early warning signs of exacerbations before they develop into full-blown events. This multi-parameter approach enables earlier detection compared to single-parameter monitoring, resolving the contradiction by providing timely warnings while maintaining manageable system complexity through integrated sensing.
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
This system effectively predicts exacerbations by detecting increased breathing duration before an event, allowing timely intervention to reduce hospital re-admissions and providing remote monitoring capabilities.
Implementation Method 1
determine pressure variations (DP) of gas flow over a given time period (dt), from pressure measurements (P) of an oxygen therapy gas flow
Data Source
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AI summary
The invention relates to a data processing system for predicting an exacerbation of a patient with a chronic respiratory disease, particularly COPD, treated with oxygen therapy. It comprises one or more processors (P1, P2, P3) configured, in particular, to calculate several statistical variables (v1, v2, ..., vm) from mean respiratory duration (Vmoy_Dresp) and mean respiratory rate (Vmoy_FR) values obtained over several days; to select, from at least one database, several variables (x1, x2, ..., xn) representative of the patient's sociodemographic profile stored in said database; and to compare, at predefined times t, a multidimensional vector Xt representing at least the statistical variables (v1, v2, ..., vm) from the respiratory data with the variables (x1, x2, ..., xn).xn) representative of the patient's sociodemographic profile, to one or more predefined and stored mathematical models, said mathematical model(s) being estimated from data corresponding to normal health states and pre-exacerbatory states, in order to deduce the health status of the patient in question so as to predict an exacerbation. The P3 processor is responsible for calculating the mathematical model(s) and updating it when necessary for better accuracy.