Lung Function Prediction via Diurnal Variation Correction
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Solution Overview
Problem
Current methods for diagnosing and predicting exacerbations of airways diseases like asthma and COPD are inaccurate and unreliable due to wide variance in lung function between patients and flawed diurnal variation calculations, leading to ineffective treatment and high healthcare costs.
Innovation Solution
A system and method using electronically monitored expiratory lung function data and bronchodilator usage timing to compute personalized reference values, estimate responsiveness, and predict exacerbations through non-linear time series regression and machine learning models, correcting for diurnal variation and bronchodilator effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard reference values based on population statistics are used to diagnose exacerbations, then the method is simple to apply, but the diagnostic accuracy is poor due to wide variance in normal lung function between patients
Solution Approach 1:
The system performs preliminary actions by collecting extensive lung function data over time before diagnosis, building personalized baseline profiles and diurnal variation patterns in advance. This preliminary data accumulation enables accurate exacerbation detection when combined with bronchodilator responsiveness modeling, resolving the contradiction between diagnostic accuracy and method simplicity.
Solution Approach 2:
The invention changes from using fixed population-based reference values to dynamic, personalized parameters including individual baseline lung function, patient-specific diurnal variation patterns, and bronchodilator responsiveness metrics. These parameter changes enable accurate diagnosis while the automated calculation methods keep the system manageable in complexity.
2Measurement precision
If diurnal variation is calculated using standard morning and evening measurements, then the method is easy to implement, but the measurement precision is poor because these measurements may not capture actual maximum and minimum values
Solution Approach 1:
The system replaces manual timing and calculation methods with electronic monitoring and automated computational algorithms. Electronic devices automatically track lung function measurements throughout the day, identify true peak and trough values, and calculate diurnal variation parameters, thereby achieving high precision without burdening patients with complex measurement protocols.
Solution Approach 2:
The system enables self-service through automated electronic monitoring that continuously tracks lung function parameters and automatically determines diurnal variation patterns without requiring patient intervention for timing or calculation. Patients simply provide measurements, and the system handles the complex analysis autonomously.
3Measurement precision
If bronchodilator responsiveness is assessed using fixed time intervals, then the protocol is simple to follow, but the prediction accuracy is poor because responsiveness varies with current lung function state
Solution Approach 1:
The system transitions from static, fixed-interval bronchodilator testing to dynamic assessment that adapts to the patient's current lung function state. The bronchodilator responsiveness model incorporates real-time lung function measurements and adjusts predictions based on the patient's physiological state, enabling accurate forecasting of exacerbations while maintaining protocol feasibility through automated implementation.
Solution Approach 2:
The system implements feedback mechanisms where bronchodilator responsiveness measurements feed into updated predictions of future lung function. The model continuously learns from actual patient responses to bronchodilators and adjusts its predictions accordingly, improving accuracy over time while the automated feedback loop manages the complexity of the modeling process.
4Reliability
If patient self-reporting of symptoms is used to detect exacerbations, then the method requires minimal resources, but the reliability is poor due to subjective variability
Solution Approach 1:
The system replaces subjective patient self-reporting with objective electronic monitoring of lung function parameters. Automated devices continuously measure peak expiratory flow rate and other respiratory metrics, providing reliable, quantifiable data for exacerbation detection without requiring patient interpretation or reporting, thereby achieving high reliability while managing system complexity through automation.
Data Source
AI summary
The present invention relates to a system and method to diagnose and predict an exacerbation for an individual, using electronically monitored expiratory lung function data and electronically monitored timing of inhalation of a bronchodilator. The method comprises the steps of computing a reference value or resting state value of at least one parameter indicating expiratory lung function of an individual, determining responsiveness of the individual to a bronchodilator using a non-linear time-series regression model, and estimation of amplitude and phase of the diurnal variation of the parameter for the individual using a regression analysis model. The reference value, responsiveness to bronchodilator, and the phase and amplitude of diurnal variation, are used to diagnose and predict occurrences of exacerbations for the individual.