Spirometry Alert Thresholds Using Best-of-Day Lung Function
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Solution Overview
Problem
Existing systems for monitoring lung function in patients with respiratory dysfunction lack patient-specific and dynamic alert thresholds, as they rely on universal or manually set values that do not account for individual variations and changes over time, making them inefficient and burdensome to manage.
Innovation Solution
A method using spirometry data to calculate dynamic and patient-specific alert thresholds based on best-of-day measurements, adjusting target values with one standard deviation and maximum values to ensure accurate monitoring, and generating alerts when lung function drops below these thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If universal fixed thresholds are used for alerting, then the system is simple to operate, but it cannot accurately reflect individual patient variations and changes in lung function
Solution Approach 1:
The patent implements dynamic target values that automatically update based on patient-specific spirometry data trends over time. The system calculates moving averages and standard deviations from historical measurements to establish personalized thresholds that adapt as the patient's lung function changes, replacing static universal thresholds with dynamically adjusting patient-specific thresholds.
Solution Approach 2:
The system automatically calculates and updates target values using algorithms that process spirometry data without requiring manual provider input. The algorithm independently determines mean values, standard deviations, and alert thresholds based on the patient's own historical data, eliminating the need for manual threshold setting while maintaining high accuracy.
2Measurement precision
If manual target value setting is performed for each patient, then patient-specific monitoring is achieved, but the process becomes onerous and time-consuming for providers managing multiple patients
Solution Approach 1:
The system automatically calculates target values using algorithms that process spirometry data without requiring manual provider input. The algorithm independently determines mean values, standard deviations, and alert thresholds based on the patient's own historical data, eliminating the need for manual threshold setting while maintaining high accuracy.
Solution Approach 2:
The system continuously monitors spirometry measurements and uses feedback from the data to automatically adjust target values. The algorithm recalculates thresholds based on trends in the patient's lung function, comparing new measurements against historical data to dynamically update alert thresholds without manual intervention.
3Measurement precision
If conventional methods using mean values are used for target setting, then the calculation is simple, but aberrant data points artificially change the target and do not represent the patient's best lung function
Solution Approach 1:
The patent implements dynamic target values that automatically update based on patient-specific spirometry data trends over time. The system calculates moving averages and standard deviations from historical measurements to establish personalized thresholds that adapt as the patient's lung function changes, replacing static universal thresholds with dynamically adjusting patient-specific thresholds.
Solution Approach 2:
The system collects and analyzes more data points than the conventional single mean value approach. By gathering multiple spirometry measurements over time and calculating statistics across this larger dataset, the system obtains a more robust and representative target value that is less influenced by aberrant individual readings.
Data Source
AI summary
A method for monitoring lung function in a subject with respiratory dysfunction includes the steps of receiving a plurality of initial measurement data from a spirometry system indicative of a first plurality of respiratory efforts from the subject, calculating a first target value utilizing best-of-day data from the plurality of initial measurement data, receiving a plurality of subsequent measurement data from the spirometry system indicative of a second plurality of respiratory efforts from the subject, calculating a second target value utilizing best-of-day data from the plurality of subsequent measurement data, receiving a plurality of measurement data from the spirometry system indicative of a third plurality of respiratory efforts from the subject, and generating an alert if best-of-day data from the plurality of measurement data drops below a threshold set based on the second target value.