Capnometry Nonlinearity Score for Respiratory Distress Prediction
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Solution Overview
Problem
Current methods for predicting acute respiratory distress in patients with obstructive lung diseases like asthma and COPD are inadequate, as they are invasive, expensive, and fail to accurately predict future events, leading to mixed effectiveness and susceptibility to fraud, with existing systems being insensitive to emotional triggers and diurnal variations, and lacking immunity to individual anatomical and behavioral variations.
Innovation Solution
A wearable, mobile, noninvasive device equipped with capnometry sensors and signal-processing software that calculates nonlinear properties of capnometry time series, providing a capnometry nonlinearity score to predict near-term respiratory distress, integrated with case-management software and electronic health records for timely notification and preventive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional respiratory monitoring methods are used, then measurement precision may be adequate, but the device complexity and cost increase, and patient comfort decreases due to invasiveness
Solution Approach 1:
The patent extracts the essential predictive function from complex traditional respiratory monitoring systems. By focusing specifically on capnometry nonlinearity analysis rather than comprehensive multi-parameter monitoring, the system achieves accurate prediction of respiratory distress while using simpler, less invasive equipment that can be integrated into routine care.
Solution Approach 2:
The patent replaces complex mechanical respiratory monitoring systems with a computational approach. Instead of using elaborate physical sensors and mechanical measurement devices, the system uses signal-processing software to analyze nonlinearity in capnometry time series data, substituting mechanical complexity with computational analysis.
2Reliability
If traditional respiratory monitoring methods are used, then some prediction capability is provided, but reliability is insufficient due to susceptibility to fraud and inability to detect emotional triggers and diurnal variations
Solution Approach 1:
The patent implements dynamic analysis by examining the nonlinearity of capnometry time series data over time. The system continuously monitors changes in respiratory patterns and detects dynamic variations that indicate impending respiratory distress, allowing it to adapt to individual patient variations and detect emotional triggers and diurnal patterns that static methods miss.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously analyzing capnometry data and comparing it against established nonlinearity thresholds. When abnormal patterns are detected, the system provides early warning signals that allow clinicians to intervene before full respiratory distress occurs, improving reliability through continuous monitoring and adaptive response.
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
The solution enables early and accurate prediction of acute respiratory distress, allowing for proactive preventive measures, reducing hospital admissions and healthcare costs, while being less susceptible to fraud and adaptable to individual anatomical and behavioral variations.
Implementation Method 1
capnometry sensors and signal-processing software that calculates nonlinear properties of capnometry time series
Data Source
AI summary
A system, methods, and computer-readable media are provided for the automatic identification of patients having an elevated near-term risk of pulmonary function deterioration or respiratory distress. Embodiments of the invention are directed to event prediction, risk stratification, and optimization of the assessment, communication, and decision-making to prevent respiratory events in humans, and in one embodiment take the form of a platform for wearable, mobile, untethered monitoring devices with embedded decision support. Respiratory information is obtained over one or a plurality of previous time intervals, to classify a likelihood of events leading to an acute respiratory decompensation event within a future time interval. In an embodiment, the risk prediction is based a plurality of nonlinearity measures of capnometry information over the previous time interval(s), and the risk for an acute respiratory decompensation event determined using an ensemble model predictor on the nonlinearity measures.