Ventilator Missed Breath Detection via Segmented Trigger Analysis
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Solution Overview
Problem
Current ventilator systems fail to effectively detect and display missed breaths, leading to inefficiencies in patient-ventilator synchronization, with clinicians able to detect less than one-third of ineffective patient efforts, which can occur in up to 80% of mechanically ventilated patients.
Innovation Solution
The implementation of a system that monitors respiratory data using at least one sensor, analyzes it with both a first and second trigger detection application to detect patient inspiratory efforts, calculates a missed breaths metric, and displays a missed breath indicator, utilizing a graphical user interface to provide clinicians with real-time and historical data on ineffective and effective triggers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current ventilator systems use standard trigger detection methods, then the device complexity remains low, but the measurement precision of missed breath detection is insufficient (clinicians detect less than one-third of ineffective patient efforts)
Solution Approach 1:
The trigger detection system is segmented into multiple independent trigger detection applications (first, second, and third applications), each using different detection methods. This segmentation allows comparison of results across multiple applications to improve detection accuracy without requiring a single overly complex detection mechanism.
Solution Approach 2:
A missed breath module acts as an intermediary that receives and compares trigger detections from multiple trigger detection applications. This intermediary component coordinates the multiple detection methods and synthesizes their results to determine missed breaths, improving precision while managing system complexity through modular architecture.
2Measurement precision
If multiple trigger detection applications are implemented to improve missed breath detection, then the measurement precision increases, but the device complexity increases
Solution Approach 1:
The system divides trigger detection into separate applications with specialized functions. Each application can focus on specific detection criteria, making individual applications simpler while the collective system achieves high precision through their coordinated operation and comparison.
Solution Approach 2:
The missed breath module serves multiple functions: it receives inputs from various trigger detection applications, compares their results, identifies discrepancies, and determines missed breaths. This multi-functional component manages the complexity of multiple detection applications through a unified processing approach.
3Loss of information
If real-time monitoring and analysis of respiratory data is performed, then the information availability for clinicians is improved, but the loss of time for data processing increases
Solution Approach 1:
The system continuously monitors respiratory data and pre-processes it through multiple trigger detection applications in real-time, preparing detection results in advance. This preliminary action ensures that when missed breaths occur, the information is already available for immediate clinician review, reducing the time lag between event occurrence and information availability.
Solution Approach 2:
The monitoring and analysis operations run continuously rather than intermittently, maintaining constant surveillance of respiratory data. This continuous operation ensures that no missed breaths go undetected and provides uninterrupted information flow to clinicians, eliminating gaps in detection coverage while managing processing loads through steady-state operation.
4Measurement precision
If detailed respiratory data analysis is performed to detect missed breaths, then the measurement precision improves, but the productivity of clinical workflow decreases
Solution Approach 1:
The system provides feedback to clinicians through visual indicators (checkmarks for effective triggers, X marks for missed breaths) on the graphical user interface. This immediate feedback allows clinicians to quickly identify and respond to missed breaths without manually analyzing raw data, maintaining high detection precision while preserving clinical workflow efficiency through intuitive presentation.
Solution Approach 2:
The system automatically performs the complex analysis of comparing multiple trigger detections and determining missed breaths without requiring clinician intervention. This self-service capability handles the computationally intensive comparison operations autonomously, providing accurate missed breath detection while freeing clinicians to focus on patient care rather than data analysis.
Data Source
AI summary
This disclosure describes improved systems and methods for displaying respiratory data to a clinician in a ventilatory system. Respiratory data may be displayed by any number of suitable means, for example, via appropriate graphs, diagrams, charts, waveforms, and other graphic displays. The disclosure describes novel systems and methods for determining and displaying ineffective patient inspiratory or expiratory efforts or missed breaths in a manner easily deciphered by a clinician.