Evolving approaches to assessing and monitoring patient-ventilator interactions.
PURPOSE OF REVIEW: Patient-ventilator dyssynchrony (PVD) is prevalent in critically ill patients and causes increased work of breathing, which can lead to ventilatory muscle overload and fatigue as well as impairment in sleep efficiency, both important factors in determining ventilator duration. RECENT FINDINGS: New ways to analyze and communicate information, such as embedded computerized algorithms that are able to calculate a dyssynchrony index and enhanced ventilator graphical displays, may assist the clinician with recognition of PVD. Esophageal pressure monitoring allows the clinician to address trigger, flow and cycle dyssynchrony as well as quantify the incremental work of breathing imposed by PVD. An analysis using relationships in the equation of motion allows for onset and cycling off of breaths that match closer with the patient effort compared with current algorithms. Monitoring diaphragmatic electrical activity permits the accurate assessment of relationships between neural drive and ventilator flow delivery, and can assess the workload of the diaphragm. SUMMARY: Although computerized algorithms and user-friendly graphic displays show promise in minimizing the time to recognition of dyssynchrony, monitoring diaphragmatic electrical activity comes closest to representing the ideal in ventilator monitoring. Further work, however, is needed to demonstrate outcomes benefit to patients and to make this a reliable and user-friendly system for clinicians.
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Related Subject Headings
- Respiratory Mechanics
- Respiration, Artificial
- Monitoring, Physiologic
- Humans
- Emergency & Critical Care Medicine
- Diaphragm
- Critical Illness
- Algorithms
- 1103 Clinical Sciences
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Respiratory Mechanics
- Respiration, Artificial
- Monitoring, Physiologic
- Humans
- Emergency & Critical Care Medicine
- Diaphragm
- Critical Illness
- Algorithms
- 1103 Clinical Sciences