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Classification of patient-ventilator asynchronies, development of an updated conceptual framework: Scoping review
Michel Perez-Garzon1, Henry Robayo-Amortegui2, Jaime Fernandez-Sarmiento3
1Department of Critical Care Medicine, Extracorporeal Life Support Unit, Fundación Clínica Shaio, Bogotá DC, Colombia; PhD Programm in Clinical Science, School of Medicine, Universidad de La Sabana, Cundinamarca, Chía, Colombia.
Respiratory Medicine
|August 5, 2026
Summary
Patient-ventilator asynchrony (PVA) impacts many invasive mechanical ventilation patients, leading to worse outcomes. This review proposes a new, flexible five-level PVA classification framework for better clinical use and AI integration.
Area of Science:
- Critical Care Medicine
- Respiratory Physiology
- Biomedical Engineering
Background:
- Patient-ventilator asynchrony (PVA) affects up to 85% of patients on invasive mechanical ventilation (IMV).
- PVA is linked to prolonged ventilation duration, increased complications, and higher mortality rates.
- Current PVA classification systems are inconsistent, difficult to apply clinically, and not validated for advanced technology integration.
Purpose of the Study:
- To evaluate existing patient-ventilator asynchrony (PVA) classification systems.
- To propose an operational and flexible PVA classification framework for intensive care unit (ICU) settings.
- To facilitate future integration of PVA detection with artificial intelligence (AI).
Main Methods:
- A comprehensive literature search was performed across major databases and gray literature.
- The review adhered to Joanna Briggs Institute methodology for study selection and analysis.
- Two independent reviewers selected and analyzed studies proposing, evaluating, or validating PVA classifications.
Main Results:
- Fifteen studies met the inclusion criteria, revealing significant methodological heterogeneity and limited external validity.
- Identified PVA detection approaches included waveform analysis, respiratory drive assessment, and AI models.
- Inconsistencies in terminology, thresholds, and detection methods hindered clinical implementation, leading to the development of a novel five-level hierarchical framework.
Conclusions:
- Existing PVA classification systems lack standardization and practical clinical applicability.
- A novel five-level classification framework is proposed, designed to be operational, ventilator-mode agnostic, reproducible, and adaptable for AI systems.
- This framework aims to enhance bedside decision-making, standardize research, and enable AI-based automation in PVA detection and management.
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