A single compartment model to describe lung functionality: A comprehensive study.

Area of Science:

• Biomedical Engineering
• Respiratory Physiology
• Computational Modeling

Background:

• Mechanical Ventilation (MV) is crucial for patients with severe respiratory failure.
• Computational models, especially the Single Compartment Lung Model (SCLM), are vital for understanding lung mechanics during MV.
• SCLM aids in optimizing treatment strategies for conditions like pneumonia and COVID-19.

Purpose of the Study:

• To critically review existing literature on Single Compartment Lung Model (SCLM) applications in mechanical ventilation.
• To identify trends, inconsistencies, and research gaps in SCLM methodologies, parameters, and clinical applications.
• To provide guidance for refining SCLM approaches for personalized mechanical ventilation.

Main Methods:

• Systematic literature review of SCLM applications in mechanical ventilation.
• Analysis of key parameters: lung elastance (E), airway resistance (Rrs), and Dynamic Functional Residual Capacity (dFRC).
• Examination of methodologies, evaluation metrics, and clinical applications of SCLM.

Main Results:

• Lung elastance (E) is the most frequently analyzed parameter, often alongside PEEP, PIP, PIV, and Vt.
• Airway resistance (Rrs) and Functional Residual Capacity (FRC) are considered in some SCLM applications.
• Identified inconsistencies in evaluation metrics and a need for disease-specific adaptations.

Conclusions:

• Standardized evaluation protocols and simplified input models are necessary for enhanced clinical applicability of SCLM.
• Further research should focus on disease-specific adaptations to refine SCLM for personalized mechanical ventilation.
• Refined SCLM approaches can significantly improve patient outcomes under mechanical ventilation.