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Mechanical ventilation energy analysis: Recruitment focuses injurious power in the ventilated lung.

Proceedings of the National Academy of Sciences of the United States of America2025-03-03PubMed
Total: 76.0Innovation: 8Impact: 8Rigor: 7Citation: 8

Summary

Using a porcine ARDS model, the authors decomposed dissipated energy during mechanical ventilation into airflow, tissue viscoelasticity, and recruitment/derecruitment (RD) components. RD, though only 2–5% of total dissipation, concentrated high power intensity over small regions and was the only component correlating with physiologic metrics, implicating RD-focused power as a key driver of VILI.

Key Findings

  • Developed a technique to quantify total and component dissipated energies during mechanical ventilation in a porcine ARDS model.
  • Recruitment/derecruitment accounted for only 2–5% of total dissipated energy yet exhibited high power intensity localized to small regions.
  • Only the RD component correlated with physiologic metrics over time; final injury was confirmed histologically.

Clinical Implications

Supports strategies that minimize cyclic recruitment/derecruitment (e.g., adequate PEEP to stabilize alveoli, low tidal volumes), and motivates bedside monitoring approaches that approximate RD-related power.

Why It Matters

Provides a quantitative framework linking specific energy pathways to injury, shifting focus from global energy to RD power intensity and informing ventilation strategies.

Limitations

  • Animal model with short (6-hour) observation; human validation and feasibility of bedside energy partitioning are unknown.
  • Sample size and variability across animals are not specified in the abstract.

Future Directions

Translate RD power metrics to bedside surrogates (e.g., impedance-based measures), validate in human cohorts, and test ventilation protocols that explicitly minimize RD intensity.

Study Information

Study Type
Cohort
Research Domain
Pathophysiology
Evidence Level
V - Preclinical mechanistic study in a porcine ARDS model.
Study Design
OTHER