Stochastic integrated model-based protocol for volume-controlled ventilation setting.

Area of Science:

• Biomedical Engineering
• Computational Physiology
• Critical Care Medicine

Background:

• Mechanical ventilation (MV) is crucial for respiratory failure but often relies on non-patient-specific settings.
• Current MV approaches can lead to suboptimal or harmful patient care due to generalized guidelines.
• There is a need for personalized MV strategies that account for patient variability.

Purpose of the Study:

• To introduce the Stochastic integrated VENT (SiVENT) protocol for personalized mechanical ventilation.
• To integrate stochastic modeling into a model-based approach to account for respiratory elastance variability.
• To evaluate the SiVENT protocol's efficacy in reducing mechanical ventilation setting combinations.

Main Methods:

• Developed the SiVENT protocol by integrating a stochastic model of respiratory elastance (Ers) into the existing VENT protocol.
• Validated the SiVENT protocol using a cohort of 20 virtual mechanical ventilation patients based on retrospective data.
• Compared SiVENT and VENT protocols across 1080 instances each for volume-controlled (VC) ventilation to assess reduction in possible settings.

Main Results:

• The VENT protocol reduced 189,000 possible MV settings to a median of 10,612 (94.4% reduction).
• The SiVENT protocol further reduced settings to a median of 9,329 (95.1% reduction), a difference of over 1,000 combinations.
• SiVENT demonstrated a superior ability to narrow down mechanical ventilation settings compared to the VENT protocol.

Conclusions:

• Integrating stochastic modeling enhances decision support systems for patient-specific MV settings.
• SiVENT effectively considers inter- and intra-patient variability in respiratory elastance and eliminates harmful settings.
• Further clinical validation trials are warranted to confirm SiVENT's prediction accuracy and clinical utility.