STOP algorithm for bedside mechanical ventilation: Standardized, evidence-based management of critically ill patients

Area of Science:

• Critical Care Medicine
• Respiratory Therapy
• Medical Device Technology

Background:

• COVID-19 pandemic exposed significant variability in mechanical ventilation practices.
• Need for standardized, actionable protocols in managing critically ill patients on mechanical ventilation.

Purpose of the Study:

• To develop the Standard Training and Operating Procedure (STOP), an evidence-based algorithm.
• To standardize management and troubleshooting of patient-ventilator interactions for mechanically ventilated patients.

Main Methods:

• Utilized the Successive Approximation Model (SAM) with iterative refinement involving 33 frontline clinicians.
• External validation through a Modified-Delphi process with 50 clinicians across 19 countries (CERTAIN network).
• Consensus threshold of ≥70% for tool validation.

Main Results:

• STOP comprises eight sequential bedside checkpoints for mechanical ventilation management.
• Delphi agreement on checkpoints ranged from 82% to 96%, indicating strong face validity and clinical relevance.
• The algorithm addresses key aspects from vital signs to readiness for spontaneous breathing trials.

Conclusions:

• STOP provides a practical framework to minimize practice variability in mechanical ventilation.
• The tool aims to enhance the safety and effectiveness of mechanical ventilation in critically ill patients.
• Further prospective studies are needed to evaluate STOP's impact on adherence and patient outcomes.