STOP algorithm for bedside mechanical ventilation: Standardized, evidence-based management of critically ill patients
Year: 2026
Type: article
Abstract: The COVID-19 pandemic revealed significant variability in mechanical ventilation training and bedside practices, highlighting the necessity for standardized, actionable protocols. This study aimed to develop the Standard Training and Operating Procedure (STOP), an evidence-based algorithm designed for managing mechanically ventilated critically ill patients and troubleshooting patient-ventilator interactions. Utilizing the Successive Approximation Model (SAM), we reviewed current guidelines and expert recommendations, created a minimum-viable prototype during a multidisciplinary "savvy start," and refined it through seven iterative review cycles involving 33 frontline clinicians. The finalized tool underwent external evaluation via a Modified-Delphi process within the Checklist for early recognition and treatment of acute illness and injury (CERTAIN) network, engaging 50 clinicians from 19 countries across four continents, with a consensus threshold of ≥70%. STOP consists of eight sequential bedside checkpoints: abnormal vital signs/ventilator alarms, assessment of ventilation adequacy, elevated peak pressure, elevated plateau pressure, lung protection against ventilator-induced lung injury, risk of oxygen toxicity, patient-ventilator asynchrony, and readiness for spontaneous awakening and breathing trials. The Delphi agreement across these steps ranged from 82% to 96%, supporting the tool's face validity and clinical relevance. STOP offers a practical framework to minimize practice variability and enhance the safety of mechanical ventilation; however, prospective implementation studies are necessary to assess its impact on adherence and patient outcomes.
Source: Biomolecules and Biomedicine
Institutions: Mayo Clinic, Virginia Mason Medical Center, Mayo Clinic in Florida
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7123346405