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Background <p>Lung-protective ventilation is a cornerstone of modern mechanical ventilation, yet real-world adherence to lung-protective targets remains suboptimal. While previous studies have established the physiological benefits of low tidal volume and driving pressure, clinical implementation is hindered by limited monitoring granularity and lack of real-time actionable feedback. This trial aims to evaluate whether a real-time, cloud-based algorithmic feedback platform can improve lung-protective ventilation delivery and contribute to better clinical outcomes in mechanically ventilated patients with ARDS.</p> Methods <p>This multicentre, parallel-group, open-label randomised controlled trial will enrol 208 adult mechanically ventilated ICU patients with ARDS from nine adult ICUs across tertiary academic hospitals and regional referral centres in multiple provinces and municipalities in mainland China. Participants will be randomly assigned in blocks to receive either standard monitoring (Control group) or real-time respiratory mechanics feedback through a cloud-based platform (Intervention group). The intervention group will receive real-time alerts for lasting 72 h and ventilator reports every 24 h, integrating tidal volume, plateau pressure, driving pressure, mechanical power, and detected patient–ventilator asynchrony events. The primary outcome is the lung-protective ventilation achievement rate, defined as compliance with VT &lt; 8 mL/kg predicted body weight, driving pressure &lt; 15 cmH₂O, plateau pressure &lt; 30 cmH₂O, and mechanical power &lt; 17 J/min during the first 72 h after randomisation. Secondary outcomes include ventilator-free days at day 28, ICU length of stay, ventilator-associated complications, inflammatory biomarkers, clinician satisfaction, and predefined safety outcomes, including severe hypoxemia, severe hypercapnia/acidemia, barotrauma, and hemodynamic instability temporally associated with ventilator adjustments.</p> Discussion <p>This study is, to our knowledge, among the first multicentre randomised controlled trials to evaluate a real-time algorithmic feedback platform designed to enhance lung-protective ventilation. The intervention is designed to provide continuous bedside feedback on ventilation mechanics and may enable more timely and standardised clinical adjustments, with the potential to facilitate lung-protective ventilation delivery.</p> <p>Triaiontl registration</p> <p>ClinicalTrials.gov Identifier NCT07307066 (Registration Date: 2025/12/02).</p>

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Real-time algorithm-driven ventilation feedback to improve lung-protective ventilation in patients with ARDS (REALVENT-study): study protocol for a multicentre randomised controlled trial

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Longxiang Su,
  • Yingying Yang,
  • Ye Wang,
  • Jinyan Lan,
  • Chaofu Yue,
  • Mei Yang,
  • Joris Pensier,
  • Song Zhang,
  • Jin Yang,
  • Jicheng Zhang,
  • Huanzhang Shao,
  • Yu Wang,
  • Jiangyong Zhao,
  • Xiaojun Song,
  • Haiquan Cao,
  • Huiping Wu,
  • Fuhong Cai,
  • Yue Ma,
  • Zhangwei Song,
  • Daniel Talmor,
  • Elias Baedorf-Kassis,
  • Yun Long

摘要

Background

Lung-protective ventilation is a cornerstone of modern mechanical ventilation, yet real-world adherence to lung-protective targets remains suboptimal. While previous studies have established the physiological benefits of low tidal volume and driving pressure, clinical implementation is hindered by limited monitoring granularity and lack of real-time actionable feedback. This trial aims to evaluate whether a real-time, cloud-based algorithmic feedback platform can improve lung-protective ventilation delivery and contribute to better clinical outcomes in mechanically ventilated patients with ARDS.

Methods

This multicentre, parallel-group, open-label randomised controlled trial will enrol 208 adult mechanically ventilated ICU patients with ARDS from nine adult ICUs across tertiary academic hospitals and regional referral centres in multiple provinces and municipalities in mainland China. Participants will be randomly assigned in blocks to receive either standard monitoring (Control group) or real-time respiratory mechanics feedback through a cloud-based platform (Intervention group). The intervention group will receive real-time alerts for lasting 72 h and ventilator reports every 24 h, integrating tidal volume, plateau pressure, driving pressure, mechanical power, and detected patient–ventilator asynchrony events. The primary outcome is the lung-protective ventilation achievement rate, defined as compliance with VT < 8 mL/kg predicted body weight, driving pressure < 15 cmH₂O, plateau pressure < 30 cmH₂O, and mechanical power < 17 J/min during the first 72 h after randomisation. Secondary outcomes include ventilator-free days at day 28, ICU length of stay, ventilator-associated complications, inflammatory biomarkers, clinician satisfaction, and predefined safety outcomes, including severe hypoxemia, severe hypercapnia/acidemia, barotrauma, and hemodynamic instability temporally associated with ventilator adjustments.

Discussion

This study is, to our knowledge, among the first multicentre randomised controlled trials to evaluate a real-time algorithmic feedback platform designed to enhance lung-protective ventilation. The intervention is designed to provide continuous bedside feedback on ventilation mechanics and may enable more timely and standardised clinical adjustments, with the potential to facilitate lung-protective ventilation delivery.

Triaiontl registration

ClinicalTrials.gov Identifier NCT07307066 (Registration Date: 2025/12/02).