Real-Time Algorithm-Driven Ventilation Feedback to Improve Lung-Protective Ventilation in Patients With ARDS (REALVENT-study)

Overview

The REALVENT trial is designed to evaluate whether a real-time, algorithm-driven ventilation feedback strategy can improve lung-protective ventilation (LPV) achievement rates in critically ill patients receiving invasive mechanical ventilation. This multicentre randomised controlled trial will compare real-time respiratory waveform monitoring with automated feedback against standard ICU care. The primary endpoint is the LPV achievement rate over the first 72 hours.

Detailed description

Mechanical ventilation is essential in modern intensive care but may cause ventilator-induced lung injury (VILI) when delivered with excessive tidal volume, airway pressure, or mechanical power, or in the presence of unrecognised patient-ventilator asynchrony. Despite guideline recommendations to limit tidal volume, plateau pressure, and driving pressure, real-world adherence to lung-protective ventilation (LPV) remains suboptimal, and clinicians often rely on intermittent, manual review of ventilator settings and waveforms.

The REALVENT trial tests a cloud-based respiratory dynamics monitoring and feedback system that continuously acquires high-frequency ventilator waveforms (pressure, flow, volume) and automatically computes key LPV metrics, including tidal volume indexed to predicted body weight, driving pressure, plateau pressure, mechanical power, and patient-ventilator asynchrony events. For patients in the intervention arm, the platform provides three layers of feedback over the first 72 hours after randomisation: (1) real-time alerts when LPV thresholds are exceeded; (2) 4-hour window indicator checks to capture sustained deviations; and (3) standardised 24-hour summary reports with recommendations for ventilator adjustment. These reports are reviewed by bedside clinicians and a central monitoring team, but all treatment decisions remain at the discretion of the local ICU team.

The control group receives usual care with standard bedside ventilator monitoring but without structured feedback from the platform. All other aspects of care, including fluid management, sedation, prone positioning, neuromuscular blockade, and adjunct respiratory monitoring (e.g., esophageal manometry or EIT), are left to clinician judgement and recorded.

The primary hypothesis is that algorithm-driven feedback will increase the proportion of time during the first 72 hours that all four LPV targets are simultaneously achieved compared with standard care. Secondary hypotheses are that improved LPV adherence will translate into more ventilator-free days, fewer ventilator-associated complications, lower inflammatory biomarker levels, and acceptable clinician workload and usability ratings.

Interventions

• Device REal-time Algorithm-driven Ventilation feedback to improve lung-protective ventilation in critically
  Patients in the intervention arm will receive real-time ventilator waveform monitoring through the respiratory dynamics monitoring and feedback RemoteVentilate ViewTM system. The system continuously collects high-frequency waveform data (flow, pressure, volume) directly from the ventilator interface and analyses the following metrics: Tidal volume (VT) indexed to predicted body weight, Driving pressure (ΔP), Plateau pressure (Pplat), and Mechanical power (MP). Patient-ventilator asynchrony (PVA)
• Other Standard ICU care
  The control group will receive standard ICU care, including routine monitoring of ventilator parameters such as tidal volume, plateau pressure, and oxygenation status. No structured feedback or external ventilation reports will be provided. This reflects the prevailing standard of care in Chinese ICUs and is thus an appropriate comparator for assessing the added value of a real-time respiratory feedback platform.

Primary outcome measures

• The daily lung-protective ventilation achievement rate [Time frame: Over the first 72 hours following randomisation]

Secondary outcome measures (11)

• Ventilator-free days at day 28 (VFD-28) [Time frame: Day 28 after trial enrollment]
• ICU length of stay [Time frame: 28 days after ICU admission]
• Serum concentration of interleukin-1 beta (IL-1β) [Time frame: Baseline (within 24hours) and 72 hours after trial enrollment]
• Serum concentration of interleukin-6 (IL-6) [Time frame: Baseline (within 24hours) and 72 hours after trial enrollment]
• Serum concentration of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) [Time frame: Baseline (within 24hours) and 72 hours after trial enrollment]
• Incidence of ventilator-associated pneumonia (VAP) [Time frame: 72 hours after trial enrollment]
• Incidence of barotrauma [Time frame: 72 hours after trial enrollment]
• ECMO initiation rate [Time frame: 72 hours after trial enrollment]
• Mortality at day 28 [Time frame: Day 28 after trial enrollment]
• Modified NASA Task Load Index (NASA-TLX) score (0-100) [Time frame: 72 hours after trial enrollment]
• Clinician-reported usability score (mean of 5-item, 5-point Likert scale; range 1-5) [Time frame: 72 hours after trial enrollment]

Eligibility criteria

Inclusion criteria

• Age between 18 and 75 years
• Receiving invasive mechanical ventilation via endotracheal intubation at the time of screening
• Initiation of invasive mechanical ventilation within the past 24 hours
• PaO₂/FiO₂ ≤ 200 mmHg on PEEP ≥ 8 cmH₂O or, if arterial blood gas is unavailable: SpO₂/FiO₂ ≤ 235 with SpO₂ ≤ 97%
• Chest imaging (chest X-ray or CT) showing bilateral pulmonary infiltrates not fully explained by pleural effusions, lobar collapse, or pulmonary nodules
• Respiratory failure not fully explained by cardiac failure or fluid overload
• Expected to require invasive mechanical ventilation for ≥ 72 hours after enrollment

Exclusion criteria

• Receipt of extracorporeal membrane oxygenation (ECMO) or high-frequency oscillatory ventilation at screening
• Brain death or anticipated withdrawal of life-sustaining treatment within 72 hours
• Pregnancy
• Known neuromuscular disease affecting spontaneous respiratory effort
• Prisoners or individuals unable to provide informed consent or surrogate consent
• Simultaneous enrollment in another interventional ICU study
• Lack of digital infrastructure for real-time ventilator waveform acquisition

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Single blind

Primary purpose

Treatment

Study locations

China · 1 center

• Qujing Central Hospital of Yunnan Province — Qujing

Publications

• Liu S, Zhao Z, Chen X, Chi Y, Yuan S, Cai F, Song Z, Ma Y, He H, Su L, Long Y. Evaluation of health care providers' ability to identify patient-ventilator triggering asynchrony in intensive care unit: a translational observational study in China. BMC Med Educ. 2025 Feb 4;25(1):182. doi: 10.1186/s12909-025-06638-5. PMID 39905371
• Chen X, Yuan S, Kassis EB, Zhang S, Chi Y, Liu S, Cai F, Ma Y, Li Y, Su L, Long Y. Methodological development of the remote ventilate view platform for real-time monitoring of patient-ventilator asynchrony and respiratory parameters in severe pneumonia patients. J Intensive Med. 2025 Sep 23;5(4):367-376. doi: 10.1016/j.jointm.2025.07.003. eCollection 2025 Oct. PMID 41180101
• Chen X, Fan J, Zhao W, Shi R, Guo N, Chang Z, Song M, Wang X, Chen Y, Li T, Li GG, Su L, Long Y; on bahalf of Beijing Dongcheng Critical Care Quality Control Centre Group. Application of a cloud platform that identifies patient-ventilator asynchrony and enables continuous monitoring of mechanical ventilation in intensive care unit. Heliyon. 2024 Jun 27;10(13):e33692. doi: 10.1016/j.heliyon.2024.e3 PMID 39055813
• Su L, Lan Y, Chi Y, Cai F, Bai Z, Liu X, Huang X, Zhang S, Long Y. Establishment and Application of a Patient-Ventilator Asynchrony Remote Network Platform for ICU Mechanical Ventilation: A Retrospective Study. J Clin Med. 2023 Feb 16;12(4):1570. doi: 10.3390/jcm12041570. PMID 36836113
• Su L, Yang Y, Wang Y, Lan J, Yue C, Yang M, Pensier J, Zhang S, Yang J, Zhang J, Shao H, Wang Y, Zhao J, Song X, Cao H, Wu H, Cai F, Ma Y, Song Z, Talmor D, Baedorf-Kassis E, Long Y. 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. Respir Res. 2026 Jul 4. doi: PMID 42401979

Identifiers

NCT: NCT07307066 · K6526

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗