Real-Time Algorithm-Driven Ventilation Feedback to Improve Lung-Protective Ventilation in Critically Ill Patients
NCT ID: NCT07307066
Last Updated: 2025-12-29
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
NA
208 participants
INTERVENTIONAL
2025-12-30
2026-07-30
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
Control group 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 reflect
TREATMENT
SINGLE
Study Groups
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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) events will be also collected in the system, including double triggering, ineffective efforts, reverse triggering, and flow starvation, ect
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) events will be also collected in the system, including double triggering, ineffective efforts, reverse triggering, and flow starvation, ect..
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.
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.
Interventions
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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) events will be also collected in the system, including double triggering, ineffective efforts, reverse triggering, and flow starvation, ect..
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.
Eligibility Criteria
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Inclusion Criteria
* 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
* Chronic ventilator dependence, defined as ≥ 21 consecutive days of mechanical ventilation prior to the current admission
* 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
18 Years
75 Years
ALL
No
Sponsors
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Beijing Hepingli Hospital
UNKNOWN
Beijing No.6 Hospital
UNKNOWN
Jinzhou Medical University
OTHER
Henan Provincial People's Hospital
OTHER
Binzhou Second People's Hospital
UNKNOWN
Chongqing General Hospital
OTHER
Qujing Central Hospital of Yunnan Province
UNKNOWN
Shandong Provincial Hospital
OTHER_GOV
Capital Medical University Affiliated Beijing Anzhen Hospital, Nanchong Center
UNKNOWN
Peking Union Medical College Hospital
OTHER
Responsible Party
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Central Contacts
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References
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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.
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.
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.e33692. eCollection 2024 Jul 15.
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.
Other Identifiers
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K6526
Identifier Type: -
Identifier Source: org_study_id