Research on Respiratory Support Decision-Making for Mechanically Ventilated ICU Patients Based on a Physiological World Model
Mechanical ventilation is essential for ICU patients with respiratory failure, yet ventilator adjustment and weaning decisions remain experience-dependent and highly variable. Current single-point metrics such as the oxygenation index cannot distinguish true recovery from support-dependent stability-the same oxygenation level may reflect either. This study uses EHR data from 4,232 invasively ventilated ICU patients at Ruijin Hospital (2016-2026) to develop LAVENT, a deep learning-based physiological world model of respiratory dynamics. LAVENT learns the co-evolution of patient physiology and respiratory support, generates 96-hour multivariate trajectories under candidate FiO₂/PEEP settings, and compares alternative ventilation strategies within the same patient state. External validation uses the MIMIC-IV database (n = 29,899). This retrospective observational study uses only existing EHR data, with no prospective intervention or biospecimen collection.
Study Overview
Status
Conditions
Detailed Description
Mechanical ventilation is the cornerstone of life support for critically ill patients with respiratory failure in the ICU. However, adjustments to ventilator settings and decisions regarding weaning remain highly dependent on clinical experience, resulting in substantial inter-clinician variability. A fundamental limitation of current practice is that commonly used single-point metrics-such as the oxygenation index-cannot reliably reflect a patient's true recovery state across different levels of respiratory support. The same oxygenation level may indicate genuine recovery in a patient on low support, or persistent respiratory failure masked by high ventilatory support. This conflation of "true recovery" with "support-dependent stability" is a major contributor to weaning failure and delayed extubation.
To address this challenge, this study will leverage electronic health record (EHR) data from ICU patients who received invasive mechanical ventilation at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, between 2016 and 2026 (RuiICU-P1 cohort, n = 4,232), to develop a deep learning-based physiological world model of respiratory dynamics (LAVENT). The model is designed to: (1) learn the co-evolutionary dynamics between a patient's physiological state and respiratory support; (2) generate 96-hour multivariate clinical trajectories given the current patient state and candidate ventilator settings (FiO₂, PEEP); and (3) compare the differential clinical outcomes of alternative ventilator adjustment strategies within the same patient state. External validation will be performed using the public MIMIC-IV database (n = 29,899) to assess model transferability across healthcare systems.
This is a retrospective, observational study using only existing EHR data, without any prospective patient intervention, additional examinations, or biospecimen collection.
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Jing Xu
- Phone Number: +8618217732985
- Email: xj40952@rjh.com.cn
Study Locations
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Shanghai Municipality
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Shanghai, Shanghai Municipality, China, 200025
- Ruijin hospital
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Contact:
- Jing Xu
- Phone Number: +8618217732985
- Email: xj40952@rjh.com.cn
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Adult ICU patients (≥18 years) who received invasive mechanical ventilation at the Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, between January 2016 and June 2026.
Description
Inclusion Criteria:
- Inclusion criteria:
- age ≥ 18 years
- received invasive mechanical ventilation during ICU stay
- had complete 48hour baseline data and 96hour followup data.
Exclusion Criteria:
- age < 18 years
- duration of mechanical ventilation < 24 hours
- missing rate > 50% for key variables (FiO₂, PEEP, SpO₂, respiratory rate)
- received ECMO therapy.
Study Plan
How is the study designed?
Design Details
- Observational Models: COHORT
- Time Perspectives: RETROSPECTIVE
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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success rate of ventilator weaning
Time Frame: 28 days
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The proportion of participants who successfully achieve liberation from invasive mechanical ventilation (weaning success), expressed as a percentage of all participants who undergo a weaning attempt |
28 days
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Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
- RJ2026681
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Privacy and confidentiality constraints: The source data contain sensitive clinical information derived from electronic medical records. Although all data are de-identified before analysis, the combination of detailed longitudinal physiological variables, ventilator parameters, and clinical outcomes carries a residual risk of re-identification. Institutional privacy policies and the approved ethics protocol do not permit public deposition of individual-level data.
Ethics approval and consent limitations: This study is approved with a waiver of informed consent for retrospective use of existing EHR data. The ethics approval covers secondary analysis for the specified research purpose only, and does not extend to unrestricted sharing or redistribution of individual-level records.
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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