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.
age ≥ 18 years
received invasive mechanical ventilation during ICU stay
had complete 48hour baseline data and 96hour followup data.
age < 18 years
duration of mechanical ventilation < 24 hours
missing rate > 50% for key variables (FiO₂, PEEP, SpO₂, respiratory rate)
received ECMO therapy.
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.
Cohort
Retrospective
Not listed
success rate of ventilator weaning
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
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