Development and validation of VentPilot: an AI-based recommendation system for mechanical ventilation
Abstract
Background
Mechanical ventilation requires repeated adjustment to changing patient physiology, but consistent individualized management remains challenging. We developed VentPilot, an artificial intelligence-based system for recommending ventilator settings, and evaluated it in multicenter retrospective validation cohorts.
Methods
VentPilot was developed using offline reinforcement learning on high-resolution physiologic and ventilator trajectories from Seoul National University Hospital (SNUH), with a reward combining ventilator-free days and intensivist-derived preference feedback. We evaluated VentPilot in an internal validation cohort from SNUH and an external validation cohort from Mayo Clinic. Fitted Q-evaluation assessed the estimated return of VentPilot relative to observed clinician behavior under the prespecified reward function. In a complementary inverse probability-weighted analysis, clinical outcomes were compared between patients with higher versus lower concordance between observed ventilator settings and VentPilot recommendations.
Results
Among 4296 mechanically ventilated adults, 3002 were included in the derivation cohort, 283 in the internal validation cohort, and 1011 in the external validation cohort. Under the prespecified reward function, fitted Q-evaluation estimated higher returns for VentPilot than for observed clinician behavior in both validation cohorts, with differences in overall reward of 1.6 (95% CI 0.8-2.5) and 1.3 (95% CI 0.6-2.0), respectively. In inverse probability-weighted analyses, higher concordance with VentPilot recommendations was associated with more ventilator-free days within 28 days, with mean differences of 5.0 days (95% CI 2.4-7.6) and 3.1 days (95% CI 1.7-4.6), respectively. Higher concordance was also associated with lower 28-day mortality and shorter ICU length of stay.
Conclusions
In multicenter retrospective validation cohorts, fitted Q-evaluation estimated higher returns for VentPilot than for observed clinician behavior under the prespecified reward function, while greater concordance between observed care and VentPilot recommendations was associated with more favorable clinical outcomes. Further clinical evaluation is warranted to establish VentPilot's safety, usability, and clinical impact.