Background: It remains controversial as how to set positive end-expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) patients. This study aims to provide suggestions to the clinicians in selecting PEEP for ARDS patients receiving invasive mechanical ventilation based on artificial intelligence (AI).
Methods: Invasively ventilated ARDS patients in MIMIC-IV and eICU databases were enrolled in the observational cohort study. An AI model trained by awarding survival for suggesting optimal PEEP was developed and tested on the MIMIC-IV database and externally validated on the eICU database. Three subgroups were defined in which the PEEP grades set by the AI model are lower, equal, and higher than that set by the clinicians (denoted as , , and , respectively). Intensive care unit (ICU) mortality and 28-day ventilation-free days are the primary and secondary outcomes.
Results: 6839 (MIMIC-IV) and 2117 (eICU) ARDS admissions were included in the study. The ICU mortalities are 10.8% and 8.6% in the subgroup in the MIMIC-IV and eICU databases, respectively, and become higher in the and subgroups (MIMIC-IV: 25.6% and 23.7%, eICU: 26.9% and 27.6%). An explainable analysis reflects that the clinicians’ PEEP setting relates more to the oxygenation, respiratory mechanics, and ventilatory settings, while the RL model also pays attention to the more comprehensive parameters concerning patient characteristics such as Sequential Organ Failure Assessment (SOFA) and age.
Conclusions: AI-based PEEP selection tends to consider clinical measurements and patient characteristics comprehensively, and is promising to improve the ICU outcomes for ARDS patients.