This prospective, randomized controlled trial aims to evaluate whether an AI-driven decision support system can improve clinical outcomes for mechanically ventilated pediatric patients (aged 1 month to 18 years) in the PICU, compared to standard care. The primary question addressed is: Do patients whose ventilator parameter optimization decisions are guided by AI assistance achieve a greater number of ventilator-free days within 28 days compared to those managed with standard care by medical staff? Eligible pediatric patients requiring mechanical ventilation following tracheal intubation will be randomly assigned (1:1) to either the AI-guided intervention group or the standard care control group. In the intervention group, physicians will receive real-time, AI-generated recommendations for ventilator parameters to inform clinical adjustments. In contrast, the control group will be managed according to standard clinical protocols. This study seeks to assess whether AI-driven ventilator optimization can effectively improve clinical outcomes and shorten ventilation duration for pediatric patients in the PICU.
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Exclusion Criteria:
Using Artificial Intelligence To Improve Ventilator Settings For Intensive Care Patients
Effort of Breathing Guided Ventilator Protocol
Real-time Effort Driven VENTilator Management
Machine Learning Ventilator Decision System VS. Standard Controlled Ventilation
Evaluation and Further Development of an Artificial Intelligence-based Algorithm for Clinical Decision Support
Real-Time Algorithm-Driven Ventilation Feedback to Improve Lung-Protective Ventilation in Patients With ARDS (REALVENT-study)
Home-based Optimization of Mechanical Ventilation in Children
Clinical Decision Support Tool in PARDS Pilot Study