PEEP, Occult

Out-of-hospital cardiac arrest (OHCA) remains a major cause of mortality, with low survival probabilities to hospital discharge. Despite the frequent use of airway management and mechanical ventilation during resuscitation, there is limited evidence regarding the optimal ventilation strategy to improve oxygen delivery and patient outcomes. The present study aims to investigate the effects of positive-end-expiratory-pressure (PEEP) set at 5 mbar compared to zero-end-expiratory-pressure (ZEEP) on the return of spontaneous-circulation (ROSC) in adult patients with OHCA.

his study is designed as a prospective observational clinical trial. Patients over 18 years old who are hemodynamically stable and require mechanical ventilation in the Intensive Care Unit (ICU) will be included. The inclusion criteria ensure that participants require individualized ventilatory optimization. The study will involve a comparison between Artificial Intelligence (AI)-generated Positive End-Expiratory Pressure (PEEP) recommendations and expert-determined PEEP levels. ICU specialists will perform PEEP titration manually based on standardized protocols, identifying the lower inflection point (LIP) and upper inflection point (UIP) to optimize ventilation. The pressure-volume (P-V) curve will be analyzed to ensure optimal alveolar recruitment and prevent overdistension. Study Procedures Participants will: Undergo systematic mechanical ventilation assessments, including inspiratory hold and expiratory hold maneuvers, compliance, elastance, auto-PEEP, and time constant evaluations. Have ventilation data collected and analyzed using three AI models: ChatGPT, DeepSeek, and Gemini. Receive AI-generated recommendations regarding optimal PEEP levels, abnormal ventilation parameters, and potential treatment suggestions. Have their AI-based PEEP recommendations compared with those determined by ICU specialists with at least five years of experience. Outcome Measures The study will compare AI and expert assessments based on the following primary and secondary measures: Primary Outcome: Agreement between AI-generated PEEP levels and expert-determined PEEP levels. Secondary Outcomes: AI sensitivity and specificity in detecting abnormal ventilation parameters. Accuracy of AI-generated diagnoses. Clinical applicability of AI-recommended treatment strategies. This study aims to determine whether AI models can serve as reliable clinical decision support tools for ventilator management in ICU patients.