Evaluation of the Success of Artificial Intelligence Models in Determining the Optimal Positive End-Expiratory Pressure (PEEP) in Mechanical Ventilation in Intensive Care
Trial ID: NCT06844916
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-03-01
Completion date: 2026-02-02
Sponsor: Kanuni Sultan Suleyman Training and Research Hospital
Conditions: PEEP, Occult
Interventions: AI-Assisted PEEP Optimization
Locations: Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Countries: Turkey (Türkiye)
Enrollment: 145
Age range: 18 Years - None
Sex: ALL

Summary:
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.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years or older (adult patient group).
* Patients requiring mechanical ventilation in the intensive care unit (ICU).
* Hemodynamically stable patients with stable blood pressure and heart rate.
* Patients with complete medical records, including arterial blood gas values and ventilation parameters.
* Patients whose legal representatives (if applicable) have provided informed consent for study participation.

Exclusion Criteria:

* Patients receiving extracorporeal membrane oxygenation (ECMO).
* Patients with severe hemodynamic instability, such as refractory hypotension or arrhythmias requiring continuous vasopressor support.
* Patients with incomplete medical records, particularly those missing critical data on ventilation parameters or arterial blood gas analysis.
* Patients or their legal representatives who decline participation in the study.

Outcomes:
Agreement Between AI-Generated and Expert-Determined PEEP Levels