Evaluation of AI Models in Determining the Optimal PEEP
ConditionPEEP, Occult
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorKanuni Sultan Suleyman Training and Research Hospital
About this trial
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
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial population
This study will include adult patients (≥18 years old) requiring mechanical ventilation in the intensive care unit (ICU). The target population consists of hemodynamically stable patients who require individualized ventilator management and PEEP titration for optimized respiratory support.
Trial design
Design model
Case-control
Time perspective
Prospective
Treatments tested in this trial
AI-Assisted PEEP Optimization
Other interventionIn this study, three artificial intelligence (AI) models (ChatGPT, DeepSeek, and Gemini) will analyze mechanical ventilation data, including compliance, elastance, auto-PEEP, time constant, and pressure-volume (P-V) curves, to generate patient-specific PEEP recommendations. These AI-generated recommendations will be compared with manual PEEP titration performed by experienced ICU specialists. The AI models will also provide abnormal ventilation parameter detection, diagnostic suggestions, and treatment recommendations. The study aims to evaluate the reliability, accuracy, and clinical applicability of AI-generated outputs in optimizing PEEP settings for mechanically ventilated ICU patients.
Treatment groups
145 Participants
are divided into 2 treatment groupsGroup A: AI-Generated PEEP Group (Experimental Group)1 intervention
Group B: Expert-Determined PEEP Group (Control Group)0 interventions
Trial outcomes
Primary outcomes
1
Agreement Between AI-Generated and Expert-Determined PEEP Levels
The primary outcome will be the agreement between the PEEP levels recommended by AI models (ChatGPT, DeepSeek, Gemini) and those determined manually by ICU specialists. Agreement will be assessed using Bland-Altman analysis and intraclass correlation coefficient (ICC) to measure consistency
Time frame
7 daySecondary outcomes
1
Sensitivity and Specificity of AI Models in Detecting Abnormal Ventilation Parameters
AI models will be evaluated for their ability to correctly identify abnormal ventilation parameters (e.g., auto-PEEP, poor compliance, overdistension) compared to expert evaluations. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) will be calculated.
Time frame
7 days2
Accuracy of AI-Generated Diagnostic Predictions
The accuracy of diagnostic suggestions made by AI models will be compared with ICU specialists' clinical assessments. Agreement will be measured using Cohen's kappa statistic.
Time frame
7 days3
Clinical Applicability of AI-Generated Treatment Recommendations
The feasibility and clinical relevance of treatment recommendations provided by AI models will be assessed by ICU specialists using a 5-point Likert scale (1 = Not useful, 5 = Highly useful).
Time frame
Within the first 24 hours of mechanical ventilation.Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Source ClinicalTrials.gov