Evaluation of AI Models in Determining the Optimal PEEP

NCT06844916 | Recruiting

• 1 other identifier: Peep Titration
• Study Type: observational
• Target: 145
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

PEEP, Occult

Outcome Measures

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

  7 day

Secondary Outcomes (3)

• Sensitivity and Specificity of AI Models in Detecting Abnormal Ventilation Parameters

  7 days

• Accuracy of AI-Generated Diagnostic Predictions

  7 days

• Clinical Applicability of AI-Generated Treatment Recommendations

  Within the first 24 hours of mechanical ventilation.

Study Arms (2)

AI-Generated PEEP Group (Experimental Group)

Patients in this group will have their optimal PEEP levels determined using AI models (ChatGPT, DeepSeek, and Gemini). AI models will analyze mechanical ventilation data, including compliance, elastance, auto-PEEP, time constant, and pressure-volume (P-V) curves, to generate PEEP recommendations. These AI-generated values will be recorded and analyzed for accuracy, clinical relevance, and agreement with expert decisions.

Other: AI-Assisted PEEP Optimization

Expert-Determined PEEP Group (Control Group)

In this group, PEEP titration will be performed manually by ICU specialists using standard clinical protocols. Experts will determine PEEP levels based on lower and upper inflection point identification and pressure-volume curve analysis. Their decisions will serve as the reference standard for evaluating the AI-generated recommendations.

Interventions

AI-Assisted PEEP Optimization OTHER

In 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.

AI-Generated PEEP Group (Experimental Group)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Kanuni Sultan Suleyman Training and Research Hospital: lead

Study Sites (1)

Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital

Istanbul, 34303, Turkey (Türkiye)

RECRUITING

MeSH Terms

Conditions

Positive-Pressure Respiration, Intrinsic

Condition Hierarchy (Ancestors)

Respiratory Insufficiency; Respiration Disorders; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Target Duration: 7 Days
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: anesthesiology and reanimation specialist

Study Record Dates

• First Submitted: February 20, 2025
• First Posted: February 25, 2025
• Study Start: March 1, 2025
• Primary Completion: February 1, 2026
• Study Completion: February 2, 2026
• Last Updated: June 4, 2025

Record last verified: 2025-05

Locations

TU (1)