PEEP, OccultNCT06844916
Evaluation of AI Models in Determining the Optimal PEEP
An observational study of PEEP, Occult, run by Kanuni Sultan Suleyman Training and Research Hospital at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- PEEP, Occult
- What is being tested?
- AI-Assisted PEEP Optimization
- How many people
- 145 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Mar 2025to 2 Feb 2026
- How long will I take part?
- 7 Days
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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 locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site, 1 recruiting
- Recruiting
Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Istanbul, 34303
Engin ihsan Turan, 05382431114, enginihsan@hotmail.com
About this study
Official title: Evaluation of the Success of Artificial Intelligence Models in Determining the Optimal Positive End-Expiratory Pressure (PEEP) in Mechanical Ventilation in Intensive Care
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.
Read more
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.
Read the detailed description
Patients receiving mechanical ventilation support in the intensive care unit (ICU), who are hemodynamically stable and over 18 years old, will be considered eligible for the study. These criteria have been set to ensure that the patients require adequate respiratory support while being suitable for individualized ventilator management optimization.
During the patient selection process, the medical records of ICU patients will be reviewed, and those meeting the criteria will be included in the study. To ensure the accuracy of clinical data, the patient selection process will be carried out by intensive care specialists.
The mechanical ventilation parameters of the included patients will be systematically recorded. In this context, inspiratory hold and expiratory hold maneuvers will be applied to evaluate the respiratory mechanics of the patients. The recorded measurements will include compliance (lung elasticity), elastance (lung stiffness), auto-PEEP (presence of air trapping), and time constant (gas exchange duration).
In addition to these fundamental ventilation parameters, the pressure-volume (P-V) curve will be analyzed to collect critical data for ventilation optimization. These procedures will be performed using advanced analysis modules integrated into ICU ventilators and patient monitoring systems. The collected data will be analyzed both manually by experts and by artificial intelligence (AI) models throughout the study.
In the study, PEEP titration will be performed manually by intensive care specialists. This process will be applied immediately after intubation for intubated patients and will continue daily. The procedure will be standardized according to a predefined protocol and applied consistently across all patients.
The lower inflection point (LIP) will be identified, and PEEP will be applied at the point where alveolar recruitment begins. This step is critical to preventing lung collapse and ensuring optimal oxygenation. Additionally, the upper inflection point (UIP) will be determined to prevent overdistension, ensuring that tidal volume remains below this pressure threshold. If the lower inflection point is not clearly identified, a PEEP level of approximately 10 cmH₂O will be applied as a standard approach.
The pressure-volume curve of the ventilator will be analyzed to assess whether the optimal PEEP level has been achieved. If the curve shifts upward, it will be considered an indicator of successful alveolar recruitment. If the curve shifts to the right, it will indicate a risk of overdistension, prompting a reduction in PEEP. By this approach, the most suitable PEEP level for each patient will be determined, and ventilator settings will be optimized accordingly. These procedures are routinely applied in our hospital and globally and are among the most commonly preferred methods for PEEP titration.
All collected ventilation parameters will be input into three different artificial intelligence models: ChatGPT, DeepSeek, and Gemini. These systems will be asked to provide:
An optimal PEEP level recommendation for the patient The ability to detect abnormal ventilation parameters Three possible diagnostic suggestions Three proposed treatment approaches The data provided by the AI models will be recorded and compared with manual assessments performed by experienced ICU specialists. This comparison aims to determine the extent to which AI models can serve as clinical decision support tools. The reliability, accuracy, and clinical applicability of AI-generated recommendations will be thoroughly analyzed.
In the final phase of the study, the PEEP levels determined by AI models will be compared with those identified by ICU specialists with at least five years of experience. The following criteria will be evaluated:
Agreement between AI models and expert opinions on PEEP levels (primary outcome measure) Sensitivity and specificity of AI in detecting abnormal ventilation parameters Accuracy of AI-generated diagnoses Clinical applicability of AI-recommended treatment approaches
- Conditions
- PEEP, Occult
- Being tested
- AI-Assisted PEEP Optimization
- Medical subject headings
- Positive-Pressure Respiration, Intrinsic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Assisted PEEP OptimizationOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 7 day
Secondary outcomes (3)
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.
Measured: 7 days
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.
Measured: 7 days
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).
Measured: Within the first 24 hours of mechanical ventilation.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Feb 2025
- Study start1 Mar 2025
- Primary completion1 Feb 2026 (estimated)
- Study completion2 Feb 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PEEP, OccultStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 145 participants (estimated)
- Target duration
- 7 Days
Sponsor and investigators
- Lead sponsor
- Kanuni Sultan Suleyman Training and Research Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Engin Ihsan Turan, Kanuni Sultan Suleyman Training and Research Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06844916
- Sponsor's protocol ID
- Peep Titration
- Status last verified
- May 2025
- First submitted
- 20 Feb 2025
- Last updated
- 4 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 4 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of the Success of Artificial Intelligence Models in Determining the Optimal Positive End-Expiratory Pressure (PEEP) in Mechanical Ventilation in Intensive Care
- ClinicalTrials.gov ID
- NCT06844916
- Acronym / sponsor ID
- Peep Titration
- Conditions
- PEEP, Occult
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 145 participants (estimated)
- Lead sponsor
- Kanuni Sultan Suleyman Training and Research HospitalUniversity, hospital or other
- Study start
- 1 Mar 2025 (actual)
- Primary completion
- 1 Feb 2026 (estimated)
- Study completion
- 2 Feb 2026 (estimated)
- First posted
- 25 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Patients receiving mechanical ventilation support in the intensive care unit (ICU), who are hemodynamically stable and over 18 years old, will be considered eligible for the study. These criteria have been set to ensure that the patients require adequate respiratory support while being suitable for individualized ventilator management optimization.
During the patient selection process, the medical records of ICU patients will be reviewed, and those meeting the criteria will be included in the study. To ensure the accuracy of clinical data, the patient selection process will be carried out by intensive care specialists.
The mechanical ventilation parameters of the included patients will be systematically recorded. In this context, inspiratory hold and expiratory hold maneuvers will be applied to evaluate the respiratory mechanics of the patients. The recorded measurements will include compliance (lung elasticity), elastance (lung stiffness), auto-PEEP (presence of air trapping), and time constant (gas exchange duration).
In addition to these fundamental ventilation parameters, the pressure-volume (P-V) curve will be analyzed to collect critical data for ventilation optimization. These procedures will be performed using advanced analysis modules integrated into ICU ventilators and patient monitoring systems. The collected data will be analyzed both manually by experts and by artificial intelligence (AI) models throughout the study.
In the study, PEEP titration will be performed manually by intensive care specialists. This process will be applied immediately after intubation for intubated patients and will continue daily. The procedure will be standardized according to a predefined protocol and applied consistently across all patients.
The lower inflection point (LIP) will be identified, and PEEP will be applied at the point where alveolar recruitment begins. This step is critical to preventing lung collapse and ensuring optimal oxygenation. Additionally, the upper inflection point (UIP) will be determined to prevent overdistension, ensuring that tidal volume remains below this pressure threshold. If the lower inflection point is not clearly identified, a PEEP level of approximately 10 cmH₂O will be applied as a standard approach.
The pressure-volume curve of the ventilator will be analyzed to assess whether the optimal PEEP level has been achieved. If the curve shifts upward, it will be considered an indicator of successful alveolar recruitment. If the curve shifts to the right, it will indicate a risk of overdistension, prompting a reduction in PEEP. By this approach, the most suitable PEEP level for each patient will be determined, and ventilator settings will be optimized accordingly. These procedures are routinely applied in our hospital and globally and are among the most commonly preferred methods for PEEP titration.
All collected ventilation parameters will be input into three different artificial intelligence models: ChatGPT, DeepSeek, and Gemini. These systems will be asked to provide:
An optimal PEEP level recommendation for the patient The ability to detect abnormal ventilation parameters Three possible diagnostic suggestions Three proposed treatment approaches The data provided by the AI models will be recorded and compared with manual assessments performed by experienced ICU specialists. This comparison aims to determine the extent to which AI models can serve as clinical decision support tools. The reliability, accuracy, and clinical applicability of AI-generated recommendations will be thoroughly analyzed.
In the final phase of the study, the PEEP levels determined by AI models will be compared with those identified by ICU specialists with at least five years of experience. The following criteria will be evaluated:
Agreement between AI models and expert opinions on PEEP levels (primary outcome measure) Sensitivity and specificity of AI in detecting abnormal ventilation parameters Accuracy of AI-generated diagnoses Clinical applicability of AI-recommended treatment approaches
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Target follow-up
- 7 Days
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-Generated PEEP Group (Experimental Group) | – | Other: AI-Assisted PEEP OptimizationPatients 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. |
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Assisted PEEP OptimizationIn 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. | Other | AI-Generated PEEP Group (Experimental Group) |
Primary outcome
Agreement Between AI-Generated and Expert-Determined PEEP Levels
Time frame: 7 day
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
3 secondary outcomes
Sensitivity and Specificity of AI Models in Detecting Abnormal Ventilation Parameters
Time frame: 7 days
Accuracy of AI-Generated Diagnostic Predictions
Time frame: 7 days
Clinical Applicability of AI-Generated Treatment Recommendations
Time frame: Within the first 24 hours of mechanical ventilation.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jun 2025.
- Individual participant dataSharing planned: Undecided
Related trials for PEEP, Occult
Recruiting
Effect of Individualized Positive End-expiratory Pressure (PEEP) in Patients Who Have an Intrinsic PEEP During One-lung Ventilation
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Not yet recruitingObservational study
End-Expiratory Diaphragmatic Residual Contraction Index for Identifying Intrinsic PEEP and Assisting PEEP Titration: A Prospective Physiological Study
Speckle Tracking, Diaphragm
Active, not recruiting
Physiological Effects of External PEEP in Controlled Mechanical Ventiated Patients With autoPEEP
Obstructive Airway Diseases
Recruiting
PEEP vs. ZEEP in Out-of-Hospital-Cardiac-Arrest
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Istanbul, Turkey (Türkiye)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of PEEP, Occult, run by Kanuni Sultan Suleyman Training and Research Hospital at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 4 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement Between AI-Generated and Expert-Determined PEEP Levels (time frame: 7 day). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Jun 2025.