Deep LearningNCT06751693
Development of a Scoring and Prediction Model for Weaning Success in ARDS Patients Using Ventilation Parameters Combined with Artificial Intelligence and Deep Learning Techniques
An observational study of Deep Learning and Artificial Intelegence, run by Bakirkoy Dr. Sadi Konuk Research and Training Hospital at 1 site in Turkey (Türkiye).
Trial summary
This study aims to develop an AI-supported scoring model to optimize the weaning processes of ARDS patients from mechanical ventilation. Retrospective analysis will be conducted on the data of 25,000 patients, focusing on ventilator parameters and hemodynamic variables. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Deep Learning, Artificial Intelegence, ARDS (Acute Respiratory Distress Syndrome)
- How many people
- 25,000 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 10 Dec 2024to 24 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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: Intubated patients which diagnosed with ARDS and followed up in the ICU for more than 48 hours.
Who may be able to join
- ARDS diagnosis
- Aged 18 years and older
- Intubated and followed by Mechanical ventilation
- Admission on Intensive care unit
- Complete data on clinical support and desicion system
Who cannot join
- Missing data
- Under 18 years of age
- Followed by non-ARDS conditions
- Terminal status
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Bakirkoy Dr Sadi Konuk Research and Training Hospital
Istanbul
About this study
This study aims to develop an AI-supported scoring model to optimize the weaning processes of ARDS patients from mechanical ventilation. Retrospective analysis will be conducted on the data of 25,000 patients, focusing on ventilator parameters and hemodynamic variables. The model will be designed to contribute to clinical decision support systems.
Read the detailed description
The aim of this study is to develop an artificial intelligence and deep learning-supported scoring system using ventilator parameters obtained during the mechanical ventilation process in patients diagnosed with ARDS. This system seeks to predict and optimize the weaning process, facilitating successful liberation from mechanical ventilation.
In this context, our study will analyze data from 25,000 patients obtained from the Metavision system. From this data pool, ARDS patients will be filtered and divided into two groups: those successfully weaned from mechanical ventilation (weaned) and those who were not (non-weaned). The ventilator parameters of both groups, including oxygenation indices, driving pressure, and total mechanical power, will be examined in detail.
The collected data will be analyzed using artificial intelligence and deep learning algorithms to develop a scoring system capable of predicting patients' weaning processes. This system is designed to guide clinicians in patient management and enhance the success of weaning procedures.
The results of this study aim to contribute to more efficient and safer management of the weaning process for ARDS patients. Furthermore, the implementation of AI-supported scoring systems in intensive care units is expected to promote widespread adoption and improve the quality of patient care.
- Keywords
- Deep Learning
- Medical subject headings
- Acute Lung Injury
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Weaned | Those successfully weaned from mechanical ventilation | |
| Non-weaned | Those who not weaned from mechanical ventilation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Successful Weaning
The primary outcome of this study will be the successful weaning from mechanical ventilation.
Measured: 48 hours
Secondary outcomes (1)
Mechanical Ventilatory Parameters
Determining the impact of mechanical power on patient outcomes.
Measured: 48 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Dec 2024
- Study start10 Dec 2024
- Primary completion24 Dec 2024
- Study completion24 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Deep LearningStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 25,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Bakirkoy Dr. Sadi Konuk Research and Training Hospital
- Sponsor type
- Other government
- Responsible party
- Sponsor
- Investigators
- Zafer Cukurova, M.D, Study chair, Bakirkoy Dr. Sadi Konuk Research and Training Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06751693
- Sponsor's protocol ID
- 2024-12-07
- Status last verified
- Dec 2024
- First submitted
- 11 Dec 2024
- Last updated
- 30 Dec 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Scoring and Prediction Model for Weaning Success in ARDS Patients Using Ventilation Parameters Combined with Artificial Intelligence and Deep Learning Techniques
- ClinicalTrials.gov ID
- NCT06751693
- Acronym / sponsor ID
- 2024-12-07
- Keywords
- Deep Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 25,000 participants (actual)
- Lead sponsor
- Bakirkoy Dr. Sadi Konuk Research and Training HospitalOther government
- Study start
- 10 Dec 2024 (actual)
- Primary completion
- 24 Dec 2024 (actual)
- Study completion
- 24 Dec 2024 (actual)
- First posted
- 30 Dec 2024 (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.
This study aims to develop an AI-supported scoring model to optimize the weaning processes of ARDS patients from mechanical ventilation. Retrospective analysis will be conducted on the data of 25,000 patients, focusing on ventilator parameters and hemodynamic variables. The model will be designed to contribute to clinical decision support systems.
Detailed description
The aim of this study is to develop an artificial intelligence and deep learning-supported scoring system using ventilator parameters obtained during the mechanical ventilation process in patients diagnosed with ARDS. This system seeks to predict and optimize the weaning process, facilitating successful liberation from mechanical ventilation.
In this context, our study will analyze data from 25,000 patients obtained from the Metavision system. From this data pool, ARDS patients will be filtered and divided into two groups: those successfully weaned from mechanical ventilation (weaned) and those who were not (non-weaned). The ventilator parameters of both groups, including oxygenation indices, driving pressure, and total mechanical power, will be examined in detail.
The collected data will be analyzed using artificial intelligence and deep learning algorithms to develop a scoring system capable of predicting patients' weaning processes. This system is designed to guide clinicians in patient management and enhance the success of weaning procedures.
The results of this study aim to contribute to more efficient and safer management of the weaning process for ARDS patients. Furthermore, the implementation of AI-supported scoring systems in intensive care units is expected to promote widespread adoption and improve the quality of patient care.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Weaned | – | –Those successfully weaned from mechanical ventilation |
| Non-weaned | – | –Those who not weaned from mechanical ventilation |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Intubated patients which diagnosed with ARDS and followed up in the ICU for more than 48 hours.
Key inclusion criteria (5 listed)
- ARDS diagnosis
- Aged 18 years and older
- Intubated and followed by Mechanical ventilation
- Admission on Intensive care unit
- Complete data on clinical support and desicion system
Key exclusion criteria (4 listed)
- Missing data
- Under 18 years of age
- Followed by non-ARDS conditions
- Terminal status
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Successful Weaning
Time frame: 48 hours
The primary outcome of this study will be the successful weaning from mechanical ventilation.
1 secondary outcome
Mechanical Ventilatory Parameters
Time frame: 48 hours
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Dec 2024.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Zafer Cukurova, M.D study chair
Bakirkoy Dr. Sadi Konuk Research and Training Hospital
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 Deep Learning and Artificial Intelegence, run by Bakirkoy Dr. Sadi Konuk Research and Training 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: finished, as of the sponsor's last update on 30 Dec 2024. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Successful Weaning (time frame: 48 hours). 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 30 Dec 2024; AzTrials last refreshed its copy on 6 Nov 2026.