Respiratory FailureNCT07001696
Combining Chest X-Ray and Arterial Blood Gas Findings to Predict Need for Mechanical Ventilation in Critically Ill Patients
An observational study of Respiratory Failure and Critical Illness, run by Zagazig University at 1 site in Egypt.
Trial summary
This prospective cross-sectional study aims to develop and validate a machine learning model that combines chest X-ray findings with arterial blood gas (ABG) analysis to assess the necessity for mechanical ventilation in critically ill adult patients. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Respiratory Failure, Critical Illness, Mechanical Ventilation
- How many people
- 2,160 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Jun 2025to 30 Jan 2026
- 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
- Adult, Older adult
Study population: The study will enroll adult critically ill patients presenting to the Emergency Department and Intensive Care Units (ICUs) at Zagazig University Hospitals. Eligible patients include those evaluated for potential mechanical ventilation based on clinical judgment, arterial blood gas (ABG) results, and chest X-ray findings. A matched control group of critically ill patients who do not require mechanical ventilation will also be included. The study population will be diverse in terms of age, sex, and underlying diagnoses to ensure generalizability of the machine learning model.
Who may be able to join
- Critically ill adult patients aged 18 years or older.
- Patients assessed to require mechanical ventilation.
- Control group: Age- and sex-matched critically ill patients not requiring mechanical ventilation.
- Availability of both chest X-ray and arterial blood gas (ABG) analysis at the time of evaluation.
Who cannot join
- Patients with missing or incomplete data (e.g., absent chest X-ray or ABG results).
- Patients with chronic lung diseases unrelated to the current admission (e.g., COPD, pulmonary fibrosis).
- Pregnant females.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Egypt1 site, 1 recruiting
- Recruiting
Faculty of medicine, zagazig university
Zagazig, Al Sharqia, 44151
Prof, +201005288595, zuhmc_info@zu.edu.eg
About this study
Official title: Combining Chest X-Ray Findings With Arterial Blood Gas Analysis for Generation of Machine Learning Model Assessing the Need for Mechanical Ventilation in Critically Ill Patients
This prospective cross-sectional study aims to develop and validate a machine learning model that combines chest X-ray findings with arterial blood gas (ABG) analysis to assess the necessity for mechanical ventilation in critically ill adult patients. Conducted at Zagazig University Hospitals, the study seeks to improve clinical decision-making by integrating radiological and biochemical data using artificial intelligence. The model's predictive performance will be evaluated against standard clinical assessments.
Read the detailed description
The study is a prospective cross-sectional investigation conducted at Zagazig University Hospitals, aiming to develop a machine learning model that integrates chest X-ray findings and arterial blood gas (ABG) analysis to assess the necessity for mechanical ventilation in critically ill adult patients. While current clinical decision-making relies on separate interpretation of radiologic and biochemical data, this study proposes a novel model that synthesizes both sources of information using artificial intelligence to improve predictive accuracy and reduce subjectivity.
A total of approximately 2,160 patients will be enrolled over a 6-month period. Data collected will include demographic and clinical characteristics, ABG parameters (e.g., pH, PaO2, PaCO2, HCO3), and radiological features (e.g., infiltrates, effusions, consolidation). Patients will be categorized based on whether they require mechanical ventilation.
The machine learning model will be trained on 70% of the dataset and validated on the remaining 30%. Performance metrics such as accuracy, R-squared values, and root mean square error (RMSE) will be used to assess predictive capacity. The study will adhere to ethical guidelines and has obtained IRB approval from the Faculty of Medicine at Zagazig University (Approval No. 1138).
By combining imaging and laboratory data, this study seeks to deliver a practical decision-support tool that enhances the objectivity and efficiency of critical care management.
- Keywords
- Mechanical Ventilation, Respiratory Failure, Critical Illness, Artificial Intelligence, Chest X-Ray, Arterial Blood Gas
- Medical subject headings
- Respiratory Insufficiency, Critical Illness
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 |
|---|---|---|
| Group 1 - Patients Requiring Mechanical Ventilation | Critically ill adult patients who are clinically assessed to require mechanical ventilation. Data collected include chest X-ray findings and ABG parameters. | |
| Group 2 - Control Group (No Mechanical Ventilation Required) | Age- and sex-matched critically ill patients who do not require mechanical ventilation. Data collected similarly for model comparison. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of Machine Learning Model in Predicting the Need for Mechanical Ventilation
Comparison of the machine learning model's prediction with actual clinical decision regarding mechanical ventilation. Accuracy will be measured using sensitivity, specificity, area under the ROC curve (AUC), and confusion matrix.
Measured: Within 24 hours of patient presentation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Jun 2025
- Study start1 Jun 2025 (estimated)
- Primary completion31 Dec 2025 (estimated)
- Study completion30 Jan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Respiratory FailureStudies of the same condition, open and under way.
- More trials run by Zagazig UniversityThe sponsor's other registered studies.
- 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
- Other
- Time perspective
- Prospective
- Enrolment
- 2,160 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zagazig University
- Sponsor type
- Other government
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07001696
- Sponsor's protocol ID
- ZU-IRB 1138
- Status last verified
- May 2025
- First submitted
- 24 May 2025
- Last updated
- 3 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 3 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Combining Chest X-Ray Findings With Arterial Blood Gas Analysis for Generation of Machine Learning Model Assessing the Need for Mechanical Ventilation in Critically Ill Patients
- ClinicalTrials.gov ID
- NCT07001696
- Acronym / sponsor ID
- ZU-IRB 1138
- Keywords
- Mechanical Ventilation, Respiratory Failure, Critical Illness, Artificial Intelligence, Chest X-Ray, Arterial Blood Gas
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,160 participants (estimated)
- Lead sponsor
- Zagazig UniversityOther government
- Study start
- 1 Jun 2025 (estimated)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 30 Jan 2026 (estimated)
- First posted
- 3 Jun 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.
This prospective cross-sectional study aims to develop and validate a machine learning model that combines chest X-ray findings with arterial blood gas (ABG) analysis to assess the necessity for mechanical ventilation in critically ill adult patients. Conducted at Zagazig University Hospitals, the study seeks to improve clinical decision-making by integrating radiological and biochemical data using artificial intelligence. The model's predictive performance will be evaluated against standard clinical assessments.
Detailed description
The study is a prospective cross-sectional investigation conducted at Zagazig University Hospitals, aiming to develop a machine learning model that integrates chest X-ray findings and arterial blood gas (ABG) analysis to assess the necessity for mechanical ventilation in critically ill adult patients. While current clinical decision-making relies on separate interpretation of radiologic and biochemical data, this study proposes a novel model that synthesizes both sources of information using artificial intelligence to improve predictive accuracy and reduce subjectivity.
A total of approximately 2,160 patients will be enrolled over a 6-month period. Data collected will include demographic and clinical characteristics, ABG parameters (e.g., pH, PaO2, PaCO2, HCO3), and radiological features (e.g., infiltrates, effusions, consolidation). Patients will be categorized based on whether they require mechanical ventilation.
The machine learning model will be trained on 70% of the dataset and validated on the remaining 30%. Performance metrics such as accuracy, R-squared values, and root mean square error (RMSE) will be used to assess predictive capacity. The study will adhere to ethical guidelines and has obtained IRB approval from the Faculty of Medicine at Zagazig University (Approval No. 1138).
By combining imaging and laboratory data, this study seeks to deliver a practical decision-support tool that enhances the objectivity and efficiency of critical care management.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Group 1 - Patients Requiring Mechanical Ventilation | – | –Critically ill adult patients who are clinically assessed to require mechanical ventilation. Data collected include chest X-ray findings and ABG parameters. |
| Group 2 - Control Group (No Mechanical Ventilation Required) | – | –Age- and sex-matched critically ill patients who do not require mechanical ventilation. Data collected similarly for model comparison. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The study will enroll adult critically ill patients presenting to the Emergency Department and Intensive Care Units (ICUs) at Zagazig University Hospitals. Eligible patients include those evaluated for potential mechanical ventilation based on clinical judgment, arterial blood gas (ABG) results, and chest X-ray findings. A matched control group of critically ill patients who do not require mechanical ventilation will also be included. The study population will be diverse in terms of age, sex, and underlying diagnoses to ensure generalizability of the machine learning model.
Key inclusion criteria (4 listed)
- Critically ill adult patients aged 18 years or older.
- Patients assessed to require mechanical ventilation.
- Control group: Age- and sex-matched critically ill patients not requiring mechanical ventilation.
- Availability of both chest X-ray and arterial blood gas (ABG) analysis at the time of evaluation.
Key exclusion criteria (3 listed)
- Patients with missing or incomplete data (e.g., absent chest X-ray or ABG results).
- Patients with chronic lung diseases unrelated to the current admission (e.g., COPD, pulmonary fibrosis).
- Pregnant females.
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
Accuracy of Machine Learning Model in Predicting the Need for Mechanical Ventilation
Time frame: Within 24 hours of patient presentation
Comparison of the machine learning model's prediction with actual clinical decision regarding mechanical ventilation. Accuracy will be measured using sensitivity, specificity, area under the ROC curve (AUC), and confusion matrix.
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 3 Jun 2025.
- Individual participant dataSharing planned: Undecided
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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.
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 Respiratory Failure and Critical Illness, run by Zagazig University at 1 site in Egypt.
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 4 inclusion and 3 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 3 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 Egypt. 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?
Contact Omaima Ibrahim Prof (+201001664310, OIAbdelhamid@medicine.zu.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of Machine Learning Model in Predicting the Need for Mechanical Ventilation (time frame: Within 24 hours of patient presentation). 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 3 Jun 2025.