Combining Chest X-Ray and Arterial Blood Gas Findings to Predict Need for Mechanical Ventilation in Critically Ill Patients

NCT07001696 | Recruiting

• 1 other identifier: ZU-IRB 1138
• Study Type: observational
• Target: 2,160
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Respiratory Failure; Critical Illness; Mechanical Ventilation

Keywords

Mechanical Ventilation; Respiratory Failure; Critical Illness; Artificial Intelligence; Chest X-Ray; Arterial Blood Gas

Outcome Measures

Primary Outcomes (1)

• 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.

  Within 24 hours of patient presentation

Study Arms (2)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Zagazig University: lead

Study Sites (1)

Faculty of medicine, zagazig university

Zagazig, Al Sharqia, 44151, Egypt

RECRUITING

MeSH Terms

Conditions

Respiratory Insufficiency; Critical Illness

Condition Hierarchy (Ancestors)

Respiration Disorders; Respiratory Tract Diseases; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Omaima Ibrahim Prof

CONTACT

+201001664310; OIAbdelhamid@medicine.zu.edu.eg

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 24, 2025
• First Posted: June 3, 2025
• Study Start: June 1, 2025
• Primary Completion: December 31, 2025
• Study Completion: January 30, 2026
• Last Updated: June 3, 2025

Record last verified: 2025-05

Locations

EG (1)