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

NCT ID: NCT07001696

Last Updated: 2025-06-03

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 2160 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-06-01
• Study Completion Date: 2026-01-30

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.

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.

Conditions

Respiratory Failure; Critical Illness; Mechanical Ventilation

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Study Groups

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.

No interventions assigned to this group

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.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

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.

Exclusion Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Zagazig University

OTHER_GOV

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Faculty of medicine, zagazig university

Zagazig, Al Sharqia, Egypt

Site Status: RECRUITING

Countries

Egypt

Central Contacts

Omaima Ibrahim Prof

Role: CONTACT

OIAbdelhamid@medicine.zu.edu.eg

+201001664310

Facility Contacts

Prof

Role: primary

zuhmc_info@zu.edu.eg

+201005288595

Other Identifiers

ZU-IRB 1138

Identifier Type: -

Identifier Source: org_study_id