Clinical trial NCT07001696: Combining Chest X-Ray and Arterial Blood Gas Findings to Predict Need for Mechanical Ventilation in Critically Ill Patients
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
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Respiratory Failure
Critical Illness
Mechanical Ventilation
Keywords: Mechanical Ventilation
Respiratory Failure
Critical Illness
Artificial Intelligence
Chest X-Ray
Arterial Blood Gas
Lead sponsor: Zagazig University
Sponsor class: OTHER_GOV
First posted: 2025-06-03
Enrollment: 2160
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: OTHER
Time perspective: PROSPECTIVE
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.

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.

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.

Primary outcomes:
measure: Accuracy of Machine Learning Model in Predicting the Need for Mechanical Ventilation
timeFrame: Within 24 hours of patient presentation
Secondary outcomes:
Other outcomes:
Locations:
city: Zagazig
country: Egypt
facility: Faculty of medicine, zagazig university
state: Al Sharqia
Study officials:
References: