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

Overview

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.

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