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