Critical Illness, Mechanical Ventilation, Respiratory Failure
Conditions
Keywords
Mechanical Ventilation, Respiratory Failure, Critical Illness, Artificial Intelligence, Chest X-Ray, Arterial Blood Gas
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.
Interventions
None listed
Sponsors
Zagazig University
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of Machine Learning Model in Predicting the Need for Mechanical Ventilation | Within 24 hours of patient presentation | 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. |
Countries
Egypt
Contacts
Primary ContactOmaima Ibrahim Prof
Outcome results
None listed