Combining Chest X-Ray and Arterial Blood Gas Findings to Predict Need for Mechanical Ventilation in Critically Ill Patients
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Respiratory Failure, Critical Illness, Mechanical Ventilation. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Egypt
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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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
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.
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.
Primary outcome measures
- Accuracy of Machine Learning Model in Predicting the Need for Mechanical Ventilation [Time frame: Within 24 hours of patient presentation]
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Other
Study locations
Egypt · 1 center
- Faculty of medicine, zagazig university — Zagazig
Identifiers
NCT: NCT07001696 · ZU-IRB 1138