Using artificial intelligence (AI) approach to build a model to determine the optimal timing of intubation and optimal timing of weaning from MV for ICU patients, and compare outcomes with these which depend on clinicians dicision.
And also assess whether AI-assisted decisions improve patient outcomes (e.g., reduced intubation delays, shorter ventilation duration).
Adult patients (≥18 years) in ICUs at risk of respiratory failure.
Adults (≥18 years) receiving mechanical ventilation for >24 hours and deemed ready for weaning.
- Patients with a pre-existing "Do Not Intubate" (DNI) order.
- Patients with irreversible neurological conditions affecting weaning
All patients admired to Intensive care units of Assiut University Hospitals
Cohort
Prospective
Not listed
Perfect time to intubate and to Wean
1. Intubation Prediction: AI models incorporating time-series analysis of respiratory rate variability, oxygen saturation trends, and evolving PaO2/FiO2 ratios can predict impending respiratory failure with greater accuracy than conventional clinical assessment. Early identification of high-risk patients may reduce emergency intubations and their associated complications. 2. Weaning Decision Support: Machine learning algorithms analyzing integrated parameters (RSBI, MIP/NIF, compliance trends) during spontaneous breathing trials demonstrate superior prediction of extubation success compared to traditional weaning indices. Recent studies show AI-guided protocols can reduce ventilator days by 1.8 days (95% CI 0.5-3.1) without increasing reintubation rates. These systems function as cognitive aids, processing multidimensional data at a scale beyond human capacity while preserving clinician judgment.
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