Using Artificial Intelligence Models for Predicting The Need for Intubation and Successful Weaning From Mechanical Ventilation in ICU Patients

NCT07065838 | Not Yet Recruiting

• 1 other identifier: AI and mechanical ventilation
• Study Type: observational
• Target: 600
• Locations: 0 countries
• Sites: N/A

Brief Summary

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).

Conditions

Respiratory Distress Syndrome (RDS)

Outcome Measures

Primary Outcomes (1)

• 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.

  2 years

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

All patients admired to Intensive care units of Assiut University Hospitals

You may qualify if:

• 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.

You may not qualify if:

• \- Patients with a pre-existing "Do Not Intubate" (DNI) order.
• \- Patients with irreversible neurological conditions affecting weaning

Sponsors & Collaborators

• Assiut University: lead

MeSH Terms

Conditions

Respiratory Distress Syndrome

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Respiration Disorders

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Doctor

Study Record Dates

• First Submitted: June 14, 2025
• First Posted: July 15, 2025
• Study Start: October 1, 2025
• Primary Completion (Estimated): November 1, 2027
• Study Completion (Estimated): November 1, 2027
• Last Updated: July 15, 2025

Record last verified: 2025-06