Real-Time Predictive Analysis of ICU Ventilator Weaning Failure: A Prospective Validation Study

Area of Science:

• Critical Care Medicine
• Respiratory Therapy
• Medical Informatics

Background:

• Ventilator weaning failure is a significant challenge in ICUs, leading to increased morbidity and mortality.
• Predicting weaning outcomes is crucial for optimizing patient management and resource allocation.
• Existing prediction methods may not fully capture the complexity of patient-specific factors.

Purpose of the Study:

• To develop and validate a nomogram model for predicting ICU ventilator weaning failure.
• To identify independent clinical indicators associated with weaning failure.
• To provide a tool for personalized weaning strategy formulation.

Main Methods:

• A cohort of 485 ICU patients requiring mechanical ventilation weaning was analyzed.
• Patients were divided into training (n=340) and validation (n=145) sets.
• Multivariate logistic regression identified independent risk factors; a nomogram was constructed and validated using C-index, AUC, and calibration plots.

Main Results:

• The weaning failure rate was approximately 29% in both training and validation sets.
• Independent predictors of weaning failure included age, APACHE II score, ventilation duration, spontaneous breathing frequency, GCS score, and sedative use.
• The nomogram demonstrated strong predictive performance with a C-index of 0.829 (training) and 0.826 (validation), and good calibration.

Conclusions:

• The developed nomogram effectively predicts the risk of ICU ventilator weaning failure.
• The model integrates multidimensional clinical indicators for improved risk stratification.
• This tool can guide the development of individualized mechanical ventilation weaning plans.