Construction and Validation of a Risk Prediction Model for Ventilator-Associated Pneumonia in Mechanically Ventilated...

Area of Science:

• Pediatric Critical Care Medicine
• Infectious Disease Epidemiology
• Nursing Science

Background:

• Mechanical ventilation (MV) is crucial for critically ill pediatric patients but carries risks.
• Ventilator-associated pneumonia (VAP) is a significant complication in mechanically ventilated children.
• Identifying risk factors and developing predictive models are essential for VAP prevention.

Purpose of the Study:

• To identify independent risk factors for VAP in pediatric patients receiving mechanical ventilation.
• To develop and validate a predictive model for VAP risk in this population.
• To guide precision nursing interventions for VAP prevention.

Main Methods:

• Retrospective analysis of pediatric patients undergoing MV in a Pediatric Intensive Care Unit (PICU).
• Binary logistic regression to identify independent VAP risk factors.
• Development and validation of a risk prediction model using R software.

Main Results:

• Absence of early enteral nutrition, duration of MV, endotracheal suctioning frequency, central venous catheterization, and antibiotic type were significant VAP risk factors (p < 0.05).
• The prediction model showed strong discriminative ability with ROC-AUCs of 0.870 (derivation) and 0.761 (validation).
• The model was well-calibrated in both cohorts (Hosmer-Lemeshow tests, p > 0.05).

Conclusions:

• A validated risk prediction model effectively stratifies VAP risk in mechanically ventilated children.
• Early identification of high-risk patients enables tailored nursing strategies.
• Targeting modifiable risk factors can potentially reduce VAP incidence in pediatric patients.