A Ventilator-associated Pneumonia Prediction Model in Patients With Acute Respiratory Distress Syndrome

Abstract Background Mechanical ventilation is crucial for acute respiratory distress syndrome (ARDS) patients and diagnosis of ventilator-associated pneumonia (VAP) in ARDS patients is challenging. Hence, an effective model to predict VAP in ARDS is urgently needed. Methods We performed a secondary analysis of patient-level data from the Early versus Delayed Enteral Nutrition (EDEN) of ARDSNet randomized controlled trials. Multivariate binary logistic regression analysis established a predictive model, incorporating characteristics selected by systematic review and univariate analyses. The model’s discrimination, calibration, and clinical usefulness were assessed using the C-index, calibration plot, and decision curve analysis (DCA). Results Of the 1000 unique patients enrolled in the EDEN trials, 70 (7%) had ARDS complicated with VAP. Mechanical ventilation duration and intensive care unit (ICU) stay were significantly longer in the VAP group than non-VAP group (P < .001 for both) but the 60-day mortality was comparable. Use of neuromuscular blocking agents, severe ARDS, admission for unscheduled surgery, and trauma as primary ARDS causes were independent risk factors for VAP. The area under the curve of the model was .744, and model fit was acceptable (Hosmer-Lemeshow P = .185). The calibration curve indicated that the model had proper discrimination and good calibration. DCA showed that the VAP prediction nomogram was clinically useful when an intervention was decided at a VAP probability threshold between 1% and 61%. Conclusions The prediction nomogram for VAP development in ARDS patients can be applied after ICU admission, using available variables. Potential clinical benefits of using this model deserve further assessment.