Prediction models for the risk of ventilator-associated pneumonia in patients on mechanical ventilation: A systematic review and meta-analysis
Abstract
BACKGROUND
Identifying patients at risk for ventilator-associated pneumonia (VAP) through prediction models can facilitate medical decision-making. Our objective was to systematically evaluate the current models for VAP in patients with mechanical ventilation (MV).
METHODS
Nine databases systematically retrieved from establishment to March 6, 2024. Two independent reviewers performed study selection, data extraction, and quality assessment, respectively. The Prediction Model Risk of Bias Assessment Tool was used to evaluate the risk of model bias and applicability. Stata 17.0 was used for conducting a meta-analysis of discrimination of model validation.
RESULTS
The total of 34 studies were included, with reported 52 prediction models. More than 50% of the models were developed using logistic regression, and the AUCs of the included models ranged from 0.509 to 0.982. Predictors that appeared more frequently in the models were MV duration, length of ICU stay, age. Each study was essentially considered having an overall high risk of bias. A meta-analysis of 17 studies containing 33 models with validated and complete data was performed with a pooled AUC of 0.80 (95% CI: 0.78-0.83).
CONCLUSION
Despite the relatively excellent performance of the models, there is a high risk of bias of the model development process. Enhancing the methodological quality and revealing the details of study process, especially the external validation, practical application and optimization of the…
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