Prediction models for the risk of ventilator-associated pneumonia in patients on mechanical ventilation: A systematic review and meta-analysis.

Autor: Li J; School of Nursing, Ningxia Medical University, Yinchuan, Ningxia, China., Li G; Department of Critical Care Medicine, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China. Electronic address: lgf3446@163.com., Liu Z; School of Nursing, Ningxia Medical University, Yinchuan, Ningxia, China., Yang X; School of Nursing, Ningxia Medical University, Yinchuan, Ningxia, China., Yang Q; School of Nursing, Ningxia Medical University, Yinchuan, Ningxia, China.
Jazyk: angličtina
Zdroj: American journal of infection control [Am J Infect Control] 2024 Dec; Vol. 52 (12), pp. 1438-1451. Date of Electronic Publication: 2024 Jul 25.
DOI: 10.1016/j.ajic.2024.07.006
Abstrakt: Background: Identifying patients at risk of ventilator-associated pneumonia through prediction models can facilitate medical decision-making. Our objective was to evaluate the current models for ventilator-associated pneumonia in patients with mechanical ventilation.
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 to conduct a meta-analysis of discrimination of model validation.
Results: The total of 34 studies were included, with reported 52 prediction models. The most frequent predictors in the models were mechanical ventilation duration, length of intensive care unit stay, and age. Each study was essentially considered having a high risk of bias. A meta-analysis of 17 studies containing 33 models with validation was performed with a pooled area under the receiver-operating curve of 0.80 (95% confidence interval: 0.78-0.83).
Conclusions: Despite the relatively excellent performance of the models, there is a high risk of bias of the model development process. Enhancing the methodological quality, especially the external validation, practical application, and optimization of the models need urgent attention.
(Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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