Ventilator-Associated Pneumonia Prediction Models Based on AI: Scoping Review

Abstract

Background: Ventilator-associated pneumonia (VAP) is a serious complication of mechanical ventilation therapy that affects patient treatment and prognosis. Owing to its excellent data mining capabilities, artificial intelligence (AI) has been increasingly used to predict VAP. Objective: This article reviews the prediction models for VAP based on AI, providing a reference for the early identification of high-risk groups in future clinical practice. Methods: A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension guidelines. The Wanfang, Chinese BioMedical Literature Database, Cochrane Library, Web of Science, PubMed, MEDLINE, and Embase databases were searched to identify relevant articles. Study selection and data extraction were independently conducted by two reviewers. The data extracted from the included studies were synthesized narratively. Results: From 137 publications, 11 were included in the scoping review. The included studies reported the use of AI for predicting VAP. All of the 11 studies predicted VAP occurrence, and studies on VAP prognosis were excluded. Further, these studies used text data, and none of them involved imaging data. Public databases were used as the primary data choice for model building (6/11, 55 %), whereas the remaining studies had sample sizes smaller than 1000. Machine learning is the primary algorithm for studying the VAP prediction models. However, deep learning and large language models are not used to construct VAP prediction models. Random forest is the most commonly used algorithm (5/11, 45 %). All studies are internal validations, and none of them address how the model is used. Conclusions: This review presents an overview of studies based on AI used to predict and diagnose VAP. AI models have better predictive performance than traditional methods and are expected to provide an indispensable tool for the risk prediction of VAP in the future. However, the current research is in the stage of model construction and validation, and the implementation and guidance for the clinical prediction of VAP require further research.