Abstract
ABSTRACT
Background and Aims
Despite widespread Infection Prevention and Control (IPC) measures, Healthcare‐Associated Infections (HAIs) remain a major concern in Intensive Care Units (ICUs), leading to increased mortality, prolonged hospital stays, and additional healthcare costs. Prognostic prediction models support identification of patients at high infection risk, and Machine Learning and Artificial Intelligence (ML/AI) methods offer the potential to improve such predictions. However, no systematic review has specifically examined ML/AI‐based prognostic models for HAI prediction in adult ICU populations. This review aims to identify ML/AI‐based prognostic models for estimating the HAI risk in adult ICU‐patients, investigate their methodological characteristics, risk of bias and reporting quality.
Methods
A systematic search will be conducted in PubMed, Scopus, and Web of Science to identify studies developing and/or validating ML/AI‐based prognostic models of HAI prediction in adult ICU‐admitted patients. Any study developing or evaluating supervised ML/AI‐based models for the prediction of any HAI occurring after ICU admission will be included. Three reviewers will independently screen articles, extract data, and assess risk of bias using the Risk Of Bias ASsessment Tool for prediction models using Artificial Intelligence (PROBAST + AI). Extracted information will include study characteristics, patients and setting, predictors, sample size, missing data, analysis methods, performance, and model presentation and interpretation. A narrative synthesis will be conducted for all included studies. Finally, the SPIN‐Prediction Models (SPIN‐PM) framework will be used to assess quality of reporting. This systematic review protocol was registered on the Open Science Framework (osf. io/bzqfp).
Conclusion
This systematic review's findings on methodological aspects, risk of bias, and applicability of ML/AI‐based prognostic models for HAI prediction in adult ICU patients will help inform researchers, clinicians, and IPC decision‐makers about these models' reliability and readiness for clinical use.