AI for Healthcare-Associated Infection prediction in adult ICUs: prognostic models systematic review
Abstract
Background: Identifying patients at high risk of Healthcare-Associated Infections (HAIs) is still a major challenge in Intensive Care Units (ICUs). Prognostic prediction models based on Machine Learning and Artificial Intelligence (ML/AI) methods can enhance individual-patient HAI risk estimation and enable preventive interventions. This systematic review (protocol:osf.io/bzqfp) aims to identify ML/AI-based prognostic models for estimating the HAI risk in adult ICU-patients and investigate their methodological characteristics.
Methods: PubMed, Scopus, and Web of Science were searched to identify studies developing and/or evaluating ML/AI prognostic models of HAI prediction in adult ICUs. Data on study characteristics, data sources, HAI types, setting, and performance assessment were extracted and synthesized narratively.
Results: Out of 3930 unique screened records, 33 studies (2013-2025) were included, primarily from China (60.6%), USA (15.2%), and Europe (15.2%). Data sources were approximately half public repositories (e.g., MIMIC-III/IV, eICU) (45.5%), and half primary medical records, surveillance or cohort study data (54.5%), mostly from general/mixed ICUs (81.8%). Bloodstream infections (BSIs) (54.4%) and ventilator-associated/hospital-acquired pneumonias (21.2%) were the most common HAI outcomes. Most studies aimed to only develop ML/AI models (72.7%), and only 27.3% performed external evaluation. On average 5 models per study were developed (range: 1-12), with tree-based methods being the most frequently used (66.7% Random Forests and 63.6% XGBoost). All studies reported model discrimination assessment (Area Under the Curve), but only 12.1% assessed calibration.
Conclusions: Different methodological gaps emerged among ML/AI-based models for HAI prediction in the ICU, that indicate potential overoptimism in model performance, such as the widespread use of a few public datasets, limited external evaluation, and a lack of model calibration assessment.
Key messages: • Despite growing interest in ML/AI-based methods for HAI prediction in the ICU, methodological gaps could limit the applicability of these models in clinical settings.
• Future research should shift focus from model proliferation to rigorous external evaluation and appropriate assessment of model reliability across diverse clinical environments.
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BibTeX
@article{sciurti2026healthcareassociated,
title = {{AI for Healthcare-Associated Infection prediction in adult ICUs: prognostic models systematic review}},
author = {A Sciurti and G Migliara and LM Siena and G Bonanno and J Iera and C Marzuillo and P Villari and V Baccolini},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.136},
url = {https://doi.org/10.1093/eurpub/ckag169.136},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - AI for Healthcare-Associated Infection prediction in adult ICUs: prognostic models systematic review
AU - A Sciurti
AU - G Migliara
AU - LM Siena
AU - G Bonanno
AU - J Iera
AU - C Marzuillo
AU - P Villari
AU - V Baccolini
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.136
UR - https://doi.org/10.1093/eurpub/ckag169.136
AB - Background: Identifying patients at high risk of Healthcare-Associated Infections (HAIs) is still a major challenge in Intensive Care Units (ICUs). Prognostic prediction models based on Machine Learning and Artificial Intelligence (ML/AI) methods can enhance individual-patient HAI risk estimation and enable preventive interventions. This systematic review (protocol:osf.io/bzqfp) aims to identify ML/AI-based prognostic models for estimating the HAI risk in adult ICU-patients and investigate their methodological characteristics. Methods: PubMed, Scopus, and Web of Science were searched to identify studies developing and/or evaluating ML/AI prognostic models of HAI prediction in adult ICUs. Data on study characteristics, data sources, HAI types, setting, and performance assessment were extracted and synthesized narratively. Results: Out of 3930 unique screened records, 33 studies (2013-2025) were included, primarily from China (60.6%), USA (15.2%), and Europe (15.2%). Data sources were approximately half public repositories (e.g., MIMIC-III/IV, eICU) (45.5%), and half primary medical records, surveillance or cohort study data (54.5%), mostly from general/mixed ICUs (81.8%). Bloodstream infections (BSIs) (54.4%) and ventilator-associated/hospital-acquired pneumonias (21.2%) were the most common HAI outcomes. Most studies aimed to only develop ML/AI models (72.7%), and only 27.3% performed external evaluation. On average 5 models per study were developed (range: 1-12), with tree-based methods being the most frequently used (66.7% Random Forests and 63.6% XGBoost). All studies reported model discrimination assessment (Area Under the Curve), but only 12.1% assessed calibration. Conclusions: Different methodological gaps emerged among ML/AI-based models for HAI prediction in the ICU, that indicate potential overoptimism in model performance, such as the widespread use of a few public datasets, limited external evaluation, and a lack of model calibration assessment. Key messages: • Despite growing interest in ML/AI-based methods for HAI prediction in the ICU, methodological gaps could limit the applicability of these models in clinical settings. • Future research should shift focus from model proliferation to rigorous external evaluation and appropriate assessment of model reliability across diverse clinical environments.
SN - 1101-1262
SN - 1464-360X
ER -