Skip to main content

ORIGINAL RESEARCH article

Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1444176
This article is part of the Research Topic Outbreak Investigations of Nosocomial Infections View all 3 articles

Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units

Provisionally accepted
Jin Wang Jin Wang 1Gan Wang Gan Wang 2,3*Yujie Wang Yujie Wang 4Yun Wang Yun Wang 5
  • 1 Department of Healthcare-associated Infection Management, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, China
  • 2 Shanghai Institute of Infectious Diseases and Biosecurity, Fudan University, Shanghai, China
  • 3 School of Public Health, Fudan University, Shanghai, Shanghai Municipality, China
  • 4 Department of Clinical Laboratory, Qingdao Municipal Hospital,University of Health and Rehabilitation Sciences, Qingdao, China
  • 5 Emergency Intensive Care Unit, Qingdao Municipal Hospital,University of Health and Rehabilitation Sciences, Qingdao, China

The final, formatted version of the article will be published soon.

    This retrospective study used 10 machine learning algorithms to predict the risk of healthcareassociated infections (HAIs) in patients admitted to intensive care units (ICUs). A total of 2517 patients treated in the ICU of a tertiary hospital in China from January 2019 to December 2023 were included, of whom 455 (18.1%) developed an HAI. Data on 32 potential risk factors for infection were considered, of which 18 factors that were statistically significant on single-factor analysis were used to develop a machine learning prediction model using the synthetic minority oversampling technique (SMOTE). The main HAIs were respiratory tract infections (28.7%) and ventilatorassociated pneumonia (25.0%), and were predominantly caused by gram-negative bacteria (78.8%).The CatBoost model showed good predictive performance (area under the curve: 0.944, and sensitivity 0.872). The 10 most important predictors of HAIs in this model were the Penetration Aspiration Scale score, Braden score, high total bilirubin level, female, high white blood cell count, Caprini Risk Score, Nutritional Risk Screening 2002 score, low eosinophil count, medium white blood cell count, and the Glasgow Coma Scale score. The CatBoost model accurately predicted the occurrence of HAIs and could be used in clinical practice.

    Keywords: machine learning, prediction, Risk factors, Intensive Care, Healthcare-associated infections

    Received: 05 Jun 2024; Accepted: 28 Aug 2024.

    Copyright: © 2024 Wang, Wang, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Gan Wang, Shanghai Institute of Infectious Diseases and Biosecurity, Fudan University, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.