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Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study

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  • Jens Kjølseth Møller
  • Martin Sørensen
  • Christian Hardahl

Abstract

Background: Healthcare associated infections (HAI) are a major burden for the healthcare system and associated with prolonged hospital stay, increased morbidity, mortality and costs. Healthcare associated urinary tract infections (HA-UTI) accounts for about 20–30% of all HAI’s, and with the emergence of multi-resistant urinary tract pathogens, the total burden of HA-UTI will most likely increase. Objective: The aim of the current study was to develop two predictive models, using data from the index admission as well as historic data on a patient, to predict the development of UTI at the time of entry to the hospital and after 48 hours of admission (HA-UTI). The ultimate goal is to predict the individual patient risk of acquiring HA-UTI before it occurs so that health care professionals may take proper actions to prevent it. Methods: Retrospective cohort analysis of approx. 300 000 adult admissions in a Danish region was performed. We developed models for UTI prediction with five machine-learning algorithms using demographic information, laboratory results, data on antibiotic treatment, past medical history (ICD10 codes), and clinical data by transformation of unstructured narrative text in Electronic Medical Records to structured data by Natural Language Processing. Results: The five machine-learning algorithms have been evaluated by the performance measures average squared error, cumulative lift, and area under the curve (ROC-index). The algorithms had an area under the curve (ROC-index) ranging from 0.82 to 0.84 for the entry model (T = 0 hours after admission) and from 0.71 to 0.77 for the HA-UTI model (T = 48 hours after admission). Conclusion: The study is proof of concept that it is possible to create machine-learning models that can serve as early warning systems to predict patients at risk of acquiring urinary tract infections during admission. The entry model and the HA-UTI models perform with a high ROC-index indicating a sufficient sensitivity and specificity, which may make both models instrumental in individualized prevention of UTI in hospitalized patients. The favored machine-learning methodology is Decision Trees to ensure the most transparent results and to increase clinical understanding and implementation of the models.

Suggested Citation

  • Jens Kjølseth Møller & Martin Sørensen & Christian Hardahl, 2021. "Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.
  • Handle: RePEc:plo:pone00:0248636
    DOI: 10.1371/journal.pone.0248636
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    References listed on IDEAS

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    1. Li Luo & Jialing Li & Chuang Liu & Wenwu Shen, 2019. "Using machine‐learning methods to support health‐care professionals in making admission decisions," International Journal of Health Planning and Management, Wiley Blackwell, vol. 34(2), pages 1236-1246, April.
    2. R Andrew Taylor & Christopher L Moore & Kei-Hoi Cheung & Cynthia Brandt, 2018. "Predicting urinary tract infections in the emergency department with machine learning," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-15, March.
    3. Kristin M Corey & Sehj Kashyap & Elizabeth Lorenzi & Sandhya A Lagoo-Deenadayalan & Katherine Heller & Krista Whalen & Suresh Balu & Mitchell T Heflin & Shelley R McDonald & Madhav Swaminathan & Mark , 2018. "Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-19, November.
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