Modest Clostridiodes difficile infection prediction using machine learning models in a tertiary care hospital

Previous studies have shown promising results of machine learning (ML) models for predicting health outcomes. We develop and test ML models for predicting Clostridioides difficile infection (CDI) in hospitalized patients. This is a retrospective cohort study conducted during 2015–2017. All inpatient...

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Bibliographic Details
Published in:Diagnostic microbiology and infectious disease 2020-10, Vol.98 (2), p.115104-115104, Article 115104
Main Authors: Marra, Alexandre R., Alzunitan, Mohammed, Abosi, Oluchi, Edmond, Michael B., Street, W. Nick, Cromwell, John W., Salinas, Jorge L.
Format: Article
Language:English
Subjects:
Aged
Anti-Bacterial Agents - therapeutic use
Area Under Curve
Bayes Theorem
Clostridioides difficile
Clostridium Infections - diagnosis
Diarrhea
Diarrhea - microbiology
Female
Forecasting - methods
Humans
Infection control
Logistic Models
Machine Learning
Male
Middle Aged
Retrospective Studies
ROC Curve
Tertiary Care Centers
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Summary:Previous studies have shown promising results of machine learning (ML) models for predicting health outcomes. We develop and test ML models for predicting Clostridioides difficile infection (CDI) in hospitalized patients. This is a retrospective cohort study conducted during 2015–2017. All inpatients tested for C. difficile were included. CDI was defined as having a positive glutamate dehydrogenase and toxin results. We restricted analyses to the first record of C. difficile testing per patient. Of 3514 patients tested, 136 (4%) had CDI. Age and antibiotic use within 90 days before C. difficile testing were associated with CDI (P 
ISSN:0732-8893
1879-0070
DOI:10.1016/j.diagmicrobio.2020.115104