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

Alexandre R Marra; Mohammed Alzunitan; Oluchi Abosi; Michael B Edmond; W Nick Street; John W Cromwell; Jorge L Salinas

doi:10.1016/j.diagmicrobio.2020.115104

Back

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

Journal article

Peer reviewed

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

Alexandre R Marra, Mohammed Alzunitan, Oluchi Abosi, Michael B Edmond, W Nick Street, John W Cromwell and Jorge L Salinas

Diagnostic microbiology and infectious disease, Vol.98(2), pp.115104-115104

10/2020

DOI: https://doi.org/10.1016/j.diagmicrobio.2020.115104

PMID: 32650284

Abstract

Aged

Anti-Bacterial Agents - therapeutic use

Area Under Curve

Bayes Theorem

Clostridioides difficile

Clostridium Infections - diagnosis

Diarrhea - microbiology

Female

Forecasting - methods

Humans

Logistic Models

Machine Learning

Male

Middle Aged

Retrospective Studies

ROC Curve

Tertiary Care Centers

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 < 0.01). We tested 10 ML methods with and without resampling. Logistic regression, random forest and naïve Bayes models yielded the highest AUC ROC performance: 0.6. Predicting CDI was difficult in our cohort of patients tested for CDI. Multiple ML models yielded only modest results in a real-world population of hospitalized patients tested for CDI.

Metrics

1 Record Views

See more details

Details

Title: Modest Clostridiodes difficile infection prediction using machine learning models in a tertiary care hospital
Creators - without role: Alexandre R Marra - University of Iowa Hospitals and Clinics
Mohammed Alzunitan - University of Iowa
Oluchi Abosi - University of Iowa Hospitals and Clinics
Michael B Edmond - University of Iowa
W Nick Street - University of Iowa
John W Cromwell - University of Iowa Hospitals and Clinics
Jorge L Salinas - University of Iowa
Publication Details: Diagnostic microbiology and infectious disease, Vol.98(2), pp.115104-115104
Identifiers: 9920669308331
Academic Unit: King Abdulaziz University
Language: English
Resource Type: Journal article