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Generalisability of predictive models for Clostridioides difficile infection, severity, and recurrence at an urban safety-net hospital
Journal article   Open access   Peer reviewed

Generalisability of predictive models for Clostridioides difficile infection, severity, and recurrence at an urban safety-net hospital

Nicole Rafalko, Jessica L Webster, Gunnar Jacob, Michele Kutzler and Neal D Goldstein
The Journal of hospital infection, v 146, pp 10-20
12 Jan 2024
DOI: https://doi.org/10.1016/j.jhin.2024.01.001
PMID: 38219834
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
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https://doi.org/10.1016/j.jhin.2024.01.001View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2024CC BY V4.0,  Open

Abstract

catchment Clostridioides difficile prediction modelling urban safety-net hospital electronic health record external validity
Predictive models for Clostridioides difficile (C. difficile) infection can identify high risk patients and aid clinicians in preventing infection. Issues of generalisability regarding current predictive models have been acknowledged but to our knowledge have never been quantified. Through case-control sampling from an urban safety-net hospital, we created C. difficile infection, severity, and recurrence predictive models using multivariate logistic regression. Models were validated using 5-fold cross-validation and inverse probability weights (IPW) based on two different catchment area definitions were used to improve external validity. Akaike Information Criterion (AIC), area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity with bootstrapped confidence intervals were used to assess and compare model fit and performance. Changes in performance before and after weighting were small across all models; although, differences were more apparent after weighting the recurrence model (AUROCs of 0.78, 0.76, 0.71 for the unweighted and two weighted models, respectively). Overall, the infection model performed the best (AUROC=0.82 [95%CI: 0.78, 0.85]) followed by the recurrence model (AUROC=0.78 [95%CI: 0.69, 0.86]) and then the severity model (AUROC=0.70 [95%CI: 0.63, 0.78]. The performance of our models after weighting did not change drastically suggesting that our model predicting C. difficile infection, severity, and recurrence may not be impacted by patient selection factors; however, other researchers may want to consider addressing these catchment forces using IPW.

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Web of Science research areas
Infectious Diseases
Public, Environmental & Occupational Health
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