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Development of a Larval Zebrafish Infection Model for Clostridioides difficile
Published on: February 14, 2020
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Development and validation of a recurrent Clostridium difficile risk-prediction model.
Marya D Zilberberg1, Kimberly Reske, Margaret Olsen
1EviMed Research Group, LLC, Goshen, Massachusetts; School of Public Health and Health Sciences, University of Massachusetts, Amherst, Massachusetts.
Journal of Hospital Medicine
|April 5, 2014
Summary
Identifying patients at high risk for recurrent Clostridium difficile infection (rCDI) at initial CDI (iCDI) onset is crucial. A predictive model identified key factors including healthcare-associated onset, prior hospitalizations, and specific medication use.
Area of Science:
- Infectious Diseases
- Epidemiology
- Clinical Prediction Modeling
Background:
- Recurrent Clostridium difficile infection (rCDI) impacts 10-25% of initial CDI (iCDI) cases.
- Identifying high-risk patients at iCDI onset is vital for new therapies.
Purpose of the Study:
- To develop a predictive model for rCDI using factors present at iCDI onset.
- To identify key predictors of rCDI for early intervention.
Main Methods:
- Retrospective cohort study of adult patients with iCDI.
- Logistic regression model developed and cross-validated to identify rCDI predictors.
- Analysis of demographic, chronic, acute disease, and process-of-care factors.
Main Results:
- 10.1% of 4196 patients developed rCDI.
- Six factors predicted rCDI: healthcare-associated onset, prior hospitalizations, gastric acid suppression, fluoroquinolone/high-risk antibiotic use, and age.
- Model showed moderate discrimination (C-statistic 0.643) and high negative predictive value (≥90%).
Conclusions:
- Multiple factors at iCDI onset increase rCDI risk.
- Early identification of high-risk patients enables tailored treatment and prevention strategies.
