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Updated: Sep 13, 2025

Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291
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Risk Factors for Recurrence and In-Hospital Mortality in Patients with Clostridioides difficile: A Nationwide Study.

Rafael Garcia-Carretero1, Oscar Vazquez-Gomez1, Belen Rodriguez-Maya1

  • 1Department of Internal Medicine, Mostoles University Hospital, 28935 Madrid, Spain.

Journal of Clinical Medicine
|July 29, 2025
PubMed
Summary
This summary is machine-generated.

This study identified key predictors for Clostridioides difficile infection (CDI) mortality and recurrence using machine learning. Older age, comorbidities, and ICU admission significantly increase mortality risk in CDI patients.

Keywords:
CDI incidenceClostridioides difficile infectionepidemiologymachine learningmortalityoutcomesrecurrence

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Area of Science:

  • Infectious Diseases
  • Medical Informatics
  • Epidemiology

Background:

  • Clostridioides difficile infection (CDI) presents a significant challenge in healthcare settings, contributing to substantial morbidity and mortality.
  • Identifying predictors for in-hospital mortality and CDI recurrence is crucial for enhancing patient outcomes and managing the infection effectively.

Purpose of the Study:

  • To identify and validate risk factors associated with in-hospital mortality and recurrence of Clostridioides difficile infection.
  • To leverage a combination of traditional statistical methods and machine learning for robust prediction of CDI outcomes.

Main Methods:

  • A nationwide retrospective study was conducted using the Spanish Minimum Basic Data Set at Hospitalization (2020-2022).
  • Analyzed 34,557 admissions for Clostridioides difficile infection.
  • Employed logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) for predictor identification and Cox regression with LASSO for time-to-mortality analysis.

Main Results:

  • Mortality and recurrence rates for CDI showed an increasing trend during the study period.
  • Key predictors for all-cause hospital mortality included older age, high Charlson Comorbidity Index, ICU admission, congestive heart failure, malignancies, and dementia.
  • Strongest predictors for CDI recurrence were advanced age (≥75 years), chronic kidney disease, and chronic liver disease; malignancy showed a protective association, potentially due to survivor bias.

Conclusions:

  • The study established a reliable framework for predicting CDI outcomes by integrating statistical methods and machine learning on a large dataset.
  • Findings underscore the necessity for targeted interventions in high-risk patient groups and highlight the potential of machine learning in clinical risk stratification.
  • Further research is recommended to validate these predictive models in external cohorts and investigate survivor bias in malignancy-associated outcomes.