Risk Factors for Recurrence and In-Hospital Mortality in Patients with Clostridioides difficile: A Nationwide Study

Background: Clostridioides difficile infection (CDI) is a major cause of healthcare-associated morbidity and mortality. Understanding the predictors of in-hospital mortality and recurrence of CDI is key for improving outcomes. This study combined traditional statistical methods and machine learning approaches to identify risk factors for these outcomes. Methods: We conducted a nationwide, retrospective study using the Spanish Minimum Basic Data Set at Hospitalization, analyzing 34,557 admissions with CDI from 2020 to 2022. Logistic regression combined with the least absolute shrinkage and selection operator (LASSO) was used to identify the most relevant predictors. Survival analyses using Cox regression and LASSO were also performed to assess time-to-mortality predictors. Results: Mortality and recurrence rates increased over the study period, reflecting the increasing burden of CDI. LASSO identified a parsimonious subset of predictors while maintaining predictive accuracy (area under the curve: 0.71). Older age (OR = 2.10, 95%CI: 1.98-2.22), Charlson Comorbidity Index ≥ 2 (OR = 1.42, 95%CI: 1.33-1.52), admission to the intensive care unit (OR = 3.09, 95%CI: 2.88-3.32), congestive heart failure (OR = 1.71, 95%CI: 1.61-1.82), malignancies (OR = 1.76, 95%CI: 1.66-1.87), and dementia (OR = 1.36, 95%CI: 1.25-1.48) were strongly associated with all-cause hospital mortality. For recurrence, age ≥ 75 years (OR = 1.19, 95%CI: 1.12-1.27), chronic kidney disease (OR = 1.15, 95%CI: 1.08-1.23), and chronic liver disease (OR = 1.43, 95%CI: 1.16-1.74) were the strongest predictors, while malignancy appeared protective, likely due to survivor bias. Conclusions: Our study provides a robust framework for predicting CDI outcomes. The integration of traditional statistical methods and machine learning applied to a large dataset may improve the reliability of the results. Our findings highlight the need for targeted interventions in high-risk populations and emphasize the potential utility of machine learning in risk stratification. Future studies should validate these models in external cohorts and explore survivor bias in malignancy-associated outcomes.