Predicting Clostridioides difficile infection outcomes with explainable machine learning

Area of Science:

• Medical Informatics
• Infectious Diseases
• Machine Learning

Background:

• Clostridioides difficile infection (CDI) presents significant short-term risks and potential for recurrence.
• Predicting CDI outcomes at diagnosis is challenging but crucial for clinical decision-making.

Purpose of the Study:

• To develop and validate a predictive model for severe outcomes and recurrence of CDI.
• To identify key clinical features for accurate CDI prognostication.

Main Methods:

• Retrospective collection of 52 clinical features from 1660 inpatient CDI cases.
• Development of a modified desirability of outcome ranking (DOOR) model using deep neural networks and SHAPley Additive exPlanations (SHAP).
• Comparison of model performance against existing severity and recurrence prediction models using AUROC.

Main Results:

• The full 52-feature model achieved AUROCs of 0.823 for severity and 0.678 for recurrence.
• SHAP identified 13 high-importance features, enabling a reduced model with similar performance.
• The reduced model significantly outperformed the top existing severity model (AUROC 0.837 vs. 0.749).

Conclusions:

• The developed model demonstrates superior performance in predicting CDI severity compared to existing tools.
• The model requires external validation but offers explainable predictions for clinical implementation.
• A web application with real-time SHAP explanations was developed for feasible use.