Available online 17 August 2022, 102628

Assess prediction of recurrence of C. difficile infection and mortality using Japanese patient data.

Developed a machine learning model to evaluate rCDI incidences in Japan.

Machine learning model predicted marginally better recurrence outcome.

Traditional logistic regression performed slightly better in predicting mortality.

Future machine learning may provide useful low-cost bedside setting tools.

Clostridioides difficile infection (CDI) is associated with a large burden of morbidity and mortality worldwide. Previous studies have developed models for predicting recurrence and mortality following CDI, but no machine learning predictive models have been developed specifically using data from Japanese patients.

Using a database of records from acute care hospitals in Japan, we extracted records from January 2012 to September 2016 (plus a 60-day lookback window). A total of 19,159 patients were included. We used a machine learning approach, XGBoost, and compared it to a traditional unregularized logistic regression model. The first 80% of the dataset (by patient index date) was used to optimize model hyperparameters and train the final models, and evaluation was performed on the remaining 20%. We measured model performance by the area under the receiver operator curve and assessed feature importance using Shapley additive explanations.

Performance was similar between the machine learning approach and the classical logistic regression model. Logistic regression performed slightly better than XGBoost for predicting mortality.

XGBoost performed slightly better than logistic regression for predicting recurrence, but it was not competitive with existing published models. Despite this, a future machine learning-based application provided in a bedside setting at low cost might be a clinically useful tool.

Clostridioides difficile infection

Machine learning

Logistic regression

Clinical prediction model

Recurrence

© 2022 Published by Elsevier Ltd.