Development and validation of a Clostridium difficile infection risk prediction model

Abstract

Objective: To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease.

Design and setting: Retrospective cohort study in a tertiary care medical center.

Patients: Patients admitted to the hospital for at least 48 hours during the calendar year 2003.

Methods: Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.

Results: A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).

Conclusions: The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.

Figure 1

CDI Risk Prediction Model Calibration Plot Key: The ideal line represents a scenario when all cases are predicted by the model. The apparent is the performance of the model on the derivation cohort. The bias-corrected is the performance on the model after boot-strapping.

Figure 2

ROC Curve for CDI Risk Prediction Model