Development and Validation of a Clostridium difficile Infection Risk Prediction Model

By E. Dubberke, Yan Yan, K. Reske, A. Butler, Joshua A. Doherty, Victor Pham, Victoria J. Fraser. 2011

103 citations recorded by the source corpus.

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.

Subfields: Applied Microbiology and Biotechnology, Critical Care and Intensive Care Medicine, Infectious Diseases

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Learning Credible Models (2017, background): “We identified these variables working with experts in infectious disease who identified key factors based on the literature [6, 8, 34].”
Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile (2014, methodology): “More recently, Dubberke et al [23] developed and validated a risk-prediction model based on variables collected at the time of admission and also during the hospitalization to identify patients at high risk of CDI.”