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Development and Validation of a Clostridium difficile Infection Risk Prediction Model

Part of: BARRY FARR AWARD RECIPIENTS

Published online by Cambridge University Press:  02 January 2015

Erik R. Dubberke ,
Yan Yan ,
Kimberly A. Reske ,
Anne M. Butler ,
Joshua Doherty ,
Victor Pham  and
Victoria J. Fraser
Erik R. Dubberke*
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
Yan Yan
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
Kimberly A. Reske
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
Anne M. Butler
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
Joshua Doherty
Affiliation:
Barnes-Jewish Hospital, St. Louis, Missouri
Victor Pham
Affiliation:
Barnes-Jewish Hospital, St. Louis, Missouri
Victoria J. Fraser
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
*
Box 8051, 660 South Euclid, St. Louis, MO 63110 ([email protected])
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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.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 32 , Issue 4 , April 2011 , pp. 360 - 366
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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