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Predicting the Risk for Hospital-Onset Clostridium difficile Infection (HO-CDI) at the Time of Inpatient Admission: HO-CDI Risk Score

Published online by Cambridge University Press:  10 March 2015

Ying P. Tabak ,
Richard S. Johannes ,
Xiaowu Sun ,
Carlos M. Nunez  and
L. Clifford McDonald
Ying P. Tabak
Affiliation:
Clinical Research, Clinical Operation, CareFusion, San Diego, California
Richard S. Johannes
Affiliation:
Clinical Research, Clinical Operation, CareFusion, San Diego, California Division of Gastroenterology, Harvard Medical School and Brigham and Women’s Hospital Boston, Massachusetts
Xiaowu Sun
Affiliation:
Clinical Research, Clinical Operation, CareFusion, San Diego, California
Carlos M. Nunez
Affiliation:
Clinical Research, Clinical Operation, CareFusion, San Diego, California The Biomedical Informatics Research Center, San Diego State University, San Diego, California
L. Clifford McDonald*
Affiliation:
Centers for Disease Control and Prevention, Atlanta, Georgia
*
Address all correspondence to L. Clifford McDonald, MD, FACP, Senior Advisor for Science and Integrity, Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, GA 30341-3724 (cmcdonald1@cdc.gov).
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Abstract

OBJECTIVE

To predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admission

DESIGN

Retrospective data analysis

SETTING

Six US acute care hospitals

PATIENTS

Adult inpatients

METHODS

We used clinical data collected at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a nonduplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations.

RESULTS

Among 78,080 adult admissions, 323 HO-CDI cases were identified (ie, a rate of 4.1 per 1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age ≥65, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin ≤3 g/dL, creatinine >2.0 mg/dL, bands >32%, platelets ≤150 or >420 109/L, and white blood cell count >11,000 mm3. The model had a c-statistic of 0.78 (95% confidence interval [CI], 0.76–0.81) with good calibration. Among 79% of patients with risk scores of 0–7, 19 HO-CDIs occurred per 10,000 admissions; for patients with risk scores >20, 623 HO-CDIs occurred per 10,000 admissions (P<.0001).

CONCLUSION

Using clinical parameters available at the time of admission, this HO-CDI model demonstrated good predictive ability, and it may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.

Infect Control Hosp Epidemiol 2015;00(0):1–7

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 36 , Issue 6 , June 2015 , pp. 695 - 701
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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Footnotes

PREVIOUS PRESENTATION. The preliminary data were presented in part as a poster at the IDWEEK, October, 2012, San Diego, California.

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