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Thursday, April 26 • 9:00am - 9:20am
Development of a Risk Prediction Model for Hospital-onset Clostridium difficile Infection in Patients Receiving Antibiotics

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Development of a Risk Prediction Model for Hospital-onset Clostridium difficile Infection in Patients Receiving Antibiotics
Carrie Tilton, Steven Johnson
Novant Health Forsyth Medical Center - Winston-Salem, NC

Background/Purpose: Clostridium difficile infection (CDI) is recognized as a significant challenge in healthcare. The objective of this study is to develop an easily implementable risk prediction model for hospital-onset CDI in patients receiving systemic antimicrobials.

Methodology: This is a retrospective, case-control, multi-center study approved by the Institutional Review Board. Adult patients (18 years old and above) with hospital-onset CDI (defined as: positive Clostridium difficile polymerase chain reaction more than 48 hours into admission) who are receiving systemic antibiotics will be included as cases. Controls will consist of adult patients without a CDI diagnosis who are receiving systemic antibiotics. Cases will be matched 1:1 with controls by admitted medical unit type. Medical records for eligible subjects will be reviewed to collect relevant clinical data including: patient age, sex, date of admission, admission unit, admission from, discharge date, length of stay, most recent prior hospitalization date, deceased, outpatient systemic antibiotics prior to admission, inpatient systemic antibiotics, duration of antibiotics, diarrhea noted less than 48 hours into admission, antacid use, gastrointestinal surgery, and gastrointestinal manipulation. Variables identified as statistically significantly associated with CDI will be incorporated into a multivariate analysis. A logistic regression model will be utilized to formulate a point-based risk prediction model. Positive predictive value, negative predictive value, sensitivity, and specificity will be determined at various point cutoffs of the model.

Presentation Objective: Describe the application of a risk prediction model in the inpatient setting to identify patients at high risk for hospital-onset Clostridium difficile infection.

Self-Assessment: Which of the included variables increase risk of Clostridium difficile infection the most in the inpatient setting?

Speakers

Thursday April 26, 2018 9:00am - 9:20am EDT
Athena I