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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
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6 2015
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Source: Infect Control Hosp Epidemiol. 36(6):695-701
Details:
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Alternative Title:Infect Control Hosp Epidemiol
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Personal Author:
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Description: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 present 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 non-duplicate 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 (4.1/1,000 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 WBC >11,000 mm3. The model had a c-statistic of 0.78 (95% CI: 0.76, 0.81) with good calibration. For 79% patients with risk score of 0-7, there were 19 HO-CDIs per 10,000 admissions; for patients with risk score of 20+, there were 623 HO-CDIs per 10, 000 admissions (P<0.0001).
Conclusion
Using clinical parameters available at the time of admission, this HO-CDI model displayed a good predictive ability. It may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.
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Pubmed ID:25753106
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Pubmed Central ID:PMC5768429
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