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. 2015 Jun;36(6):695-701.
doi: 10.1017/ice.2015.37. Epub 2015 Mar 10.

Predicting the risk for hospital-onset Clostridium difficile infection (HO-CDI) at the time of inpatient admission: HO-CDI risk score

Affiliations

Predicting the risk for hospital-onset Clostridium difficile infection (HO-CDI) at the time of inpatient admission: HO-CDI risk score

Ying P Tabak et al. Infect Control Hosp Epidemiol. 2015 Jun.

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.

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Conflict of interest statement

Conflict of Interest:

Dr. McDonald has no conflict of interest to disclose.

Drs. Tabak, Johannes, Sun, and Nunez are employees of CareFusion. No other conflict of interest to disclose.

Figures

Figure 1
Figure 1
Receiver Operating Curve for HO-CDI Risk Score Models. Model with CO-CDI Pressure had a c-statistic of 0.78. Model with All-CO-CDI Pressure had a c-statistic of 0.79.
Figure 2
Figure 2
Hosmer-Lemeshow Calibration
Figure 3
Figure 3
Distribution of HO-CDI Risk Score and Corresponding Admission Volume. *Score 20 and above were collapsed because of small numbers. **Model with CO-CDI Pressure was presented. Model with All-CO-CDI Pressure yielded similar distribution. Data were not shown.
Figure 4
Figure 4
Linearization of HO-CDI Risk and Corresponding Proportion of Admission. * Model with CO-CDI Pressure was presented. Model with All-CO-CDI Pressure yielded similar distribution. Data were not shown.

Comment in

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