Identification of patients at high-risk for Clostridium difficile infection. Development and validation of a risk prediction model in hospitalized patients treated with antibiotics.

Link to article at PubMed

Identification of patients at high-risk for Clostridium difficile infection. Development and validation of a risk prediction model in hospitalized patients treated with antibiotics.

Clin Microbiol Infect. 2015 Apr 15;

Authors: van Werkhoven CH, van der Tempel J, Jajou R, Thijsen SF, Diepersloot RJ, Bonten MJ, Postma DF, Oosterheert JJ

Abstract
To develop and validate a prediction model for Clostridium difficile infection (CDI) in hospitalized patients treated with systemic antibiotics, we performed a case-cohort study in a tertiary (derivation) and secondary care hospital (validation). Cases had a positive Clostridium test and were treated with systemic antibiotics before suspicion of CDI. Controls were randomly selected from hospitalized patients treated with systemic antibiotics. Potential predictors were selected from the literature. Logistic regression was used to derive the model. Discrimination and calibration of the model was tested in internal and external validation. 180 cases and 330 controls were included for derivation. Age >65, recent hospitalization, CDI history, malignancy, chronic renal failure, use of immunosuppressants, antibiotics before admission, non-surgical admission, ICU admission, gastric tube feeding, treatment with cephalosporins and presence of an underlying infection were independent predictors of CDI. The area under the receiver operating characteristic curve of the model in the derivation cohort was 0.84 (95% CI 0.80 to 0.87), and was reduced to 0.81 after internal validation. In external validation, consisting of 97 cases and 417 controls, the model AUC was 0.81 (95% confidence interval 0.77 to 0.85) and model calibration was adequate (Brier score 0.004). A simplified risk score was derived. Using a cut-off of 7 points, the positive predictive value, sensitivity and specificity were 1.0%, 72% and 73%, respectively. In conclusion, a risk prediction model was developed and validated, with good discrimination and calibration, that can be used to target preventive interventions in patients with increased risk of Clostridium difficile infection.

PMID: 25889357 [PubMed - as supplied by publisher]

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