Identification of patients at high risk for Clostridium difficile infection: development and validation of a risk prediction model in hospitalized patients treated with antibiotics: Infectious Diseases

• Published in: Clinical microbiology and infection Vol. 21; no. 8; pp. 786.e1 - 786.e8
• Main Authors: van Werkhoven, CH, van der Tempel, J, Jajou, R, Thijsen, SFT, Diepersloot, RJA, Bonten, MJM, Postma, D F, Oosterheert, J J
• Format: Journal Article
• Language: English
• Published: 01.08.2015
• Subjects: Clostridium; Clostridium difficile
• ISSN: 1198-743X
• DOI: 10.1016/j.cmi.2015.04.005

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 were tested in internal and external validation. A total of 180 cases and 330 controls were included for derivation. Age >65 years, recent hospitalization, CDI history, malignancy, chronic renal failure, use of immunosuppressants, receipt of antibiotics before admission, nonsurgical admission, admission to the intensive care unit, 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% confidence interval 0.80-0.87), and was reduced to 0.81 after internal validation. In external validation, consisting of 97 cases and 417 controls, the model area under the curve was 0.81 (95% confidence interval 0.77-0.85) and model calibration was adequate (Brier score 0.004). A simplified risk score was derived. Using a cutoff 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 CDI.