4.8 Article

Prospective Derivation and Validation of a Clinical Prediction Rule for Recurrent Clostridium difficile Infection

Journal

GASTROENTEROLOGY
Volume 136, Issue 4, Pages 1206-1214

Publisher

W B SAUNDERS CO-ELSEVIER INC
DOI: 10.1053/j.gastro.2008.12.038

Keywords

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Funding

  1. National Institutes of Health [R0-1 AI053069]
  2. Harvard Medical School [K30-HL04095]
  3. Irish Health Research Board [RP/2005/72]
  4. [T32-DK0776]

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Background & Aims: Prevention of recurrent Clostridium difficile infection (CDI) is a substantial therapeutic challenge. A previous prospective study of 63 patients with CDI identified risk factors associated with recurrence. This study aimed to develop a prediction rule for recurrent CDI using the above derivation cohort and prospectively evaluate the performance of this rule in an independent validation cohort. Methods: The clinical prediction rule was developed by multivariate logistic regression analysis and included the following variables: age >65 years, severe or fulminant illness (by the Horn index), and additional antibiotic use after CDI therapy. A second rule combined data on serum concentrations of immunoglobulin G (IgG) against toxin A with the clinical predictors. Both rules were then evaluated prospectively in an independent cohort of 89 patients with CDI. Results: The clinical prediction rule discriminated between patients with and without recurrent CDI, with an area under the curve of the receiver-operating-characteristic curve of 0.83 (95% confidence interval [CI]: 0.70-0.95) in the derivation cohort and 0.80 (95% Cl: 0.67-0.92) in the validation cohort. The rule correctly classified 77.3% (95% CI: 62.2%-88.5%) and 71.9% (95% CI: 59.2%-82.4%) of patients in the derivation and validation cohorts, respectively. The combined rule performed well in the derivation cohort but not in the validation cohort (area under the curve of the receiver-operating-characteristic curve, 0.89 vs 0.62; diagnostic accuracy, 93.8% vs 69.2%, respectively). Conclusions: We prospectively derived and validated a clinical prediction rule for recurrent CDI that is si le, reliable, and accurate and can be used to identify high-risk patients most likely to benefit from measures to prevent recurrence.

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