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Updated: Mar 20, 2026

Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291
Published on: December 10, 2016
Elevated fecal calprotectin associates with adverse outcomes from Clostridium difficile infection in older adults
Krishna Rao1,2,3, Kavitha Santhosh1,2, Jill A Mogle1,2
1a Divisions of Infectious Diseases , University of Michigan School of Medicine , Ann Arbor , MI , USA ;
Background:
Clostridium difficile infection (CDI) causes a mild to moderate colitis in most patients, but some, especially older adults, develop severe, adverse outcomes. Biomarkers predicting outcomes are needed to optimize treatments. This study tested whether fecal calprotectin associated with a composite primary outcome of complicated CDI (intensive care unit admission, colectomy, or death due to CDI within 30 days of diagnosis) and/or 8-week recurrence.
Methods:
Stool was collected in Cary-Blair media at the time of diagnosis from inpatients of age >60 years that tested positive for C. difficile (enzyme immunoassay [EIA] for toxin A/B or polymerase chain reaction for the tcdB gene). Fecal calprotectin was measured and normalized to solid stool weight. Analysis was performed using logistic regression. Variables were selected for the final model using likelihood ratio tests.
Results:
Fifty patients were included with a mean age 72.8 (± 7.5), and 13 (26%) developed the primary outcome. Clinical variables such as age, gender, and comorbid disease did not associate with complicated CDI/recurrence, nor did traditional biomarkers such as serum albumin or white blood cell count. A high normalized fecal calprotectin (>2000 μg/g) associated with the primary outcome in the final model after adjustment for gender and detectable fecal toxin(s) by EIA (OR 24.9, 95% CI 2.4-257.9, p = 0.007) with a specificity of 91.9%.
Conclusion:
This study provides evidence that fecal calprotectin level associates with complications from CDI in older adults. Further studies are required to validate these findings in larger cohorts and incorporate them into clinical prediction algorithms.
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