Predictors of mortality attributable to Clostridium difficile infection in patients with underlying malignancy

Young Kyung Yoon, Min Ja Kim, Jang Wook Sohn, Hye Suk Kim, Yoon Ji Choi, Jung Sun Kim, Seung Tae Kim, Kyong Hwa Park, Seok Jin Kim, Byung Soo Kim, Sang Won Shin, Yeul Hong Kim, Yong Park
Supportive Care in Cancer 2014, 22 (8): 2039-48

PURPOSE: This study aimed at evaluating the clinical severity and treatment outcomes of Clostridium difficile infections (CDI) and identifying predictors associated with mortality in patients with malignancy.

METHODS: A retrospective study was conducted in a teaching hospital from January 2004 to June 2013. The subjects included adult patients (aged ≥ 18 years) receiving treatment for malignancy whose conditions were complicated by CDI. Clinical severity was determined using the guidelines from the Society for Healthcare Epidemiology of America and the Infectious Diseases Society of America (SHEA/IDSA). Multivariate logistic regression analysis was performed to identify predictors independently associated with CDI-related mortality.

RESULTS: Of the 5,594 patients treated for malignancy at the Division of Hematology/Oncology during the study period, 61 (1.1%) had CDI complications. CDI-related mortality was 19.7% (12/61). Twenty-seven (44.3%) patients were diagnosed with neutropenia (ANC ≤ 500/mm(3)) at initial CDI presentation. Forty-one patients (67.2%) received antimicrobial therapy for CDI. Based on the SHEA/IDSA guidelines, only 12 patients (19.7%) presented with severe CDI, but 25 (61.0%) patients experienced treatment failure. Multiple logistic regression modeling showed neutropenia to be an independent risk factor for CDI-related mortality (odds ratio, 5.17; 95% confidence interval, 1.24-21.59).

CONCLUSIONS: This study tracked poor CDI treatment outcomes in patients with malignancy and identified neutropenia as a previously unrecognized risk factor of CDI-related mortality. Alternative definitions of severe CDI that include neutropenia might be necessary to more accurately determine clinical severity.

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