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Open Access Highly Accessed Research article

Derivation and validation of a simple clinical bedside score (ATLAS) for Clostridium difficile infection which predicts response to therapy

Mark A Miller1*, Thomas Louie2, Kathleen Mullane3, Karl Weiss4, Arnold Lentnek5, Yoav Golan6, Yin Kean7 and Pam Sears7

Author Affiliations

1 Division of Infectious Diseases, Jewish General Hospital, 3755 Cote-Ste-Catherine Rd, Montreal, QC, Canada

2 University of Calgary, Calgary, AB, Canada

3 University of Chicago, Chicago, IL, USA

4 Hopital Maisonneuve-Rosemont, Montreal, QC, Canada

5 Wellstar Infectious Disease, Marietta, GA, USA

6 Tufts Medical Center, Boston, MA, USA

7 Optimer Pharmaceutical, Inc, San Diego, CA, USA

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BMC Infectious Diseases 2013, 13:148  doi:10.1186/1471-2334-13-148

Published: 25 March 2013

Abstract

Background

Clostridium difficile infection (CDI) continues to be a frequent and potentially severe infection. There is currently no validated clinical tool for use at the time of CDI diagnosis to categorize patients in order to predict response to therapy.

Methods

Six clinical and laboratory variables, measured at the time of CDI diagnosis, were combined in order to assess their correlation with treatment response in a large CDI clinical trial database (derivation cohort). The final categorization scheme was chosen in order to maximize the number of categories (discrimination) while maintaining a high correlation with clinical cure assessed two days after the end of therapy. Validation of the derived scoring scheme was done on a second large CDI clinical trial database (validation cohort). A third comparison was done on the two pooled databases (pooled cohort).

Results

In the derivation cohort, the best discrimination and correlation with cure was seen with a five-component ATLAS score (age, treatment with systemic antibiotics, leukocyte count, albumin and serum creatinine as a measure of renal function), which divided CDI patients into 11 groups (scores of 0 to 10 inclusive) and was highly correlated with treatment outcome (R2=0.95; P<0.001). This scheme showed excellent prediction of cure in the validation cohort (overall Kappa=95.2%; P<0.0001), as well as in the pooled cohort, regardless of treatment (fidaxomicin or vancomycin).

Conclusions

A combination of five simple and commonly available clinical and laboratory variables measured at the time of CDI diagnosis, combined into a scoring system (ATLAS), are able to accurately predict treatment response to CDI therapy. The ATLAS scoring system may be useful in stratifying CDI patients so that appropriate therapies can be chosen to maximize cure rates, as well as for categorization of patients in CDI therapeutic studies in order allow comparisons of patient groups.