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

BMC Infectious Diseases Derivation and validation of a simple clinical bedside score (ATLAS) for Clostridium difficile infection which predicts response to therapy Mark A Miller 0 Thomas Louie 2 Kathleen Mullane 1 Karl Weiss 6 Arnold Lentnek 5 Yoav Golan 4 Yin Kean 3 Pam Sears 3 0 Division of Infectious Diseases, Jewish General Hospital , 3755 Cote-Ste- Catherine Rd, Montreal, QC , Canada 1 University of Chicago , Chicago, IL , USA 2 University of Calgary , Calgary, AB , Canada 3 Optimer Pharmaceutical, Inc , San Diego, CA , USA 4 Tufts Medical Center , Boston, MA , USA 5 Wellstar Infectious Disease , Marietta, GA , USA 6 Hopital Maisonneuve- Rosemont , Montreal, QC , Canada 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. - Background Clostridium difficile infection (CDI) has emerged during the last decade as a serious and increasingly common healthcare-associated infection [1]. The emergence of hypervirulent strains has resulted in elevated rates of CDIrelated complications (e.g. colectomy, need for intensive care) and increased mortality, especially among the elderly [2,3]. Newer treatment options, such as novel antibacterials [4], immune modulators [5] and immunotherapeutics [6], have led to a recent expansion in the number of clinical trials involving subjects with this serious infection. Despite more than 3 decades of research involving this condition, a validated severity scale has yet to be developed which correlates with treatment response, which is predictive of severe outcomes (i.e. colectomy, need for intensive care, or attributable mortality) or which predicts CDI recurrence. Although several predictive scoring systems and clinical variables have been described in limited CDI populations or case series, none have been validated on large CDI databases [7-16]. While the choice of therapy for malignant neoplasms and subjects with sepsis is often based on validated criteria which consist of both patient and disease-related variables in order to maximize treatment response [17,18], no such validated scheme exists for CDI. The absence of such a categorization system means that the choice of therapies for a particular patient is often not evidence-based, and clinical trials investigating CDI outcomes may be comparing dissimilar patient populations. Recent guidelines which have put forward severity categories for CDI have not validated these categorizations and their correlation with treatment outcome, disease outcome and CDI recurrence are unknown [19,20]. We have used 2 large clinical therapeutic trials for treating CDI in order to derive and then validate a categorization system to discriminate among CDI patients and correlate the grouping with treatment response. Methods Two large databases, which were derived while conducting therapeutic trials that compared fidaxomicin and vancomycin for the treatment of CDI, were used for the present analyses [4,21]. The clinical and trial details for the two identical CDI therapy studies are described elsewhere [4]. Briefly, 10 days of therapy with either vancomycin or fidaxomicin was administered to CDI patients. The first trial (003) enrolled patients in the United States and Canada; the second trial (004) enrolled patients in those two countries as well as in Europe. The response to treatment was assessed two days following the last day of therapy. Patients considered as a cure were then followed for an additional 28 days to evaluate them for a CDI recurrence. This present analyses used all patients included in each of the respective trials if they had a confirmed diagnosis of CDI and received at least 1 dose of study medication (modified intent to treat group; mITT). Since the vancomycin and fidaxomicin arms had nearly identical cure rates [4], all patients in each study were combined into a single group regardless of the therapy they were randomized to receive. The mITT group of patients consisted of 596 individuals in the 003 study, 509 individuals in the 004 study, and a total of 1105 subjects in both combined studies. All subjects in both studies gave informed consent which also allowed for secondary analyses of the Leukocyte count (total) Serum creatinine (as a measure of renal function) Table 1 Clinical and laboratory variables, along with their respective values and points, for determining the optimal scoring system which correlates with cure after CDI therapy databases such as in the present investigation. No additional form of ethical approval was required to do this subgroup risk analysis, as the original ethical approval for the trial covered such analyses. The study sponsor permitted the authors to access the trial data for this analysis; the dataset used was preexisting, deidentified and required no further collection of data from patients. The six clinical and laboratory parameters used in the analyses were chosen for their ready availability, their ease of calculation, prior correlation with CDI outcome in case series [7-16], and the fact that they had been collected and were available in the two CDI clinical trials of interest. (...truncated)