- Research article
- Open Access
- Open Peer Review
This article has Open Peer Review reports available.
Derivation and validation of a simple clinical bedside score (ATLAS) for Clostridium difficile infection which predicts response to therapy
© Miller et al.; licensee BioMed Central Ltd. 2013
Received: 25 May 2012
Accepted: 6 February 2013
Published: 25 March 2013
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.
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).
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).
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.
Clostridium difficile infection (CDI) has emerged during the last decade as a serious and increasingly common healthcare-associated infection . The emergence of hypervirulent strains has resulted in elevated rates of CDI-related complications (e.g. colectomy, need for intensive care) and increased mortality, especially among the elderly [2, 3]. Newer treatment options, such as novel antibacterials , immune modulators  and immunotherapeutics , 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.
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 . 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 , 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 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, de-identified and required no further collection of data from patients.
Clinical and laboratory variables, along with their respective values and points, for determining the optimal scoring system which correlates with cure after CDI therapy
< 60 years
60 – 79 years
≥ 80 years
Treatment with systemic antibiotics during CDI therapy (≥ 1 day)
37.6 – 38.5°C
Leukocyte count (total)
16,000 – 25,000
> 35 g/L
26 – 35 g/L
≤ 25 g/L
Serum creatinine (as a measure of renal function)
≤ 120 μmol/L
121 – 179 μmol/L
≥ 180 μmol/L
During the first step of this analysis, the derivation process, the 003 trial database was used to test the combinations for their correlation with treatment response.
Correlation with cure having an R2 value ≥ 0.9
Correlation with cure having a P value ≤ 0.01
If multiple combinations met the above 2 conditions, the combination consisting of the largest number of variables was considered as the optimal combination, since this scheme would be able to categorize the patients into the largest number of distinct groups (i.e. highest discriminative ability and category utility) .
During the second step of this analysis, the validation process, the predicted cure rate from the optimum combination chosen in step one (using the derived regression formula) was compared to the actual CDI cure rate as seen in the 004 clinical trial database. This was done by means of Kappa statistics of the cure rate for each score category.
During a final step of this analysis, the two clinical studies (003 and 004) were pooled and the optimum combination which had been derived in the first step was used to compare the predicted and actual cure rates for this entire cohort, again using a Chi-Square analysis for each score group.
Step 1. Derivation of the optimal categorization scoring system.
Age, leukocyte count
Age, treatment with systemic antibiotics, leukocyte count
Age, temperature, serum creatinine
Age, temperature, leukocyte count
Temperature, albumin, leukocyte count
Age, treatment with systemic antibiotics, temperature, serum creatinine
Age, temperature, albumin, serum creatinine
Age, temperature, leukocyte count, albumin
Temperature, leukocyte count, albumin, serum creatinine
Age, treatment with systemic antibiotics, leukocyte count, albumin, serum creatinine
All possible combinations of the six clinical and laboratory variables, along with the respective linear correlation coefficient (R 2 ) and its significance (P value), when correlated with CDI cure for patients in the 003 trial database with available values for all analyzed variables; n=515
Ag, Tr, Te, S *
Ag, Tr, Te, Al
Ag, Tr, Te, L
Ag, Tr, Al, S
Ag, Tr, L, S
Ag, Tr, L, Al
Ag, Te, Al, S *
Ag, Te, L, S
Ag, Te, L, Al *
Ag, L, Al, S
Tr, Te, Al, S
Ag, Al *
Tr, Te, L, S
Tr, Te, L, Al
Tr, L, Al, S
Te, L, Al, S *
Ag, Tr, Te, Al, S
Te, Al *
Ag, Tr, Te, L, S
Ag, Tr, S, L, Al **
Ag, Te, L, Al, S
Ag, Tr, Te, L, Al
L, Al *
Tr, Te, L, Al, S
Ag, Tr, Te
Ag, Tr, Te, L, Al, S
Ag, Tr, S
Ag, Tr, Al
Ag, Tr, L *
Ag, Te, S *
Ag, Te, Al
Ag, Te, L *
Ag, Al, S
Ag, L, S
Ag, L, Al
Tr, Te, S
Tr, Te, Al;
Tr, Te, L
Tr, Al, S
Tr, L, S
Tr, L, Al
Te, Al, S
Te, L, S
Te, L, Al *
L, Al, S
Of these 12 candidate combinations, the most discriminating (containing the largest number of variables and thereby separating the patients into the largest number of unique groups) was the combination of age (Ag), treatment with systemic antibiotics (Tr), leukocyte count (L), serum albumin (Al) and serum creatinine as a measure of renal function (S) (abbreviated herewith as ATLAS). The ATLAS combination produced a scoring system which was able to place the CDI patients into 11 unique categories (scores 0 to 10, inclusive) and this correlated with treatment cure with an R-squared value of 0.95 and a highly significant P value of < 0.001. The regression equation for this correlation was shown to be: cure rate = 100 – [5.08 × (ATLAS score)]. The receiver operating characteristics of the ATLAS score for predicting treatment cure are shown in Figure 1 (area under the curve was calculated to be 0.71).
The predicted cure rate and the actual cure rate for CDI patients in the validation database (all mITT patients in the 004 clinical trial with available values for all analyzed variables; n=452)
ATLAS score (nT)
Predicted cure rate* (%) (nc §/nT)
Actual cure rate (%) (nc/nT)
All scores (452)
The ATLAS categorization scheme was able to very closely (overall Kappa=95.2%; P<0.0001) predict the actual cure rate for these patients in every score category.
Step 3. Capacity of the ATLAS score to predict cure for the pooled CDI patient databases, and by treatment allocation.
The predicted and actual cure rates for CDI patients in the combined 003 and 004 trial databases (mITT patients who have available values for all analyzed variables), and also categorized by treatment assignment on entry into the studies (fidaxomicin or vancomycin)
All patients (n=967)
Vancomycin patients (n=494)
Fidaxomicin patients (n=473)
Predicted cure rate* (%) (nc/nT)
Actual cure rate (%) (nc/nT)
Predicted cure rate* (%) (nc/nT)
Actual cure rate (%) (nc/nT)
Predicted cure rate* (%) (nc/nT)
Actual cure rate (%) (nc/nT)
The current classifications of “mild”, “moderate”, “severe”, and “fulminant” CDI are unvalidated, subjective, and have not yet been shown to be clinically useful. An easy to use, objective, clinically relevant and validated system of categorizing CDI patients is needed in order to choose among the growing number of available therapies, to decide which patients might benefit from adjunctive therapies, to facilitate communication among healthcare providers, to prognosticate the outcome of therapy, and to categorize patients for CDI intervention trials.
Two large CDI clinical trial databases, the fidaxomicin/vancomycin comparative trials, were used to derive and then to validate a CDI patient categorization scheme which could predict cure at the end of therapy. This derived scoring system, the ATLAS score, was able to categorize CDI patients into 11 distinct categories and was able to predict clinical cure with a high degree of accuracy.
Thirdly, a different use of the clinical variables with other weighting schemes, might give a more accurate predictive model. The variations in factor weighting and combinations are virtually limitless, and multivariate regression analysis was not helpful in determining the weighting scheme, since many of the factors highly predictive of outcome (i.e. albumin, age, serum creatinine) are very highly correlated with each other and negated each other in the multivariate models. Fourthly, it should be noted that only clinical cure was assessed in these analyses. We did a preliminary analysis investigating the capacity of this scoring system to predict CDI recurrence at 28 days post-therapy, and the five-component ATLAS score performed poorly in this regard. Other combinations performed better for predicting CDI recurrence, and those analyses will be undertaken at a later time. Lastly, this scoring system worked well in predicting response to both fidaxomicin and to vancomycin. Since no patients received metronidazole in these 2 clinical studies, it remains unknown how the ATLAS score would perform in predicting cure rates with this agent. However, the ATLAS categorization of CDI patients may now allow post-hoc comparisons of CDI patients treated with vancomycin and metronidazole in other databases in order to examine if there is truly a difference in outcome with these two agents in sub-groups of patients with specific scores.
In conclusion, a combination of five simple clinical and laboratory variables measured at the time of CDI diagnosis, combined into an 11-category scoring system (ATLAS), seems to be able to accurately predict treatment response to CDI therapy by either vancomycin or fidaxomicin. The ATLAS scoring system may be useful to stratify CDI patients in order to prospectively evaluate and compare CDI therapies among patient categories, to choose therapies for patient sub-groups to maximize cure rates, to categorize patients in different CDI therapeutic studies in order to allow between-study comparisons of patient groups, and to stratify patients upon entry into CDI therapeutic trials.
We would like to acknowledge and thank all of the site investigators, the site research personnel, and the patients themselves who participated in the two large clinical trials comparing fidaxomicin and vancomycin, from which this extensive and rich database has arisen.
Various portions of this analysis were presented at the Interscience Conference on Antimicrobial Agents and Chemotherapy in September 2010 and at the Annual Meeting of the Infectious Diseases Society of America in October 2010.
The statistical programming was performed and paid for by Optimer Pharmaceuticals.
- Jarvis WR, Schlosser J, Jarvis AA, Chinn RY: National point prevalence of clostridium difficile in US health care facility inpatients, 2008. Am J Infect Control. 2009, 37: 263-270. 10.1016/j.ajic.2009.01.001.View ArticlePubMedGoogle Scholar
- Loo VG, Poirier L, Miller MA: A predominantly clonal multi-insitutional outbreak of clostridium difficile-associated diarrhea with high morbidity and mortality. New Engl J Med. 2005, 353: 2442-2449. 10.1056/NEJMoa051639.View ArticlePubMedGoogle Scholar
- Goorhuis A, Bakker D, Corver J: Emergence of clostridium difficile infection due to a new hypervirulent strain, polymerase chain reaction ribotype 078. Clin Infect Dis. 2008, 47: 1162-1170. 10.1086/592257.View ArticlePubMedGoogle Scholar
- Louie TG, Miller MA, Mullane KM: Fidaxomicin versus vancomycin for clostridium difficile infection. New Engl J Med. 2011, 364: 422-431. 10.1056/NEJMoa0910812.View ArticlePubMedGoogle Scholar
- Sougioultzis S, Kyne L, Drudy D, Keates S, Maroo S, Pothoulakis C, Giannasca PJ, Lee CK, Warny M, Monath TP, Kelly CP, Clostridium difficile toxoid vaccine in recurrent C. difficile-associated diarrhea: Clostridium difficile toxoid vaccine in recurrent C. Difficile-associated diarrhea. Gastroenterology. 2005, 128: 764-770. 10.1053/j.gastro.2004.11.004.View ArticlePubMedGoogle Scholar
- Lowy I, Molrine DC, Leav BA: Treatment with monoclonal antibodies against clostridium difficile toxins. N Engl J Med. 2010, 362: 197-205. 10.1056/NEJMoa0907635.View ArticlePubMedGoogle Scholar
- Rubin MS, Bodenstein LE, Kent KC: Severe clostridium difficile colitis. Dis Colon Rectum. 1995, 38: 350-354. 10.1007/BF02054220.View ArticlePubMedGoogle Scholar
- Belmares J, Gerding DN, Parada JP, Miskevics S, Weaver F, Johnson S: Outcome of metronidazole therapy for clostridium difficile disease and correlation with a scoring system. J Infect. 2007, 55: 495-501. 10.1016/j.jinf.2007.09.015.View ArticlePubMedGoogle Scholar
- McEllistrem MC, Carman RJ, Gerding DN, Genheimer CW, Zheng L: A hospital outbreak of clostridium difficile disease associated with isolates carrying binary toxin genes. Clin Infect Dis. 2005, 40: 265-272. 10.1086/427113.View ArticlePubMedGoogle Scholar
- Zar FA, Bakkanagari SR, Moorthi KM, Davis MB: A comparison of vancomycin and metronidazole for the treatment of clostridium difficile-associated diarrhea, stratified by disease severity. Clin Infect Dis. 2007, 45: 302-307. 10.1086/519265.View ArticlePubMedGoogle Scholar
- Henrich TJ, Krakower D, Bitton A, Yokoe DS: Clinical risk factors for severe clostridium difficile-associated disease. Emerg Infect Dis. 2009, 15: 415-422. 10.3201/eid1503.080312.View ArticlePubMedPubMed CentralGoogle Scholar
- Louie T, Gerson M, Grimard D: Results of a phase III trial comparing tolevamer, vancomycin and metronidazole in patients with clostridium difficile-associated diarrhea (CDAD). program and abstracts of the 47th annual interscience conference on antimicrobial agents and chemotherapy. 2007, Chicago, Illinois: Abstract K-425a, September 17–20Google Scholar
- Gujja D, Friedenberg FK: Predictors of serious complications due to clostridium difficile infection. Aliment Pharmacol Ther. 2009, 29: 635-642. 10.1111/j.1365-2036.2008.03914.x.View ArticlePubMedGoogle Scholar
- UPHS guidelines for antimicrobial therapy. 2011, http://www.uphs.upenn.edu/bugdrug/. Last accessed November 7
- Fujitani S, George WL, Murthy AR: Comparison of clinical severity score indices for clostridium difficile infection. Infection Control & Hospital Epidemiology. 2011, 32: 220-228. 10.1086/658336.View ArticleGoogle Scholar
- Toro DH, Amaral-Mojica KM, Rocha-Rodriguez R, Gutierrez-Nunez J: An innovative severity score index for C. Difficile infection: a prospective study. Infect Dis in Clin Practice. 2011, 19: 336-339. 10.1097/IPC.0b013e31821895a8.View ArticleGoogle Scholar
- Brouwers MC, Rawski E, Spithoff K, Oliver TK: Inventory of cancer guidelines: a tool to advance the guideline enterprise and improve the uptake of evidence. Expert Review of Pharmacoeconomics & Outcomes Research. 2011, 11: 151-161. 10.1586/erp.11.11.View ArticleGoogle Scholar
- Levy MM, Dellinger RP, Townsend SR: The surviving sepsis campaign: results of an international guideline-based performance improvement program targeting severe sepsis. Intensive Care Med. 2010, 36: 222-231. 10.1007/s00134-009-1738-3.View ArticlePubMedPubMed CentralGoogle Scholar
- Cohen SH, Gerding DN, Johnson S: Clinical practice guidelines for clostridium difficile infection in adults: 2010 update by the society for healthcare epidemiology of America (SHEA) and the infectious diseases society of America (IDSA). Infection Control & Hospital Epidemiology. 2010, 31: 431-455. 10.1086/651706.View ArticleGoogle Scholar
- Bauer MP, Kuijper EJ, van Dissel JT: European society of clinical microbiology and infectious diseases (ESCMID): treatment guidance document for clostridium difficile infection (CDI). Clin Microbiol Infect. 2009, 15: 1067-1079. 10.1111/j.1469-0691.2009.03099.x.View ArticlePubMedGoogle Scholar
- Crook DW, Walker AS, Kean Y: Fidaxomicin versus vancomycin for Clostridium difficile infection: meta-analysis of pivotal randomized controlled trials. Clin Infect Dis. 2012, 55 (Supp 2): S93-S103.View ArticlePubMedPubMed CentralGoogle Scholar
- Corter JE, Gluck MA: Explaining basic categories: feature predictability and information. Psychol Bull. 1992, 111: 291-303.View ArticleGoogle Scholar
- Miller M, Gravel D, Mulvey M: Health care-associated clostridium difficile infection in Canada: patient age and infecting strain type are highly predictive of severe outcome and mortality. Clin Infect Dis. 2010, 50: 194-201. 10.1086/649213.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2334/13/148/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.