Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-12T23:39:49.716Z Has data issue: false hasContentIssue false
  • English
  • Français

External Validation of Three Prediction Tools for Patients at Risk of a Complicated Course of Clostridium difficile Infection: Disappointing in an Outbreak Setting

Published online by Cambridge University Press:  08 June 2017

Yvette H. van Beurden ,
Marjolein P. M. Hensgens ,
Olaf M. Dekkers ,
Saskia Le Cessie ,
Chris J. J. Mulder  and
Christina M. J. E. Vandenbroucke-Grauls
Yvette H. van Beurden*
Affiliation:
Department of Gastroenterology and Hepatology, VU Medical Center, Amsterdam, The Netherlands Department of Medical Microbiology and Infection Control, VU Medical Center, Amsterdam, The Netherlands
Marjolein P. M. Hensgens
Affiliation:
Department of Internal Medicine, Meander Medical Center, Amersfoort, The Netherlands
Olaf M. Dekkers
Affiliation:
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, The Netherlands
Saskia Le Cessie
Affiliation:
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands Department of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands
Chris J. J. Mulder
Affiliation:
Department of Gastroenterology and Hepatology, VU Medical Center, Amsterdam, The Netherlands
Christina M. J. E. Vandenbroucke-Grauls
Affiliation:
Department of Medical Microbiology and Infection Control, VU Medical Center, Amsterdam, The Netherlands
*
Address correspondence to Yvette H. van Beurden, MD, Department of Gastroenterology & Hepatology, VU University medical center, De Boelelaan 1118, Room PK 2 X 132, 1081 HV Amsterdam, The Netherlands (y.vanbeurden@vumc.nl).
Get access Rights & Permissions [Opens in a new window]

Abstract

OBJECTIVE

Estimating the risk of a complicated course of Clostridium difficile infection (CDI) might help doctors guide treatment. We aimed to validate 3 published prediction models: Hensgens (2014), Na (2015), and Welfare (2011).

METHODS

The validation cohort comprised 148 patients diagnosed with CDI between May 2013 and March 2014. During this period, 70 endemic cases of CDI occurred as well as 78 cases of CDI related to an outbreak of C. difficile ribotype 027. Model calibration and discrimination were assessed for the 3 prediction rules.

RESULTS

A complicated course (ie, death, colectomy, or ICU admission due to CDI) was observed in 31 patients (21%), and 23 patients (16%) died within 30 days of CDI diagnosis. The performance of all 3 prediction models was poor when applied to the total validation cohort with an estimated area under the curve (AUC) of 0.68 for the Hensgens model, 0.54 for the Na model, and 0.61 for the Welfare model. For those patients diagnosed with CDI due to non-outbreak strains, the prediction model developed by Hensgens performed the best, with an AUC of 0.78.

CONCLUSION

All 3 prediction models performed poorly when using our total cohort, which included CDI cases from an outbreak as well as endemic cases. The prediction model of Hensgens performed relatively well for patients diagnosed with CDI due to non-outbreak strains, and this model may be useful in endemic settings.

Infect Control Hosp Epidemiol 2017;38:897–905

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 38 , Issue 8 , August 2017 , pp. 897 - 905
Copyright
© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Lessa, FC, Mu, Y, Bamberg, WM, et al. Burden of Clostridium difficile infection in the United States. N Engl J Med 2015;372:825–834.CrossRefGoogle ScholarPubMed
2. Leffler, DA, Lamont, JT. Clostridium difficile infection. N Engl J Med 2015;372:1539–1548.CrossRefGoogle ScholarPubMed
3. O’Connor, JR, Johnson, S, Gerding, DN. Clostridium difficile infection caused by the epidemic BI/NAP1/027 strain. Gastroenterology 2009;136:1913–1924.Google Scholar
4. Cohen, SH, Gerding, DN, Johnson, S, et al. 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). Infect Control Hosp Epidemiol 2010;31:431–455.Google Scholar
5. Keller, JJ, Kuijper, EJ. Treatment of recurrent and severe Clostridium difficile infection. Annu Rev Med 2015;66:373–386.CrossRefGoogle ScholarPubMed
6. van Nood, E, Vrieze, A, Nieuwdorp, M, et al. Duodenal infusion of donor feces for recurrent Clostridium difficile . N Engl J Med 2013;368:407–415.Google Scholar
7. Louie, TJ, Miller, MA, Mullane, KM, et al. Fidaxomicin versus vancomycin for Clostridium difficile infection. N Engl J Med 2011;364:422–431.Google Scholar
8. Hensgens, MPM, Dekkers, OM, Goorhuis, A, LeCessie, S, Kuijper, EJ. Predicting a complicated course of Clostridium difficile infection at the bedside. Clin Microbiol Infect 2014;20:O301–O308.Google Scholar
9. Na, X, Martin, AJ, Sethi, S, et al. A multi-center prospective derivation and validation of a clinical prediction tool for severe Clostridium difficile infection. PLoS One 2015;10:e0123405.Google Scholar
10. Arora, V, Kachroo, S, Ghantoji, SS, Dupont, HL, Garey, KW. High Horn’s index score predicts poor outcomes in patients with Clostridium difficile infection. J Hosp Infect 2011;79:23–26.Google Scholar
11. Valiquette, L, Pepin, J, Do, XV, et al. Prediction of complicated Clostridium difficile infection by pleural effusion and increased wall thickness on computed tomography. Clin Infect Dis 2009;49:554–560.CrossRefGoogle ScholarPubMed
12. Velazquez-Gomez, I, Rocha-Rodreiguez, R, Toro, DH, Gutierrez-Nun, JJ, Gonzalez, G, Saavedra, S. A severity score index for Clostridium difficile infection. Infect Dis Clin Pract 2008;16:376–378.Google Scholar
13. Rubin, MS, Bodenstein, LE, Kent, KC. Severe Clostridium difficile colitis. Dis Colon Rectum 1995;38:350–354.Google Scholar
14. Lungulescu, OA, Cao, W, Gatskevich, E, et al. CSI: a severity index for Clostridium difficile infection at the time of admission. J Hosp Infect 2011;79:151–154.Google Scholar
15. Drew, RJ, Boyle, B. RUWA scoring system: a novel predictive tool for the identification of patients at high risk for complications from Clostridium difficile infection. J Hosp Infect 2009;71:93–95.Google Scholar
16. Bhangu, S, Bhangu, A, Nightingale, P, Michael, A. Mortality and risk stratification in patients with Clostridium difficile-associated diarrhoea. Colorectal Dis 2010;12:241–246.CrossRefGoogle ScholarPubMed
17. Welfare, MR, Lalayiannis, LC, Martin, KE, Corbett, S, Marshall, B, Sarma, JB. Comorbidities as predictors of mortality in Clostridium difficile infection and derivation of the ARC predictive score. J Hosp Infect 2011;79:359–363.Google Scholar
18. Zilberberg, MD, Shorr, AF, Micek, ST, Doherty, JA, Kollef, MH. Clostridium difficile-associated disease and mortality among the elderly critically ill. Crit Care Med 2009;37:2583–2589.Google Scholar
19. Butt, E, Foster, JA, Keedwell, E, et al. Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection. BMC Infect Dis 2013;13:316.Google Scholar
20. Abou Chakra, CN, Pepin, J, Valiquette, L. Prediction tools for unfavourable outcomes in Clostridium difficile infection: a systematic review. PLoS One 2012;7:e30258.Google Scholar
21. Toro, DH, KM, Amaral-Mojica, Rocha-Rodriguez, R, Gutierrez-Nunez, J. An innovative severity score index for Clostridium difficile infection: a prospective study. Infect Dis Clin Pract 2011;16:336–339.CrossRefGoogle Scholar
22. van Beurden, YH, Dekkers, OM, Bomers, MK, et al. An outbreak of Clostridium difficile Ribotype 027 associated with length of stay in the intensive care unit and use of selective decontamination of the digestive tract: a case control study. PLoS One 2016;11:e0160778.Google Scholar
23. Charlson, ME, Pompei, P, Ales, KL, MacKenzie, CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373–383.CrossRefGoogle ScholarPubMed
24. Kuijper, EJ, Coignard, B, Tull, P. Emergence of Clostridium difficile-associated disease in North America and Europe. Clin Microbiol Infect 2006;12(Suppl 6):2–18.CrossRefGoogle Scholar
25. McDonald, LC, Coignard, B, Dubberke, E, Song, X, Horan, T, Kutty, PK. Recommendations for surveillance of Clostridium difficile-associated disease. Infect Control Hosp Epidemiol 2007;28:140–145.Google Scholar
26. Sterne, JA, White, IR, Carlin, JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 2009;338:b2393.Google Scholar
27. Tazhibi, M, Bashardoost, N, Ahmadi, M. Kernel smoothing for ROC curve and estimation for thyroid stimulating hormone. Int J Public Health Res 2011:239–242.Google Scholar
28. Loo, VG, Poirier, L, Miller, MA, et al. A predominantly clonal multi-institutional outbreak of Clostridium difficile-associated diarrhea with high morbidity and mortality. N Engl J Med 2005;353:2442–2449.CrossRefGoogle ScholarPubMed
29. See, I, Mu, Y, Cohen, J, et al. NAP1 strain type predicts outcomes from Clostridium difficile infection. Clin Infect Dis 2014;58:1394–1400.Google Scholar
30. Cloud, J, Noddin, L, Pressman, A, Hu, M, Kelly, C. Clostridium difficile strain NAP-1 is not associated with severe disease in a nonepidemic setting. Clin Gastroenterol Hepatol 2009;7:868–873.Google Scholar
31. Pepin, J, Valiquette, L, Gagnon, S, Routhier, S, Brazeau, I. Outcomes of Clostridium difficile-associated disease treated with metronidazole or vancomycin before and after the emergence of NAP1/027. Am J Gastroenterol 2007;102:2781–2788.Google Scholar
32. Bauer, MP, Hensgens, MP, Miller, MA, et al. Renal failure and leukocytosis are predictors of a complicated course of Clostridium difficile infection if measured on day of diagnosis. Clin Infect Dis 2012;55(Suppl 2):S149–S153.CrossRefGoogle ScholarPubMed
33. Steyerberg, EW. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2009.Google Scholar
34. Justice, AC, Covinsky, KE, Berlin, JA. Assessing the generalizability of prognostic information. Ann Intern Med 1999;130:515–524.Google Scholar
35. Stevens, VW, Nelson, RE, Schwab-Daugherty, EM, et al. Comparative effectiveness of vancomycin and metronidazole for the prevention of recurrence and death in patients with Clostridium difficile infection. JAMA Intern Med 2017;177:546–553.CrossRefGoogle ScholarPubMed
36. Nelson, RL, Suda, KJ, Evans, CT. Antibiotic treatment for Clostridium difficile-associated diarrhoea in adults. Cochrane Database Syst Rev 2017;3:CD004610.Google Scholar
Supplementary material: File

Van Beurden supplementary material S1

Appendix

Download Van Beurden supplementary material S1(File)
File 53.3 KB
Supplementary material: File

Van Beurden supplementary material S2

Appendix

Download Van Beurden supplementary material S2(File)
File 17.4 KB
Supplementary material: File

Van Beurden supplementary material S3

Appendix

Download Van Beurden supplementary material S3(File)
File 17.5 KB
Supplementary material: File

Van Beurden supplementary material S4

Appendix

Download Van Beurden supplementary material S4(File)
File 21.6 KB