Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-22T22:23:49.186Z Has data issue: false hasContentIssue false
  • English
  • Français

Electronic Health Record–Based Detection of Risk Factors for Clostridium difficile Infection Relapse

Published online by Cambridge University Press:  02 January 2015

Courtney Hebert ,
Hongyan Du ,
Lance R. Peterson  and
Ari Robicsek
Courtney Hebert*
Affiliation:
Department of Biomedical Informatics, Ohio State University Wexner Medical Center, Columbus, Ohio
Hongyan Du
Affiliation:
Center for Clinical and Research Informatics, NorthShore University HealthSystem, Evanston, Illinois
Lance R. Peterson
Affiliation:
Department of Pathology, NorthShore University HealthSystem, Evanston, Illinois
Ari Robicsek
Affiliation:
Department of Medicine, Department of Health Information Technology, Center for Clinical and Research Informatics, NorthShore University HealthSystem, Evanston, Illinois Pritzker School of Medicine, University of Chicago, Chicago, Illinois
*
Department of Bio-medical Informatics, Ohio State University Wexner Medical Center, 3190 Graves Hall, 333 West Tenth Avenue, Columbus, OH 43210 ([email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective.

A major challenge in treating Clostridium difficile infection (CDI) is relapse. Many new therapies are being developed to help prevent this outcome. We sought to establish risk factors for relapse and determine whether fields available in an electronic health record (EHR) could be used to identify high-risk patients for targeted relapse prevention strategies.

Design.

Retrospective cohort study.

Setting.

Large clinical data warehouse at a 4-hospital healthcare organization.

Participants.

Data were gathered from January 2006 through October 2010. Subjects were all inpatient episodes of a positive C. difficile test where patients were available for 56 days of follow-up.

Methods.

Relapse was defined as another positive test between 15 and 56 days after the initial test. Multivariable regression was performed to identify factors independently associated with CDI relapse.

Results.

Eight hundred twenty-nine episodes met eligibility criteria, and 198 resulted in relapse (23.9%). In the final multivariable analysis, risk of relapse was associated with age (odds ratio [OR], 1.02 per year [95% confidence interval (CI), 1.01–1.03]), fluoroquinolone exposure in the 90 days before diagnosis (OR, 1.58 [95% CI, 1.11–2.26]), intensive care unit stay in the 30 days before diagnosis (OR, 0.47 [95% CI, 0.30–0.75]), cephalosporin (OR, 1.80 [95% CI, 1.19–2.71]), proton pump inhibitor (PPI; OR, 1.55 [95% CI, 1.05–2.29]), and metronidazole exposure after diagnosis (OR, 2.74 [95% CI, 1.64–4.60]). A prediction model tuned to ensure a 50% probability of relapse would flag 14.6% of CDI episodes.

Conclusions.

Data from a comprehensive EHR can be used to identify patients at high risk for CDI relapse. Major risk factors include antibiotic and PPI exposure.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 34 , Issue 4 , April 2013 , pp. 407 - 414
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2013

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

1.Redelings, MD, Sorvillo, F, Mascola, L. Increase in Clostridium difficile–related mortality rates, United States, 1999–2004. Emerg Infect Dis 2007;13(9):1417–1419.Google Scholar
2.Kelly, CP, LaMont, JT. Clostridium difficile: more difficult than ever. N Engl J Med 2008;359(18):1932–1940.Google Scholar
3.McDonald, LC, Owings, M, Jernigan, DB. Clostridium difficile infection in patients discharged from US short-stay hospitals, 1996–2003. Emerg Infect Dis 2006;12(3):409–415.Google Scholar
4.McDonald, LC, Killgore, GE, Thompson, A, et al.An epidemic, toxin gene–variant strain of Clostridium difficile. N Engl J Med 2005;353(23):2433–2441.Google Scholar
5.Cohen, SH, Gerding, DN, Johnson, S, et al.Clinical practice guide-lines 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(5):431–455.Google Scholar
6.Louie, TJ, Miller, MA, Mullane, KM, et al.Fidaxomicin versus vancomycin for Clostridium difficile infection. N Engl J Med 2011;364(5):422–431.Google Scholar
7.Lowy, I, Molrine, DC, Leav, BA, et al.Treatment with monoclonal antibodies against Clostridium difficile toxins. N Engl J Med 2010;362(3):197–205.Google Scholar
8.Shakov, R, Salazar, RS, Kagunye, SK, Baddoura, WJ, DeBari, VA. Diabetes mellitus as a risk factor for recurrence of Clostridium difficile infection in the acute care hospital setting. Am J Infect Control 2011;39(3):194–198.Google Scholar
9.Linsky, A, Gupta, K, Lawler, EV, Fonda, JR, Hermos, JA. Proton pump inhibitors and risk for recurrent Clostridium difficile infection. Arch Intern Med 2010;170(9):772–778.Google Scholar
10.Cadena, J, Thompson, GR IIIPatterson, JE, et al.Clinical predictors and risk factors for relapsing Clostridium difficile infection. Am J Med Sci 2010;339(4):350–355.CrossRefGoogle ScholarPubMed
11.Pepin, J, Alary, ME, Valiquette, L, et al.Increasing risk of relapse after treatment of Clostridium difficile colitis in Quebec, Canada. Clin Infect Dis 2005;40(11):1591–1597.Google Scholar
12.Johnson, S, Schriever, C, Galang, M, Kelly, CP, Gerding, DN. Interruption of recurrent Clostridium difficile–associated diarrhea episodes by serial therapy with vancomycin and rifaximin. Clin Infect Dis 2007;44(6):846–848.Google Scholar
13.McFarland, LV, Elmer, GW, Surawicz, CM. Breaking the cycle: treatment strategies for 163 cases of recurrent Clostridium difficile disease. Am J Gastroenterol 2002;97(7):1769–1775.Google Scholar
14.O'Horo, J, Safdar, N. The role of immunoglobulin for the treatment of Clostridium difficile infection: a systematic review. Int J Infect Dis 2009;13(6):663–667.Google Scholar
15.Choi, HK, Kim, KH, Lee, SH, Lee, SJ. Risk factors for recurrence of Clostridium difficile infection: effect of vancomycin-resistant enterococci colonization. J Korean Med Sci 2011;26(7):859–864.Google Scholar
16.Hu, MY, Katchar, K, Kyne, L, et al.Prospective derivation and validation of a clinical prediction rule for recurrent Clostridium difficile infection. Gastroenterology 2009;136(4):1206–1214.Google Scholar
17.Metz, CE, Kronman, HB. Statistical significance tests for binormal ROC curves. J Math Psychol 1980;22(3):218–243.Google Scholar
18.Metz, C, Wang, P, Kronman, H. A new approach for testing the significance of differences between ROC curves from correlated data. In: Deconinck, F, ed. Information Processing in Medical Imaging. The Hague: Nijhoff, 1984:432–445.Google Scholar
19.Garey, KW, Sethi, S, Yadav, Y, DuPont, HL. Meta-analysis to assess risk factors for recurrent Clostridium difficile infection. J Hosp Infect 2008;70(4):298–304.CrossRefGoogle ScholarPubMed
20.Owens, RC JrDonskey, CJ, Gaynes, RP, Loo, VG, Muto, CA. Antimicrobial-associated risk factors for Clostridium difficile infection. Clin Infect Dis 2008;46(suppl):S19–S31.Google Scholar
21.Howell, MD, Novack, V, Grgurich, P, et al.Iatrogenic gastric acid suppression and the risk of nosocomial Clostridium difficile infection. Arch Intern Med 2010;170(9):784–790.Google Scholar
22.Dial, S, Alrasadi, K, Manoukian, C, Huang, A, Menzies, D. Risk of Clostridium difficile diarrhea among hospital inpatients prescribed proton pump inhibitors: cohort and case-control studies. CMAJ 2004;171(1):33–38.Google Scholar
23.Vesteinsdottir, I, Gudlaugsdottir, S, Einarsdottir, R, Kalaitzakis, E, Sigurdardottir, O, Bjornsson, ES. Risk factors for Clostridium difficile toxin-positive diarrhea: a population-based prospective case-control study. Eur J Clin Microbiol Infect Dis 2012;31:2601–2610.Google Scholar
24.Kwok, CS, Arthur, AK, Anibueze, CI, Singh, S, Cavallazzi, R, Loke, YK. Risk of Clostridium difficile infection with acid suppressing drugs and antibiotics: meta-analysis. Am J Gastroenterol 2012;107:1011–1019.Google Scholar
25.Deshpande, A, Pant, C, Pasupuleti, V, et al.Association between proton pump inhibitor therapy and Clostridium difficile infection in a meta-analysis. Clin Gastroenterol Hepatol 2012;10(3):225–233.Google Scholar
26.Kuehn, BM. Reflux drugs linked to C. difficile–related diarrhea. JAMA 2012;307(10):1014.Google Scholar
27.Kim, YG, Graham, DY, Jang, BI. Proton pump inhibitor use and recurrent Clostridium difficile–associated disease: a case-control analysis matched by propensity score. J Clin Gastroenterol 2012;46(5):397–400.Google Scholar
28.Kim, JW, Lee, KL, Jeong, JB, et al.Proton pump inhibitors as a risk factor for recurrence of Clostridium difficile–associated diarrhea. World J Gastroenterol 2010;16(28):3573–3577.CrossRefGoogle ScholarPubMed
29.Cadle, RM, Mansouri, MD, Logan, N, Kudva, DR, Musher, DM. Association of proton-pump inhibitors with outcomes in Clostridium difficile colitis. Am J Health Syst Pharm 2007;64(22):2359–2363.Google Scholar
30.Peterson, LR, Gerding, DN. Antimicrobial agents in Clostridium difficile–associated intestinal disease. In: Rambaud, J-C, Ducluzeau, R, eds. Clostridium difficile–associated intestinal diseases. Paris: Springer, 1990:115–127.Google Scholar
31.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(3):302–307.Google Scholar
32.Black, SR, Weaver, KN, Jones, RC, et al.Clostridium difficile outbreak strain BI is highly endemic in Chicago area hospitals. Infect Control Hosp Epidemiol 2011;32(9):897–902.Google Scholar
33.Robicsek, A, Beaumont, JL, Wright, MO, Thomson, RB JrKaul, KL, Peterson, LR. Electronic prediction rules for methicillin-resistant Staphylococcus aureus colonization. Infect Control Hosp Epidemiol 2011;32(1):9–19.Google Scholar