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Comorbidity and severity-of-illness risk adjustment for hospital-onset Clostridioides difficile infection using data from the electronic medical record

Published online by Cambridge University Press:  17 December 2020

Stephanie M. Cabral Open the ORCID record for Stephanie M. Cabral [Opens in a new window] ,
Katherine E. Goodman Open the ORCID record for Katherine E. Goodman [Opens in a new window] ,
Natalia Blanco ,
Surbhi Leekha Open the ORCID record for Surbhi Leekha [Opens in a new window] ,
Larry S. Magder ,
Gita Nadimpalli Open the ORCID record for Gita Nadimpalli [Opens in a new window] ,
Lisa Pineles Open the ORCID record for Lisa Pineles [Opens in a new window] ,
Kerri A. Thom  and
Anthony D. Harris
Stephanie M. Cabral
Affiliation:
Department of Epidemiology of Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Katherine E. Goodman
Affiliation:
Department of Epidemiology of Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Natalia Blanco
Affiliation:
Department of Epidemiology of Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Surbhi Leekha
Affiliation:
Department of Epidemiology of Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Larry S. Magder
Affiliation:
Department of Epidemiology of Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Gita Nadimpalli
Affiliation:
Department of Epidemiology of Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Lisa Pineles
Affiliation:
Department of Epidemiology of Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Kerri A. Thom
Affiliation:
Department of Epidemiology of Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Anthony D. Harris*
Affiliation:
Department of Epidemiology of Public Health, University of Maryland School of Medicine, Baltimore, Maryland
*
Author for correspondence: Anthony D. Harris E-mail: aharris@som.umaryland.edu
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Abstract

Objective:

To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment.

Patients:

All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018.

Methods:

Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities.

Results:

At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change.

Conclusions:

Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 42 , Issue 8 , August 2021 , pp. 955 - 961
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Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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References

Antibiotic resistance threats in the United States. 2013. Centers for Disease Control and Prevention website. https://www-cdc-gov.proxy-hs.researchport.umd.edu/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf#page=51. Published 2013. Accessed February 18, 2018.Google Scholar
Lucado, J, Gould, C, Elixhauser, A. Clostridium difficile infections (CDI) in hospital stays, 2009: statistical brief #124. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville, MD: Agency for Healthcare Research and Quality; 2009.Google Scholar
Bouwknegt, M, van Dorp, S, Kuijper, E. Burden of Clostridium difficile infection in the United States. N Engl J Med 2015;372:2368.Google ScholarPubMed
Hospital-Acquired Conditional Reduction Program (HACRP). Center for Medicare and Medicaid Services website. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/HAC/Hospital-Acquired-Conditions.html. Updated July 30, 2018. Accessed August 3, 2018.Google Scholar
Healthcare-associated infections. Center for Medicare and Medicaid Services website. https://www.medicare.gov/hospitalcompare/Data/Healthcare-Associated-Infections.html. Accessed February 18, 2018.Google Scholar
The NHSN guide to the standardized infection ratio: a guide to the SIR. Centers for Disease Control and Prevention website. https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf. Published 2017. Accessed February 6, 2018.Google Scholar
Garey, KW, Sethi, S, Yadav, Y, DuPont, HL. Meta-analysis to assess risk factors for recurrent Clostridium difficile infection. J Hosp Infect 2008;70:298–304.CrossRefGoogle ScholarPubMed
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;171:33–38.CrossRefGoogle ScholarPubMed
Eze, P, Balsells, E, Kyaw, MH, Nair, H. Risk factors for Clostridium difficile infections—an overview of the evidence base and challenges in data synthesis. J Glob Health 2017;7(1):010417. doi: 10.7189/jogh.07.010417.CrossRefGoogle ScholarPubMed
Evans, CT, Safdar, N. Current trends in the epidemiology and outcomes of Clostridium difficile infection. Clin Infect Dis 2015;60 suppl 2:S66–S71.CrossRefGoogle Scholar
Khanafer, N, Vanhems, P, Barbut, F, et al. Factors associated with Clostridium difficile infection: a nested case-control study in a three year prospective cohort. Anaerobe 2017;44:117–123.CrossRefGoogle Scholar
Howell, MD, Novack, V, Grgurich, P, et al. Iatrogenic gastric acid suppression and the risk of nosocomial Clostridium difficile infection. Arch Intern Med 2010;170:784–790.CrossRefGoogle ScholarPubMed
Dubberke, ER, Reske, KA, Olsen, MA, et al. Risk for Clostridium difficile infection after allogeneic hematopoietic cell transplant remains elevated in the postengraftment period. transplant direct. 2017;3(4):e145.CrossRefGoogle ScholarPubMed
Rodemann, JF, Dubberke, ER, Reske, KA, Seo, DH, Stone, CD. Incidence of Clostridium difficile infection in inflammatory bowel disease. Clin Gastroenterol Hepatol 2007;5:339–344.CrossRefGoogle ScholarPubMed
Harris, AD, Sbarra, AN, Leekha, S, et al. Electronically available comorbid conditions for risk prediction of healthcare-associated Clostridium difficile infection. Infect Control Hosp Epidemiol 2018;39:297–301.CrossRefGoogle ScholarPubMed
Tabak, YP, Johannes, RS, Sun, X, Nunez, CM, McDonald, LC. Predicting the risk for hospital-onset Clostridium difficile infection (HO-CDI) at the time of inpatient admission: HO-CDI risk score. Infect Control Hosp Epidemiol 2015;36:695–701.CrossRefGoogle Scholar
Harris, AD, Jackson, SS, Robinson, G, et al. Pseudomonas aeruginosa colonization in the intensive care unit: prevalence, risk factors, and clinical outcomes. Infect Control Hosp Epidemiol 2016;37:544–548.CrossRefGoogle ScholarPubMed
Pepin, CS, Thom, KA, Sorkin, JD, et al. Risk factors for central-line–associated bloodstream infections: a focus on comorbid conditions. Infect Control Hosp Epidemiol 2015;36:479–481.CrossRefGoogle ScholarPubMed
Harris, AD, Johnson, JK, Thom, KA, et al. Risk factors for development of intestinal colonization with imipenem-resistant Pseudomonas aeruginosa in the intensive care unit setting. Infect Control Hosp Epidemiol 2011;32:719–722.CrossRefGoogle ScholarPubMed
Harris, AD, Fleming, B, Bromberg, JS, et al. Surgical site infection after renal transplantation. Infect Control Hosp Epidemiol 2015;36:417–423.CrossRefGoogle ScholarPubMed
Clostridium difficile infection (CDI) tracking. Centers for Disease Control and Prevention website. https://www.cdc.gov/hai/eip/clostridium-difficile.html. Updated 2017. Accessed February 25, 2018.Google Scholar
Quan, H, Sundararajan, V, Halfon, P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43:1130–1139.CrossRefGoogle ScholarPubMed
Elixhauser, A, Steiner, C, Harris, DR, Coffey, RM. Comorbidity measures for use with administrative data. Med Care 1998;36:8–27.CrossRefGoogle ScholarPubMed
van Walraven, C, Austin, PC, Jennings, A, Quan, H, Forster, AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care 2009;47:626–633.CrossRefGoogle ScholarPubMed
Fortin, Y, Crispo, JAG, Cohen, D, McNair, DS, Mattison, DR, Krewski, D. External validation and comparison of two variants of the Elixhauser comorbidity measures for all-cause mortality. PLoS One 2017;12(3):e0174379.CrossRefGoogle ScholarPubMed
Moore, BJ, White, S, Washington, R, Coenen, N, Elixhauser, A. Identifying increased risk of readmission and in-hospital mortality using hospital administrative data: the AHRQ Elixhauser comorbidity index. Med Care 2017;55:698–705.CrossRefGoogle ScholarPubMed
Quan, H, Parsons, GA, Ghali, WA. Validity of information on comorbidity derived rom ICD-9-CCM administrative data. Med Care 2002;40:675–685.CrossRefGoogle ScholarPubMed
Schneeweiss, S, Maclure, M. Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol 2000;29:891–898.CrossRefGoogle ScholarPubMed
Leal, JR, Laupland, KB. Validity of ascertainment of comorbid illness using administrative databases: a systematic review. Clin Microbiol Infect 2010;16:715–721.CrossRefGoogle Scholar
Quan, H, Li, B, Duncan Saunders, L, et al. Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database. Health Serv Res 2008;43:1424–1441.CrossRefGoogle Scholar