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Which Comorbid Conditions Should We Be Analyzing as Risk Factors for Healthcare-Associated Infections?

Published online by Cambridge University Press:  29 December 2016

Anthony D. Harris ,
Lisa Pineles ,
Deverick Anderson ,
Keith F. Woeltje ,
William E. Trick ,
Keith S. Kaye ,
Deborah S. Yokoe ,
Ann-Christine Nyquist ,
David P. Calfee  and
Surbhi Leekha
Anthony D. Harris*
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
Lisa Pineles
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
Deverick Anderson
Affiliation:
Duke University Medical Center, Department of Medicine, Division of Infectious Diseases, Durham, North Carolina Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, North Carolina
Keith F. Woeltje
Affiliation:
Department of Medicine, Washington University Medical Center, St Louis, Missouri BJC HealthCare, St Louis, Missouri
William E. Trick
Affiliation:
Rush University Medical Center, Chicago, Illinois Cook County Health and Hospitals System, Chicago, Illinois
Keith S. Kaye
Affiliation:
Division of Infectious Diseases, Detroit Medical Center, Wayne State University, Detroit, Michigan
Deborah S. Yokoe
Affiliation:
Department of Medicine, Brigham & Women’s Hospital, Boston, Massachusetts
Ann-Christine Nyquist
Affiliation:
Children’s Hospital Colorado, Aurora, Colorado
David P. Calfee
Affiliation:
Weill Cornell Medicine, New York, New York
Surbhi Leekha
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
*
Address correspondence to Anthony D. Harris, MD MPH, 10 S. Pine St, MSTF 330, Baltimore, MD 21201 (aharris@epi.umaryland.edu).
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Abstract

OBJECTIVE

To determine which comorbid conditions are considered causally related to central-line associated bloodstream infection (CLABSI) and surgical-site infection (SSI) based on expert consensus.

DESIGN

Using the Delphi method, we administered an iterative, 2-round survey to 9 infectious disease and infection control experts from the United States.

METHODS

Based on our selection of components from the Charlson and Elixhauser comorbidity indices, 35 different comorbid conditions were rated from 1 (not at all related) to 5 (strongly related) by each expert separately for CLABSI and SSI, based on perceived relatedness to the outcome. To assign expert consensus on causal relatedness for each comorbid condition, all 3 of the following criteria had to be met at the end of the second round: (1) a majority (>50%) of experts rating the condition at 3 (somewhat related) or higher, (2) interquartile range (IQR)≤1, and (3) standard deviation (SD)≤1.

RESULTS

From round 1 to round 2, the IQR and SD, respectively, decreased for ratings of 21 of 35 (60%) and 33 of 35 (94%) comorbid conditions for CLABSI, and for 17 of 35 (49%) and 32 of 35 (91%) comorbid conditions for SSI, suggesting improvement in consensus among this group of experts. At the end of round 2, 13 of 35 (37%) and 17 of 35 (49%) comorbid conditions were perceived as causally related to CLABSI and SSI, respectively.

CONCLUSIONS

Our results have produced a list of comorbid conditions that should be analyzed as risk factors for and further explored for risk adjustment of CLABSI and SSI.

Infect Control Hosp Epidemiol 2017;38:449–454

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 38 , Issue 4 , April 2017 , pp. 449 - 454
Copyright
© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

1. Olsen, MA, Higham-Kessler, J, Yokoe, DS, et al. Developing a risk stratification model for surgical site infection after abdominal hysterectomy. Infect Control Hosp Epidemiol 2009;30:1077–1083.Google Scholar
2. Xue, DQ, Qian, C, Yang, L, Wang, XF. Risk factors for surgical site infections after breast surgery: a systematic review and meta-analysis. Eur J Surg Oncol 2012;38:375–381.Google Scholar
3. Jackson, SS, Leekha, S, Pineles, L, et al. Improving risk adjustment above current centers for disease control and prevention methodology using electronically available comorbid conditions. Infect Control Hosp Epidemiol 2016;37:1173–1178.Google Scholar
4. Chopra, T, Marchaim, D, Lynch, Y, et al. Epidemiology and outcomes associated with surgical site infection following bariatric surgery. Am J Infect Control 2012;40:815–819.Google Scholar
5. McGregor, JC, Perencevich, EN, Furuno, JP, et al. Comorbidity risk-adjustment measures were developed and validated for studies of antibiotic-resistant infections. J Clin Epidemiol 2006;59:1266–1273.CrossRefGoogle ScholarPubMed
6. Safdar, N, Maki, DG. The commonality of risk factors for nosocomial colonization and infection with antimicrobial-resistant Staphylococcus aureus, enterococcus, gram-negative bacilli, Clostridium difficile, and Candida . Ann Intern Med 2002;136:834–844.CrossRefGoogle ScholarPubMed
7. McKinnell, JA, Miller, LG, Eells, SJ, Cui, E, Huang, SS. A systematic literature review and meta-analysis of factors associated with methicillin-resistant Staphylococcus aureus colonization at time of hospital or intensive care unit admission. Infect Control Hosp Epidemiol 2013;34:1077–1086.Google Scholar
8. 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.Google Scholar
9. Lissauer, ME, Leekha, S, Preas, MA, Thom, KA, Johnson, SB. Risk factors for central line-associated bloodstream infections in the era of best practice. J Trauma Acute Care Surg 2012;72:1174–1180.Google Scholar
10. Moehring, RW, Anderson, DJ. “But my patients are different!”: risk adjustment in 2012 and beyond. Infect Control Hosp Epidemiol 2011;32:987–989.Google Scholar
11. 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.Google Scholar
12. Elixhauser, A, Steiner, C, Harris, DR, Coffey, RM. Comorbidity measures for use with administrative data. Med Care 1998;36:8–27.Google Scholar
13. Dalkey, N, Helmer, O. An experimental application of the Delphi method to the use of experts. Manag Sci 1963;9:458.Google Scholar
14. Powell, C. Methodological issues in nursing research. The Delphi technique: myths and realities. J Adv Nursing 2003;4:376.Google Scholar
15. Fitch, K, Bernstein, SJ, Aguilar, MS, et al. eds The RAND/UCLA Appropriateness Method User’s Manual. Santa Monica CA: Rand; 2001.Google Scholar
16. Deyo, RA, Cherkin, DC, Ciol, MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613–619.CrossRefGoogle ScholarPubMed
17. Holey, EA, Feeley, JL, Dixon, J, Whittaker, VJ. An exploration of the use of simple statistics to measure consensus and stability in Delphi studies. BMC Med Res Methodol 2007;7:52.Google Scholar
18. Dudeck, MA, Horan, TC, Peterson, KD, et al. National Healthcare Safety Network (NHSN) Report, data summary for 2010, device-associated module. Am J Infect Control 2011;39:798–816.Google Scholar
19. Haley, RW, Culver, DH, Morgan, WM, White, JW, Emori, TG, Hooton, TM. Identifying patients at high risk of surgical wound infection. A simple multivariate index of patient susceptibility and wound contamination. Am J Epidemiol 1985;121:206–215.Google Scholar
20. Mu, Y, Edwards, JR, Horan, TC, Berrios-Torres, SI, Fridkin, SK. Improving risk-adjusted measures of surgical site infection for the national healthcare safety network. Infect Control Hosp Epidemiol 2011;32:970–986.Google Scholar
21. Anderson, DJ, Chen, LF, Sexton, DJ, Kaye, KS. Complex surgical site infections and the devilish details of risk adjustment: important implications for public reporting. Infect Control Hosp Epidemiol 2008;29:941–946.Google Scholar
22. Roy, MC, Herwaldt, LA, Embrey, R, Kuhns, K, Wenzel, RP, Perl, TM. Does the Centers for Disease Control’s NNIS system risk index stratify patients undergoing cardiothoracic operations by their risk of surgical-site infection? Infect Control Hosp Epidemiol 2000;21:186–190.Google Scholar
23. Bergen, GA, Toney, JF. Infection versus colonization in the critical care unit. Crit Care Clin 1998;14:71–90.Google Scholar
24. Rothman, KJ, Greenland, S, Lash, TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.Google Scholar
25. Gordis, L. Epidemiology. 2nd ed. Philadelphia: W.B. Saunders Company; 2000.Google Scholar
26. Harrell, FE Jr, Lee, KL, Mark, DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361–387.Google Scholar
27. Greenland, S, Morgenstern, H. Confounding in health research. Annu Rev Public Health 2001;22:189–212.Google Scholar
28. O’Leary, DP, Lynch, N, Clancy, C, Winter, DC, Myers, E. International, expert-based, consensus statement regarding the management of acute diverticulitis. JAMA Surg 2015;150:899–904.Google Scholar
29. McGregor, JC, Kim, PW, Perencevich, EN, et al. Utility of the chronic disease score and Charlson comorbidity index as comorbidity measures for use in epidemiologic studies of antibiotic-resistant organisms. Am J Epidemiol 2005;161:483–493.CrossRefGoogle ScholarPubMed
30. Stevens, V, Concannon, C, van Wijngaarden, E, McGregor, J. Validation of the chronic disease score-infectious disease (CDS-ID) for the prediction of hospital-associated Clostridium difficile infection (CDI) within a retrospective cohort. BMC Infect Dis 2013;13:150.Google Scholar