Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-26T01:21:59.410Z Has data issue: false hasContentIssue false
  • English
  • Français

Improving Risk Adjustment Above Current Centers for Disease Control and Prevention Methodology Using Electronically Available Comorbid Conditions

Published online by Cambridge University Press:  15 July 2016

Sarah S. Jackson ,
Surbhi Leekha ,
Lisa Pineles ,
Laurence S. Magder ,
Kerri A. Thom ,
Yuan Wang  and
Anthony D. Harris
Sarah S. Jackson*
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
Surbhi Leekha
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
Lisa Pineles
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
Laurence S. Magder
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
Kerri A. Thom
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
Yuan Wang
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
Anthony D. Harris
Affiliation:
University of Maryland School of Medicine, Baltimore, Maryland
*
Address correspondence to Sarah S. Jackson, MPH, 10 S. Pine St, MSTF 362A, Baltimore, MD 21201 ([email protected]).
Get access Rights & Permissions [Opens in a new window]

Abstract

OBJECTIVE

To identify comorbid conditions associated with surgical site infection (SSI) among patients undergoing renal transplantation and improve existing risk adjustment methodology used by the Centers for Disease Control and Prevention National Healthcare Safety Network (NHSN).

PATIENTS

Patients (≥18 years) who underwent renal transplantation at University of Maryland Medical Center January 1, 2010-December 31, 2011.

METHODS

Trained infection preventionists reviewed medical records to identify surgical site infections that developed within 30 days after transplantation, using NHSN criteria. Patient demographic characteristics and risk factors for surgical site infections were identified through a central data repository. International Statistical Classification of Disease, Ninth Revision, Clinical Modification codes were used to analyze individual component comorbid conditions and calculate the Charlson and Elixhauser comorbidity indices. These indices were compared with the current NHSN risk adjustment methodology.

RESULTS

A total of 441 patients were included in the final cohort. In bivariate analysis, the Charlson components of cerebrovascular disease, peripheral vascular disease, and rheumatologic disorders and Elixhauser components of obesity, rheumatoid arthritis, and weight loss were significantly associated with the outcome. A model utilizing the variables from the NHSN methodology had a c-statistic of 0.56 (95% CI, 0.48–0.63), whereas a model that also included comorbidities from the Charlson and Elixhauser indices had a c-statistic of 0.65 (95% CI, 0.58–0.73). The model with all 3 risk adjustment scores performed best and was statistically different from the NHSN model alone, demonstrated by improvement in the c statistic (0.65 vs 0.56).

CONCLUSION

Risk adjustment models should incorporate electronically available comorbid conditions.

Infect Control Hosp Epidemiol 2016;1–6

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 37 , Issue 10 , October 2016 , pp. 1173 - 1178
Copyright
© 2016 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. Moehring, RW, Anderson, DJ. “But my patients are different!”: risk adjustment in 2012 and beyond. Infect Control Hosp Epidemiol 2011;32:987–989.CrossRefGoogle Scholar
2. 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.CrossRefGoogle ScholarPubMed
3. 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.CrossRefGoogle ScholarPubMed
4. 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.CrossRefGoogle ScholarPubMed
5. 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.CrossRefGoogle ScholarPubMed
6. Harris, AD, Fleming, B, Bromberg, JS, et al. Surgical site infection after renal transplantation. Infect Control Hosp Epidemiol 2015;36:417–423.CrossRefGoogle ScholarPubMed
7. Centers for Disease Control and Prevention. CDC/NHSN surveillance definitions for specific types of infections. CDC website. http://www.cdc.gov/nhsn/PDFs/pscManual/17pscNosInfDef_current.pdf. Updated 2015. Accessed June 4, 2016.Google Scholar
8. Horan, TC, Andrus, M, Dudeck, MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control 2008;36:309–332.CrossRefGoogle ScholarPubMed
9. 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
10. Elixhauser, A, Steiner, C, Harris, DR, Coffey, RM. Comorbidity measures for use with administrative data. Med Care 1998;36:8–27.CrossRefGoogle ScholarPubMed
11. Tacconelli, E, Cataldo, MA, De Pascale, G, et al. Prediction models to identify hospitalized patients at risk of being colonized or infected with multidrug-resistant Acinetobacter baumannii calcoaceticus complex. J Antimicrob Chemother 2008;62:1130–1137.CrossRefGoogle ScholarPubMed
12. 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
13. Verceles, AC, Lechner, EJ, Halpin, D, Scharf, SM. The association between comorbid illness, colonization status, and acute hospitalization in patients receiving prolonged mechanical ventilation. Respir Care 2013;58:250–256.CrossRefGoogle ScholarPubMed
14. 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
15. Merkow, RP, Hall, BL, Cohen, ME, et al. Relevance of the c-statistic when evaluating risk-adjustment models in surgery. J Am Coll Surg 2012;214:822–830.CrossRefGoogle ScholarPubMed
16. Yokoe, DS, Anderson, DJ, Berenholtz, SM, et al. A compendium of strategies to prevent healthcare-associated infections in acute care hospitals: 2014 updates. Infect Control Hosp Epidemiol 2014;35:967–977.CrossRefGoogle ScholarPubMed
17. Esemuede, IO, Murray, AC, Lee-Kong, SA, Feingold, DL, Kiran, RP. Obesity, regardless of comorbidity, influences outcomes after colorectal surgery—time to rethink the pay-for-performance metrics? J Gastrointest Surg 2014;18:2163–2168.CrossRefGoogle ScholarPubMed
18. Sivrikoz, E, Karamanos, E, Beale, E, Teixeira, P, Inaba, K, Demetriades, D. The effect of diabetes on outcomes following emergency appendectomy in patients without comorbidities: a propensity score-matched analysis of National Surgical Quality Improvement Program database. Am J Surg 2015;209:206–211.CrossRefGoogle ScholarPubMed
19. Southern, DA, Quan, H, Ghali, WA. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care 2004;42:355–360.CrossRefGoogle ScholarPubMed
20. Menendez, ME, Neuhaus, V, van Dijk, CN, Ring, D. The Elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery. Clin Orthop Relat Res 2014;472:2878–2886.CrossRefGoogle ScholarPubMed
21. Huttunen, R, Syrjanen, J. Obesity and the risk and outcome of infection. Int J Obes (Lond) 2013;37:333–340.CrossRefGoogle ScholarPubMed
22. Menezes, FG, Wey, SB, Peres, CA, Medina-Pestana, JO, Camargo, LF. Risk factors for surgical site infection in kidney transplant recipients. Infect Control Hosp Epidemiol 2008;29:771–773.CrossRefGoogle ScholarPubMed
23. Dryden, M, Baguneid, M, Eckmann, C, et al. Pathophysiology and burden of infection in patients with diabetes mellitus and peripheral vascular disease: focus on skin and soft-tissue infections. Clin Microbiol Infect 2015;21:S27–S32.CrossRefGoogle ScholarPubMed
24. Edwards, JR, Peterson, KD, Mu, Y, et al. National Healthcare Safety Network (NHSN) report: data summary for 2006 through 2008, issued December 2009. Am J Infect Control 2009;37:783–805.CrossRefGoogle ScholarPubMed