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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 (ssjackson@umaryland.edu).
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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 

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