Literature DB >> 27418295

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

Sarah S Jackson1, Surbhi Leekha1, Lisa Pineles1, Laurence S Magder1, Kerri A Thom1, Yuan Wang1, Anthony D Harris1.   

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.

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Year:  2016        PMID: 27418295      PMCID: PMC6341979          DOI: 10.1017/ice.2016.140

Source DB:  PubMed          Journal:  Infect Control Hosp Epidemiol        ISSN: 0899-823X            Impact factor:   3.254


  3 in total

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

Authors:  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; Surbhi Leekha
Journal:  Infect Control Hosp Epidemiol       Date:  2016-12-29       Impact factor: 3.254

2.  Electronically Available Comorbid Conditions for Risk Prediction of Healthcare-Associated Clostridium difficile Infection.

Authors:  Anthony D Harris; Alyssa N Sbarra; Surbhi Leekha; Sarah S Jackson; J Kristie Johnson; Lisa Pineles; Kerri A Thom
Journal:  Infect Control Hosp Epidemiol       Date:  2018-02-05       Impact factor: 3.254

3.  Electronically Available Comorbidities Should Be Used in Surgical Site Infection Risk Adjustment.

Authors:  Sarah S Jackson; Surbhi Leekha; Laurence S Magder; Lisa Pineles; Deverick J Anderson; William E Trick; Keith F Woeltje; Keith S Kaye; Timothy J Lowe; Anthony D Harris
Journal:  Clin Infect Dis       Date:  2017-09-01       Impact factor: 9.079

  3 in total

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