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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
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

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
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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