Brooke M Miller1, Steven W Johnson2. 1. Novant Health Forsyth Medical Center, Winston-Salem, NC. 2. Novant Health Forsyth Medical Center, Winston-Salem, NC; Campbell University College of Pharmacy and Health Sciences, Buies Creek, NC. Electronic address: johnsonsw@campbell.edu.
Abstract
BACKGROUND: The objective of this study was to identify risk factors associated with the presence of carbapenem-resistant Enterobacteriaceae (CRE) infections to develop a clinical prediction model that can be used at patient bedside to identify subjects likely infected with a CRE pathogen. METHODS: This case-control study included patients aged ≥18 years admitted to Novant Health Forsyth Medical Center between January 1, 2012, and December 31, 2013, with CRE infections (cases) or non-CRE infections (controls). Controls were matched to their corresponding resistant case (3:1) based on pathogen, place of likely acquisition, isolate source, year of admission, and level of care. A risk prediction model was developed using variables independently associated with CRE isolation. Sensitivities and specificities were obtained at various point cutoffs, and a determination of the receiver operator characteristic (ROC) area under the curve (AUC) was performed. RESULTS: A total of 164 subjects were included. Independent risk factors for CRE included recent antibiotic therapy, recent immunosuppression, and Charlson Comorbidity Index score ≥4. Adjusted odds ratios were 13.37 (95% confidence interval [CI], 4.16-61.19), 6.69 (95% CI, 1.85-29.65), and 3.30 (95% CI, 1.34-8.40), respectively. Diagnostic performance of various score cutoffs for the model indicated a score ≥5 correlated with the highest accuracy (79%). The ROC AUC was 0.83. CONCLUSION: The risk prediction model displayed good discrimination and was an excellent predictor of CRE infection.
BACKGROUND: The objective of this study was to identify risk factors associated with the presence of carbapenem-resistant Enterobacteriaceae (CRE) infections to develop a clinical prediction model that can be used at patient bedside to identify subjects likely infected with a CRE pathogen. METHODS: This case-control study included patients aged ≥18 years admitted to Novant Health Forsyth Medical Center between January 1, 2012, and December 31, 2013, with CRE infections (cases) or non-CRE infections (controls). Controls were matched to their corresponding resistant case (3:1) based on pathogen, place of likely acquisition, isolate source, year of admission, and level of care. A risk prediction model was developed using variables independently associated with CRE isolation. Sensitivities and specificities were obtained at various point cutoffs, and a determination of the receiver operator characteristic (ROC) area under the curve (AUC) was performed. RESULTS: A total of 164 subjects were included. Independent risk factors for CRE included recent antibiotic therapy, recent immunosuppression, and Charlson Comorbidity Index score ≥4. Adjusted odds ratios were 13.37 (95% confidence interval [CI], 4.16-61.19), 6.69 (95% CI, 1.85-29.65), and 3.30 (95% CI, 1.34-8.40), respectively. Diagnostic performance of various score cutoffs for the model indicated a score ≥5 correlated with the highest accuracy (79%). The ROC AUC was 0.83. CONCLUSION: The risk prediction model displayed good discrimination and was an excellent predictor of CRE infection.
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