Heather Henderson1, Courtney L Luterbach2, Eric Cober3, Sandra S Richter4, Robert A Salata5, Robert C Kalayjian6, Richard R Watkins7,8, Yohei Doi9, Keith S Kaye10, Scott Evans11, Vance G Fowler12,13, Robert A Bonomo14,15,16,17, Anthony Harris18, Sonia Napravnik1, David Van Duin1. 1. Division of Infectious Diseases, University of North Carolina, Chapel Hill. 2. Institute for Global Health and Infectious Diseases, University of North Carolina, Chapel Hill. 3. Department of Infectious Diseases, Cleveland Clinic Akron General, Ohio. 4. Department of Laboratory Medicine, Cleveland Clinic, Ohio. 5. Division of Infectious Diseases and Human Immunodeficiency Virus Medicine, Department of Medicine, Case Western Reserve University School of Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio. 6. Department of Medicine, MetroHealth Medical Center, Cleveland, Ohio. 7. Department of Internal Medicine, Northeast Ohio Medical University, Rootstown, Ohio. 8. Division of Infectious Diseases, Cleveland Clinic Akron General, Ohio. 9. Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania. 10. Division of Infectious Diseases, University of Michigan, Ann Arbor. 11. Department of Biostatistics, George Washington University, Washington, DC. 12. Division of Infectious Diseases, Duke University, Durham, North Carolina. 13. Duke Clinical Research Institute, Duke University, Durham, North Carolina. 14. Department of Medicine, Case Western Reserve University School of Medicine Ohio. 15. Louis Stokes Cleveland Department of Veterans Affairs Medical Center Ohio. 16. Departments of Pharmacology, Molecular Biology and Microbiology, Biochemistry, and Proteomics and Bioinformatics, Case Western Reserve University School of Medicine Ohio, Ohio. 17. Case Western Reserve University Cleveland Veterans Administration Medical Center Center for Antimicrobial Resistance and Epidemiology, Ohio. 18. Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore.
Abstract
BACKGROUND: Predicting mortality risk in patients is important in research settings. The Pitt bacteremia score (PBS) is commonly used as a predictor of early mortality risk in patients with bloodstream infections (BSIs). We determined whether the PBS predicts 14-day inpatient mortality in nonbacteremia carbapenem-resistant Enterobacteriaceae (CRE) infections. METHODS: Patients were selected from the Consortium on Resistance Against Carbapenems in Klebsiella and Other Enterobacteriaceae, a prospective, multicenter, observational study. We estimated risk ratios to analyze the predictive ability of the PBS overall and each of its components individually. We analyzed each component of the PBS in the prediction of mortality, assessed the appropriate cutoff value for the dichotomized score, and compared the predictive ability of the qPitt score to that of the PBS. RESULTS: In a cohort of 475 patients with CRE infections, a PBS ≥4 was associated with mortality in patients with nonbacteremia infections (risk ratio [RR], 21.9; 95% confidence interval [CI], 7.0, 68.8) and with BSIs (RR, 6.0; 95% CI, 2.5, 14.4). In multivariable analysis, the hypotension, mechanical ventilation, mental status, and cardiac arrest parameters of the PBS were independent risk factors for 14-day all-cause inpatient mortality. The temperature parameter as originally calculated for the PBS was not independently associated with mortality. However, a temperature <36.0°C vs ≥36°C was independently associated with mortality. A qPitt score ≥2 had similar discrimination as a PBS ≥4 in nonbacteremia infections. CONCLUSIONS: Here, we validated that the PBS and qPitt score can be used as reliable predictors of mortality in nonbacteremia CRE infections.
BACKGROUND: Predicting mortality risk in patients is important in research settings. The Pitt bacteremia score (PBS) is commonly used as a predictor of early mortality risk in patients with bloodstream infections (BSIs). We determined whether the PBS predicts 14-day inpatient mortality in nonbacteremia carbapenem-resistant Enterobacteriaceae (CRE) infections. METHODS: Patients were selected from the Consortium on Resistance Against Carbapenems in Klebsiella and Other Enterobacteriaceae, a prospective, multicenter, observational study. We estimated risk ratios to analyze the predictive ability of the PBS overall and each of its components individually. We analyzed each component of the PBS in the prediction of mortality, assessed the appropriate cutoff value for the dichotomized score, and compared the predictive ability of the qPitt score to that of the PBS. RESULTS: In a cohort of 475 patients with CRE infections, a PBS ≥4 was associated with mortality in patients with nonbacteremia infections (risk ratio [RR], 21.9; 95% confidence interval [CI], 7.0, 68.8) and with BSIs (RR, 6.0; 95% CI, 2.5, 14.4). In multivariable analysis, the hypotension, mechanical ventilation, mental status, and cardiac arrest parameters of the PBS were independent risk factors for 14-day all-cause inpatient mortality. The temperature parameter as originally calculated for the PBS was not independently associated with mortality. However, a temperature <36.0°C vs ≥36°C was independently associated with mortality. A qPitt score ≥2 had similar discrimination as a PBS ≥4 in nonbacteremia infections. CONCLUSIONS: Here, we validated that the PBS and qPitt score can be used as reliable predictors of mortality in nonbacteremia CRE infections.
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