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Validation of a Bacteremia Prediction Model

Published online by Cambridge University Press:  02 January 2015

Joseph M. Mylotte ,
Mario A. Pisano ,
Sanjay Ram ,
Sharlene Nakasato  and
Denise Rotella
Joseph M. Mylotte*
Affiliation:
Departments of Medicine, Buffalo, New York Microbiology, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, New York
Mario A. Pisano
Affiliation:
Department of Pharmaceutical Care Services, Erie County Medical Center, Buffalo, New York
Sanjay Ram
Affiliation:
Departments of Medicine, Buffalo, New York
Sharlene Nakasato
Affiliation:
Department of Pharmaceutical Care Services, Erie County Medical Center, Buffalo, New York
Denise Rotella
Affiliation:
Department of Pharmaceutical Care Services, Erie County Medical Center, Buffalo, New York Department of Pharmacy, School of Pharmacy, State University of New York at Buffalo, Buffalo, New York
*
Infectious Diseases, Erie County Medical Center, 462 Grider St., Buffalo, NY 14215
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Abstract

Objective:

To validate a previously published model for predicting bacteremia in hospitalized patients.

Design:

Application of a published bacteremia prediction model to a prospective validation cohort of patients and comparison of its predictability to that found in the derivation cohort.

Setting:

Urban, university-affiliated, 550-bed public hospital.

Patients:

The validation cohort consisted of 342 patients with 559 blood culture episodes between October 14, 1992, and December 5, 1992. Each blood culture episode was scored based on the presence or absence of seven predictors of bacteremia and the findings compared with published results (derivation cohort).

Interventions:

None.

Results:

Application of the bacteremia prediction model to the validation cohort identified episodes with a low risk (3%) and a high risk (17%) for true bacteremia, similar to the findings in the derivation cohort (1% and 16%, respectively). Comparison of the predictions of the model in the two cohorts by receiver operator characteristic curve analysis revealed that the overall predictability of the model in the validation cohort was not as good as in the derivation cohort.

Conclusions:

Although the bacteremia prediction model did not perform as well overall in the validation cohort, the model still was able to clearly define two extreme groups: those with a low risk and those with a high risk for true bacteremia. This predictive capability may aid physicians in prescribing empiric antimicrobial therapy and also may be useful to hospital epidemiologists in assessing quality of care

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 16 , Issue 4 , April 1995 , pp. 203 - 209
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1995

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References

1. Wasson, JH, Sox, HC, Neff, RK, Goldman, L. Clinical prediction rules. Applications and methodological standards. N Engl J Med 1985;313:793–799.CrossRefGoogle ScholarPubMed
2. Joshi, N, Localio, AR, Hamory, BH. A predictive risk index for nosocomial pneumonia in the intensive care unit. Am J Med 1992;93:135–142.Google Scholar
3. Garibaldi, RA, Cushing, D, Lerer, T. Risk factors for postoperative infection Am J Med 1991:91 (suppl 3B):158S–163S.CrossRefGoogle ScholarPubMed
4. Shapiro, M, Simchen, E, Izraeli, S, et al. A multivariate analysis of risk factors for acquiring bacteriuria in patients with indwelling urinary catheters for longer than 24 hours. Infect Control Hosp Epidemiol 1984;5:525–532.10.1017/S019594170006104XCrossRefGoogle Scholar
5. Peduzzi, P, Shatney, C, Sheagren, J, Sprung, C. Predictors of bacteremia and Gram-negative bacteremia in patients with sepsis. Arch Intern Med 1992;152:529–535.10.1001/archinte.1992.00400150059010CrossRefGoogle ScholarPubMed
6. Mellors, JW, Horowitz, RI, Harvey, MR, et al. A simple index to identify occult bacterial infection in adults with acute unexplained fever. Arch Intern Med 1987;147:666–671.CrossRefGoogle ScholarPubMed
7. Leibovici, L, Cohen, O, Wysenbeek, AJ. Occult bacterial infection in adults with unexplained fever. Validation of a diagnostic index. Arch Intern Med 1990;150:1270–1272.10.1001/archinte.1990.00390180088016Google Scholar
8. Aube, H, Milan, C, Blettery, B. Risk factors for septic shock in the early management of bacteremia. Am J Med 1992;93:283–288.Google Scholar
9. Leibovici, L, Konisberger, H, Pitlik, SD, Samra, Z, Drucker, M. Predictive index for optimizing empiric treatment of Gram-negative bacteremia . J Infect Dis 1991;163:193–196.10.1093/infdis/163.1.193Google Scholar
10. Gransden, WR, Phillips, I. Predictive index for optimizing empiric treatment of Gram-negative bacteremia. J Infect Dis 1991;164:211–212. Letter.Google Scholar
11. Gransden, WR. Predictive indices for optimizing empiric treatment of bacteremia. Infections in Medicine 1992;May:61–68.Google Scholar
12. Bates, DW, Cook, F, Goldman, L, Lee, TH. Predicting bacteremia in hospitalized patients. A prospectively validated model. Ann Intern Med 1990;113:495–500.10.7326/0003-4819-113-7-495Google Scholar
13. Gross, PA, Barrett, TL, Dellinger, P, et al. Quality standard for the treatment of bacteremia. Infect Control Hosp Epidemiol. 1994;15:189–192.CrossRefGoogle ScholarPubMed
14. McCabe, WR, Jackson, GG. Gram-negative bacteremia, I: etiology and ecology. Arch Intern Med 1962;110:847–855.Google Scholar
15. Knaus, WA, Draper, WA, Wagner, DP, Zimmerman, JE. APACHE 11: a severity of disease classification system. Crit Care Med 1985;13:818–829.CrossRefGoogle Scholar
16. MacGregor, RR, Beaty, HN. Evaluation of positive blood cultures. Guidelines for early differentiation of contaminated from valid positive cultures. Arch Intern Med 1972;130:84–87.10.1001/archinte.1972.03650010072013Google Scholar
17. Centor, RM, Schwartz, JS. An evaluation of methods for estimating the area under the receiver operating characteristic (ROC) curve. Med Decis Making 1985;5:149–156.Google Scholar
18. Centor, RM. A Visicale program for estimating the area under a receiver operating charateristic (ROC) curve. Med Decis Making 1985;5:139–148.10.1177/0272989X8500500203Google Scholar
19. Nettleman, MD. Receiver operator characteristic (ROC) curves. Infect Control Hosp Epidemiol 1988;9:374–377.Google Scholar
20. Hanley, JA, McNeil, BI. The meaning and use of the area under the receiver operating characteristic (ROC) curve. Radiology 1982;143:29–36.Google Scholar
21. Bates, DW, Goldman, L, Lee, TH. Contaminant blood cultures and resource utilization. The true consequences of false-positive results. JAMA 1991;265:365–369.Google Scholar
22. Bates, DW, Lee, TH. Rapid classification of positive blood cultures. Prospective validation of a multivariate algorithm. JAMA 1992;267:1962–1966.CrossRefGoogle ScholarPubMed
23. Fontanarosa, PB, Kaeberiein, FJ, Gerson, LW, Thomson, RB. Difficulty in predicting bacteremia in elderly emergency patients. Ann Emerg Med 1992;21:842–848.Google Scholar
24. Centers for Disease Control. Increase in national hospital discharge survey rates for septicemia-United States, 1979-87. MMWR 1990;39:31–34.Google Scholar
25. Gross, PA, Barrett, TL, Dellinger, P, et al. Consensus development of quality standards. Infect Control Hosp Epidemiol 1994;15:180–181.Google Scholar
26. Aronson, MD, Bor, DH. Blood cultures. Ann Intern Med 1987;106:246–253.10.7326/0003-4819-106-2-246Google Scholar
27. Lorian, V, Amaral, L. Predictive value of blood cultures. Infect Control Hosp Epidemiol 1992;13:293–294.Google Scholar