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Controlling for Severity of Illness in Assessment of the Association Between Antimicrobial-Resistant Infection and Mortality: Impact of Calculation of Acute Physiology and Chronic Health Evaluation (APACHE) II Scores at Different Time Points

Published online by Cambridge University Press:  02 January 2015

Keith W. Hamilton ,
Warren B. Bilker  and
Ebbing Lautenbach
Keith W. Hamilton
Affiliation:
Center for Research and Education on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia
Warren B. Bilker
Affiliation:
Center for Research and Education on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia
Ebbing Lautenbach*
Affiliation:
Center for Research and Education on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia Division of Infectious Diseases, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia
*
University of Pennsylvania School of Medicine, Center for Clinical Epidemiology and Biostatistics, 825 Blockley Hall, 423 Guardian Dr., Philadelphia, PA 19104-6021 (ebbing@mail.med.upenn.edu)
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Abstract

Background.

In studies of the association between antibiotic-resistant infection and mortality, the importance of controlling for the underlying severity of illness is well recognized. However, it is unclear when the severity of illness should be assessed. Controlling for severity of illness on the day the culture specimen is obtained may underestimate the true association between resistance and mortality.

Objective.

TO assess the impact of calculating the Acute Physiology and Chronic Health Evaluation (APACHE) II score at different time points on the association between antimicrobial resistance and mortality.

Methods.

We used an existing data set from a study that investigated the association between fluoroquinolone resistance and mortality. The APACHE II score was calculated at 3 time points: the day the culture specimen was obtained, 1 day before the culture specimen was obtained, and 2 days before the culture specimen was obtained. Separate multivariable models were constructed using the 3 different APACHE II scores. These models were compared qualitatively.

Results.

Of 91 total subjects, 51 were infected with a fluoroquinolone-resistant strain and 40 with a fluoroquinolone-susceptible strain. The median APACHE II score for all subjects was 13 (95% confidence interval [CI], 11-15) when calculated on the day the culture specimen was obtained, 12 (95% CI, 11-13) when calculated 1 day before, and 11 (95% CI, 10-13) when calculated 2 days before the culture specimen was obtained. Of 91 subjects, 12 (13.2%) died. The 3 multivariable models (each with the APACHE II score calculated on a different day) were not substantively different; the adjusted odds ratio for the association between fluoroquinolone-resistant infection and mortality varied only from 1.38 to 1.65 in the 3 models.

Conclusions.

APACHE II scores calculated at different time points relative to obtainment of the culture specimen did not differ substantively. Furthermore, when the adjusted association between fluoroquinolone resistance and mortality was assessed, there were no substantive differences across multivariable models that incorporated APACHE II scores calculated at different time points.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 28 , Issue 7 , July 2007 , pp. 832 - 836
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2007

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References

1.Cosgrove, SE, Carmeli, Y. The impact of antimicrobial resistance on health and economic outcomes. Clin Infect Dis 2003;36:1433–1437.Google ScholarPubMed
2.French, GL. Clinical impact and relevance of antibiotic resistance. Adv Drug Deliv Rev 2005;57:1514–1527.Google Scholar
3.Cosgrove, SE. The relationship between antimicrobial resistance and patient outcomes: mortality, length of hospital stay, and health care costs. Clin Infect Dis 2006;42(suppl 2):S82–S89.Google Scholar
4.Cosgrove, SE, Sakoulas, G, Perencevich, EN, Schwaber, MJ, Karchmer, AW, Carmeli, Y. Comparison of mortality associated with methicillin-resistant and methicillin-susceptible Staphylococcus aureus bacteremia: a metaanalysis. Clin Infect Dis 2003;36:53–59.Google Scholar
5.Salgado, CD, Farr, BM, Calfee, DP. Community-acquired methicillin-resistant Staphylococcus aureus: a meta-analysis of prevalence and risk factors. Clin Infect Dis 2003;36:131–139.Google Scholar
6.DiazGranados, CA, Zimmer, SM, Klein, M, Jernigan, JA. Comparison of mortality associated with vancomycin-resistant and vancomycin-suscep-tible enterococcal bloodstream infections: a meta-analysis. Clin Infect Dis 2005;41:327–333.Google Scholar
7.Tleyjeh, IM, Tlaygeh, HM, Hejal, R, Montori, VM, Baddour, LM. The impact of penicillin resistance on short-term mortality in hospitalized adults with pneumococcal pneumonia: a systematic review and metaanalysis. Clin Infect Dis 2006;42:788–797.Google Scholar
8.D'Agata, EM. Methodologie issues of case-control studies: a review of established and newly recognized limitations. Infect Control Hosp Epidemiol 2005;26:338–341.Google Scholar
9.Robins, JM. The control of confounding by intermediate variables. Stat Med 1989;8:679–701.Google Scholar
10.Perencevich, EN. Excess shock and mortality in Staphylococcus aureus related to methicillin resistance. Clin Infect Dis 2000;31:1311.CrossRefGoogle ScholarPubMed
11.Lautenbach, E, Metlay, JP, Bilker, WB, Edelstein, PH, Fishman, NO. Association between fluoroquinolone resistance and mortality in Escherichia coli and Klebsiella pneumoniae infections: role of inadequate empiric antimicrobial therapy. Clin Infect Dis 2005;41:923–929.Google Scholar
12.Clinical and Laboratory Standards Institute (CLSI). Performance Standards for Antimicrobial Susceptibility Testing: Sixteenth Informational Supplement. Wayne, PA: CLSI; 2006. Document M100-S16.Google Scholar
13.Lautenbach, E, Strom, BL, Bilker, WB, Patel, JB, Edelstein, PH, Fishman, NO. Epidemiological investigation of fluoroquinolone resistance in infections due to extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella pneumoniae. Clin Infect Dis 2001;33:1288–1294.Google Scholar
14.Garner, JS, Jarvis, WR, Emori, TG, Horau, TC, Hughes, JM. CDC definitions for nosocomial infections, 1988. Am J Infect Control 1988;16:128–140.Google Scholar
15.Knaus, WA, Drapier, EA, Wagner, DP, Zimmerman, JE. APACHE II: a severity of disease classification system. Crit Care Med 1985;13:818–829.Google Scholar
16.Kleinbaum, DG, Kupper, LL, Morgenstern, H. Epidemiologie Research: Principles and Quantitative Methods. New York: Van Nostrand Reinhold; 1982.Google Scholar
17.Hosmer, DW, Lemeshow, SL. Applied Logistic Regression. 2nd ed. New York: Wiley Interscience; 2000.Google Scholar
18.Sun, GW, Shook, TL, Kay, GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 1996;49:907–916.CrossRefGoogle ScholarPubMed
19.Greenland, S. Modeling and variable selection in epidemiologic analysis. Am J Public Health 1989;79:340–349.Google Scholar
20.Maldonado, G, Greenland, S. Simulation study of confounder-selection strategies. Am J Epidemiol 1993;138:923–936.Google Scholar
21.Robins, JM, Greenland, S. The role of model selection in causal inference from nonexperimental data. Am J Epidemiol 1986;123:392–402.Google Scholar