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Controlling for Severity of Illness in Outcome Studies Involving Infectious Diseases: Impact of Measurement at Different Time Points

Published online by Cambridge University Press:  02 January 2015

Kerri A. Thom*
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland
Michelle D. Shardell
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland
Regina B. Osih
Affiliation:
Department of Medicine, Centre Hospitalier Universitaire Vaudois, and University of Lausanne, Lausanne, Switzerland
Marin L. Schweizer
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland
Jon P. Furuno
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland
Eli N. Perencevich
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland Veterans Affairs Maryland Health Care System, Baltimore, Maryland
Jessina C. McGregor
Affiliation:
College of Pharmacy, Oregon State University, Corvallis, Oregon
Anthony D. Harris
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland Veterans Affairs Maryland Health Care System, Baltimore, Maryland
*
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, 100 North Greene St., Lower Level, Baltimore, MD 21201 (kthoms@medicine.umaryland.edu)

Abstract

Background.

Severity of illness is an important confounder in outcome studies involving infectious diseases. However, it is unclear whether the time at which severity of illness is measured is important.

Methods.

We performed a retrospective study of 328 episodes of gram-negative bacteremia in adult patients to assess the impact of the time of measurement of severity of illness on the association between empirical antimicrobial therapy received and in-hospital mortality. Using a modified Acute Physiology Score (APS), severity of illness was measured at 2 time points: (1) hospital admission and (2) 24 hours before the first culture-positive blood sample was collected. Multivariate logistic regression was used to estimate the impact of adjusting for the APS on the relationship between empirical therapy received (ie, the exposure) and in-hospital mortality (ie, the outcome).

Results.

The mean APS ( ± standard deviation) of patients with bacteremia increased during their hospital stay (from 19.2 ± 11.6 at admission to 24.2 ± 13.6 at the second time point; P < .01). When examining the association between empirical antimicrobial therapy received and in-hospital mortality, and controlling for the APS, there was a trend toward a decreased impact of appropriate therapy received on in-hospital mortality. The unadjusted odds ratio (OR) for the association between appropriate therapy received and in-hospital mortality was 0.83 (95% confidence interval [CI], 0.51-1.34). After controlling for the APS at admission, this association was attenuated (OR, 0.94 [95% CI, 0.57-1.55]), and when a change in the APS was also included in the multivariate logistic regression model, the association was further attenuated (OR, 0.99 [95% CI, 0.58-1.69]).

Conclusions.

The magnitude of the association between appropriate antimicrobial therapy received and in-hospital mortality among patients with gram-negative bacteremia was sensitive to the timing of adjustment for severity of illness.

Type
Original Article
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2008

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References

1.Perencevich, EN. Excess shock and mortality in Staphylococcus aureus related to methicillin resistance. Clin Infect Dis 2000;31:13111313.CrossRefGoogle Scholar
2.McGregor, JC, Rich, SE, Harris, AD, et al.A systematic review of the methodologies used to assess the association between appropriate antibiotic therapy and outcomes in bacteremic patients. Clin Infect Dis 2007;45:329337.CrossRefGoogle Scholar
3.Hamilton, KW, Bilker, WB, Lautenbach, E. 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. Infect Control Hosp Epidemiol 2007;28:832836.CrossRefGoogle ScholarPubMed
4.Marra, AR, Bearman, GM, Wenzel, RP, Edmond, MB. Comparison of severity of illness scoring systems for patients with nosocomial bloodstream infection due to Pseudomonas aeruginosa. BMC Infect Dis 2006;6:132.CrossRefGoogle Scholar
5.Osih, RB, McGregor, JC, Rich, SE, et al.Impact of empiric antibiotic therapy on outcomes in patients with Pseudomonas aeruginosa bacteremia. Antimicrob Agents Chemother 2007;51:839844.CrossRefGoogle Scholar
6.Sunenshine, RH, Wright, MO, Maragakis, LL, et al.Multidrug-resistant Acinetobacter infection mortality rate and length of hospitalization. Emerg Infect Dis 2007;13:97103.CrossRefGoogle ScholarPubMed
7.Knaus, WA, Wagner, DP, Draper, EA, et al.The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991;100:16191636.CrossRefGoogle ScholarPubMed
8.Harbarth, S, Garbino, J, Pugin, J, Romand, JA, Lew, D, Pittet, D. Inappropriate initial antimicrobial therapy and its effect on survival in a clinical trial of immunomodulating therapy for severe sepsis. Am J Med 2003;115:529535.CrossRefGoogle Scholar
9.Kang, CI, Kim, SH, Park, WB, et al.Bloodstream infections due to extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella pneumoniae: risk factors for mortality and treatment outcome, with special emphasis on antimicrobial therapy. Antimicrob Agents Chemother 2004;48:45744581.CrossRefGoogle Scholar
10.McGregor, JC, Kim, PW, Perencevich, EN, et al.Utility of the Chronic Disease Score and Charlson Comorbidity Index as comorbidity measures for use in epidemiologic studies of antibiotic-resistant organisms. Am f Epidemiol 2005;161:483493.CrossRefGoogle Scholar
11.CLSI. Performance standards for antimicrobial susceptibility testing: 16th informational supplement. CLSI document. Wayne, PA: CLSI, 2006: M100-S16.Google Scholar
12.Kang, CI, Kim, SH, Kim, HB, et al.Pseudomonas aeruginosa bacteremia: risk factors for mortality and influence of delayed receipt of effective antimicrobial therapy on clinical outcome. Clin Infect Dis 2003;37:745751.CrossRefGoogle ScholarPubMed
13.Hansen, DS, Gottschau, A, Kolmos, HJ. Epidemiology of Klebsiella bacteremia: a case control study using Escherichia coli bacteraemia as control. J Hosp Infect 1998;38:119132.CrossRefGoogle ScholarPubMed
14.Olesen, B, Kolmos, HJ, Orskov, F, Orskov, I, Gottschau, A. Bacteraemia due to Escherichia coli in a Danish university hospital, 1986-1990. Scand J Infect Dis 1995;27:253257.CrossRefGoogle Scholar
15.Wisplinghoff, H, Bischoff, T, Tallent, SM, Seifert, H, Wenzel, RP, Edmond, MB. Nosocomial bloodstream infections in US hospitals: analysis of 24,179 cases from a prospective nationwide surveillance study. Clin Infect Dis 2004;39:309317.CrossRefGoogle Scholar
16.Watanakunakorn, C, Jura, J. Klebsiella bacteremia: a review of 196 episodes during a decade (1980-1989). Scand J Infect Dis 1991;23:399405.CrossRefGoogle Scholar
17.Marra, AR, Wey, SB, Castelo, A, et al.Nosocomial bloodstream infections caused by Klebsiella pneumoniae: impact of extended-spectrum β-lac-tamase (ESBL) production on clinical outcome in a hospital with high ESBL prevalence. BMC Infect Dis 2006;6:24.CrossRefGoogle Scholar
18.Bryan, CS, Reynolds, KL, Brenner, ER. Analysis of 1,186 episodes of gram-negative bacteremia in non-university hospitals: the effects of antimicrobial therapy. Rev Infect Dis 1983;5:629638.CrossRefGoogle Scholar
19.Vidal, F, Mensa, J, Almela, M, et al.Epidemiology and outcome of Pseudomonas aeruginosa bacteremia, with special emphasis on the influence of antibiotic treatment: analysis of 189 episodes. Arch Intern Med 1996;156:21212126.CrossRefGoogle Scholar
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