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Predicting Bacteremia among Patients Hospitalized for Skin and Skin-Structure Infections: Derivation and Validation of a Risk Score

Published online by Cambridge University Press:  02 January 2015

Benjamin A. Lipsky ,
Marin H. Kollef ,
Loren G. Miller ,
Xiaowu Sun ,
Richard S. Johannes  and
Ying P. Tabak
Benjamin A. Lipsky
Affiliation:
General Medical Service, Veterans Affairs Puget Sound Health Care System, Seattle, Washington Department of Medicine, University of Washington, Seattle, Washington
Marin H. Kollef
Affiliation:
Washington University School of Medicine, St Louis, Missouri
Loren G. Miller
Affiliation:
UCLA School of Medicine, Los Angeles, California Harbor-UCLA Medical Center, Los Angeles, California
Xiaowu Sun
Affiliation:
CareFusion, MedMined Services, Clinical Research, Marlborough, Massachusetts
Richard S. Johannes
Affiliation:
CareFusion, MedMined Services, Clinical Research, Marlborough, Massachusetts Harvard Medical School, Boston, Massachusetts
Ying P. Tabak
Affiliation:
CareFusion, MedMined Services, Clinical Research, Marlborough, Massachusetts
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Abstract

Objective.

Bacteremia is relatively common in patients with skin and skin-structure infection (SSSI) severe enough to require hospitalization. We used selected demographic and clinical characteristics easily assessable at initial evaluation to develop a model for the early identification of patients with SSSI who are at higher risk for bacteremia.

Participants.

A large database of adults hospitalized with SSSI at 97 hospitals in the United States during the period from 2003 through 2007 and from whom blood samples were obtained for culture at admission.

Methods.

We compared selected candidate predictor variables for patients shown to have bacteremia and patients with no demonstrated bacteremia. Using stepwise logistic regression to identify independent risk factors for bacteremia, we derived a model by using 75% of a randomly split cohort, converted the model coefficients into a risk score system, and then we validated it by using the remaining 25% of the cohort.

Results.

Bacteremia was documented in 1,021 (11.7%) of the 8,747 eligible patients. Independent predictors of bacteremia (P<.001) were infected device or prosthesis, respiratory rate less than 10 or more than 29 breaths per minute, pulse rate less than 49 or more than 125 beats per minute, temperature less than 35.6°C or at least 38.0°C, white blood cell band percentage of 7% or more, white blood cell count greater than 11 x 109/L, healthcare-associated infection, male sex, and older age. The bacteremia rates ranged from 3.7% (lowest decile) to 30.6% (highest decile) (P< .001). The model C statistic was 0.71; the Hosmer-Lemeshow test P value was .36, indicating excellent model calibration.

Conclusions.

Using data available at hospital admission, we developed a risk score that differentiated SSSI patients at low risk for bacteremia from patients at high risk. This score may help clinicians identify patients who require more intensive monitoring or antimicrobial regimens appropriate for treating bacteremia.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 31 , Issue 8 , August 2010 , pp. 828 - 837
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2010

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References

1.Stevens, DL, Bisno, AL, Chambers, HF, et al.Practice guidelines for the diagnosis and management of skin and soft-tissue infections. Clin Infect Dis 2005;41:1373–1406.CrossRefGoogle ScholarPubMed
2.DeFrances, CJ, Lucas, CA, Buie, VC, Golosinskiy, A. 2006 National Hospital Discharge Survey. National Center for Health Statistics Web site. http://www.cdc.gov/nchs/data/nhsr/nhsr005.pdf. Published July 30, 2008. Accessed September 13, 2009.Google Scholar
3.Levit, K, Wier, L, Stranges, E, Elixhauser, A. HCUP facts and figures: statistics on hospital-based care in the United States, 2007. Healthcare Cost and Utilization Web site, http://www.hcup-us.ahrq.gov/reports/factsandfigures/2007/pdfs/FF_report_2007.pdf. Published 2009. Accessed Sept 24, 2009.Google Scholar
4.Lipsky, BA, Weigelt, JA, Gupta, V, Killian, A, Peng, MM. Skin, soft tissue, bone, and joint infections in hospitalized patients: epidemiology and microbiological, clinical, and economic outcomes. Infect Control Hosp Epidemiol 2007;28:1290–1298.CrossRefGoogle ScholarPubMed
5.Eady, EA, Cove, JH. Staphylococcal resistance revisited: community-acquired methicillin resistant Staphylococcus aureus—an emerging problem for the management of skin and soft tissue infections. Curr Opin Infect Dis 2003;16:103–124.CrossRefGoogle ScholarPubMed
6.Fridkin, SK, Hageman, JC, Morrison, M, et al.Methicillin-resistant Staphylococcus aureus disease in three communities. N Engl J Med 2005;352:1436–1444.CrossRefGoogle ScholarPubMed
7.Kielhofner, MA, Brown, B, Dall, L. Influence of underlying disease process on the utility of cellulitis needle aspirates. Arch Intern Med 1988;148:2451–2452.CrossRefGoogle ScholarPubMed
8.Perl, B, Gottehrer, NP, Raveh, D, Schlesinger, Y, Rudensky, B, Yinnon, AM. Cost-effectiveness of blood cultures for adult patients with cellulitis. Clin Infect Dis 1999;29:1483–1488.CrossRefGoogle ScholarPubMed
9.Sachs, MK. The optimum use of needle aspiration in the bacteriologie diagnosis of cellulitis in adults. Arch Intern Med 1990;150:1907–1912.CrossRefGoogle Scholar
10.Sigurdsson, AF, Gudmundsson, S. The etiology of bacterial cellulitis as determined by fine-needle aspiration. Scand J Infect Dis 1989;21:537–542.CrossRefGoogle ScholarPubMed
11.Björnsdottir, S, Gottfredsson, M, Thórisdóttir, AS, et al.Risk factors for acute cellulitis of the lower limb: a prospective case-control study. Clin Infect Dis 2005;41:1416–1422.CrossRefGoogle ScholarPubMed
12.Carratalà, J, Rosón, B, Fernández-Sabé, N, et al.Factors associated with complications and mortality in adult patients hospitalized for infectious cellulitis. Eur J Clin Microbiol Infect Dis 2003;22:151–157.CrossRefGoogle ScholarPubMed
13.Lark, RL, Saint, S, Chenoweth, C, Zemencuk, JK, Lipsky, BA, Plorde, JJ. Four-year prospective evaluation of community-acquired bacteremia: epidemiology, microbiology, and patient outcome. Diagn Microbiol Infect Dis 2001;41:15–22.CrossRefGoogle ScholarPubMed
14.Micek, ST, Hoban, AP, Pham, V, et al.Bacteremia increases the risk of death among patients with soft-tissue infections. Surg Infect (Lardimi) 2010;11:169–176.CrossRefGoogle ScholarPubMed
15.Peralta, G, Padron, E, Roiz, MP, et al.Risk factors for bacteremia in patients with limb cellulitis. Eur J Clin Microbiol Infect Dis 2006;25:619–626.CrossRefGoogle ScholarPubMed
16.Sharkawy, A, Low, DE, Saginur, R, et al.Severe group A streptococcal soft-tissue infections in Ontario:1992-1996. Clin Infect Dis 2002;34:454–460.CrossRefGoogle Scholar
17.Zahar, J-R, Goveia, J, Lesprit, P, Brun-Buisson, C. Severe soft tissue infections of the extremities in patients admitted to an intensive care unit. Clin Microbiol Infect 2005;11:79–82.CrossRefGoogle Scholar
18.Weigelt, JA, Lipsky, BA, Tabak, YP, Derby, KG, Kim, M, Gupta, V. Surgical site infections: causative pathogens and associated outcomes. Am J Infect Control 2010;38:112–120.CrossRefGoogle ScholarPubMed
19.Fine, MJ, Auble, TE, Yealy, DM, et al.A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 1997;336:243–250.CrossRefGoogle ScholarPubMed
20.Kollef, MH, Shorr, A, Tabak, YP, Gupta, V, Liu, LZ, Johannes, RS. Epidemiology and outcomes of health-care-associated pneumonia: results from a large US database of culture-positive pneumonia. Chest 2005;128:3854–3862.CrossRefGoogle ScholarPubMed
21.Shorr, AF, Tabak, YP, Killian, AD, Gupta, V, Liu, LZ, Kollef, MH. Healthcare-associated bloodstream infection: a distinct entity? Insights from a large U.S. database. Crif Care Med 2006;34:2588–2595.CrossRefGoogle ScholarPubMed
22.Tabak, YP, Johannes, RS, Silber, JH. Using automated clinical data for risk adjustment: development and validation of six disease-specific mortality predictive models for pay-for-performance. Med Care 2007;45:789–805.CrossRefGoogle ScholarPubMed
23.Tabak, YP, Sun, X, Johannes, RS, Gupta, V, Shorr, AF. Mortality and need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: development and validation of a simple risk score. Arch Intern Med 2009;169:1595–1602.CrossRefGoogle ScholarPubMed
24.Jernigan, JA, Farr, BM. Short-course therapy of catheter-related Staphylococcus aureus bacteremia: a meta-analysis. Ann Intern Med 1993;119:304–311.CrossRefGoogle ScholarPubMed
25.Bates, DW, Pruess, KE, Lee, TH. How bad are bacteremia and sepsis? Outcomes in a cohort with suspected bacteremia. Arch Intern Med 1995;155:593–598.CrossRefGoogle Scholar
26.Ibrahim, EH, Sherman, G, Ward, S, Fraser, VJ, Kollef, MH. The influence of inadequate antimicrobial treatment of bloodstream infections on patient outcomes in the ICU setting. Chest 2000;118:146–155.CrossRefGoogle ScholarPubMed
27.Miller, LG, Perdreau-Remington, F, Bayer, AS, et al.Clinical and epidemiologic characteristics cannot distinguish community-associated methicillin-resistant Staphylococcus aureus infection from methicillin-suscep-tible S. aureus infection: a prospective investigation. Clin Infect Dis 2007;44:471–482.CrossRefGoogle ScholarPubMed
28.Rodriguez-Bano, J, Millan, AB, Dominguez, MA, et al.Impact of inappropriate empirical therapy for sepsis due to health care-associated methicillin-resistant Staphylococcus aureus. J Infect 2009;58:131–137.CrossRefGoogle ScholarPubMed
29.Jaimes, F, Arango, C, Ruiz, G, et al.Predicting bacteremia at the bedside. Clin Infect Dis 2004;38:357–362.CrossRefGoogle ScholarPubMed
30.Iyer, RP, Duckett, GK, Brogan, TD, Tweedy, PS, Sharpe, TC. Prognostic indicators of septicaemia—a two year prospective evaluation. Postgrad Med J 1987;63:1049–1053.CrossRefGoogle ScholarPubMed
31.Bates, DW, Cook, EF, Goldman, L, Lee, TH. Predicting bacteremia in hospitalized patients: a prospectively validated model. Ann Intern Med 1990;113:495–500.CrossRefGoogle ScholarPubMed
32.Garnacho-Montero, J, Garcia-Garmendia, JL, Barrero-Almodovar, A, Jimenez-Jimenez, FJ, Perez-Paredes, C, Ortiz-Leyba, C. Impact of adequate empirical antibiotic therapy on the outcome of patients admitted to the intensive care unit with sepsis. Crif Care Med 2003;31:2742–2751.CrossRefGoogle Scholar
33.Kollef, MH, Sherman, G, Ward, S, Fraser, VJ. Inadequate antimicrobial treatment of infections: a risk factor for hospital mortality among critically ill patients. Chest 1999;115:462–474.CrossRefGoogle ScholarPubMed
34.Kuti, EL, Patel, AA, Coleman, CI. Impact of inappropriate antibiotic therapy on mortality in patients with ventilator-associated pneumonia and blood stream infection: a meta-analysis. J Crit Care 2008;23:91–100.CrossRefGoogle Scholar
35.Vallès, J, Rello, J, Ochagavia, A, Garnacho, J, Alcalá, MA. Community-acquired bloodstream infection in critically ill adult patients: impact of shock and inappropriate antibiotic therapy on survival. Chest 2003;123:1615–1624.CrossRefGoogle ScholarPubMed
36.Roy, PM, Durieux, P, Gillaizeau, F, et al.A computerized handheld decision-support system to improve pulmonary embolism diagnosis: a randomized trial. Ann Intern Med 2009;151:677–686.CrossRefGoogle ScholarPubMed