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Utility of a Clinical Risk Factor Scoring Model in Predicting Infection with Extended-Spectrum β-Lactamase-Producing Enterobacteriaceae on Hospital Admission

Published online by Cambridge University Press:  02 January 2015

Steven W. Johnson ,
Deverick J. Anderson ,
D. Byron May  and
Richard H. Drew
Steven W. Johnson*
Affiliation:
Duke University Hospital, Durham, North Carolina Campbell University College of Pharmacy and Health Sciences, Buies Creek, North Carolina
Deverick J. Anderson
Affiliation:
Duke University Hospital, Durham, North Carolina
D. Byron May
Affiliation:
Duke University Hospital, Durham, North Carolina Campbell University College of Pharmacy and Health Sciences, Buies Creek, North Carolina
Richard H. Drew
Affiliation:
Duke University Hospital, Durham, North Carolina Campbell University College of Pharmacy and Health Sciences, Buies Creek, North Carolina
*
Campbell University College of Pharmacy, and Health Sciences, 1612 Barndale Glen Court, Winston-Salem, NC 27106 (johnsonsw@campbell.edu)
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Abstract

Objective.

To validate the utility of a previously published scoring model (Italian) to identify patients infected with community-onset extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-EKP) and develop a new model (Duke) based on local epidemiology.

Methods.

This case-control study included patients 18 years of age or more admitted to Duke University Hospital between January 1, 2008, and December 31, 2010, with culture-confirmed infection due to an ESBL-EKP (cases). Uninfected controls were matched to cases (3 : 1). The Italian model was applied to our patient population for validation. The Duke model was developed through logistic-regression-based prediction scores calculated on variables independently associated with ESBL-EKP isolation. Sensitivities and specificities at various point cutoffs were determined, and determination of the area under the receiver operating characteristic curve (ROC AUC) was performed.

Results.

A total of 123 cases and 375 controls were identified. Adjusted odds ratios and 95% confidence intervals for variables previously identified in the Italian model were as follows: hospitalization (3.20 [1.62–6.55]), transfer (4.31 [2.15–8.78]), urinary catheterization (5.92 [3.09–11.60]), β-lactam and/or fluoroquinolone therapy (3.76 [2.06–6.95]), age 70 years or more (1.55 [0.79–3.01]), and Charlson Comorbidity Score of 4 or above (1.06 [0.55–2.01]). Sensitivity and specificity were, respectively, more than or equal to 95% and less than or equal to 47% for scores 3 or below and were less than or equal to 50% and more than or equal to 96% for scores 8 or above. The ROC AUC was 0.88. Variables identified in the Duke model were as follows: hospitalization (2.63 [1.32–5.41]), transfer (5.30 [2.67–10.71]), urinary catheterization (6.89 [3.62–13.38]), β-lactam and/or fluoroquinolone therapy (3.47 [1.91–6.41]), and immunosuppression (2.34 [1.14–4.80]). Sensitivity and specificity were, respectively, more than or equal to 94% and less than or equal to 65% for scores 3 or below and were less than or equal to 58% and more than or equal to 95% for scores 8 or above. The ROC AUC was 0.89.

Conclusion.

While the previously reported model was an excellent predictor of community-onset ESBL-EKP infection, models utilizing factors based on local epidemiology may be associated with improved performance.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 34 , Issue 4 , April 2013 , pp. 385 - 392
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2013

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References

1.Aloush, V, Navon-Venezia, S, Seigman-Igra, Y, Cabili, S, Carmeli, Y. Multidrug-resistant Pseudomonas aeruginosa: risk factors and clinical impact. Antimicrob Agents Chemother 2006;50(1):43–48.Google Scholar
2.Furuno, JP, Harris, AD, Wright, MO, et al.Prediction rules to identify patients with methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci upon hospital admission. Am J Infect Control 2004;32(8):436–440.Google Scholar
3.Giske, CG, Monnet, DL, Cars, O, Carmeli, Y. Clinical and economic impact of common multidrug-resistant gram-negative bacilli. Antimicrob Agents Chemother 2008;52(3):813–821.Google Scholar
4.Harris, AD, McGregor, JC, Johnson, JA, et al.Risk factors for colonization with extended-spectrum β-lactamase–producing bacteria and intensive care unit admission. Emerg Infect Dis 2007;13(8):1144–1149.CrossRefGoogle ScholarPubMed
5.Tumbarello, M, Trecarichi, EM, Bassetti, M, et al.Identifying patients harboring extended-spectrum-β-lactamase-producing Enterobacteriaceae on hospital admission: derivation and validation of a scoring system. Antimicrob Agents Chemother 2011;55(7):3485–3490.CrossRefGoogle ScholarPubMed
6.Wright, SW, Wrenn, KD, Haynes, M, Haas, DW. Prevalence and risk factors for multidrug resistant uropathogens in ED patients. Am J Emerg Med 2000;18(2):143–146.Google Scholar
7.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
8.Kim, BN, Woo, JH, Kim, MN, Ryu, J, Kim, YS. Clinical implications of extended-spectrum β-lactamase-producing Klebsiella pneumoniae bacteraemia. J Hosp Infect 2002;52(2):99–106.Google Scholar
9.Lautenbach, E, Patel, JB, Bilker, WB, Edelstein, PH, Fishman, NO. Extended-spectrum β-lactamase–producing Escherichia coli and Klebsiella pneumoniae: risk factors for infection and impact of resistance on outcomes. Clin Infect Dis 2001;32(8):1162–1171.CrossRefGoogle ScholarPubMed
10.Melzer, M, Petersen, I. Mortality following bacteraemic infection caused by extended spectrum beta-lactamase (ESBL) producing E. coli compared to non-ESBL producing E. coli. J Infect 2007;55(3):254–259.CrossRefGoogle ScholarPubMed
11.Peralta, G, Sanchez, MB, Garrido, JC, et al.Impact of antibiotic resistance and of adequate empirical antibiotic treatment in the prognosis of patients with Escherichia coli bacteraemia. J Antimicrob Chemother 2007;60(4):855–863.Google Scholar
12.Schwaber, MJ, Navon-Venezia, S, Kaye, KS, Ben-Ami, R, Schwartz, D, Carmeli, Y. Clinical and economic impact of bacteremia with extended-spectrum-β-lactamase-producing Enterobacteriaceae. Antimicrob Agents Chemother 2006;50(4):1257–1262.CrossRefGoogle ScholarPubMed
13.Schwaber, MJ, Carmeli, Y. Mortality and delay in effective therapy associated with extended-spectrum β-lactamase production in Enterobacteriaceae bacteraemia: a systematic review and meta-analysis. J Antimicrob Chemother 2007;60(5):913–920.CrossRefGoogle ScholarPubMed
14.Tumbarello, M, Sanguinetti, M, Montuori, E, et al.Predictors of mortality in patients with bloodstream infections caused by extended-spectrum-β-lactamase-producing Enterobacteriaceae: importance of inadequate initial antimicrobial treatment. Antimicrob Agents Chemother 2007;51(6):1987–1994.Google Scholar
15.Harbarth, S, Sax, H, Fankhauser-Rodriguez, C, Schrenzel, J, Agostinho, A, Pittet, D. Evaluating the probability of previously unknown carriage of MRSA at hospital admission. Am J Med 2006;119(3):275.e15–275.e23.Google Scholar
16.Clinical and Laboratory Standards Institute (CLSI). 2009. Performance Standards for Antimicrobial Susceptibility Testing: 19th Informational Supplement. Wayne, PA: CLSI, 2012. CLSI document M100-S19.Google Scholar
17.Charlson, ME, Pompei, P, Ales, KL, MacKenzie, CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40(5):373–383.Google Scholar
18.Fawcett, T. An introduction to ROC analysis. Pattern Recognit Lett 2006;27:861–874.CrossRefGoogle Scholar
19.Sullivan, LM, Massaro, JM, D'Agostino, RB Sr.Presentation of multivariate data for clinical use: the Framingham study risk score functions. Stat Med 2004;23(10):1631–1660.CrossRefGoogle ScholarPubMed
20.Apisarnthanarak, A, Kiratisin, P, Saifon, P, Kitphati, R, Dejsirilert, S, Mundy, LM. Predictors of mortality among patients with community-onset infection due to extended-spectrum β-lactamase–producing Escherichia coli in Thailand. Infect Control Hosp Epidemiol 2008;29(1):80–82.Google Scholar
21.Azap, OK, Arslan, H, Serefhanoglu, K, et al.Risk factors for extended-spectrum β-lactamase positivity in uropathogenic Escherichia coli isolated from community-acquired urinary tract infections. Clin Microbiol Infect 2010;16(2):147–151.CrossRefGoogle ScholarPubMed
22.Coque, TM, Baquero, F, Canton, R. Increasing prevalence of ESBL-producing Enterobacteriaceae in Europe. Euro Surveill 2008;13(47):pii= 19044.Google Scholar
23.Hawser, SP, Bouchillon, SK, Hoban, DJ, Badal, RE, Canton, R, Baquero, F. Incidence and antimicrobial susceptibility of Escherichia coli and Klebsiella pneumoniae with extended-spectrum β-lactamases in community- and hospital-associated intra-abdominal infections in Europe: results of the 2008 Study for Monitoring Antimicrobial Resistance Trends (SMART). Antimicrob Agents Chemother 2010;54(7):3043–3046.Google Scholar
24.Laupacis, A, Sekar, N, Stiell, IG. Clinical prediction rules: a review and suggested modifications of methodological standards. JAMA 1997;277(6):488–494.CrossRefGoogle ScholarPubMed
25.Tacconelli, E, Karchmer, AW, Yokoe, D, D'Agata, EM. Preventing the influx of vancomycin-resistant enterococci into health care institutions, by use of a simple validated prediction rule. Clin Infect Dis 2004;39(7):964–970.Google Scholar
26.Tacconelli, E, Cataldo, MA, De, AG, Cauda, R. Risk scoring and bloodstream infections. Int J Antimicrob Agents 2007;30(suppl 1):S88–S92.CrossRefGoogle ScholarPubMed
27.Aliberti, S, Di, PM, Zanaboni, AM, et al.Stratifying risk factors for multidrug-resistant pathogens in hospitalized patients coming from the community with pneumonia. Clin Infect Dis 2012;54(4):470–478.Google Scholar
28.Calbo, E, Romani, V, Xercavins, M, et al.Risk factors for community-onset urinary tract infections due to Escherichia coli harbouring extended-spectrum β-lactamases. J Antimicrob Chemother 2006;57(4):780–783.Google Scholar
29.Demirdag, K, Hosoglu, S. Epidemiology and risk factors for ESBL-producing Klebsiella pneumoniae: a case control study. J Infect Dev Ctries 2010;4(11):717–722.Google Scholar
30.Graffunder, EM, Preston, KE, Evans, AM, Venezia, RA. Risk factors associated with extended-spectrum β-lactamase-producing organisms at a tertiary care hospital. J Antimicrob Chemother 2005;56(1):139–145.Google Scholar
31.van, GJ, Florence, E, Van den Ende, J. Validation of clinical scores for risk assessment. Clin Infect Dis 2012;54(10):1520–1521.Google Scholar
32.Zweig, MH, Campbell, G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 1993;39(4):561–577.Google Scholar