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A new simplified predictive model for mortality in methicillin-resistant Staphylococcus aureus bacteremia

  • Sarah C. J. Jorgensen
  • Abdalhamid M. Lagnf
  • Sahil Bhatia
  • Michael J. RybakEmail author
Original Article
  • 27 Downloads

Abstract

Adjustment for confounding is important in observational methicillin-resistant Staphylococcus aureus bacteremia (MRSAB) studies due to the wide spectrum of disease severity and baseline health status that patients present with. The objectives of this study were to develop a simplified MRSAB-specific scoring model to estimate the risk of 30-day all-cause mortality and to compare its performance to the APACHE II and Pitt Bacteremia scores. Retrospective, singe-center, cohort study in adults with MRSAB 2008 to 2018. Independent predictors of mortality were identified through multivariable logistic regression. A scoring model was derived using a regression coefficient-based scoring method. Discriminatory ability was assessed using the c statistic. A total of 455 patients were included. Thirty-day mortality was 16.3%. The MRSAB score consisted of six variables: age, respiratory rate, Glasgow Coma scale, renal failure, hospital-acquired MRSAB, and infective endocarditis or lower respiratory tract infection source. The score demonstrated very good discrimination (c statistic 0.8662, 95% CI 0.824–0.909) and was superior to the APACHE II (P = 0.043) and the Pitt bacteremia (P < 0.001) scores. A weighted combination of six independent variables routinely measured in patients with MRSAB can be used to predict, with high discrimination, 30-day all-cause mortality. External validation is required before widespread use.

Keywords

Methicillin-resistant Staphylococcus aureus Bacteremia Risk stratification APACHE II Pitt bacteremia score 

Notes

Acknowledgments

This study was presented, in part, at IDWeek 2018, October 5, 2018, San Francisco, CA, USA; abstracts 1061 and 1227.

Funding

This study was carried out as part of the authors’ routine work with no external funding.

Compliance with ethical standards

Conflict of interest

MJR has received funding support, consulted or participated in speaking bureaus for Allergan, Achaogen, Bayer, Melinta, Merck, Theravance, The Medicine Company, Sunovian and Zavante, and NIAID (all unrelated to this study). SCJJ, AML, and SB have nothing to declare.

Ethical approval

This study was approved by the Wayne State University Institutional Review Board (IRB# 122916MP2E) and the Detroit Medical Center Research Committee (14081) with a waiver for informed consent.

Informed consent

Due to the retrospective nature of this study, informed consent was not required.

Supplementary material

10096_2018_3464_MOESM1_ESM.docx (29 kb)
ESM 1 (DOCX 28 kb)

References

  1. 1.
    Doernberg SB, Lodise TP, Thaden JT et al (2017) Gram-positive bacterial infections: research priorities, accomplishments, and future directions of the antibacterial resistance leadership group. Clin Infect Dis 64(suppl_1):S24–S29CrossRefGoogle Scholar
  2. 2.
    Guillamet MCV, Vazquez R, Deaton B, Shroba J, Vazquez L, Mercier RC (2018) Host-pathogen-treatment triad: host factors matter most in methicillin-resistant Staphylococcus aureus bacteremia outcomes. Antimicrob Agents Chemother 62(2)Google Scholar
  3. 3.
    Minejima E, Bensman J, She RC et al (2016) A dysregulated balance of proinflammatory and anti-inflammatory host cytokine response early during therapy predicts persistence and mortality in Staphylococcus aureus bacteremia. Crit Care Med 44(4):671–679Google Scholar
  4. 4.
    Thwaites GE, Edgeworth JD, Gkrania-Klotsas E et al (2011) Clinical management of Staphylococcus aureus bacteraemia. Lancet Infect Dis 11(3):208–222CrossRefGoogle Scholar
  5. 5.
    Dantes R, Mu Y, Belflower R et al (2013) National burden of invasive methicillin-resistant Staphylococcus aureus infections, United States, 2011. JAMA Intern Med 173(21):1970–1978Google Scholar
  6. 6.
    Kullar R, McKinnell JA, Sakoulas G (2014) Avoiding the perfect storm: the biologic and clinical case for reevaluating the 7-day expectation for methicillin-resistant Staphylococcus aureus bacteremia before switching therapy. Clin Infect Dis 59(10):1455–1461CrossRefGoogle Scholar
  7. 7.
    Liu C, Bayer A, Cosgrove SE et al (2011) Clinical practice guidelines by the infectious diseases society of america for the treatment of methicillin-resistant Staphylococcus aureus infections in adults and children. Clin Infect Dis 52(3):e18–e55CrossRefGoogle Scholar
  8. 8.
    Deresinski S (2007) Counterpoint: vancomycin and Staphylococcus aureus—an antibiotic enters obsolescence. Clin Infect Dis 44(12):1543–1548CrossRefGoogle Scholar
  9. 9.
    Sakoulas G, Moise-Broder PA, Schentag J, Forrest A, Moellering RC Jr, Eliopoulos GM (2004) Relationship of MIC and bactericidal activity to efficacy of vancomycin for treatment of methicillin-resistant Staphylococcus aureus bacteremia. J Clin Microbiol 42(6):2398–2402CrossRefGoogle Scholar
  10. 10.
    Fowler VG Jr, Boucher HW, Corey GR et al (2006) Daptomycin versus standard therapy for bacteremia and endocarditis caused by Staphylococcus aureus. N Engl J Med 355(7):653–665CrossRefGoogle Scholar
  11. 11.
    Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985) APACHE II: a severity of disease classification system. Crit Care Med 13(10):818–829CrossRefGoogle Scholar
  12. 12.
    Korvick JA, Bryan CS, Farber B et al (1992) Prospective observational study of Klebsiella bacteremia in 230 patients: outcome for antibiotic combinations versus monotherapy. Antimicrob Agents Chemother 36(12):2639–2644CrossRefGoogle Scholar
  13. 13.
    Paterson DL, Ko WC, Von Gottberg A et al (2004) International prospective study of Klebsiella pneumoniae bacteremia: implications of extended-spectrum beta-lactamase production in nosocomial infections. Ann Intern Med 140(1):26–32CrossRefGoogle Scholar
  14. 14.
    Stevens V, Lodise TP, Tsuji B et al (2012) The utility of acute physiology and chronic health evaluation II scores for prediction of mortality among intensive care unit (ICU) and non-ICU patients with methicillin-resistant Staphylococcus aureus bacteremia. Infect Control Hosp Epidemiol 33(6):558–564CrossRefGoogle Scholar
  15. 15.
    Kaasch AJ, Fatkenheuer G, Prinz-Langenohl R et al (2015) Early oral switch therapy in low-risk Staphylococcus aureus bloodstream infection (SABATO): study protocol for a randomized controlled trial. Trials 16:450CrossRefGoogle Scholar
  16. 16.
    Horan TC, Andrus M, Dudeck MA (2008) CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control 36(5):309–332CrossRefGoogle Scholar
  17. 17.
    Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG (2009) Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42(2):377–381CrossRefGoogle Scholar
  18. 18.
    Durack DT, Lukes AS, Bright DK (1994) New criteria for diagnosis of infective endocarditis: utilization of specific echocardiographic findings. Duke Endocarditis Service. Am J Med 96(3):200–209CrossRefGoogle Scholar
  19. 19.
    Kan LP, Lin JC, Chiu SK et al (2014) Methicillin-resistant Staphylococcus aureus bacteremia in hemodialysis and nondialysis patients. J Microbiol Immunol Infect 47(1):15–22CrossRefGoogle Scholar
  20. 20.
    Lee SC, Lee CW, Shih HJ, Chiou MJ, See LC, Siu LK (2013) Clinical features and risk factors of mortality for bacteremia due to community-onset healthcare-associated methicillin-resistant S. aureus. Diagn Microbiol Infect Dis 76(1):86–92CrossRefGoogle Scholar
  21. 21.
    van Hal SJ, Jensen SO, Vaska VL, Espedido BA, Paterson DL, Gosbell IB (2012) Predictors of mortality in Staphylococcus aureus bacteremia. Clin Microbiol Rev 25(2):362–386CrossRefGoogle Scholar
  22. 22.
    Lodise TP, McKinnon PS, Swiderski L, Rybak MJ (2003) Outcomes analysis of delayed antibiotic treatment for hospital-acquired Staphylococcus aureus bacteremia. Clin Infect Dis 36(11):1418–1423CrossRefGoogle Scholar
  23. 23.
    Zhang H, Singer B (1999) Recursive partitioning in the health sciences. Springer, New YorkCrossRefGoogle Scholar
  24. 24.
    Moons KG, Harrell FE, Steyerberg EW (2002) Should scoring rules be based on odds ratios or regression coefficients? J Clin Epidemiol 55(10):1054–1055CrossRefGoogle Scholar
  25. 25.
    Sullivan LM, Massaro JM, D’Agostino RB Sr (2004) Presentation of multivariate data for clinical use: the Framingham Study risk score functions. Stat Med 23(10):1631–1660CrossRefGoogle Scholar
  26. 26.
    Van Houwelingen JC, Le Cessie S (1990) Predictive value of statistical models. Stat Med 9(11):1303–1325CrossRefGoogle Scholar
  27. 27.
    Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36CrossRefGoogle Scholar
  28. 28.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845CrossRefGoogle Scholar
  29. 29.
    Soriano A, Martinez JA, Mensa J et al (2000) Pathogenic significance of methicillin resistance for patients with Staphylococcus aureus bacteremia. Clin Infect Dis 30(2):368–373CrossRefGoogle Scholar
  30. 30.
    Ganga R, Riederer K, Sharma M et al (2009) Role of SCCmec type in outcome of Staphylococcus aureus bacteremia in a single medical center. J Clin Microbiol 47(3):590–595CrossRefGoogle Scholar
  31. 31.
    Maor Y, Hagin M, Belausov N, Keller N, Ben-David D, Rahav G (2009) Clinical features of heteroresistant vancomycin-intermediate Staphylococcus aureus bacteremia versus those of methicillin-resistant S. aureus bacteremia. J Infect Dis 199(5):619–624CrossRefGoogle Scholar
  32. 32.
    Big C, Malani PN (2010) Staphylococcus aureus bloodstream infections in older adults: clinical outcomes and risk factors for in-hospital mortality. J Am Geriatr Soc 58(2):300–305CrossRefGoogle Scholar
  33. 33.
    Roth JA, Tschudin-Sutter S, Dangel M, Frei R, Battegay M, Widmer AF (2017) Value of the Pitt Bacteraemia score to predict short-term mortality in Staphylococcus aureus bloodstream infection: a validation study. Swiss Med Wkly 147:w14482Google Scholar
  34. 34.
    Siontis GC, Tzoulaki I, Ioannidis JP (2011) Predicting death: an empirical evaluation of predictive tools for mortality. Arch Intern Med 171(19):1721–1726CrossRefGoogle Scholar
  35. 35.
    Rose WE, Eickhoff JC, Shukla SK et al (2012) Elevated serum interleukin-10 at time of hospital admission is predictive of mortality in patients with Staphylococcus aureus bacteremia. J Infect Dis 206(10):1604–1611CrossRefGoogle Scholar
  36. 36.
    Norgaard M, Ehrenstein V, Vandenbroucke JP (2017) Confounding in observational studies based on large health care databases: problems and potential solutions—a primer for the clinician. Clin Epidemiol 9:185–193CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Sarah C. J. Jorgensen
    • 1
  • Abdalhamid M. Lagnf
    • 1
  • Sahil Bhatia
    • 1
  • Michael J. Rybak
    • 1
    • 2
    • 3
    Email author
  1. 1.Anti-Infective Research Laboratory, Eugene Applebaum College of Pharmacy and Health SciencesWayne State UniversityDetroitUSA
  2. 2.Department of PharmacyDetroit Medical CenterDetroitUSA
  3. 3.School of MedicineWayne State UniversityDetroitUSA

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