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Evaluating antibiotic stewardship programs in patients with bacteremia using administrative data: a cohort study

Abstract

When introducing new antibiotic guidelines for empirical treatment of bacteremia, it is imperative to evaluate the performance of the new guideline. We examined the utility of administrative data to evaluate the effect of new antibiotic guidelines and the prognostic impact of appropriate empirical treatment. We categorized 2,008 adult patients diagnosed with bacteremia between 2010 and 2012 according to whether they received cephalosporins or fluoroquinolones (old regimen) or not (new regimen). We used administrative data to extract individual level data on mortality, readmission, and appropriateness of treatment, and computed adjusted hazard ratios (HRs) and 95 % confidence intervals (CIs) for 30-day mortality and post-discharge readmission by regimen and appropriateness of treatment. In total, 945 (47.1 %) were treated by the old regimen and 1,063 (52.9 %) by the new. The median length of stay (8 days) did not differ by regimen and neither did the proportion of those receiving appropriate empirical treatment (84.1 % vs. 85.5 %). However, fewer patients with the new regimen were admitted to the intensive care unit (ICU; 3.8 % vs. 12.0 %) and they had lower 30-day mortality (16.4 % vs. 23.4 %). The adjusted 30-day mortality HR for appropriate versus inappropriate treatment was 0.79 (95 % CI 0.62–1.01) and 0.83 (95 % CI 0.66–1.05) for the new versus the old regimen. The HR for 30-day readmission for appropriate versus inappropriate treatment was 0.91 (95 % CI 0.73–1.13) and 1.05 (95 % CI 0.87–1.25) for the new versus the old regimen. This study demonstrates that administrative data can be useful for evaluating the effect and quality of new bacteremia treatment guidelines.

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References

  1. 1.

    van der Velden LBJ, Tromp M, Bleeker-Rovers CP, Hulscher M, Kullberg BJ, Mouton JW et al (2012) Non-adherence to antimicrobial treatment guidelines results in more broad-spectrum but not more appropriate therapy. Eur J Clin Microbiol Infect Dis 31:1561–1568. doi:10.1007/s10096-011-1478-5

    PubMed Central  PubMed  Article  Google Scholar 

  2. 2.

    Bearman GML, Wenzel RP (2005) Bacteremias: a leading cause of death. Arch Med Res 36:646–659. doi:10.1016/j.arcmed.2005.02.005

    PubMed  Article  Google Scholar 

  3. 3.

    Lynge E, Sandegaard JL, Rebolj M (2011) The Danish National Patient Register. Scand J Public Health 39:30–33. doi:10.1177/1403494811401482

    PubMed  Article  Google Scholar 

  4. 4.

    Pedersen CB (2011) The Danish Civil Registration System. Scand J Public Health 39:22–25. doi:10.1177/1403494810387965

    PubMed  Article  Google Scholar 

  5. 5.

    Gradel KO, Knudsen JD, Arpi M, Østergaard C, Schønheyder HC, Søgaard M (2012) Classification of positive blood cultures: computer algorithms versus physicians’ assessment—development of tools for surveillance of bloodstream infection prognosis using population-based laboratory databases. BMC Med Res Methodol 12:139. doi:10.1186/1471-2288-12-139

    PubMed Central  PubMed  Article  Google Scholar 

  6. 6.

    Hota B, Lin M, Doherty JA, Borlawsky T, Woeltje K, Stevenson K et al (2010) Formulation of a model for automating infection surveillance: algorithmic detection of central-line associated bloodstream infection. J Am Med Inform Assoc 17:42–48. doi:10.1197/jamia.M3196

    PubMed Central  PubMed  Article  Google Scholar 

  7. 7.

    Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi J-C et al (2005) Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 43:1130–1139

    PubMed  Article  Google Scholar 

  8. 8.

    Ohl CA, Dodds Ashley ES (2011) Antimicrobial stewardship programs in community hospitals: the evidence base and case studies. Clin Infect Dis 53:S23–S28. doi:10.1093/cid/cir365

    PubMed  Article  Google Scholar 

  9. 9.

    McGowan JE (2012) Antimicrobial stewardship—the state of the art in 2011: focus on outcome and methods. Infect Control Hosp Epidemiol 33:331–337. doi:10.1086/664755

    PubMed  Article  Google Scholar 

  10. 10.

    Thom KA, Schweizer ML, Osih RB, McGregor JC, Furuno JP, Perencevich EN et al (2008) Impact of empiric antimicrobial therapy on outcomes in patients with Escherichia coli and Klebsiella pneumoniae bacteremia: a cohort study. BMC Infect Dis 8:116. doi:10.1186/1471-2334-8-116

    PubMed Central  PubMed  Article  Google Scholar 

  11. 11.

    Osih RB, McGregor JC, Rich SE, Moore AC, Furuno JP, Perencevich EN et al (2007) Impact of empiric antibiotic therapy on outcomes in patients with Pseudomonas aeruginosa bacteremia. Antimicrob Agents Chemother 51:839–844. doi:10.1128/AAC.00901-06

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  12. 12.

    Ortega M, Marco F, Soriano A, Almela M, Martínez JA, Muñoz A et al (2009) Analysis of 4758 Escherichia coli bacteraemia episodes: predictive factors for isolation of an antibiotic-resistant strain and their impact on the outcome. J Antimicrob Chemother 63:568–574. doi:10.1093/jac/dkn514

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Drye EE, Normand S-LT, Wang Y, Ross JS, Schreiner GC, Han L et al (2012) Comparison of hospital risk-standardized mortality rates calculated by using in-hospital and 30-day models: an observational study with implications for hospital profiling. Ann Intern Med 156:19–26. doi:10.7326/0003-4819-156-1-201201030-00004

    PubMed Central  PubMed  Article  Google Scholar 

  14. 14.

    Paul M, Shani V, Muchtar E, Kariv G, Robenshtok E, Leibovici L (2010) Systematic review and meta-analysis of the efficacy of appropriate empiric antibiotic therapy for sepsis. Antimicrob Agents Chemother 54:4851–4863. doi:10.1128/aac.00627-10

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  15. 15.

    Freundlich M, Thomsen RW, Pedersen L, West H, Schønheyder HC (2007) Aminoglycoside treatment and mortality after bacteraemia in patients given appropriate empirical therapy: a Danish hospital-based cohort study. J Antimicrob Chemother 60:1115–1123. doi:10.1093/Jac/Dkm354

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Stevens V, Dumyati G, Fine LS, Fisher SG, van Wijngaarden E (2011) Cumulative antibiotic exposures over time and the risk of Clostridium difficile infection. Clin Infect Dis 53:42–48. doi:10.1093/Cid/Cir301

    PubMed  Article  Google Scholar 

  17. 17.

    Falagas ME, Rafailidis PI, Kofteridis D, Virtzili S, Chelvatzoglou FC, Papaioannou V et al (2007) Risk factors of carbapenem-resistant Klebsiella pneumoniae infections: a matched case control study. J Antimicrob Chemother 60:1124–1130. doi:10.1093/jac/dkm356

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Plüss-Suard C, Pannatier A, Kronenberg A, Mühlemann K, Zanetti G (2013) Impact of antibiotic Use on carbapenem resistance in pseudomonas aeruginosa: is there a role for antibiotic diversity? Antimicrob Agents Chemother 57:1709–1713. doi:10.1128/AAC.01348-12

    PubMed Central  PubMed  Article  Google Scholar 

  19. 19.

    Rothman KJ (2012) Epidemiology: an introduction, 2nd edn. Oxford University Press, New York

    Google Scholar 

  20. 20.

    Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373–383. doi:10.1016/0021-9681(87)90171-8

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG et al (1991) The APACHE III prognostic system. risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100:1619–1636. doi:10.1378/chest.100.6.1619

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Rhee J-Y, Kwon KT, Ki HK, Shin SY, Jung DS, Chung D-R et al (2009) Scoring systems for prediction of mortality in patients with intensive care unit-acquired sepsis: a comparison of the Pitt bacteremia score and the Acute Physiology and Chronic Health Evaluation II scoring systems. Shock 31:146–150

    PubMed  Article  Google Scholar 

  23. 23.

    Feldman C, Alanee S, Yu VL, Richards GA, Ortqvist A, Rello J et al (2009) Severity of illness scoring systems in patients with bacteraemic pneumococcal pneumonia: implications for the intensive care unit care. Clin Microbiol Infect 15:850–857. doi:10.1111/j.1469-0691.2009.02901.x

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Quach S, Hennessy DA, Faris P, Fong A, Quan H, Doig C (2009) A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients. BMC Health Serv Res 9:129. doi:10.1186/1472-6963-9-129

    PubMed Central  PubMed  Article  Google Scholar 

  25. 25.

    Oddone EZ, Weinberger M (2012) Hospital readmission rates: are we measuring the right thing? Ann Intern Med 157:910–911. doi:10.7326/0003-4819-157-12-201212180-00013

    PubMed  Article  Google Scholar 

  26. 26.

    Hand K (2013) Antibiotic stewardship. Clin Med 13:499–503

    PubMed  Article  Google Scholar 

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All authors report no conflict of interest relevant to this article.

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Correspondence to J. Boel.

Appendix

Appendix

Table 4 Comparison of the two empirical treatment regimens
Table 5 Descriptive characteristics of 1,774 episodes with a first-time diagnosis of bacteremia according to the empirical antibiotic treatment regimen. Episodes originating from the Department of Haematology and the ICU are excluded
Table 6 Crude and adjusted 30-day mortality for first-time bacteremia episodes according to treatment regimen and appropriateness of the empirical antibiotic treatment, excluding episodes from the ICU and Department of Haematology
Table 7 Crude and adjusted 30-day readmission for first-time bacteremia episodes according to treatment regimen and appropriateness of the empirical antibiotic treatment, excluding episodes from the ICU and Department of Haematology

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Boel, J., Søgaard, M., Andreasen, V. et al. Evaluating antibiotic stewardship programs in patients with bacteremia using administrative data: a cohort study. Eur J Clin Microbiol Infect Dis 34, 1475–1484 (2015). https://doi.org/10.1007/s10096-015-2378-x

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Keywords

  • Administrative Data
  • Charlson Comorbidity Index
  • Empirical Treatment
  • Charlson Comorbidity Index Score
  • Intensive Care Unit Treatment