Impact of multi-drug resistant bacteria on economic and clinical outcomes of healthcare-associated infections in adults: Systematic review and meta-analysis

Autoři: Miquel Serra-Burriel aff001;  Matthew Keys aff001;  Carlos Campillo-Artero aff001;  Antonella Agodi aff003;  Martina Barchitta aff003;  Achilleas Gikas aff004;  Carlos Palos aff006;  Guillem López-Casasnovas aff001
Působiště autorů: Center for Research in Health and Economics, Pompeu Fabra University, Barcelona, Spain aff001;  Balearic Islands Health Service, Palma de Mallorca, Balearic Islands, Spain aff002;  Department of Medical and Surgical Sciences and Advanced Technologies “GF Ingrassia”, University of Catania, Catania, Italy aff003;  Internal Medicine Department, Infectious Diseases Unit, University Hospital of Heraklion, Crete, Greece aff004;  School of Medicine, University of Crete, Heraklion, Greece aff005;  Hospital Beatriz Ângelo, Loures, Lisbon, Portugal aff006
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227139



Infections with multidrug resistant (MDR) bacteria in hospital settings have substantial implications in terms of clinical and economic outcomes. However, due to clinical and methodological heterogeneity, estimates about the attributable economic and clinical effects of healthcare-associated infections (HAI) due to MDR microorganisms (MDR HAI) remain unclear. The objective was to review and synthesize the evidence on the impact of MDR HAI in adults on hospital costs, length of stay, and mortality at discharge.

Methods and findings

Literature searches were conducted in PubMed/MEDLINE, and Google Scholar databases to select studies that evaluated the impact of MDR HAI on economic and clinical outcomes. Eligible studies were conducted in adults, in order to ensure homogeneity of populations, used propensity score matched cohorts or included explicit confounding control, and had confirmed antibiotic susceptibility testing. Risk of bias was evaluated, and effects were measured with ratios of means (ROM) for cost and length of stay, and risk ratios (RR) for mortality. A systematic search was performed on 14th March 2019, re-run on the 10th of June 2019 and extended the 3rd of September 2019. Small effect sizes were assessed by examination of funnel plots. Sixteen articles (6,122 patients with MDR HAI and 8,326 patients with non-MDR HAI) were included in the systematic review of which 12 articles assessed cost, 19 articles length of stay, and 14 mortality. Compared to susceptible infections, MDR HAI were associated with increased cost (ROM 1.33, 95%CI [1.15; 1.54]), prolonged length of stay (ROM 1.27, 95%CI [1.18; 1.37]), and excess in-hospital mortality (RR 1.61, 95%CI [1.36; 1.90]) in the random effects models. Risk of publication bias was only found to be significant for mortality, and overall study quality good.


MDR HAI appears to be strongly associated with increases in direct cost, prolonged length of stay and increased mortality. However, further comprehensive studies in this setting are warranted.

Trial registration

PROSPERO (CRD42019126288).

Klíčová slova:

Antimicrobial resistance – Database searching – Economic impact analysis – Health economics – Hospitals – Nosocomial infections – Systematic reviews – Urinary tract infections


1. Stygall J, Newman S. Hospital acquired infection. Cambridge Handbook of Psychology, Health and Medicine, Second Edition. 2014. doi: 10.1017/CBO9780511543579.182

2. Eliopoulos GM, Cosgrove SE, Carmeli Y. The Impact of Antimicrobial Resistance on Health and Economic Outcomes. Clin Infect Dis. 2003; doi: 10.1086/375081 12766839

3. Friedman ND, Temkin E, Carmeli Y. The negative impact of antibiotic resistance. Clinical Microbiology and Infection. 2016. doi: 10.1016/j.cmi.2015.12.002 26706614

4. Hidron AI, Edwards JR, Patel J, Horan TC, Sievert DM, Pollock DA, et al. Antimicrobial-Resistant Pathogens Associated With Healthcare-Associated Infections: Annual Summary of Data Reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007. Infect Control Hosp Epidemiol. 2008; doi: 10.1086/591861 18947320

5. Livermore DM. Bacterial Resistance: Origins, Epidemiology, and Impact. Clin Infect Dis. 2003; doi: 10.1086/344654 12516026

6. Nathwani D, Raman G, Sulham K, Gavaghan M, Menon V. Clinical and economic consequences of hospital-acquired resistant and multidrug-resistant Pseudomonas aeruginosa infections: A systematic review and meta-analysis. Antimicrob Resist Infect Control. 2014; doi: 10.1186/2047-2994-3-32 25371812

7. Roberts RR, Hota B, Ahmad I, Scott R. Douglas II, Foster SD, Abbasi F, et al. Hospital and Societal Costs of Antimicrobial-Resistant Infections in a Chicago Teaching Hospital: Implications for Antibiotic Stewardship. Clin Infect Dis. 2009;49: 1175–1184. Available: doi: 10.1086/605630 19739972

8. Kirkland KB, Briggs JP, Trivette SL, Wilkinson WE, Sexton DJ. The Impact of Surgical-Site Infections in the 1990s: Attributable Mortality, Excess Length of Hospitalization, And Extra Costs. Infect Control & Hosp Epidemiol. 2015/01/02. Cambridge University Press; 1999;20: 725–730. doi: 10.1086/501572 10580621

9. Stone PW. Economic burden of healthcare-associated infections: an American perspective. Expert Rev Pharmacoecon Outcomes Res. Taylor & Francis; 2009;9: 417–422. doi: 10.1586/erp.09.53 19817525

10. Jasovský D, Littmann J, Zorzet A, Cars O. Antimicrobial resistance-a threat to the world’s sustainable development. Ups J Med Sci. 2016/07/13. Taylor & Francis; 2016;121: 159–164. doi: 10.1080/03009734.2016.1195900 27416324

11. Europe. W. Fact sheets on sustainable development goals: health targets. 2017.

12. Cassini A, Högberg LD, Plachouras D, Quattrocchi A, Hoxha A, Simonsen GS, et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet Infect Dis. 2019;19: 56–66. doi: 10.1016/S1473-3099(18)30605-4 30409683

13. O-Neill J. Tackling drug-resistant infections globally: final report and recommendations. 2016.

14. Publishing O. Stemming the Superbug Tide: Just A Few Dollars More. 2018.

15. Nikaido H. Multidrug Resistance in Bacteria. Annu Rev Biochem. 2009; doi: 10.1146/annurev.biochem.78.082907.145923 19231985

16. Alekshun MN, Levy SB. Molecular Mechanisms of Antibacterial Multidrug Resistance. Cell. 2007. doi: 10.1016/j.cell.2007.03.004 17382878

17. Blair JMA, Webber MA, Baylay AJ, Ogbolu DO, Piddock LJ V. Molecular mechanisms of antibiotic resistance. Nature Reviews Microbiology. 2015. doi: 10.1038/nrmicro3380 25435309

18. Higgins CF. Multiple molecular mechanisms for multidrug resistance transporters. Nature. 2007. doi: 10.1038/nature05630 17429392

19. Baym M, Stone LK, Kishony R. Multidrug evolutionary strategies to reverse antibiotic resistance. Science. 2016. doi: 10.1126/science.aad3292 26722002

20. Magiorakos AP, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: An international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012; doi: 10.1111/j.1469-0691.2011.03570.x 21793988

21. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Journal of clinical epidemiology. 2009. doi: 10.1016/j.jclinepi.2009.06.006 19631507

22. Mueller M, D’Addario M, Egger M, Cevallos M, Dekkers O, Mugglin C, et al. Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations. BMC Med Res Methodol. 2018;18: 44. doi: 10.1186/s12874-018-0495-9 29783954

23. Peikes DN, Moreno L, Orzol SM. Propensity Score Matching. Am Stat. 2008; doi: 10.1198/000313008X332016

24. Kurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K, et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol. 2006; doi: 10.1093/aje/kwj047 16371515

25. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. European Journal of Epidemiology. 2010. doi: 10.1007/s10654-010-9491-z 20652370

26. Hartling L, Milne A, Hamm MP, Vandermeer B, Ansari M, Tsertsvadze A, et al. Testing the Newcastle Ottawa Scale showed low reliability between individual reviewers. J Clin Epidemiol. 2013; doi: 10.1016/j.jclinepi.2013.03.003 23683848

27. Wells GA et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality if nonrandomized studies in meta-analyses. Evid based public Heal. 2012; doi: 10.2307/632432

28. (2016), R Core Team R Foundation for Statistical Computing, Vienna A. R: A language and environment for statistical computing.

29. Schwarzer G. meta: An R package for meta-analysis. R News. 2007;7: 40–45.

30. IntHout J, Ioannidis JPA, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol. 2014;14: 25. doi: 10.1186/1471-2288-14-25 24548571

31. Borenstein M, Hedges L V., Higgins JPT, Rothstein HR. Notes on Subgroup Analyses and Meta-Regression. Introduction to Meta-Analysis. 2009. doi: 10.1002/9780470743386.ch21

32. Jackson D. The power of the standard test for the presence of heterogeneity in meta-analysis. Stat Med. John Wiley & Sons, Ltd; 2006;25: 2688–2699. doi: 10.1002/sim.2481 16374903

33. Hoaglin DC. Misunderstandings about Q and ‘Cochran’s Q test” in meta-analysis.’ Stat Med. John Wiley & Sons, Ltd; 2016;35: 485–495. doi: 10.1002/sim.6632 26303773

34. IntHout J, Ioannidis JPA, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open. 2016;6: e010247. doi: 10.1136/bmjopen-2015-010247 27406637

35. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315: 629 LP– 634. doi: 10.1136/bmj.315.7109.629 9310563

36. Kopp BJ, Nix DE, Armstrong EP. Clinical and economic analysis of methicillin-susceptible and -resistant Staphylococcus aureus infections. Ann Pharmacother. 2004; doi: 10.1345/aph.1E028 15266044

37. Mauldin PD, Salgado CD, Hansen IS, Durup DT, Bosso JA. Attributable hospital cost and length of stay associated with health care-associated infections caused by antibiotic-resistant gram-negative bacteria. Antimicrob Agents Chemother. 2010; doi: 10.1128/AAC.01041-09 19841152

38. Carmeli Y, Troillet N, Karchmer AW, Samore MH. Health and economic outcomes of antibiotic resistance in Pseudomonas aeruginosa. Arch Intern Med. 1999; doi: 10.1001/archinte.159.10.1127 10335691

39. Resch A, Wilke M, Fink C. The cost of resistance: Incremental cost of methicillin-resistant Staphylococcus aureus (MRSA) in German hospitals. Eur J Heal Econ. 2009; doi: 10.1007/s10198-008-0132-3 19015905

40. Neidell MJ, Cohen B, Furuya Y, Hill J, Jeon CY, Glied S, et al. Costs of healthcare-and community-associated infections with antimicrobial-resistant versus antimicrobial-susceptible organisms. Clin Infect Dis. 2012; doi: 10.1093/cid/cis552 22700828

41. Micek ST, Wunderink RG, Kollef MH, Chen C, Rello J, Chastre J, et al. An international multicenter retrospective study of Pseudomonas aeruginosa nosocomial pneumonia: Impact of multidrug resistance. Crit Care. 2015; doi: 10.1186/s13054-015-0926-5 25944081

42. Engemann JJ, Carmeli Y, Cosgrove SE, Fowler VG, Bronstein MZ, Trivette SL, et al. Adverse Clinical and Economic Outcomes Attributable to Methicillin Resistance among Patients with Staphylococcus aureus Surgical Site Infection. Clin Infect Dis. 2003; doi: 10.1086/367653 12594640

43. Roberts RR, Hota B, Ahmad I, Scott II RD, Foster SD, Abbasi F, et al. Hospital and Societal Costs of Antimicrobial‐Resistant Infections in a Chicago Teaching Hospital: Implications for Antibiotic Stewardship. Clin Infect Dis. 2009; doi: 10.1086/605630 19739972

44. Pelz RK, Lipsett PA, Swoboda SM, Diener-West M, Powe NR, Brower RG, et al. Vancomycin-sensitive and vancomycin-resistant enterococcal infections in the ICU: Attributable costs and outcomes. Intensive Care Med. 2002; doi: 10.1007/s00134-002-1276-8 12107672

45. Riu M, Chiarello P, Terradas R, Sala M, Garcia-Alzorriz E, Castells X, et al. Cost attributable to nosocomial bacteremia. Analysis according to microorganism and antimicrobial sensitivity in a university hospital in Barcelona. PLoS One. 2016; doi: 10.1371/journal.pone.0153076 27055117

46. Magira EE, Islam S, Niederman MS. Multi-drug resistant organism infections in a medical ICU: Association to clinical features and impact upon. Med Intensiva (English Ed. Elsevier España, S.L.U. and SEMICYUC; 2018;42: 225–234. doi: 10.1016/j.medin.2017.07.006 29033075

47. Chen Z, Xu Z, Wu H, Chen L, Gao S, Chen Y. The impact of carbapenem-resistant Pseudomonas aeruginosa on clinical and economic outcomes in a Chinese tertiary care hospital: A propensity score − matched analysis. AJIC Am J Infect Control. Elsevier Inc.; 2019;47: 677–682. doi: 10.1016/j.ajic.2018.10.025 30554879

48. Fernández-Barat L, Ferrer M, De Rosa F, Gabarrús A, Esperatti M, Terraneo S, et al. Intensive care unit-acquired pneumonia due to Pseudomonas aeruginosa with and without multidrug resistance. J Infect. 2017;74: 142–152. doi: 10.1016/j.jinf.2016.11.008 27865895

49. Tedja R, Nowacki A, Fraser T, Fatica C, Griffiths L, Gordon S, et al. The impact of multidrug resistance on outcomes in ventilator-associated pneumonia. Am J Infect Control. 2014;42: 542–545. doi: 10.1016/j.ajic.2013.12.009 24630700

50. Depuydt PO, Vandijck DM, Bekaert MA, Decruyenaere JM, Blot SI, Vogelaers DP, et al. Determinants and impact of multidrug antibiotic resistance in pathogens causing ventilator-associated-pneumonia. Crit Care. 2008;12: R142. doi: 10.1186/cc7119 19014695

51. Martin-Loeches I, Torres A, Rinaudo M, Terraneo S, de Rosa F, Ramirez P, et al. Resistance patterns and outcomes in intensive care unit (ICU)-acquired pneumonia. Validation of European Centre for Disease Prevention and Control (ECDC) and the Centers for Disease Control and Prevention (CDC) classification of multidrug resistant organi. J Infect. 2015;70: 213–222. doi: 10.1016/j.jinf.2014.10.004 25445887

52. Puchter L, Chaberny IF, Schwab F, Vonberg R-P, Bange F-C, Ebadi E. Economic burden of nosocomial infections caused by vancomycin-resistant enterococci. Antimicrob Resist Infect Control. England; 2018;7: 1. doi: 10.1186/s13756-017-0291-z 29312658

53. Bonnet V, Dupont H, Glorion S, Aupee M, Kipnis E, Gerard JL, et al. Influence of bacterial resistance on mortality in intensive care units: a registry study from 2000 to 2013 (IICU Study). J Hosp Infect. England; 2019;102: 317–324. doi: 10.1016/j.jhin.2019.01.011 30659869

54. Nelson RE, Stevens VW, Jones M, Khader K, Schweizer ML, Perencevich EN, et al. Attributable Cost and Length of Stay Associated with Nosocomial Gram-Negative Bacterial Cultures. Antimicrob Agents Chemother. 2018;62: e00462–18. doi: 10.1128/AAC.00462-18 30150480

55. Cowie SE, Ma I, Lee SK, Smith RM, Hsiang YN. Nosocomial MRSA infection in vascular surgery patients: impact on patient outcome. Vasc Endovascular Surg. United States; 2005;39: 327–334. doi: 10.1177/153857440503900404 16079941

56. Blot S, Depuydt P, Vandewoude K, De Bacquer D. Measuring the impact of multidrug resistance in nosocomial infection. Curr Opin Infect Dis. United States; 2007;20: 391–396. doi: 10.1097/QCO.0b013e32818be6f7 17609598

57. Friedrich JO, Adhikari NKJ, Beyene J. The ratio of means method as an alternative to mean differences for analyzing continuous outcome variables in meta-analysis: A simulation study. BMC Med Res Methodol. 2008;8: 32. doi: 10.1186/1471-2288-8-32 18492289

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