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.
Citation: Serra-Burriel M, Keys M, Campillo-Artero C, Agodi A, Barchitta M, Gikas A, et al. (2020) Impact of multi-drug resistant bacteria on economic and clinical outcomes of healthcare-associated infections in adults: Systematic review and meta-analysis. PLoS ONE 15(1): e0227139. doi:10.1371/journal.pone.0227139
Editor: Wisit Cheungpasitporn, University of Mississippi Medical Center, UNITED STATES
Received: July 1, 2019; Accepted: December 12, 2019; Published: January 10, 2020
Copyright: © 2020 Serra-Burriel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: GLC: unrestricted grant for the study of the consequences of antimicrobial resistance. Angelini SpA.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: MSB acknowledges research grants from Horizon2020, EiTHealth and ISCI programs outside of the submitted work, MK;CCA;AA;MB;CP;GLC declare no competing interests.
Abbreviations: MDR, multidrug resistance;HAI, hospital acquired infection;ICU, intensive care unit;ECDC, European Center for Disease Prevention and Control;CAI, community acquired infection;HAI, healthcare acquired infection;SSI, surgical site infections;CL-BSI, central line bloodstream infections;CAUTIs, catheter associated urinary tract infection;VAP, ventilator associated pneumonia;WHO, World Health Organization;XDR, extensively drug resistance;PDR, pan drug resistance
The growing prevalence of bacterial infections that cannot be adequately treated by existing effective antimicrobial therapies poses a considerable threat to the effectiveness, efficiency of healthcare systems[1–6], and is currently reaching societal consequences[7–9] limiting the achievement of WHO’s Sustainable Development Goals[10,11].
Much work has been done on understanding and estimating the impact of antimicrobial resistance (AMR) in various settings. A recent article underlines that AMR is one of the greatest challenges for public health and highlights the high impact of healthcare-associated infections (HAIs) due to antimicrobial resistant bacteria in terms of number of cases, attributable deaths, and disability-adjusted life-years. 700.000 deaths due to infections caused by Multi-drug resistant (MDR) bacteria occur yearly worldwide and this number could increase to 10 million in 2050 depending upon resistance patterns evolution and effective antibiotic discovery. Estimated associated costs are estimated to be 3.8% of annual GDP (additional 1.2 trillion USD) and as a results, 28.3 million people are condemned to extreme poverty. “Three out of four deaths from superbug infections could be averted by spending just USD 2 per person a year on measures as simple as hand washing and more prudent prescription of antibiotics”.
With respect to AMR, the most commonly reported characterization is multidrug resistance (MDR)[15–19], which occurs when a bacterial infection is resistant to treatment with multiple appropriate antimicrobial drugs. MDR is currently characterized as non-susceptibility to at least one agent in three antimicrobial categories . Associations between resistant infections and a wide arrange of outcomes has been reported and their direct effects are mainly driven by the ineffectiveness of available treatment of such infections. However, the relative magnitude of effects remains unclear. Moreover, estimates of the iatrogenic burden of MDR in hospital nosocomial infections could have been biased due to small sample sizes studies, inadequate research methodology and measurement error.
The objective of this study was to systematically review the literature and to provide a quantitative synthesis of the available evidence regarding the adjusted association between HAI due to MDR bacteria and direct cost of care, extended length of stay and mortality in observational studies with the highest degree of internal validity.
Materials and methods
This systematic review and meta-analysis adheres to the PRISMA recommendations. Furthermore, specific recommendations for systematic review and metanalysis of observational studies were followed. This metanalysis was registered with PROSPERO (CRD42019126288).
Study eligibility criteria
Studies met eligibility criteria if they were case-control or cohort studies conducted in adults evaluating the effects of MDR HAI vs non-MDR HAI. We excluded non-adjusted studies due to the excess risk of bias within the observational setting of this analysis. Two consecutive rounds of eligibility criteria were applied to ensure comparability across studies.
First, each included study must: i) have been published between 1st January 1980 - 3rd September 2019 in English, ii) attempt to estimate the direct impact of MDR (MDR was defined in accordance to the current available interim expert proposal) HAIs on either clinical or economic outcomes using original data with a case-control or cohort study design, and, iii) attempt to control for confounding either through matching, inclusion of sensible covariates in a multivariate analysis, or both.
A second round of eligibility criteria was applied upon examination of the full text of each study for inclusion in the systematic review. In particular, each study must: i) have a control group that differs in the antibiotic susceptibility pattern of the microorganism causing infections (confirmed by susceptibility testing), ii) present comprehensive descriptive statistics (gender composition, age, microorganism distribution) of the case and control groups with estimated variance parameters of the dependent variables, iii) examine at least one of the following outcomes: mortality at discharge, direct hospital cost/charges or length of stay, and, iv) in case of inclusion of community-acquired infection (CAI) cases, results had to be reported separately for CAI and HAI cases while fulfilling the previous inclusion conditions.
Identification and selection of studies
A systematic search was performed on 14th March 2019,re-run on the 10th of June 2019 and extended the 3rd of September 2019. We searched PubMed/MEDLINE and Google Scholar databases for eligible studies. Two authors (MSB and MK) performed the title screening to find retrospective or prospective studies reporting an adjusted association of MDR HAI with mortality, length of stay and/or direct cost of care. A specific-search strategy was tailored to each database by using MeSH/Map terms and is presented as supplementary S3 Table. The degree of agreement across the two reviewers for inclusion assessment was assessed using kappa statistics.
Additional papers were also collected through expert consultation, and references of individual papers and previous meta-analyses. The search flow chart is presented in Fig 1.
Data extraction and risk of bias assessment
Two predefined tables were used to summarize information from the final eligible studies. In the first one, for each paper, we recorded the basic results about the estimated impact of MDR HAI on each clinical and economic outcome (adjusted difference, outcomes mean, and, sample sizes). In the second one, we recorded the descriptive statistics provided on the study samples used for the final analysis. Specific Items were extracted for each study: clinical setting, study design (prospective or retrospective), origin of infection (hospital and/or community), infection type. These data extraction procedures were performed by two authors, and disagreement was resolved by discussion with a third author (CC). Three primary outcomes were evaluated: i) direct hospitalization cost or hospital charges, ii) length of stay, and iii) mortality at discharge.
Direct hospitalization cost was defined as the total direct cost from the hospital perspective of the inpatient admission, when unavailable, hospital charges were used as a proxy. Length of stay was defined as the admission length of stay following infection up to discharge. Mortality was defined as patient death at the time of discharge.
Various methodologies are regularly employed to control for confounding and to approximate unbiased, causal estimates. In order to assess the risk of bias per study we used the Newcastle-Ottawa Scale designed for assessing the quality of non-randomized studies in a meta-analysis[25–27]. Publication bias was assessed with funnel plots.
All statistical analyses were performed in R, version 3.5.1 (2018-07-02) with the meta package. Difference in outcomes was expressed as ratio of means (ROM) for cost and length of stay outcomes, and risk ratio (RR) for mortality at discharge. The rationale for expressing outcomes as ROM, was to eliminate between-studies variability between the reporting of direct hospital cost or charges, both random-effects (RE) and fixed-effects (FE) models were fitted for statistical pooling of the data. Given the clinical heterogeneity present in the analysis, a random effects model with the Hartung-Knapp-Sidik-Jonkman method was chosen as the primary estimation method. The weighting of the results was performed in relation to the sample size of each study. Given the low likelihood of obtaining 10 or more studies by covariate, meta-regression was not performed.
Statistical heterogeneity for each pooled outcome was examined with the I2 statistic and Q-test. Significance level was established at the 5% level, and all results are expressed with 95% confidence intervals (CI). Prediction interval was also estimated in the pooling. Risk of publication bias was visually examined through funnel plots and assessed with a linear regression test of funnel plot asymmetry.
Fig 1 depicts a flow diagram of the search, following PRISMA guidelines, screening and eligibility procedures and the loss of studies that occurred throughout. Table 1 describes the studies that were included after the second round of eligibility criteria applied to the full-text studies. Out of 733 selected studies, 658 (93.5%) were excluded after screening of the abstract for the first round of inclusion criteria (kappa = 0.73). A total of 48 full-text manuscripts were assessed for the second round of inclusion criteria, with 28 (58.3%) being excluded for not fulfilling the criteria (kappa = 0.96). A total of 20 studies were included in the review, with 14,448 patients included[36–55]. Out of the 20 studies, 19 presented in hospital length of stay, 14 presented mortality data and 12 cost outcomes.
Most studies were single center (k = 13). Four studies were prospective cohort studies, while the rest (k = 16) were retrospective. Only four studies were conducted in the intensive care units (ICU), while the remaining studies were conducted in other wards, with some including both community-acquired infections and HAI reported separately (k = 6). In terms of type of infection, most studies included any HAI (k = 11), while the rest, pneumonia (k = 6), bloodstream infections (BSI, k = 2), surgical site infections (SSI, k = 2), and urinary tract infections (UTI, k = 1).
Risk of bias
Risk of bias was rated according to the Newcastle-Ottawa scale and is summarized in S1 Table. The majority of studies were rated as good quality according to the AHQR, (k = 9). Six studies were classified as fair and five of poor quality. Case definition was adequately portrayed in most studies (k = 14), selection of controls (k = 17), definition of controls (k = 12), and representativeness of the sample (k = 11). Comparability of cases and controls on the was rated at least with one point (*) in all studies (k = 20). Ascertainment of exposure was adequate in almost all studies (k = 18). Overall, the risk of bias in included studies was found to be low.
Effects of MDR-HAI
Excess attributable cost of MDR HAI was found to be statistically significant in the RE pooling (ROM 1.33 95%CI [1.15; 1.54]). Fig 2 presents the results of the pooling. FE pooling displayed similar results (ROM 1.35 95%CI [1.26; 1.44]), and the prediction interval ranged from 0.81 to 2.20. Significant heterogeneity across studies was found to be present with a Q-statistic value of 44.09 (p<0.01) and an I2 statistic of 75%. There was no evidence of publication bias in the 11 studies that reported cost outcomes with the linear regression test of funnel plot asymmetry (p = 0.7715).
Length of stay.
Prolonged length of stay of MDR HAI was found to be also significant in the RE pooling (ROM 1.27 95%CI [1.17; 1.39]). FE model yielded similar results (ROM 1.21 [1.17; 1.24]). The prediction interval was found to be narrower than for cost with values ranging from 0.97 to 1.65. Fig 3 presents the results. Heterogeneity was also found to be present, Q-statistic value of 59.38 (p<0.01) and I2 value of 69%. We were not able to reject the null hypothesis of symmetry in the funnel plot (p = 0.1258).
Mortality at discharge.
Excess mortality results from the 14 eligible studies are displayed in Fig 4. RE pooling shows a significant increase (RR 1.61 95%CI [1.36; 1.90]). FE pooled estimates were much lower than RE (RR 1.27 95%CI [1.21; 1.33]), while the prediction interval ranges from 0.94 to 2.74. Significant heterogeneity across studies was found, Q-statistic 48.49 (p<0.01) and I2 statistic of 73%.
Fig 5 displays the funnel plots of all three outcomes. Symmetry was confirmed for cost and length of stay outcomes, while for mortality a significant asymmetry towards increased RR was noted (p<0.01), highlighting a high risk of publication bias in this particular outcome.
HAIs can occur in almost every step of hospital care and are highly heterogeneous in their cause and clinical presentation. It follows that a prominent feature of the literature on the economic and clinical impact of MDR is a large degree of heterogeneity across study settings. Here we reviewed and synthesized the available evidence presented with the highest observational methodological standards due to well understood confounding problems. We limited the analysis to cohort and case-control studies in order to alleviate selection bias, excluding a significant portion of studies, and yielding a small number of studies. Overall, the quality of studies was good, we did, however, found indication of publication bias in mortality estimates.
We found consistent patterns that indicate increased mortality and resource utilization in terms of length of stay and direct cost which are associated with MDR HAI vs those associated with susceptible microorganism acquired infections. We report 1.27, 1.33 and 1.62-fold increases in excess length of stay, cost and mortality at discharge risk associated with MDR respectively. The effect size of length and cost does not vary significantly depending upon the estimation method. However, for mortality, there is a two-fold difference between RE (RR 1.62) and FE (RR 1.27). This difference highlights the potential unobserved heterogeneity across studies in terms of patient prognosis and publication bias. The interrelation of these three outcomes plays a crucial role in the interpretation of the results. Higher inpatient mortality could, theoretically, even reduce inpatient stay and direct hospital cost. However, our estimates point to an overall increase around 30% for resource utilization.
As is the case with observational studies where the exposure is not randomized, there is risk of bias in estimating the impact of exposure. Only 4 out of 20 eligible studies presented a prospective cohort study design that could mitigate selection bias in the estimates. Remaining studies presented retrospective designs and attempted to overcome other sources of bias through a process matching and/or multivariate modelling with appropriate selection of control variables.
The range of costs presented varies considerably according to the setting in which MDR HAIs were studied, ranging from average increases of 3,000 USD to 40,000 USD[36,37]. The reported mechanisms of cost impact also vary across settings. We tried to address this between-studies heterogeneity by using a ROM metric as outcome, rendering interpretable estimates regardless of institutional and national setting. The most consistent mechanism across studies refers to secondary antibiotic treatment, followed by prolonged length of stay and increased labor costs. Furthermore, the MDR cost impact has an average incremental estimate of a 1.33-fold magnitude, highlighting the relative magnitude of the problem.
Further well-designed studies, that use either a consistent control, matching or time-to-infection control, like Nelson et al. (2018) are needed in order to best approximate unbiased and consistent estimates. Their clinical and economic implications are crucial for both the development of novel antibiotics alongside their potential cost-effectiveness estimates.
There are several limitations to this systematic review. There is a general lack of comparability across studies examining the impact of MDR HAI on clinical and economic outcomes, namely in setting, MDR bacteria and infections studied, data presentation, and methodology. Given the potential lack of exogenous variation in the allocation of resistant bacterial infections, confounding may be a problem, which would require elaborate and uniform covariate choice across participating studies so that a comprehensive set of covariates could be used in a multivariate pooled analysis. No restrictions regarding time to HAI were applied for its definition. However, most studies used a 48H window since admission.
Furthermore, there are subtle differences in the definitions of primary endpoints utilized across the eligible studies and MDR definitions, which makes comparison less reliable. For example, one may formulate cost from either the patient or hospital perspective, which may be an estimate of cost rather than the true figure, which poses measurement error. Lastly, a complex grouping of studies was chosen for this meta-analysis. We considered all bacteria types presenting with resistant and many types of infections. Comparisons between these different settings are made more difficult by the inconsistencies across studies. We were also unable to perform reliable subgroup analyses due to the small sample size of eligible studies in each predefined category.
Finally, our search strategy did not include studies published in non-English languages, potentially yielding under representation of geographic areas where AMR presents an even higher burden. Fig 6 presents the geographical distribution of included studies, mainly in North America and Europe. Further research estimating the pooled burden in non-English speaking countries is warranted in order to provide accurate estimates of the magnitude of the burden.
MDR HAI appears to be strongly associated with increased resource utilization and health outcomes, as assessed by direct provider cost and/or charges, prolonged length of stay and mortality of patients in a wide array of healthcare settings. However, more research is required to fully elucidate the relationship between MDR and economic and clinical outcomes and its dependence on various settings, routes of infection and pathogens involved. Further applied research should focus on the development of prospective registries with standardized susceptibility testing and longer follow-up outside of hospital settings to better understand the actual societal burden of such a rising phenomenon.
S1 Checklist. PRISMA checklist.
S1 Table. Summary of Newcastle-Ottawa risk of bias judgements for each study.
S2 Table. Quantitative summary of included studies.
S3 Table. Search strategy by database.
- 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. pmid: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. pmid: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. pmid:18947320
- 5. Livermore DM. Bacterial Resistance: Origins, Epidemiology, and Impact. Clin Infect Dis. 2003; doi: 10.1086/344654. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:19231985
- 16. Alekshun MN, Levy SB. Molecular Mechanisms of Antibacterial Multidrug Resistance. Cell. 2007. doi: 10.1016/j.cell.2007.03.004. pmid: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. pmid:25435309
- 18. Higgins CF. Multiple molecular mechanisms for multidrug resistance transporters. Nature. 2007. doi: 10.1038/nature05630. pmid:17429392
- 19. Baym M, Stone LK, Kishony R. Multidrug evolutionary strategies to reverse antibiotic resistance. Science. 2016. doi: 10.1126/science.aad3292. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:18492289