Relationship between Klebsiella pneumoniae biofilm ...
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• 93 isolates remained upon removal of moderate biofilm formers • MDR isolates were more common among weak biofilm formers (97.9%) versus strong biofilm formers (76.1%; p=0.002) • XDR isolates were similar between groups (25.5% vs. 28.3% p=0.77) • Resistance to penicillin/β-lactamase inhibitors, cephalosporins, monobactams, carbapenems, protein synthesis inhibitors, or fluoroquinolones were more common among weak biofilm formers (p<0.05). • Predictors regression model results: o Carbapenem resistance is inversely associated with strong biofilm • OR 0.09 (95% CI 0.02 – 0.33) • Hypothesis: There is an association between specific K. pneumoniae antimicrobial resistance and biofilm formation • Objectives: o Determine biofilm formation and antimicrobial susceptibility for each K. pneumoniae isolate o Identify if an association exists between K. pneumoniae biofilm formation and specific antibiotic resistance Acknowledgements: The information presented are those of the authors and do not necessarily reflect the position or policy of the United States Department of Veterans Affairs. This work was supported in part, by the Office of Academic Affiliations (OAA) at the Department of Veterans Affairs, and by the COIN: Center of Innovation in Long-Term Services and Supports for Vulnerable Veterans; Providence, RI. Funding: This work was unfunded and the authors received no specific funding for this work. • Klebsiella pneumoniae is a frequently multidrug resistant organism with a high propensity to produce biofilm 1,2 • K. pneumoniae is the most common carbapenem-resistant Enterobacteriaceae (CRE), which have been categorized as an urgent threat by the CDC and is associated with high mortality rates up to 50% 3-5 • The relationship between K. pneumoniae biofilm formation and antimicrobial resistance has not been extensively described and data is limited: o Extensively drug resistant (XDR) K. pneumoniae urinary isolates from a rehabilitation center formed biofilm at higher rates than MDR strains and susceptible strains (91.1% vs. 67.5% vs. 70.8%, respectively) 6 o Biofilm forming K. pneumoniae from a single-center hospital more likely produced ESBL versus non-biofilm formers (83.6% vs. 14.4%, p < 0.01) 7 o bla NDM-1 K. pneumoniae isolates from a single-center hospital outbreak were more likely to produce biofilm versus carbapenem susceptible isolates (p < 0.05) 8 • Organisms: 139 K. pneumoniae isolates were assessed from the CDC (n=66), BEI (n=36), ATCC (n=3), and patient isolates from Providence VA Medical Center and Rhode Island Hospital (n=34) • Biofilm Quantification: Modified Crystal Violet Method (OD 570 ) 9 o Biofilm formation was defined by tertile cut-points 10 Figure 1. Biofilm Categorization • Previously published data describe resistant K. pneumoniae isolates to be associated with biofilm formation 6-8 . However, our study comparatively: o Consists of diverse collections of isolates from multiple centers o Uses multivariate statistical analysis o Redefines biofilm quantification to overcome methodological limitations • Standardization of biofilm methods is imperative • Carbapenem resistant K. pneumoniae were 91% less likely to form biofilm o The inverse relationship between biofilm formation and antibiotic resistance suggests that virulence may be a trade-off for survival o Potential clinical impact: allows clinicians to better understand the optimal treatment approach for their patients if biofilm formation is present References: 1 - Ribeiro SM, et al. Future Microbiol. 2016;11(4):527-38. 2 - Clegg S, Murphy CN. Microbiol Spectr. 2016;4(1). 3 - Won SY, et al. Clin Infect Dis. 2011;53(6):532-40. 4 - da Silva RM, et al. Expert Opin Biol Ther. 2012;12(6):663-71. 5 - Tuon FF, et al. Braz J Infect Dis. 2017;21(1):1-6. 6 - Vuotto C, et al. J Appl Microbiol. 2017;123(4):1003-18. Relationship between Klebsiella pneumoniae biofilm formation and antimicrobial resistance Jaclyn A. Cusumano, Pharm.D. 1,2 , Kathryn E. Daffinee, BS 1 , Megan K. Luther, Pharm.D. 1,2,3 , Vrishali Lopes, MS 1 , Aisling Caffrey, Ph.D., MS 1,2,3 , Kerry L. LaPlante, Pharm.D., FCCP, FIDSA 1,2,3,4 1 Providence Veterans Affairs Medical Center, Providence, RI; 2 University of Rhode Island, College of Pharmacy, Kingston, RI.; 3 Center of Innovation in Long Term Services and Supports, Providence Veterans Affairs Medical Center, Providence, RI; 4 Infectious Diseases Division, Warren Alpert Medical School of Brown University, Providence, RI Conclusion / Discussion Results IDWeek 2018, Abstract # 71726 Contact information: Jaclyn Cusumano, PharmD, [email protected] Hypothesis / Objectives Background Methods Results • Isolate susceptibility: based on 2017 CLSI breakpoints, except for tigecycline, colistin, and fosfomycin as CLSI breakpoints were unavailable • Categorization for MDR and XDR was based on CDC/ECDC consensus 11 o Multidrug-resistant (MDR): non-susceptible to ≥ 1 agent in ≥ 3 out of 16 antimicrobial categories o Extensively drug-resistant (XDR): non-susceptible to ≥ 1 agent in all but ≤ 2 out of 16 antimicrobial categories • The 16 antimicrobial categories were collapsed to 9 groups below (Table 1) • Statistical analysis (SAS 9.2): o Differences assessed with Chi-square or Fisher’s exact tests for dichotomous variables and student t-test for continuous variables o Predictors of strong biofilm formation were identified with logistic regression Methods Table 1. Prevalence of Antimicrobial Resistance According to Biofilm Formation Variable Total Cohort (n=93) Weak Biofilm Formation (n=47) Strong Biofilm Formation (n=46) p-value Multidrug-resistant (MDR), n (%) 81 (87.1) 46 (97.9) 35 (76.1) 0.002 Extensively drug- resistant (XDR), n (%) 25 (26.9) 12 (25.5) 13 (28.3) 0.77 Number of Resistant Categories (n=16), Med 13 13 11.5 0.01 Antimicrobial Groups (n=9) Penicillin / β-lactamase inhibitors, n (%) 79 (84.9) 46 (97.9) 33 (71.7) 0.0004 Cephalosporins, n (%) 82 (88.2) 46 (97.9) 36 (78.3) 0.003 Monobactams, n (%) 73 (78.5) 45 (95.7) 28 (60.9) <0.0001 Carbapenems, n (%) 70 (75.3) 44 (93.6) 26 (56.5) <0.0001 Protein Synthesis Inhibitors, n (%) 80 (86.0) 46 (97.9) 34 (73.9) 0.001 Fluoroquinolones, n (%) 73 (78.5) 45 (95.7) 28 (60.9) <0.0001 Folate pathway inhibitors, n (%) 66 (71.0) 37 (78.7) 29 (63.0) 0.09 Fosfomycin, n (%) 61 (65.6) 29 (61.7) 32 (69.6) 0.42 Colistin, n (%) 11 (11.8) 8 (17.0) 3 (6.5) 0.12 K. pneumoniae Isolates (n=139) Tertile Cut-Points Moderate Biofilm Formation 0.16 < OD 570 < 0.59 (n=46 isolates) Strong Biofilm Formation OD 570 ≥ 0.59 (n=46 isolates) Weak Biofilm Formation OD 570 ≤ 0.16 (n=47 isolates) Final Analysis (n=93) 1.78 0.44 0.02 2.57 7- Yang D, Zhang Z. J Hosp Infect. 2008;68(4):369-71. 8 - Bocanegra-Ibarias P, et al. PLoS ONE. 2017;12(6): e0179651. 9- Stepanovic S, et al. APMIS. 2007;115(8):891-9. 10 – Guembe M, et al. Clin Microbiol Infect. 2017; doi: 10.1016/j.cmi.2017.10.018. 11 - Magiorakos AP, et al. Clin Microbiol Infect. 2012;18(3):268-81.
Transcript of Relationship between Klebsiella pneumoniae biofilm ...
• 93 isolates remained upon removal of moderate biofilm formers • MDR isolates were more common among weak biofilm formers (97.9%)
versus strong biofilm formers (76.1%; p=0.002)• XDR isolates were similar between groups (25.5% vs. 28.3% p=0.77)• Resistance to penicillin/β-lactamase inhibitors, cephalosporins,
monobactams, carbapenems, protein synthesis inhibitors, or fluoroquinolones were more common among weak biofilm formers (p<0.05).
• Predictors regression model results:o Carbapenem resistance is inversely associated with strong biofilm • OR 0.09 (95% CI 0.02 – 0.33)
• Hypothesis: There is an association between specific K. pneumoniae antimicrobial resistance and biofilm formation
• Objectives:o Determine biofilm formation and antimicrobial susceptibility for each
K. pneumoniae isolateo Identify if an association exists between K. pneumoniae biofilm formation
and specific antibiotic resistance
Acknowledgements: The information presented are those of the authors and do not necessarily reflect the position or policy of the United States Department of Veterans Affairs. This work was supported in part, by the Office of Academic Affiliations (OAA) at the Department of Veterans Affairs, and by the COIN: Center of Innovation in Long-Term Services and Supports for Vulnerable Veterans; Providence, RI.Funding: This work was unfunded and the authors received no specific funding for this work.
• Klebsiella pneumoniae is a frequently multidrug resistant organism with a high propensity to produce biofilm1,2
• K. pneumoniae is the most common carbapenem-resistant Enterobacteriaceae (CRE), which have been categorized as an urgent threat by the CDC and is associated with high mortality rates up to 50%3-5
• The relationship between K. pneumoniae biofilm formation and antimicrobial resistance has not been extensively described and data is limited:o Extensively drug resistant (XDR) K. pneumoniae urinary isolates from a
rehabilitation center formed biofilm at higher rates than MDR strains and susceptible strains (91.1% vs. 67.5% vs. 70.8%, respectively)6
o Biofilm forming K. pneumoniae from a single-center hospital more likely produced ESBL versus non-biofilm formers (83.6% vs. 14.4%, p < 0.01)7
o blaNDM-1 K. pneumoniae isolates from a single-center hospital outbreak were more likely to produce biofilm versus carbapenem susceptible isolates (p < 0.05)8
• Organisms: 139 K. pneumoniae isolates were assessed from the CDC (n=66), BEI (n=36), ATCC (n=3), and patient isolates from Providence VA Medical Center and Rhode Island Hospital (n=34)
• Biofilm Quantification: Modified Crystal Violet Method (OD570)9
o Biofilm formation was defined by tertile cut-points10
Figure 1. Biofilm Categorization
• Previously published data describe resistant K. pneumoniae isolates to beassociated with biofilm formation6-8. However, our study comparatively:o Consists of diverse collections of isolates from multiple centerso Uses multivariate statistical analysis o Redefines biofilm quantification to overcome methodological limitations • Standardization of biofilm methods is imperative
• Carbapenem resistant K. pneumoniae were 91% less likely to form biofilmo The inverse relationship between biofilm formation and antibiotic
resistance suggests that virulence may be a trade-off for survivalo Potential clinical impact: allows clinicians to better understand the optimal
treatment approach for their patients if biofilm formation is present
References:1 - Ribeiro SM, et al. Future Microbiol. 2016;11(4):527-38.2 - Clegg S, Murphy CN. Microbiol Spectr. 2016;4(1).3 - Won SY, et al. Clin Infect Dis. 2011;53(6):532-40.4 - da Silva RM, et al. Expert Opin Biol Ther. 2012;12(6):663-71.5 - Tuon FF, et al. Braz J Infect Dis. 2017;21(1):1-6. 6 - Vuotto C, et al. J Appl Microbiol. 2017;123(4):1003-18.
Relationship between Klebsiella pneumoniae biofilm formation and antimicrobial resistance
Jaclyn A. Cusumano, Pharm.D.1,2, Kathryn E. Daffinee, BS1, Megan K. Luther, Pharm.D.1,2,3, Vrishali Lopes, MS1,
Aisling Caffrey, Ph.D., MS1,2,3, Kerry L. LaPlante, Pharm.D., FCCP, FIDSA1,2,3,4
1Providence Veterans Affairs Medical Center, Providence, RI; 2University of Rhode Island, College of Pharmacy, Kingston, RI.;3Center of Innovation in Long Term Services and Supports, Providence Veterans Affairs Medical Center, Providence, RI;
4Infectious Diseases Division, Warren Alpert Medical School of Brown University, Providence, RI
Conclusion / Discussion
Results
IDWeek 2018, Abstract # 71726 Contact information: Jaclyn Cusumano, PharmD, [email protected]
Hypothesis / Objectives
Background
Methods
Results
• Isolate susceptibility: based on 2017 CLSI breakpoints, except for tigecycline, colistin, and fosfomycin as CLSI breakpoints were unavailable
• Categorization for MDR and XDR was based on CDC/ECDC consensus11
o Multidrug-resistant (MDR): non-susceptible to ≥ 1 agent in ≥ 3 out of 16 antimicrobial categories
o Extensively drug-resistant (XDR): non-susceptible to ≥ 1 agent in all but ≤ 2 out of 16 antimicrobial categories
• The 16 antimicrobial categories were collapsed to 9 groups below (Table 1)• Statistical analysis (SAS 9.2): o Differences assessed with Chi-square or Fisher’s exact tests for
dichotomous variables and student t-test for continuous variableso Predictors of strong biofilm formation were identified with logistic
regression
Methods
Table 1. Prevalence of Antimicrobial Resistance According to Biofilm Formation
Variable
Total
Cohort
(n=93)
Weak Biofilm
Formation
(n=47)
Strong Biofilm
Formation
(n=46)
p-value
Multidrug-resistant
(MDR), n (%)81 (87.1) 46 (97.9) 35 (76.1) 0.002
Extensively drug-
resistant (XDR), n (%)25 (26.9) 12 (25.5) 13 (28.3) 0.77
Number of Resistant
Categories (n=16), Med13 13 11.5 0.01
Antimicrobial Groups (n=9)
Penicillin / β-lactamase
inhibitors, n (%)79 (84.9) 46 (97.9) 33 (71.7) 0.0004
Cephalosporins, n (%) 82 (88.2) 46 (97.9) 36 (78.3) 0.003
Monobactams, n (%) 73 (78.5) 45 (95.7) 28 (60.9) <0.0001
Carbapenems, n (%) 70 (75.3) 44 (93.6) 26 (56.5) <0.0001
Protein Synthesis
Inhibitors, n (%)80 (86.0) 46 (97.9) 34 (73.9) 0.001
Fluoroquinolones, n (%) 73 (78.5) 45 (95.7) 28 (60.9) <0.0001
Folate pathway
inhibitors, n (%)66 (71.0) 37 (78.7) 29 (63.0) 0.09
Fosfomycin, n (%) 61 (65.6) 29 (61.7) 32 (69.6) 0.42
Colistin, n (%) 11 (11.8) 8 (17.0) 3 (6.5) 0.12
K. pneumoniae Isolates (n=139)
Tertile Cut-Points
Moderate Biofilm Formation
0.16 < OD570 < 0.59(n=46 isolates)
Strong Biofilm Formation
OD570 ≥ 0.59 (n=46 isolates)
Weak Biofilm Formation
OD570 ≤ 0.16 (n=47 isolates)
Final Analysis (n=93)
1.78
0.44
0.02
2.57
7- Yang D, Zhang Z. J Hosp Infect. 2008;68(4):369-71.8 - Bocanegra-Ibarias P, et al. PLoS ONE. 2017;12(6): e0179651. 9- Stepanovic S, et al. APMIS. 2007;115(8):891-9.10 – Guembe M, et al. Clin Microbiol Infect. 2017; doi: 10.1016/j.cmi.2017.10.018.11 - Magiorakos AP, et al. Clin Microbiol Infect. 2012;18(3):268-81.
