From Epidemiology of Community-Onset Bloodstream Infections to the Development of Empirical Antimicrobial Treatment-Decision Algorithm in a Region with High Burden of Antimicrobial Resistance
Abstract
:1. Introduction
2. Results
2.1. Distribution of Pathogenic Organisms Associated with Community-Onset Bloodstream and Antimicrobial Susceptibility
2.2. Clinical Characteristics of Patients with Antimicrobial-Resistant BSIs (AMR-BSIs) and Non-Antimicrobial-Resistant BSIs (NAMR-BSIs)
2.3. Analysis of Risk Factors Associated with AMR and 30-Day All-Cause Mortality in Patients with Community-Onset Bloodstream Infections
2.4. Appropriateness of Antimicrobial Use
2.5. Proposed Empirical Antimicrobial Treatment Algorithm for Patients with Suspected Community-Onset Bloodstream Infections
3. Discussion
4. Material and Methods
4.1. Study Population
4.2. Data Collection
4.3. Definitions
4.4. Microbiological Testing
4.5. Assessment of the Appropriateness of Antimicrobial Use
4.6. Statistical Analysis
4.7. Empirical Antimicrobial Treatment-Decision Algorithm Development
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Dat, V.Q.; Vu, H.N.; The, H.N.; Nguyen, H.T.; Hoang, L.B.; Viet, D.V.T.; Bui, C.L.; Van Nguyen, K.; Nguyen, T.V.; Trinh, D.T.; et al. Bacterial bloodstream infections in a tertiary infectious diseases hospital in Northern Vietnam: Aetiology; drug resistance; and treatment outcome. BMC Infect. Dis. 2017, 17, 493. [Google Scholar] [CrossRef] [PubMed]
- Santoro, A.; Franceschini, E.; Meschiari, M.; Menozzi, M.; Zona, S.; Venturelli, C.; Digaetano, M.; Rogati, C.; Guaraldi, G.; Paul, M.; et al. Epidemiology and risk factors associated with mortality in consecutive patients with bacterial bloodstream infection: Impact of MDR and XDR bacteria. Open Forum Infect. Dis. 2020, 7, ofaa461. [Google Scholar] [CrossRef] [PubMed]
- Goodman, K.E.; Lessler, J.; Cosgrove, S.E.; Harris, A.D.; Lautenbach, E.; Han, J.H.; Milstone, A.M.; Massey, C.J.; Tamma, P.D.; Antibacterial Resistance Leadership Group. A clinical decision tree to predict whether a bacteremic patient is infected with an extended-spectrum beta-lactamase-producing organism. Clin. Infect. Dis. 2016, 63, 896–903. [Google Scholar] [CrossRef] [PubMed]
- Ponyon, J.; Kerdsin, A.; Preeprem, T.; Ungcharoen, R. Risk factors of infections due to multidrug-resistant gram-negative bacteria in a community hospital in rural Thailand. Trop. Med. Infect. Dis. 2022, 7, 328. [Google Scholar] [CrossRef] [PubMed]
- Laupland, K.B.; Svenson, L.W.; Gregson, D.B.; Church, D.L. Long-term mortality associated with community-onset bloodstream infection. Infection 2011, 39, 405–410. [Google Scholar] [CrossRef] [PubMed]
- Nielsen, S.L.; Lassen, A.T.; Gradel, K.O.; Jensen, T.G.; Kolmos, H.J.; Hallas, J.; Pedersen, C. Bacteremia is associated with excess long-term mortality: A 12-year population-based cohort study. J. Infect. 2015, 70, 111–126. [Google Scholar] [CrossRef] [PubMed]
- Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. Lancet 2022, 399, 629–655. [Google Scholar] [CrossRef]
- Kern, W.V.; Rieg, S. Burden of bacterial bloodstream infection-a brief update on epidemiology and significance of multidrug-resistant pathogens. Clin. Microbiol. Infect. 2020, 26, 151–157. [Google Scholar] [CrossRef]
- Tabak, Y.P.; Vankeepuram, L.; Ye, G.; Jeffers, K.; Gupta, V.; Murray, P.R. Blood culture turnaround time in U.S. acute care hospitals and implications for laboratory process optimization. J. Clin. Microbiol. 2018, 56, e00500-18. [Google Scholar] [CrossRef]
- Sirijatuphat, R.; Sripanidkulchai, K.; Boonyasiri, A.; Rattanaumpawan, P.; Supapueng, O.; Kiratisin, P.; Thamlikitkul, V. Implementation of global antimicrobial resistance surveillance system (GLASS) in patients with bacteremia. PLoS ONE 2018, 13, e0190132. [Google Scholar] [CrossRef]
- Lee, C.C.; Wang, J.L.; Lee, C.H.; Hung, Y.P.; Hong, M.Y.; Chang, C.M.; Ko, W.C. Age-related trends in adults with community-onset bacteremia. Antimicrob. Agents Chemother. 2017, 61, e01050-17. [Google Scholar] [CrossRef] [PubMed]
- Rothe, K.; Wantia, N.; Spinner, C.D.; Schneider, J.; Lahmer, T.; Waschulzik, B.; Schmid, R.M.; Busch, D.H.; Katchanov, J. Antimicrobial resistance of bacteraemia in the emergency department of a German university hospital (2013–2018): Potential carbapenem-sparing empiric treatment options in light of the new EUCAST recommendations. BMC Infect. Dis. 2019, 19, 1091. [Google Scholar] [CrossRef] [PubMed]
- Jernigan, J.A.; Hatfield, K.M.; Wolford, H.; Nelson, R.E.; Olubajo, B.; Reddy, S.C.; McCarthy, N.; Paul, P.; McDonald, L.C.; Kallen, A.; et al. Multidrug-resistant bacterial infections in U.S. hospitalized patients; 2012–2017. N. Engl. J. Med. 2020, 382, 1309–1319. [Google Scholar] [CrossRef] [PubMed]
- Lin, W.P.; Huang, Y.S.; Wang, J.T.; Chen, Y.C.; Chang, S.C. Prevalence of and risk factor for community-onset third-generation cephalosporin-resistant Escherichia coli bacteremia at a medical center in Taiwan. BMC Infect. Dis. 2019, 19, 245. [Google Scholar] [CrossRef] [PubMed]
- Kolar, M.; Urbanek, K.; Latal, T. Antibiotic selective pressure and development of bacterial resistance. Int. J. Antimicrob. Agents 2001, 17, 357–363. [Google Scholar] [CrossRef] [PubMed]
- Rogers, B.A.; Sidjabat, H.E.; Paterson, D.L. Escherichia coli O25b-ST131: A pandemic; multiresistant; community-associated strain. J. Antimicrob. Chemother. 2011, 66, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Pruetpongpun, N.; Khawcharoenporn, T.; Damronglerd, P.; Suwantarat, N.; Apisarnthanarak, A.; Rutjanawech, S. Inappropriate empirical treatment of uncomplicated cystitis in Thai women: Lessons learned. Clin. Infect. Dis. 2017, 64, S115–S118. [Google Scholar] [CrossRef]
- Sirijatuphat, R.; Pongsuttiyakorn, S.; Supapueng, O.; Kiratisin, P.; Thamlikitkul, V. Implementation of global antimicrobial resistance surveillance system (GLASS) in patients with bacteriuria. J. Glob. Antimicrob. Resist. 2020, 20, 60–67. [Google Scholar] [CrossRef]
- Tumbarello, M.; Viale, P.; Viscoli, C.; Trecarichi, E.M.; Tumietto, F.; Marchese, A.; Spanu, T.; Ambretti, S.; Ginocchio, F.; Cristini, F.; et al. Predictors of mortality in bloodstream infections caused by Klebsiella pneumoniae carbapenemase-producing K. pneumoniae: Importance of combination therapy. Clin. Infect. Dis. 2012, 55, 943–950. [Google Scholar] [CrossRef]
- Kim, J.H.; Yoo, S.H.; Keam, B.; Heo, D.S. The impact of palliative care consultation on reducing antibiotic overuse in hospitalized patients with terminal cancer at the end of life: A propensity score-weighting study. J. Antimicrob. Chemother. 2022, 78, 302–308. [Google Scholar] [CrossRef]
- Scarsi, K.K.; Feinglass, J.M.; Scheetz, M.H.; Postelnick, M.J.; Bolon, M.K.; Noskin, G.A. Impact of inactive empiric antimicrobial therapy on inpatient mortality and length of stay. Antimicrob. Agents Chemother. 2006, 50, 3355–3360. [Google Scholar] [CrossRef] [PubMed]
- Fitzpatrick, J.M.; Biswas, J.S.; Edgeworth, J.D.; Islam, J.; Jenkins, N.; Judge, R.; Lavery, A.J.; Melzer, M.; Morris-Jones, S.; Nsutebu, E.F.; et al. Gram-negative bacteraemia; a multi-centre prospective evaluation of empiric antibiotic therapy and outcome in English acute hospitals. Clin. Microbiol. Infect. 2016, 22, 244–251. [Google Scholar] [CrossRef] [PubMed]
- Harris, P.N.A.; Tambyah, P.A.; Lye, D.C.; Mo, Y.; Lee, T.H.; Yilmaz, M.; Alenazi, T.H.; Arabi, Y.; Falcone, M.; Bassetti, M.; et al. Effect of piperacillin-tazobactam vs meropenem on 30-day mortality for patients with E coli or Klebsiella pneumoniae bloodstream infection and ceftriaxone resistance: A randomized clinical trial. JAMA 2018, 320, 984–994. [Google Scholar] [CrossRef] [PubMed]
- Sharara, S.L.; Amoah, J.; Pana, Z.D.; Simner, P.J.; Cosgrove, S.E.; Tamma, P.D. Is piperacillin-tazobactam effective for the treatment of pyelonephritis caused by extended-spectrum beta-lactamase-producing organisms? Clin. Infect. Dis. 2020, 71, e331–e337. [Google Scholar] [CrossRef] [PubMed]
- Gould, M.; Ginn, A.N.; Marriott, D.; Norris, R.; Sandaradura, I. Urinary piperacillin/tazobactam pharmacokinetics in vitro to determine the pharmacodynamic breakpoint for resistant Enterobacteriaceae. Int. J. Antimicrob. Agents. 2019, 54, 240–244. [Google Scholar] [CrossRef] [PubMed]
- Raz, R.; Schiller, D.; Nicolle, L.E. Chronic indwelling catheter replacement before antimicrobial therapy for symptomatic urinary tract infection. J. Urol. 2000, 164, 1254–1258. [Google Scholar] [CrossRef] [PubMed]
- Denny, K.J.; Gartside, J.G.; Alcorn, K.; Cross, J.W.; Maloney, S.; Keijzers, G. Appropriateness of antibiotic prescribing in the emergency department. J. Antimicrob. Chemother. 2019, 74, 515–520. [Google Scholar] [CrossRef]
- Chotiprasitsakul, D.; Han, J.H.; Cosgrove, S.E.; Harris, A.D.; Lautenbach, E.; Conley, A.T.; Tolomeo, P.; Wise, J.; Tamma, P.D.; Antibacterial Resistance Leadership Group. Comparing the outcomes of adults with Enterobacteriaceae bacteremia receiving short-course versus prolonged-course antibiotic therapy in a multicenter; propensity score-matched cohort. Clin. Infect. Dis. 2018, 66, 172–177. [Google Scholar] [CrossRef]
- Chotiprasitsakul, D.; Bruminhent, J.; Watcharananan, S.P. Current state of antimicrobial stewardship and organ transplantation in Thailand. Transpl. Infect. Dis. 2022, 24, e13877. [Google Scholar] [CrossRef]
- Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing, M100, 32nd ed.; Clinical and Laboratory Standards Institute: Wayne, PA, USA, 2022. [Google Scholar]
- Apisarnthanarak, A.; Lapcharoen, P.; Vanichkul, P.; Srisaeng-Ngoen, T.; Mundy, L.M. Design and analysis of a pharmacist-enhanced antimicrobial stewardship program in Thailand. Am. J. Infect. Control. 2015, 43, 956–959. [Google Scholar] [CrossRef]
Variables | AMR-BSI n = 114 (%) | NAMR-BSI n = 336 (%) | p Value |
---|---|---|---|
Emergency room | 72 (63.2%) | 241 (71.7%) | |
Outpatient clinic | 42 (36.8%) | 95 (28.3%) | |
Age (years), median (IQR) | 74 (57–83) | 71 (59–80) | 0.56 |
Male | 47 (41.2%) | 147 (43.8%) | 0.64 |
Preexisting medical conditions | |||
Chronic pulmonary disease | 7 (6.1%) | 17 (5.1%) | 0.66 |
Cardiovascular disease | 35 (30.7%) | 75 (22.3%) | 0.07 |
Chronic liver disease | 9 (7.9%) | 25 (7.4%) | 0.87 |
Chronic kidney disease | 16 (14.0%) | 34 (10.1%) | 0.25 |
Neurologic disease | 30 (26.3%) | 58 (17.3%) | 0.04 |
Diabetes mellitus | 41 (36.0%) | 127 (37.8%) | 0.73 |
Hypertension | 60 (52.6%) | 168 (50%) | 0.63 |
Active solid tumor | 29 (25.4%) | 64 (19.1%) | 0.15 |
Active hematologic malignancies | 3 (2.6%) | 19 (5.7%) | 0.20 |
HIV | 0 (0%) | 5 (1.5%) | 0.19 |
Kidney transplantation | 8 (7.0%) | 11 (3.3%) | 0.09 |
Stem cell transplantation | 0 (0%) | 2 (0.6%) | 0.41 |
Connective tissue diseases | 11 (9.7%) | 15 (4.5%) | 0.04 |
Chemotherapy in 6 months | 7 (6.1%) | 27 (8.0%) | 0.51 |
Corticosteroids at ≥20 mg of prednisone daily or equivalent for ≥14 days | 4 (3.5%) | 12 (3.6%) | 0.98 |
Post-COVID-19 within 60 days | 9 (7.9%) | 13 (3.9%) | 0.09 |
Presence of hemodialysis or central venous catheters | 9 (7.9%) | 35 (10.4%) | 0.43 |
Severity of acute illness index | |||
qSOFA score, median (IQR) | 1 (0–2) | 1 (0–2) | 0.40 |
Pitt bacteremia score | 1 (0–2) | 1 (0–2) | 0.97 |
ICU admission following BSIs | 21 (18.4%) | 80 (23.8%) | 0.23 |
On mechanical ventilator | 14 (12.3%) | 51 (15.2%) | 0.45 |
On vasopressor | 14 (12.3%) | 60 (17.9%) | 0.17 |
Epidemiological risks | |||
Prior admission within 90 days | 57 (50.0%) | 94 (28.0%) | <0.001 |
Colonization or infection with MDROs during preceding 90 days | 40 (35.1%) | 27 (8.0%) | <0.001 |
Ceftriaxone-resistant Enterobactericeae | 47 (41.2%) | 16 (4.8%) | |
Carbapenem-resistant Enterobactericeae | 11 (9.6%) | 6 (1.8%) | |
Extremely drug-resistant P. aeruginosa | 6 (5.3%) | 3 (0.9%) | |
Extremely drug-resistant A. baumannii | 4 (3.5%) | 3 (0.9%) | |
Previous antibiotic exposure within 90 days | 67 (58.8%) | 95 (28.3%) | <0.001 |
Carbapenems | 21 (18.4%) | 14 (4.2%) | |
Ceftriaxone | 22 (19.3%) | 22 (6.5%) | |
Piperacillin-tazobactam | 16 (14.0%) | 26 (7.7%) | |
Fluoroquinolones | 15 (13.2%) | 16 (4.8%) | |
Amoxicillin-clavulanate | 10 (8.8%) | 17 (5.1%) | |
Vancomycin | 4 (3.5%) | 4 (1.2%) | |
Oral third generation cephalosporins | 4 (3.5%) | 3 (0.9%) | |
Ceftazidime | 3 (2.6%) | 0 | |
Cefepime | 1 (0.9%) | 5 (1.5%) | |
Type of identification | |||
Unknown primary source | 14 (12.3%) | 71 (21.1%) | 0.04 |
Pneumonia | 4 (3.5%) | 16 (4.8%) | 0.58 |
Skin and soft tissue infection | 3 (7.5%) | 8 (6.0%) | 0.72 |
Bone and joint infection | 1 (0.9%) | 12 (3.6%) | 0.14 |
Urinary tract infection | 58 (50.9%) | 100 (29.8%) | <0.001 |
Hepatobiliary infection | 12 (10.5%) | 42 (12.5%) | 0.58 |
Intra-abdominal infection | 17 (14.9%) | 38 (11.3%) | 0.31 |
Infective endocarditis | 1 (0.9%) | 13 (3.9%) | 0.11 |
Central nervous system infection | 0 (0%) | 8 (2.4%) | 0.10 |
Catheter-associated bloodstream infection | 2 (1.8%) | 20 (6.0%) | 0.07 |
Variables | Univariable Analysis | Multivariable Analysis | ||
---|---|---|---|---|
OR (95% CI) | p | aOR (95% CI) | p | |
Cardiovascular disease | 1.54 (0.96–2.48) | 0.07 | 1.51 (0.88–2.59) | 0.13 |
Neurologic disease | 1.71 (1.03–2.83) | 0.04 | 1.40 (0.79–2.46) | 0.25 |
Kidney transplantation | 2.23 (0.87–5.69) | 0.09 | 1.65 (0.56–4.80) | 0.36 |
Connective tissue diseases | 2.29 (1.02–5.13) | 0.05 | 2.26 (0.91–5.61) | 0.08 |
Prior admission within 90 days | 2.57 (1.66–3.99) | <0.001 | 1.30 (0.73–2.31) | 0.37 |
Presence of MDROs during preceding 90 days | 6.19 (3.57–10.72) | <0.001 | 3.63 (1.95–6.75) | <0.001 |
Previous antibiotic exposure within 90 days | 3.61 (2.32–5.63) | <0.001 | 1.94 (1.08–3.50) | 0.03 |
Unknown primary source | 0.52 (0.28–0.96) | 0.04 | 0.75 (0.37–1.53) | 0.43 |
Urinary source | 2.44 (1.58–3.78) | <0.001 | 1.79 (1.06–3.01) | 0.03 |
CLABSI | 0.08 (0.06–1.23) | 0.09 | 0.33 (0.07–1.62) | 0.17 |
Variables | Univariable Analysis | Multivariable Analysis | ||
---|---|---|---|---|
HR (95% CI) | p | aHR (95% CI) | p | |
Antimicrobial resistance | 0.71 (0.31–1.62) | 0.41 | ||
Inactive empirical treatment within 24 h | 0.67 (0.20–2.19) | 0.51 | ||
Pitt bacteremia score | 1.42 (1.23–1.63) | <0.001 | 1.39 (1.20–1.62) | <0.001 |
Solid malignancy | 3.03 (1.53–5.99) | 0.001 | 2.61 (1.30–5.24) | 0.01 |
Hypertension | 0.47 (0.23–0.95) | 0.04 | 0.49 (0.24–1.01) | 0.054 |
Urinary source | 0.27 (0.10–0.69) | 0.006 | 0.30 (0.11–0.79) | 0.02 |
Pneumonia source | 4.86 (1.88–12.57) | 0.001 | 2.05 (0.75–5.56) | 0.16 |
Variables | AMR-BSI | NAMR-BSI | p Value |
---|---|---|---|
Empirical antimicrobial treatment | n = 113 (%) | n = 328 (%) | |
Optimally active coverage (appropriate) | 79 (69.9) | 121 (36.9) | <0.001 |
Broad spectrum active coverage | 0 | 190 (57.9) | |
Inactive spectrum coverage | 34 (30.1) | 17 (5.2) | |
Multiple antimicrobial change in 48 h (range 2–6 times) | 27 (23.9) | 92 (28.1) | 0.39 |
Unnecessary double coverage | 2 (1.8) | 21 (6.4) | 0.06 |
Definitive antimicrobial treatment | n = 109 (%) | n = 311 (%) | |
Optimal drug and duration (appropriate) | 36 (33.0) | 113 (36.3) | 0.71 |
Narrower/simpler antimicrobial is available | 14 (12.8) | 57 (18.3) | 0.33 |
Inadequate spectrum coverage | 4 (3.7) | 2 (0.6) | 0.06 |
Step down to oral treatment is possible | 8 (7.3) | 42 (12.8) | 0.18 |
Unnecessary double coverage | 2 (1.8) | 10 (3.2) | 0.59 |
Duration of antimicrobial treatment a, median (IQR) | 13 (8–14) | 13 (9–14) | 0.74 |
Shorter duration is possible a | 52/90 (57.8) | 115/229 (50.2) | 0.22 |
All BSIs (n = 450) | Enterobacteriaceae BSIs (n = 298) | Validation Cohort (n = 227) | |
---|---|---|---|
Sensitivity (95% CI) | 49.1% (36.9–58.7%) | 57.5% (46.8–67.6%) | 55.2% (41.5–68.3%) |
Specificity (95% CI) | 85.4% (81.2–89.0%) | 81.9% (75.9–86.9%) | 93.5% (88.7–96.7%) |
PPV (95% CI) | 53.3% (45.4–61.1%) | 59.3% (50.7–67.2%) | 74.4% (58.8–86.5%) |
NPV (95% CI) | 83.2% (80.4–85.6%) | 80.7% (76.6–84.2%) | 85.9% (80.0–90.6%) |
C-statistic | 0.73 | 0.76 | 0.77 |
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Chotiprasitsakul, D.; Trirattanapikul, A.; Namsiripongpun, W.; Chaihongsa, N.; Santanirand, P. From Epidemiology of Community-Onset Bloodstream Infections to the Development of Empirical Antimicrobial Treatment-Decision Algorithm in a Region with High Burden of Antimicrobial Resistance. Antibiotics 2023, 12, 1699. https://doi.org/10.3390/antibiotics12121699
Chotiprasitsakul D, Trirattanapikul A, Namsiripongpun W, Chaihongsa N, Santanirand P. From Epidemiology of Community-Onset Bloodstream Infections to the Development of Empirical Antimicrobial Treatment-Decision Algorithm in a Region with High Burden of Antimicrobial Resistance. Antibiotics. 2023; 12(12):1699. https://doi.org/10.3390/antibiotics12121699
Chicago/Turabian StyleChotiprasitsakul, Darunee, Akeatit Trirattanapikul, Warunyu Namsiripongpun, Narong Chaihongsa, and Pitak Santanirand. 2023. "From Epidemiology of Community-Onset Bloodstream Infections to the Development of Empirical Antimicrobial Treatment-Decision Algorithm in a Region with High Burden of Antimicrobial Resistance" Antibiotics 12, no. 12: 1699. https://doi.org/10.3390/antibiotics12121699