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Antimicrobial susceptibility testing results from 13 hospitals in Viet Nam: VINARES 2016–2017

Antimicrobial susceptibility testing results from 13 hospitals in Viet Nam: VINARES 2016–2017 Objective: To analyse data from 2016–17 from a hospital-based antimicrobial resistance surveillance with national coverage in a network of hospitals Viet Nam. Methods: We analysed data from 13 hospitals, 3 less than the dataset from the 2012–13 period. Identification and antimicrobial susceptibility testing data from the clinical microbiology laboratories from samples sent in for routine diagnostics were used. Clinical and Laboratory Standards Institute 2018 guidelines were used for antimicrobial sus- ceptibility testing interpretation. WHONET was used for data entry, management and analysis. Results: 42,553 deduplicated isolates were included in this analysis; including 30,222 (71%) Gram-negative and 12,331 (29%) Gram-positive bacteria. 8,793 (21%) were from ICUs and 7,439 (18%) isolates were from invasive infec- tions. Escherichia coli and Staphylococcus aureus were the most frequently detected species with 9,092 (21%) and 4,833 isolates (11%), respectively; followed by Klebsiella pneumoniae (3,858 isolates – 9.1%) and Acinetobacter bau- mannii (3,870 isolates – 9%). Bacteria were mainly isolated from sputum (8,798 isolates – 21%), blood (7,118 isolates – 17%) and urine (5,202 isolates – 12%). Among Gram-positives 3,302/4,515 isolates (73%) of S. aureus were MRSA; 99/290 (34%) of Enterococcus faecium were resistant to vancomycin; and 58% (663/1,136) of Streptococcus pneumoniae proportion were reduced susceptible to penicillin. Among Gram-negatives 59% (4,085/6,953) and 40% (1,186/2,958) of E. coli and K. pneumoniae produced ESBL and 29% (376/1,298) and 11% (961/8,830) were resistant to carbapenems, respectively. 79% (2855/3622) and 45% (1,514/3,376) of Acinetobacter spp. and Pseudomonas aeruginosa were carbap- enem resistant, respectively. 88% (804/911) of Haemophilus influenzae were ampicillin resistant and 18/253 (7%) of Salmonella spp. and 7/46 (15%) of Shigella spp. were resistant to fluoroquinolones. The number of isolates from which data were submitted in the 2016–2017 period was twice as high as in 2012–2013. AMR proportions were higher in 2016–2017 for most pathogen-antimicrobial combinations of interest including imipenem-resistant A. baumannii, P. aeruginosa and Enterobacterales. Conclusions: The data show alarmingly high and increasing resistant proportions among important organisms in Viet Nam. AMR proportions varied across hospital types and should be interpreted with caution because existing sam- pling bias and missing information on whether isolates were community or hospital acquired. Aor ff dable and scalable *Correspondence: dungvtv@oucru.org Oxford University Clinical Research Unit, National Hospital for Tropical Diseases, 78 Giai Phong, Dong Da, Hanoi, Viet Nam Full list of author information is available at the end of the article © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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Keywords: Antimicrobial resistance, Surveillance, Viet Nam, VINARES Introduction These projects highlighted the high proportions of In a 2015 estimate based on data from the European resistance among several WHO GLASS target pathogens: Antimicrobial Resistance Surveillance Network (EARS- carbapenem-resistant Acinetobacter baumannii (40% Net), over 33,000 (out of 445 million inhabitants) peo- in the Global Antibiotic Resistance Partnership (GARP) ple die each year in the European Union as a direct in 2009 [11] and 70% in VINARES in 2012 [7]); Escheri- consequence of drug resistant infections [1]. Data from chia coli and Klebsiella pneumoniae producing extended low- and middle-income countries (LMICs) are rare, spectrum beta-lactamase (ESBL) (30% and 43% in 2009, but a recent paper from Thailand – with a population respectively); carbapenem-resistant E. coli (2% in 2009 of 69 million – estimated that 19,122 of 45,209 (43%) [11] and 6% in 2012 [7]); carbapenem-resistant K. pneu- deaths in patients with hospital-acquired infections are moniae (10% in 2009 [11] and 17% in 2012 [7]); methi- due to drug resistant infections. This higher number of cillin-resistant Staphylococcus aureus (MRSA), reported deaths per capita attributable to AMR in Thailand in at 30.1% among hospital-acquired infections in 2004 [12] comparison with the EU suggests the burden of AMR and at 69% among all isolates 2012 [7]. in LMICs may be higher [2]. In 2013, the Viet Nam Ministry of Health published In their 2014 review, Rossolini et al. indicated an out- its national action plan on AMR, including strengthen- of-control crisis for Gram-negative pathogens, par- ing and improving the national surveillance system on ticularly with the worrisome emergence and spread of the use of antimicrobials and drug resistance [13]. In carbapenem-resistant Enterobacterales, especially in 2015, Viet Nam received pilot funding from the Fleming the hospital environment, while Gram-positive patho- Fund to establish a National AMR surveillance network gens appear to be relatively under control [3]. and reference laboratory [10]. The VINARES network In May 2015, the World Health Assembly adopted a was recognised in 2016 by the Ministry of Health as the Global Action Plan on Antimicrobial Resistance, which national AMR surveillance network and continues to highlighted the need to improve awareness and under- receive support from the Fleming Fund as part of the standing of antimicrobial resistance and to strengthen country grant for Viet Nam led by FHI360. The national the knowledge and evidence-based decisions through AMR surveillance network also receives support from surveillance and research [4]. The review by the World the US Centers for Disease Control and Prevention Health Organisation (WHO) pointed out the lack of a (US CDC) and Program for Appropriate Technology global consensus on methodology and data collection in Health (PATH) as part of the Global Health Security for AMR surveillance. In addition, routine surveillance Agenda. A surveillance protocol based on GLASS and often uses samples from severe cases including those the Fleming Fund roadmap is being developed by the with hospital acquired infections and those with treat- Ministry of Health with support from US CDC, WHO ment failure, leading to an under-representation of and Oxford University Clinical Research Unit (OUCRU). samples from patients with community-acquired infec- Data collection as part of a project on development on tions (CAI) and failure of the data to properly inform evidence based guidelines restarted in 2016 [10]. treatment guidelines [5]. As a response to this situation, Here, we present the identification and antimicrobial WHO introduced that same year the Global Antimicro- susceptibility testing (AST) results from isolates from bial Resistance Surveillance System (GLASS). GLASS clinical specimens from 13 microbiology laboratories aims to enable standardized, comparable and validated participating in VINARES between June 2016 and May 2017. These results provide an insight in the dynamics AMR data collection and analysis and sharing of AMR of AMR and an update on the earlier results published data across countries to inform decision-making and based on data from the VINARES for the 2012–2013 action [6]. period [7]. AMR surveillance activities were initiated in Viet Nam in 1988 with specific programs as summarised previously [7], including VINARES, a network of 16 Materials and methods hospitals throughout the country collecting data on Data collection antimicrobial consumption and resistance and hospi- The VINARES network was described previously [7, 8]. tal-acquired infections [7]-[10]. In 2016–2017, 13 hospitals (7 provincial, 3 specialised Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 3 of 11 and 3 national) continued to participate in the network, Tropical Diseases staff since 2012, were enrolled into among which 4 were in the northern, 5 in the central and an external quality assurance scheme (UK-NEQAS) and 4 in the southern region of Viet Nam; there were 1 paedi- received Vietnamese translated Clinical and Laboratory atric and 2 infectious diseases hospitals (Fig. 1). Standards Institute (CLSI) guidelines. Thirteen hospitals submitted their routine identi - AST results were obtained by disk diffusion (DD) and fication and AST results quarterly by email from May minimum inhibitory concentration (MIC) methods. 2016 to April 2017. These were results from all bacte - The proportion of MIC testing depended on the labo - rial isolates from clinical specimens sent in for routine ratory, the specific pathogen-antimicrobial combina - diagnostics at the microbiology lab of the hospitals. tion and the period of study as detailed in Additional WHONET was used for data entry, management and file  1:  table S1. AST results were categorised according analysis [14]. Routine AST data at the participating to the CLSI 2018 guidelines as susceptible, intermedi- laboratories was entered into WHONET 5.6 by hospital ate, resistant or unknown. For each pathogen and anti- technicians or was exported from automated systems microbial under surveillance, the proportion of patients including VITEK2 (bioMérieux, Marcy l’Etoile, France) with growth of resistant bacteria was calculated in all or Phoenix automated microbiology system (BD Diag- specimens, and separately in specimens from Intensive nostic Systems, Sparks, MD, USA) using LABCONN Care Units (ICU), invasive infection (blood and cer- (LabSoft, Viet Nam). Raw data files were extracted and ebrospinal fluid (CSF)) or stool (for Shigella spp. and submitted by email. Files were converted to WHONET Salmonella spp.). AST results were interpreted using format using BacLink, a free tool included in WHONET WHONET (version 5.6) and then summarized in R [15]. All data files were combined into a single file. Data software [16]. files were checked for common errors and compatibil - MRSA was assessed using oxacillin and cefoxitin ity (language and file structure). Sites had received reg - screening. As not all hospitals used molecular or other ular training from OUCRU and National Hospital for confirmation testing, an S. aureus isolate was considered MRSA if it was resistant to one of these two antimicro- bials. In 2012–13, reduced susceptibility to penicillin in Streptococcus pneumoniae was mostly detected using oxacillin screening [7]. In 2016–17 this was more com- monly done directly by penicillin susceptibility testing using both disk diffusion and MIC by E-test or auto - mated systems. Oxacillin susceptibility results were used in case of missing penicillin susceptibility testing results. We included five antibiotic classes: carbapenems (imi - penem, meropenem and ertapenem), aminoglycosides (amikacin, gentamicin and tobramycin), fluoroqui - nolones (ciprofloxacine and levofloxacine), macrolides (azithromycin, erythromycin and clindamycin) and ceph- alosporins (ceftriaxone and cefepime). Multidrug Resistant (MDR) and Extensively drug resistant (XDR) E. coli, K. pneumoniae, Pseudomonas aeruginosa, A. baumannii and S. aureus were defined as follows: • E. coli and K. pneumoniae MDR: non-CRE and resistant to one third-generation cephalosporin, cip- rofloxacin and one aminoglycoside. • E. coli and K. pneumoniae XDR: carbapenem resist- ant and resistant to one third-generation cephalo- sporin, ciprofloxacin and one aminoglycoside. • P. aeruginosa MDR: resistant to three of the following four antibiotics: imipenem, ceftazidime, ciprofloxa - cin and tobramycin [17]. • A. baumannii MDR: resistant to at least one agent in Fig. 1 Fig. 1 Location, speciality, and type of the 13 participating hospitals in the VINARES 2016–2017 project three of the following four antibiotic classes: carbap- Vu et al. Antimicrob Resist Infect Control (2021) 10:78 Page 4 of 11 enems, aminoglycosides, fluoroquinolones and ceph - were mainly isolated from sputum (8,798 isolates – 21%), alosporins [18]. blood (7,118 isolates – 17%) and urine (5,202 isolates • S. aureus MDR: MRSA. – 12%); 321 isolates (1%) were from cerebrospinal fluid (CSF). AST results were obtained by disk diffusion (DD) and MIC methods. Details by laboratory, period, and bacte- Statistical analysis ria-drug combination are described in Table S1. Two lab- We analysed data for eleven pathogens: A. baumannii, oratories used 100% DD in the first period and 100% MIC P. aeruginosa, E. coli, K. pneumoniae, Enterobacter spp., in the second period, one laboratory used MIC in the first Enterococcus faecium, S. aureus, S. pneumoniae, Haemo- period and DD in second period, and others kept 100% philus influenzae, Salmonella spp. and Shigella spp. [19]. DD for MRSA screening in both periods. Among the 13 Data were de-duplicated, so that one isolate represents hospitals participating in the two periods, the number of one patient. Only the first isolate per patient, per path - hospitals that used MIC for ESBL testing increased from ogen, per reporting period, per stratification level (hos - 6 to 12. As a result, we observed an increase in the num- pital) was included. This also minimized bias associated ber of ESBL-producing E. coli and K. pneumoniae tests with reporting of repeat cultures [20]. Local specimen (1659 in the first period and 9911 in the second period). types were converted into specimen types compatible This increase might also be because more hospitals with WHONET. switched from manual to automated systems, and ESBL An analysis of antibiotic resistance by hospital type were tested for all samples and not just to confirm third- was carried out. Three hospital types were considered: generation cephalosporins resistance. Two laboratories national and provincial level general and specialised hos- nd in the 2 period of VINARES used MIC for imipenem- pitals, as shown in Fig.  1. Among the 16 hospitals par- st resistance testing versus only one in the 1 period. Pen- ticipating in VINARES 2012–2013, three (one national icillin-susceptibility of S. pneumoniae were tested using and two specialised, all in the northern region) did not MIC by three laboratories with 86/344 (25%) tests in the participate in 2016–2017 period. Data from each hospi- st 1 period, while they were tested using MIC in six labo- tal type were pooled and analysed. This analysis served nd ratories with 694/1,136 (61%) tests in the 2 period. to compare susceptibility between hospital types. Only the pathogen-antimicrobial combinations with the high- est sample numbers were selected, including imipenem- Antibiotic susceptibility testing results of Gram‑positive resistant A. baumannii, E. coli, ESBL-producing E. coli bacteria and MRSA. Antimicrobial susceptibility testing results of bacteria Resistant proportions of pathogen-antimicrobial com- from all specimens and from invasive infections or stool binations between two periods of VINARES were com- are shown in Tables  1, 2, 3, 4, respectively. Additional pared using Chi-square test (significance level = 0.05). file 1: Table 2a and 2b shows AST results from ICUs. Since not all isolates were tested for all listed antibi- Results otics, the denominator of each susceptible proportion Distribution of bacteria and antibiotics test was different and smaller than the total number of Between May 2016 and April 2017, hospitals submit- isolates collected. There were 4,833 S. aureus isolates, ted results from 75,051 specimens. Among them, 22,752 including 715 (15%) from blood and CSF. 690 isolates records were unknown or reported no growth, 48,084 (14%) were from ICU. 73% (3,302/4,515 isolates) of S. were from Gram-negative and Gram-positive bacteria, aureus were MRSA, 71% of S. aureus (476/674) from 882 were fungi, 1454 were anaerobes, 1,864 were myco- blood and CSF were MRSA. Among the isolates from bacteria and 15 were parasites. ICU, the proportion of MRSA was 75% (478/640). The After removal of negative cultures, fungi, anaerobes, proportion reported as non-susceptible to vancomycin mycobacteria and parasites and deduplication, results was low (2% (45/2,680) in all specimens and 1% (7/565) in from 42,553 isolates were included in the analysis; blood and CSF). No confirmatory testing for vancomycin including from 30,222 (71%) Gram-negative and 12,331 resistance was reported. The proportion resistant to mac - (29%) Gram-positive bacteria. Among all isolates, 8,793 rolides was 83% (38,61/4,661) in all specimens. (21%) were from ICUs and 7,439 (18%) were from inva- E. faecium was isolated from 296 specimens; among sive infections. which 51 (17%) were blood and CSF and 65 (22%) were E. coli and S. aureus were the most frequently iso- from ICU. 34/46 tested isolates (74%) were high level lated species with 9,092 (21%) and 4,833 isolates (11%), aminoglycoside-resistant, 7/9 isolated from blood and respectively; followed by K. pneumoniae (3,870 isolates CSF. 99/290 isolates (34%) of E. faecium were resistant to – 9%) and A. baumannii (3,710 isolates – 9%). Bacteria vancomycin (VRE) (19% of VRE tests were done by MIC Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 5 of 11 Table 1 Antimicrobial susceptibility testing results of three gram-positive bacteria in all specimens of 13 hospitals in VINARES 2016–2017 project. Denominators and numerators are the numbers of tested resistant isolates respectively. Corresponding resistant percentages are in brackets S. aureus (N = 4833) S. pneumoniae (N = 1367) E. faecium (N = 296) Aminoglycosides 1674/4090 (41%) 34/46 (74%) Fluoroquinolones 1720/4618 (37%) 31/1117 (3%) Macrolides 3861/4661 (83%) 1234/1317 (94%) 249/262 (95%) Penicillin 2347/2400 (98%) 663/1136 (58%) 111/124 (90%) SXT 1021/4158 (25%) 886/1069 (83%) 73/77 (95%) Ampicillin 57/64 (89%) 2/21 (10%) 228/253 (90%) Vancomycin 45/2680 (2%)* 16/1229 (1%) 91/290 (31%) SXT: Trimethoprim/Sulfamethoxazole Table 2 Antimicrobial susceptibility testing results of eight gram-negative bacteria in all specimens of 13 hospitals in VINARES 2016– 2017 project. Denominators and numerators are the numbers of tested and resistant isolates respectively. Corresponding resistant percentages are in brackets E. coli K. A. baumannii P. aeruginosa Enterobacter H. influenzae Salmonella Shigella spp. (N = 9092) pneumoniae (N = 3710) (N = 3461) spp. (N = 1085) spp. (N = 277) (N = 53) (N = 3870) (N = 1322) Carbapenem 961/8830 1049/3816 2855/3622 1514/3376 376/1298 0/1065 (0%) 1/195 (1%) 1/19 (5%) (11%) (27%) (79%) (45%) (29%) Aminoglyco- 4188/8785 1756/3780 2686/3641 1457/3389 637/1297 48/78 (62%) 4/5 (80%) sides (48%) (46%) (74%) (43%) (49%) Fluoroquinolo- 5813/8682 1593/3619 2929/3589 1435/3357 484/1271 7/909 (1%) 18/253 (7%) 7/46 (15%) nes (67%) (44%) (82%) (43%) (38%) Cephalospor- 5441/8195 1995/3732 2969/3549 1392/3058 675/1192 18/664 (3%) 20/217 (9%) 8/26 (31%) ins (66%) (53%) (84%) (46%) (57%) Macrolides 25/29 (86%) 4/1015 (0%) 53/137 (39%) 2/3 (67%) SXT 5704/7843 1753/3348 1329/1388 467/929 (50%) 429/470 (91%) 39/237 (16%) 44/50 (88%) (73%) (52%) (96%) AMC 1476/3251 1080/1999 461/604 (76%) 271/358 (76%) (45%) (54%) Ampicillin 5547/5938 2563/2622 476/510 (93%) 804/911 (88%) 104/252 (41%) 35/46 (76%) (93%) (98%) TCC 1317/2947 863/1449 1097/2160 297/671 (44%) (45%) (60%) (51%) SXT: Trimethoprim/Sulfamethoxazole; AMC: amoxicillin clavulanic acid; TCC: Ticarcillin/Clavulanic Acid; *: Resistant and Intermediate Table 3 Antimicrobial susceptibility testing results in blood and CSF of three gram-positive bacteria of 13 hospitals in VINARES 2016– 2017 project. Denominators and numerators are the numbers of tested and resistant isolates respectively. Corresponding resistant percentages are in brackets S. aureus (N = 715) S. pneumoniae (N = 160) E. faecium (N = 51) Aminoglycosides 294/637 (46%) 7/9 (78%) Fluoroquinolones 297/689 (43%) 2/143 (1%) Macrolides 545/693 (79%) 140/152 (92%) 46/48 (96%) Penicillin 490/504 (97%) 42/114 (37%) 19/22 (86%) SXT 233/661 (35%) 107/134 (80%) 20/20 (100%) Ampicillin 37/40 (92%) Vancomycin 7/565 (1%) 4/148 (3%) 13/51 (25%) SXT: Trimethoprim/Sulfamethoxazole Vu et al. Antimicrob Resist Infect Control (2021) 10:78 Page 6 of 11 Table 4 Antimicrobial susceptibility testing results in blood and CSF of A. baumannii, P. aeruginosa, E. coli, K. pneumoniae, Enterobacter spp., E. faecium, S. aureus, S. pneumoniae and H. influenzae; in stool for Salmonella spp. and Shigella spp. of 13 hospitals in VINARES 2016– 2017 project. Denominators and numerators are the numbers of tested and resistant isolates respectively. Corresponding resistant percentages are in brackets E. coli K. A. baumannii P. aeruginosa Enterobacter Shigella spp. Salmonella H. influenzae (N = 1535) pneumoniae (N = 187) (N = 142) spp. (N = 77) (N = 37)** spp. (N = 12) (N = 482) (N = 32)** Carbapenem 116/1483 (8%) 109/476 (23%) 110/183 (60%) 54/139 (39%) 20/77 (26%) 1/14 (7%) 0/19 (0%) 0/11 (0%) Aminoglyco- 637/1471 (43%) 195/470 (41%) 107/185 (58%) 48/138 (35%) 35/75 (47%) sides Fluoroquinolo- 953/1475 (65%) 177/459 (39%) 96/182 (53%) 37/138 (27%) 24/76 (32%) 4/31 (13%) 3/27 (11%) 0/9 (0%) nes Cephalosporins 931/1402 (66%) 221/471 (47%) 118/178 (66%) 47/120 (39%) 37/66 (56%) 7/21 (33%) 4/28 (14%) 0/11 (0%) Macrolides 1/1 (100%) 2/3 (67%) 1/5 (20%) 0/4 (0%) SXT 935/1377 (68%) 215/454 (47%) 74/82 (90%) 29/57 (51%) 31/34 (91%) 6/30 (20%) 5/8 (62%) AMC 180/577 (31%) 112/285 (39%) 26/32 (81%) 1/5 (20%) Ampicillin 928/1028 (90%) 278/287 (97%) 21/23 (91%) 23/33 (70%) 14/31 (45%) 7/8 (88%) TCC 169/356 (47%) 115/195 (59%) 46/110 (42%) 14/45 (31%) SXT: Trimethoprim/Sulfamethoxazole; AMC: amoxicillin clavulanic acid; TCC: Ticarcillin/Clavulanic Acid; *: Resistant and Intermediate method). 22 of 64 isolates (36%) from ICU were reported coli and K. pneumoniae were 29% (2,015/6,956) and 14% as vancomycin-resistant. (428/3,141), respectively. There were 514/6,956 (7%) of E. 1,367 S. pneumoniae were isolated among which 160 coli and 722/3,141 (23%) of K. pneumoniae classified as (12%) were from blood and CSF and 184 isolates (13%) XDR. were from ICU. The penicillin-resistant S. pneumoniae The number of isolates of A. baumannii and P. aer- proportion was 58% (663/1,136) in all specimens, and uginosa were similar (3,710 and 3,461, respectively). 187 lower in blood and CSF (37%, 42/114 isolates) and among (5%) isolates of A. baumannii and 482 (13%) of P. aer- isolates from specimens collected in ICU (29%, 42/146 uginosa were isolated from blood and CSF. A high pro- isolates). 691/794 (87%) of penicillin susceptibility tests portion of A. baumannii and P. aeruginosa isolates were were done by MIC method. 58/356 (16%) S. pneumoniae from ICU (32% (1,176/3,710) and 33% (1,158/3,461), isolates were cephalosporin-resistant; this proportion respectively). Ceftazidime-resistant proportions of A. was lower among ICU isolates (11%, 10/94). Two isolates baumannii in all specimens and in ICU were 2,743/3,298 (0.2%) were recorded as resistant to vancomycin, none of (83%) and 866/958 (90%). These resistant proportions them were from blood/CSF or ICU. in P. aeruginosa were 1,378/3,231 (43%) and 574/1,062 (54%). Carbapenem-resistant proportions of A. bauman- Antibiotic susceptibility testing results of Gram‑negative nii and P. aeruginosa were 79% (2,855/3,622) and 45% bacteria (1,514/3,376), respectively. Out of 1,566 P. aeruginosa The numbers of K. pneumoniae, E.  coli and Enterobac - tested with the four selected antibiotics, 660 isolates ter spp. were 3,870, 9,092 and 1,322, respectively. In (42%) were MDR. 2,781/3,442 (81%) of the tested A. bau- blood and CSF, these proportions were 12% (482/3,870), mannii isolates were MDR. 17% (1,535/9,092) and 6% (77/1,322) in same order. The Of 1,085 H. influenzae isolates submitted, 146 were proportions of K. pneumoniae, E. coli and Enterobac- from ICU and 12 were from blood and CSF. The pro - ter spp. isolated from ICUs were 28% (1,069/3,870), portion of ampicillin-resistant H. influenzae was 88% 11% (1,016/9,092 isolates) and 17% (230/1,322), respec- (804/911) among all isolates; this proportion was higher tively. The proportion of E. coli carrying ESBL was 59% among isolates collected on ICU (92/98 isolates – 94%). (4,085/6,953) and 40% (1,186/2,958) in K. pneumo- Three percent (18/664) of H. influenzae isolates were niae. Carbapenem-resistance among K. pneumoniae, cephalosporins-resistant, while none were found resist- E. coli and Enterobacter spp. was 29% (376/1,298), 11% ant to carbapenems. (961/8,830) and 27% (1,049/3,816), respectively. Tri- Salmonella spp. and Shigella spp. susceptibility were methoprim/sulfamethoxazole-resistance ranged from investigated in all specimens and in stool. Among 277 iso- 47% (215/454) of K. pneumoniae in blood and CSF to lates of Salmonella spp., there were 32 isolates from stool 76% (700/925) of E. coli in ICU. MDR proportions of E. and 18 isolates from ICU. Fluoroquinolones-resistant Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 7 of 11 Comparison with data from VINARES 2012–2013 Salmonella spp. in all specimens and in stool were 7% We compared the susceptibility of bacteria-antimicro- (18/253 and 11% (3/27), respectively. Among 53 Shigella bial combinations between the two periods of VINARES spp. isolates, 70% came from stool. 7/46 (15%) of Shigella (2012–2013 versus 2016–2017). Laboratories used simi- spp. were fluoroquinolones-resistant. lar protocols in the two periods, including antimicro- bial susceptibility testing methods using translated CLSI guidelines and data collection procedures. Laboratories Susceptibility by hospital type were enrolled in the UK-NEQAS external quality assess- Carbapenem-resistant A. baumannii, ESBL positive E. ment programme during both data collection periods. coli and MRSA in national, provincial general and spe- Since the VINARES 2016–2017 had 13 hospitals, we cialised hospitals were compared, as the number of these calculated the antimicrobial susceptibility result of VIN- pathogen-antimicrobial combinations were high enough ARES 2012–2013 in whole dataset and in a subset of 13 for reliable comparison. Details are shown in Additional hospitals. Table 5 shows resistant proportions of priority file  1: Table  3. A. baumannii had the highest carbape- pathogen-antimicrobial combinations between the two nem resistant proportion in national level hospitals, fol- periods. lowed by specialised and provincial level hospitals (82% The total number of isolates submitted in the 2016– (1,979/2,413), 77% (444/577) and 68% (432/632), respec- 2017 period was twice as high as in the 2012–2013 tively). E. coli showed a different ESBL positive propor - period; for some pathogen-antimicrobial combinations tion between national and provincial level hospitals (58% the number of isolates was up to fourfold (eg. fourfold (2,145/3,726) and 65% (1,541/2,371), respectively). MRSA for in ESBL, threefold for MRSA). Overall, antimicro- proportions were lower in provincial (71% (1,499/2,115)) bial resistant proportions were higher in 2016–2017 and specialised hospitals (72% (688/960)) than in national for almost all pathogen-antimicrobial combinations of level hospitals (77% (1,115/1,440)). Table 5 Resistance proportion of priority bacteria-antimicrobial combinations in all specimens and in blood and CSF, in 2012 and 2016. Denominators and numerators are the numbers of tested and resistant isolates respectively. Corresponding resistant percentages are in brackets Bacteria All specimens Blood and CSF (stool for Salmonella spp. and Shigella spp.) 2012 (16 2012 (13 2016 2012 (16 2012 (13 2016 hospitals) hospitals) hospitals) hospitals) ESBL E. coli 1337/1928 (69%) 626/844 (74%) 4085/6953 (59%) 126/183 (69%) 59/81 (73%) 655/1107 (59%) Imipenem E. coli 180/2 977 (6%) 145/2111 (7%) 687/8438 (8%) 15/403 (4%) 9/309 (3%) 92/1410 (7%) Ceftriaxone E. coli 2342/4 192 (56%) 776/1472 (53%) 5051/7049 (72%) 240/514 (47%) 114/234 (49%) 912/1324 (69%) MDR E. coli 453/1828 (25%) 441/1639 (27%) 2015/6956 (29%) 24/125 (19%) 24/125 (19%) 336/1204 (28%) XDR E. coli 71/1828 (4%) 63/1639 (4%) 514/6956 (7%) 2/125 (2%) 2/125 (2%) 65/1204 (5%) ESBL K. pneumoniae 887/1400 (63%) 555/815 (68%) 1186/2958 (40%) 91/172 (53%) 34/61 (56%) 128/365 (35%) Imipenem K. pneumoniae 393/2 294 (17%) 259/1697 (15%) 891/3647 (24%) 64/361 (18%) 26/233 (11%) 91/454 (20%) Ceftriaxone K. pneumoniae 1479/2 227 (66%) 626/1380 (45%) 1912/3436 (56%) 101/190 (53%) 63/175 (36%) 214/435 (49%) MDR K. pneumoniae 318/1553 (20%) 294/1315 (22%) 428/3141 (14%) 17/112 (15%) 17/112 (15%) 53/403 (13%) XDR K. pneumoniae 205/1553 (13%) 171/1315 (13%) 722/3141 (23%) 12/112 (11%) 12/112 (11%) 81/403 (20%) Imipenem A. baumannii 1495/2138 (70%) 1056/1584 (67%) 2769/3551 (78%) 110/244 (45%) 85/205 (41%) 100/178 (56%) MDR A. baumannii 897/1334 (67%) 897/1282 (70%) 2781/3442 (81%) 27/44 (61%) 27/44 (61%) 101/171 (59%) Imipenem P. aeruginosa 578/1 765 (33%) 322/996 (32%) 1403/3220 (44%) 36/129 (28%) 22/88 (25%) 49/135 (36%) MDR P. aeruginosa 178/576 (31%) 144/392 (37%) 660/1566 (42%) 4/25 (16%) 4/17 (24%) 17/70 (24%) MRSA S. aureus 1 098/1 580 (69%) 950/1303 (73%) 3302/4515 (73%) 145/197 (74%) 130/171 (76%) 476/674 (71%) Vancomycin S. aureus 28/823 (3.4%) 10/372 (2%) 45/2680 (2%) 5/135 (3.7%) 0/65 (0%) 7/565 (1%) ** ** ** ** Penicillin S. pneumoniae 115/344 (33%) 115/341 (34%) 663/1136(58%) 7/30 (23%) 7/30 (23%) 42/114 (37%) Ceftriaxone S. pneumoniae 90/358 (25%) 31/299 (10.4%) 57/352 (16%) 9/52 (17%) 4/47 (8.5%) 17/125 (14%) Vancomycin E. faecium 20/79 (25%) 20/79 (25%) 91/290 (31%) 2/14 (14%) 2/14 (14%) 13/51 (25%) Ampicillin H. influenzae 160/226 (71%) 1/1 (100%) 804/911 (88%) 3/5 (60%) 1/1 (100%) 7/8 (88%) ESBL: extended-spectrum β-lactamase; *: Intermediate and Resistant; **: Combination result of oxacillin screening and penicillin MIC test; MDR: Multi-drug resistant; XDR: Extensively drug resistant; MRSA: Methicillin-resistant Staphylococcus aureus Vu et al. Antimicrob Resist Infect Control (2021) 10:78 Page 8 of 11 interest including carbapenem-resistant A. bauman- ceftriaxone-resistant Enterobacterales and MRSA were nii, P. aeruginosa and Enterobacterales. All Chi-square seen. The number of AST tests of A. baumannii, K. pneu- test returned p-value < 0.0001 that highlight the differ - moniae and E. coli remained unchanged in blood and ence between the two periods; except the combinations CSF of 16 or 13 hospitals in VINARES 2012–2013. with only few isolates (eg. comparison of ceftriaxone- The MDR proportion of E. coli, A. baumannii, P. aerug- resistant S. pneumoniae, vancomycin-resistant E. faecium inosa in all specimens were higher in the second period. and ampicillin-resistant H. influenzae proportions from K. pneumoniae MDR went down from 22% (294/1,315 blood and CSF between two periods of VINARES). of 13 hospitals) in first period to 14% (428/3,141) in the Resistant proportions for 13 pathogen-antimicrobial second. XDR proportions of E. coli in all specimens and combinations of 13 hospitals that participated in both in blood & CSF were 7% (514/6,956) and 5% (65/1,204), periods (2012–13, 2016–17) are shown in Fig.  2. Most respectively. K. pneumoniae had a higher XDR than MDR hospitals had higher imipenem-resistant A. baumannii, proportion (23% (722/3,141) in second period compared K. pneumoniae, P. aeruginosa and penicillin non-suscep- to 14% of first period of VINARES. tible S. pneumoniae proportions in the second period. ESBL positive Enterobacterales were lower in the second period. No trends for vancomycin-resistant E. faecium, Fig. 2 Resistant proportions in 2016–2017 as a function of resistance proportions in 2012–2013 for 13 pathogen-antimicrobial combinations (one per subplot). Each dot corresponds to one of the 13 hospitals that participated in the two VINARES periods. The line is the first diagonal, showing equal proportions of resistance in the 2 periods. IPM: imipenem, CRO: ceftriaxone, VAN: vancomycin, PEN: penicillin Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 9 of 11 Discussion methods. This difference, looking at the denominators We described the identification and antimicrobial sus - for testing between 2012 and 2016, is more likely an arte- ceptibility testing results from 13 laboratories within the fact of increased ESBL testing using VITEK2 or other VINARES network in 2016–2017. Overall, we found high automated systems than that they signal a true decrease proportions of resistance among all tested priority bac- of ESBL circulation. In 2012–13 ESBL confirmation was teria and these proportions were generally higher than only done on a proportion of ceftriaxone resistant iso- those reported in 2012–2013. lates in most sites, whereas in 2016–17 a number of sites Proportions of carbapenem-resistant Gram-negative had switched to using automated systems and almost all pathogens increased gradually in the VINARES hos- isolates were screened for ESBL production. pitals. Carbapenem-resistant A. baumannii increased: According to the GLASS 2020 report, ESBL car- 40% reported from the GARP report in 2009 [11]; 70% riage among E. coli in Asian and African countries was (1,495/2,138) from VINARES 2012–2013 and 79% in the 30–70%, on par with 59% from VINARES 2016–2017. 2016–2017 period. A similar observation can be made for ESBL carriage among K. pneumoniae in VINARES 2016– carbapenem-resistant P. aeruginosa (30%, 33% and 45%, 2017 was 35%, lower in comparison with other countries respectively). (38% in Cambodia to 77% in Nigeria) [21]. In the 2012 point prevalence survey in 15 hospitals’ An increasing trend of penicillin non-susceptible S. ICU in Viet Nam, Phu et  al. reported that the two most pneumoniae could not be described properly for the common pathogens of hospital acquired infections (HAI) period between 2012–2013 and 2016–2017 period as were A. baumannii (24%) and P. aeruginosa (14%) [9]. different methods were used for assessment. There was This report showed carbapenem resistance in patients a change from oxacillin disk diffusion screening in 2012 having HAI was most common in A. baumannii (89% to penicillin susceptibility test in 2016 across sites. The [149/167]) and P. aeruginosa (56% [49/88])[9], similar to ANSORP study from 2000 to 2001 reported 91% of peni- our VINARES 2016 data. cillin non-susceptible S. pneumoniae [23] in Viet Nam, In order to understand the situation in Viet Nam from but it may not represent the true prevalence of the entire a global perspective, we compared resistant proportions country because samples were taken in only one hospital of VINARES 2016–2017 with national AMR surveillance in Ho Chi Minh city. data from LMICs which were submitted to GLASS in Results from the SOAR study (2009–2011) in 11 cen- 2018 and were published in the GLASS 2020 report [21]. tres in Viet Nam reported that 51% (100/195) of H. influ - Blood isolates of three countries in Asia (Laos, Cambodia enzae were resistant to ampicillin [24]. In the VINARES and Myanmar) and two in Africa (Nigeria and Tunisia) 2016–17 data, ampicillin resistant proportions increased were selected for comparison (Additional file  1:  Table 4). further from 71% in 2012–2013 to 88% in 2016–2017. Imipenem-resistant A. baumannii in blood isolates from Despite the number of hospitals participating in the Asian countries ranged from 33% (7/21) in Cambodia to surveillance network being lower in the second period, 59% (17/29) in Myanmar [21]. In Tunisia, this resistant the number of isolates submitted was significantly higher. proportion was 82% (173/210), while data of Nigeria was Proportions of AMR were also higher for a number of not available [6]. This proportion was 60% in the VIN - bug-drug combinations, reflecting the possibly true ARES data. The proportions of MRSA remained around increases in resistance] over time, the increasing labo- 70% in both data periods in VINARES, but was higher ratory capacity and the increasing use of microbiology than reported from GARP in 2009 (from 17 to 63% in testing as part of diagnostic and antibiotic stewardship hospitals) [11] and from the Antimicrobial Sensitivity programmes. We found a decrease of K. pneumoniae Testing Study in 2006 (42%) [22]. MDR in VINARES 2016–2017. This decrease might not MRSA proportions ranged from 11% (4/35) in Laos to reflect an actual trend, but could be explained by the defi - 74% (117/158) in Myanmar [21] in selected Asian LMICs nition of MDR as a K. pneumoniae MDR isolate is sus- and were 66% (146/222) and 21% (102/483) in Nigeria ceptible to carbapenems by definition and. carbapenem and Tunisia, respectively [21]. VINARES 2016–2017 had resistance increased. similar MRSA proportions as Myanmar and Nigeria. Our results document a higher proportion of resist- Vancomycin-intermediate and resistant S. aureus ance in national than in provincial level hospitals. Pre- remained stable (2%) over the two time periods of VIN- vious studies [25–27] have shown that the proportion ARES with no trend observed. Vancomycin-resistance of patients with hospital-acquired infection is higher in among S. aureus was not confirmed molecularly and we national hospitals. As bacteria associated with hospi- are unsure of the significance of these findings. tal acquired infections are usually more resistant, this The decrease in ESBL detection among Enterobac - may partially explain our observation. Furthermore, terales was mostly due to changes in use of detection in accordance with national health recommendations, Vu et al. Antimicrob Resist Infect Control (2021) 10:78 Page 10 of 11 patients with resistant bacterial infections or patients Supplementary Information unresponsive to therapy because of resistance are gen- The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13756- 021- 00937-4. erally transferred from provincial to national level hos- pitals, which could further explain the higher levels of Additional file 1. Supplementary materials. resistance in national level hospitals. Acknowledgements Limitations The authors would like to thank the hospitals in the VINARES project for providing their laboratory routine AST results: National Hospital for Tropical VINARES collected isolate-based data (surveillance Disease, Saint-Paul Hospital, Uong Bi Hospital, Viet Tiep Hospital, Hue Central approaches based solely on laboratory data), without General Hospital, Da Nang General Hospital, Binh Dinh General Hospital, Dak epidemiological, clinical, and population-level data. Cur- Lak General Hospital, Khanh Hoa Provincial Hospital, Cho Ray Hospital, Chil- dren’s Hospital 1, Hospital for Tropical Disease and Can Tho Central Hospital. rently, GLASS accepts both isolate-based and sample- We also appreciate the support from Prof. Nguyen Viet Tien, Prof. Luong Ngoc based data, but it encourages countries to collect and Khue, Dr. Nguyen Trong Khoa and MSc. Ngo Thi Bich Ha at the Ministry of report sample-based data, which can provide strati- Health of Viet Nam in setting up and managing the VINARES network. The VINARES consortium: National Hospital for Tropical Diseases, Saint-Paul fied and therefore more useful information [6]. Current Hospital, Uong Bi Hospital, Viet Tiep Hospital, Hue Central General Hospital, Da data collected in VINARES do not allow to differenti - Nang General Hospital, Binh Dinh General Hospital, Dak Lak General Hospital, ate between hospital or community acquired infections. Khanh Hoa Provincial Hospital, Cho Ray Hospital, Children’s Hospital 1, Hospital for Tropical Diseases and Can Tho Central Hospital. Therefore, resistant proportions may be inflated when trying to use data to inform empiric treatment for com- Authors’ contributions munity acquired infections. Sample- or case-based data HRvD, HFLW, NVT, NVK conceived the idea and designed the study. VINARES consortium and DTTN acquired the data. NVMH, JIC and LTH supported the collection may provide potential solutions for this issue. microbiological testing. VTVD performed data analysis and interpretation, and AST data were collected from isolates cultured from prepared the manuscript. The manuscript was reviewed and approved by samples sent in for routine diagnostics as part of stand- HFLW, HRvD and MC. All authors read and approved the final manuscript. ard of care. While our study did not make any selection Funding of samples and included all laboratory results, it is known This work was funded by the Department of Health and Social Care (UK) that microbiology is underused in many LMICs for vari- through the Newton Fund, grant 172698142 and the Fleming Fund Viet Nam Pilot grant. ous reasons [28] which may lead to bias, usually towards overestimating resistant proportions. Availability of data and materials A standardized sampling and data collection strategy The data sharing agreements are in place for aggregated data with Resist- anceMap (www. resis tance map. org) and in preparation for individual level data across the whole surveillance network is important to with the the Global Burden of Disease / GRAM project on AMR. We have no minimize sampling biases, enhance representativeness agreement from the hospitals to make individual level data publicly available. and interpretation of the results, and allow inference of the results to the country representativeness [6]. The Declarations change in the participation of hospitals had impact on Ethical approval the overall resistant proportions. Not required. Competing interests Conclusions None declared. We show the results from a successful continuation of Author details a large AMR surveillance network in Viet Nam. The data Oxford University Clinical Research Unit, National Hospital for Tropical Dis- show alarmingly high and increasing resistant proportions eases, 78 Giai Phong, Dong Da, Hanoi, Viet Nam. National Hospital for Tropical Diseases, Hanoi, Viet Nam. Centre for Tropical Medicine and Global Health, in important organisms causing infections in Viet Nam. Nuffield Department of Medicine, University of Oxford, Oxford, UK. Depar t- However, AMR proportions varied across hospital types ment of Medical Microbiology, Radboudumc Center for Infectious Diseases, in the network. The results may not reflect the true AMR Radboudumc, Nijmegen, Netherlands. prevalence in Viet Nam as there may be sampling biases Received: 10 June 2020 Accepted: 21 April 2021 and data on whether isolates were from hospital- or com- munity-acquired were not included. Affordable and scal - able ways to adopt a sample-or case-based approach across the network should be explored. Clinical data should also References be included in the reports from the hospitals to help pro- 1. Cassini A, et al. Attributable deaths and disability-adjusted life-years vide more informative interpretations of the surveillance caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. data. Lancet Infect Dis. Jan. 2019;19(1):56–66. https:// doi. org/ 10. 1016/ S1473- 3099(18) 30605-4. Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 11 of 11 2. Lim C, et al. Epidemiology and burden of multidrug-resistant bacterial 18. Magiorakos A-P, et al. Multidrug-resistant, extensively drug-resistant and infection in a developing country. eLife. 2016;5:e18082. https:// doi. org/ pandrug-resistant bacteria: an international expert proposal for interim 10. 7554/ eLife. 18082. standard definitions for acquired resistance. Clin Microbiol Infect. Mar. 3. Rossolini GM, Arena F, Pecile P, Pollini S. Update on the antibiotic resist- 2012;18(3):268–81. https:// doi. org/ 10. 1111/j. 1469- 0691. 2011. 03570.x. ance crisis. Curr Opin Pharmacol. Oct. 2014;18:56–60. https:// doi. org/ 10. 19. Tacconelli E, et al. Discovery, research, and development of new antibiot- 1016/j. coph. 2014. 09. 006. ics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. 4. World Health Organization. Global Action Plan on Antimicrobial Resist- Lancet Infect Dis. 2018;18(3):318–27. https:// doi. org/ 10. 1016/ S1473- ance. Geneva: Switzerland; 2015.3099(17) 30753-3. 5. WHO, Antimicrobial resistance: global report on surveillance. Geneva, Swit- 20. World Health Organization, Global antimicrobial resistance surveillance zerland: World Health Organization, 2014. system (GLASS) report: early implementation 2016–2017, Geneva, 2017. 6. World Health Organization. Global antimicrobial resistance surveillance 21. World Health Organization. Global antimicrobial resistance and use system: manual for early implementation. Geneva: Switzerland; 2015. surveillance system (GLASS) report: Early implementation 2020. Geneva: 7. Vu Tien Viet D, et al. Antimicrobial susceptibility testing and antibiotic World Health Organization; 2020. consumption results from 16 hospitals in Viet Nam- the VINARES project, 22. H. Phan Quốc, ‘ Vi khuẩn kháng thuốc kháng sinh: Thực trạng đáng báo 2012–2013. J Glob Antimicrob Resist. 2019. https:// doi. org/ 10. 1016/j. jgar. động (AMR: an alarming situation)’, Ministry of Health. http:// amr. moh. 2019. 06. 002.gov. vn/ vi- khuan- khang- thuoc- khang- sinh- thuc- trang- dang- bao- dong/ 8. Wertheim HFL, et al. Providing impetus, tools, and guidance to (accessed Dec. 26, 2019). strengthen national capacity for antimicrobial stewardship in Viet Nam. 23. Song J-H, et al. High prevalence of antimicrobial resistance among PLOS Med. 2013. https:// doi. org/ 10. 1371/ journ al. pmed. 10014 29. clinical streptococcus pneumoniae isolates in Asia (an ANSORP study). 9. Phu VD, et al. Burden of hospital acquired infections and antimicrobial Antimicrob Agents Chemother. Jun. 2004;48(6):2101–7. https:// doi. org/ use in vietnamese adult intensive care units. PLoS ONE. 2016. https:// doi. 10. 1128/ AAC. 48.6. 2101- 2107. 2004. org/ 10. 1371/ journ al. pone. 01475 44. 24. Van PH, Binh PT, Minh NHL, Morrissey I, Torumkuney D. Results from the 10. Kinh NV, et al. Developing an antimicrobial resistance reference labora- survey of antibiotic resistance (SOAR) 2009–11 in Vietnam. J Antimicrob tory and surveillance programme in Vietnam. Lancet Glob Health. Dec. Chemother. May 2016;71(Suppl 1):i93–102. https:// doi. org/ 10. 1093/ jac/ 2017;5(12):e1186–7. https:// doi. org/ 10. 1016/ S2214- 109X(17) 30370-4. dkw069. 11. Van Kinh Nguyen et al., First report on antibiotic use and resistance in 25. Labi A-K, et al. Multi-centre point-prevalence survey of hospital-acquired Vietnam hospitals in 2008–2009, 2012. infections in Ghana. J Hosp Infect. Jan. 2019;101(1):60–8. https:// doi. org/ 12. Song J-H, et al. Spread of methicillin-resistant Staphylococcus aureus 10. 1016/j. jhin. 2018. 04. 019. between the community and the hospitals in Asian countries: an 26. Wang J, et al. The prevalence of healthcare-associated infections in main- ANSORP study. J Antimicrob Chemother. 2011;66(5):1061–9. https:// doi. land China: a systematic review and meta-analysis. Infect Control Hosp org/ 10. 1093/ jac/ dkr024. Epidemiol. 2018;39(6):701–9. https:// doi. org/ 10. 1017/ ice. 2018. 60. 13. Ministry of Health of Viet Nam, National action plan on combatting Drug 27. Manosuthi W, Thientong V, Moolasart V, Rongrungrueng Y, Sangsajja C, resistance in the period from 2013–2020. 2013. Danchaivijitr S. Healthcare-associated infections at selected hospitals in 14. O’Brien TF, Stelling JM. WHONET: an information system for monitoring Thailand, Southeast Asian. J Trop Med Public Health. 2017;48(1):204–12. antimicrobial resistance. Emerg Infect Dis. 1995;1(2):66. 28. van Doorn HR, Ashley EA, Turner P. Case-based surveillance of antimicro- 15. J. Stelling, M. Kulldor, and Br ff ien, WHONET and baclink: software tools bial resistance in the ACORN (A Clinically Oriented Antimicrobial Resist- for laboratory-based surveillance of infectious diseases and antimicrobial ance Surveillance Network) study. JAC Antimicrob Resist. 2020. https:// resistance, 2007.doi. org/ 10. 1093/ jacamr/ dlaa0 18. 16. ‘R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL Publisher’s Note https:// www.R- proje ct. org.’ 2018. Springer Nature remains neutral with regard to jurisdictional claims in pub- 17. Hirsch EB, Tam VH. 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Antimicrobial susceptibility testing results from 13 hospitals in Viet Nam: VINARES 2016–2017

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Abstract

Objective: To analyse data from 2016–17 from a hospital-based antimicrobial resistance surveillance with national coverage in a network of hospitals Viet Nam. Methods: We analysed data from 13 hospitals, 3 less than the dataset from the 2012–13 period. Identification and antimicrobial susceptibility testing data from the clinical microbiology laboratories from samples sent in for routine diagnostics were used. Clinical and Laboratory Standards Institute 2018 guidelines were used for antimicrobial sus- ceptibility testing interpretation. WHONET was used for data entry, management and analysis. Results: 42,553 deduplicated isolates were included in this analysis; including 30,222 (71%) Gram-negative and 12,331 (29%) Gram-positive bacteria. 8,793 (21%) were from ICUs and 7,439 (18%) isolates were from invasive infec- tions. Escherichia coli and Staphylococcus aureus were the most frequently detected species with 9,092 (21%) and 4,833 isolates (11%), respectively; followed by Klebsiella pneumoniae (3,858 isolates – 9.1%) and Acinetobacter bau- mannii (3,870 isolates – 9%). Bacteria were mainly isolated from sputum (8,798 isolates – 21%), blood (7,118 isolates – 17%) and urine (5,202 isolates – 12%). Among Gram-positives 3,302/4,515 isolates (73%) of S. aureus were MRSA; 99/290 (34%) of Enterococcus faecium were resistant to vancomycin; and 58% (663/1,136) of Streptococcus pneumoniae proportion were reduced susceptible to penicillin. Among Gram-negatives 59% (4,085/6,953) and 40% (1,186/2,958) of E. coli and K. pneumoniae produced ESBL and 29% (376/1,298) and 11% (961/8,830) were resistant to carbapenems, respectively. 79% (2855/3622) and 45% (1,514/3,376) of Acinetobacter spp. and Pseudomonas aeruginosa were carbap- enem resistant, respectively. 88% (804/911) of Haemophilus influenzae were ampicillin resistant and 18/253 (7%) of Salmonella spp. and 7/46 (15%) of Shigella spp. were resistant to fluoroquinolones. The number of isolates from which data were submitted in the 2016–2017 period was twice as high as in 2012–2013. AMR proportions were higher in 2016–2017 for most pathogen-antimicrobial combinations of interest including imipenem-resistant A. baumannii, P. aeruginosa and Enterobacterales. Conclusions: The data show alarmingly high and increasing resistant proportions among important organisms in Viet Nam. AMR proportions varied across hospital types and should be interpreted with caution because existing sam- pling bias and missing information on whether isolates were community or hospital acquired. Aor ff dable and scalable *Correspondence: dungvtv@oucru.org Oxford University Clinical Research Unit, National Hospital for Tropical Diseases, 78 Giai Phong, Dong Da, Hanoi, Viet Nam Full list of author information is available at the end of the article © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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Keywords: Antimicrobial resistance, Surveillance, Viet Nam, VINARES Introduction These projects highlighted the high proportions of In a 2015 estimate based on data from the European resistance among several WHO GLASS target pathogens: Antimicrobial Resistance Surveillance Network (EARS- carbapenem-resistant Acinetobacter baumannii (40% Net), over 33,000 (out of 445 million inhabitants) peo- in the Global Antibiotic Resistance Partnership (GARP) ple die each year in the European Union as a direct in 2009 [11] and 70% in VINARES in 2012 [7]); Escheri- consequence of drug resistant infections [1]. Data from chia coli and Klebsiella pneumoniae producing extended low- and middle-income countries (LMICs) are rare, spectrum beta-lactamase (ESBL) (30% and 43% in 2009, but a recent paper from Thailand – with a population respectively); carbapenem-resistant E. coli (2% in 2009 of 69 million – estimated that 19,122 of 45,209 (43%) [11] and 6% in 2012 [7]); carbapenem-resistant K. pneu- deaths in patients with hospital-acquired infections are moniae (10% in 2009 [11] and 17% in 2012 [7]); methi- due to drug resistant infections. This higher number of cillin-resistant Staphylococcus aureus (MRSA), reported deaths per capita attributable to AMR in Thailand in at 30.1% among hospital-acquired infections in 2004 [12] comparison with the EU suggests the burden of AMR and at 69% among all isolates 2012 [7]. in LMICs may be higher [2]. In 2013, the Viet Nam Ministry of Health published In their 2014 review, Rossolini et al. indicated an out- its national action plan on AMR, including strengthen- of-control crisis for Gram-negative pathogens, par- ing and improving the national surveillance system on ticularly with the worrisome emergence and spread of the use of antimicrobials and drug resistance [13]. In carbapenem-resistant Enterobacterales, especially in 2015, Viet Nam received pilot funding from the Fleming the hospital environment, while Gram-positive patho- Fund to establish a National AMR surveillance network gens appear to be relatively under control [3]. and reference laboratory [10]. The VINARES network In May 2015, the World Health Assembly adopted a was recognised in 2016 by the Ministry of Health as the Global Action Plan on Antimicrobial Resistance, which national AMR surveillance network and continues to highlighted the need to improve awareness and under- receive support from the Fleming Fund as part of the standing of antimicrobial resistance and to strengthen country grant for Viet Nam led by FHI360. The national the knowledge and evidence-based decisions through AMR surveillance network also receives support from surveillance and research [4]. The review by the World the US Centers for Disease Control and Prevention Health Organisation (WHO) pointed out the lack of a (US CDC) and Program for Appropriate Technology global consensus on methodology and data collection in Health (PATH) as part of the Global Health Security for AMR surveillance. In addition, routine surveillance Agenda. A surveillance protocol based on GLASS and often uses samples from severe cases including those the Fleming Fund roadmap is being developed by the with hospital acquired infections and those with treat- Ministry of Health with support from US CDC, WHO ment failure, leading to an under-representation of and Oxford University Clinical Research Unit (OUCRU). samples from patients with community-acquired infec- Data collection as part of a project on development on tions (CAI) and failure of the data to properly inform evidence based guidelines restarted in 2016 [10]. treatment guidelines [5]. As a response to this situation, Here, we present the identification and antimicrobial WHO introduced that same year the Global Antimicro- susceptibility testing (AST) results from isolates from bial Resistance Surveillance System (GLASS). GLASS clinical specimens from 13 microbiology laboratories aims to enable standardized, comparable and validated participating in VINARES between June 2016 and May 2017. These results provide an insight in the dynamics AMR data collection and analysis and sharing of AMR of AMR and an update on the earlier results published data across countries to inform decision-making and based on data from the VINARES for the 2012–2013 action [6]. period [7]. AMR surveillance activities were initiated in Viet Nam in 1988 with specific programs as summarised previously [7], including VINARES, a network of 16 Materials and methods hospitals throughout the country collecting data on Data collection antimicrobial consumption and resistance and hospi- The VINARES network was described previously [7, 8]. tal-acquired infections [7]-[10]. In 2016–2017, 13 hospitals (7 provincial, 3 specialised Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 3 of 11 and 3 national) continued to participate in the network, Tropical Diseases staff since 2012, were enrolled into among which 4 were in the northern, 5 in the central and an external quality assurance scheme (UK-NEQAS) and 4 in the southern region of Viet Nam; there were 1 paedi- received Vietnamese translated Clinical and Laboratory atric and 2 infectious diseases hospitals (Fig. 1). Standards Institute (CLSI) guidelines. Thirteen hospitals submitted their routine identi - AST results were obtained by disk diffusion (DD) and fication and AST results quarterly by email from May minimum inhibitory concentration (MIC) methods. 2016 to April 2017. These were results from all bacte - The proportion of MIC testing depended on the labo - rial isolates from clinical specimens sent in for routine ratory, the specific pathogen-antimicrobial combina - diagnostics at the microbiology lab of the hospitals. tion and the period of study as detailed in Additional WHONET was used for data entry, management and file  1:  table S1. AST results were categorised according analysis [14]. Routine AST data at the participating to the CLSI 2018 guidelines as susceptible, intermedi- laboratories was entered into WHONET 5.6 by hospital ate, resistant or unknown. For each pathogen and anti- technicians or was exported from automated systems microbial under surveillance, the proportion of patients including VITEK2 (bioMérieux, Marcy l’Etoile, France) with growth of resistant bacteria was calculated in all or Phoenix automated microbiology system (BD Diag- specimens, and separately in specimens from Intensive nostic Systems, Sparks, MD, USA) using LABCONN Care Units (ICU), invasive infection (blood and cer- (LabSoft, Viet Nam). Raw data files were extracted and ebrospinal fluid (CSF)) or stool (for Shigella spp. and submitted by email. Files were converted to WHONET Salmonella spp.). AST results were interpreted using format using BacLink, a free tool included in WHONET WHONET (version 5.6) and then summarized in R [15]. All data files were combined into a single file. Data software [16]. files were checked for common errors and compatibil - MRSA was assessed using oxacillin and cefoxitin ity (language and file structure). Sites had received reg - screening. As not all hospitals used molecular or other ular training from OUCRU and National Hospital for confirmation testing, an S. aureus isolate was considered MRSA if it was resistant to one of these two antimicro- bials. In 2012–13, reduced susceptibility to penicillin in Streptococcus pneumoniae was mostly detected using oxacillin screening [7]. In 2016–17 this was more com- monly done directly by penicillin susceptibility testing using both disk diffusion and MIC by E-test or auto - mated systems. Oxacillin susceptibility results were used in case of missing penicillin susceptibility testing results. We included five antibiotic classes: carbapenems (imi - penem, meropenem and ertapenem), aminoglycosides (amikacin, gentamicin and tobramycin), fluoroqui - nolones (ciprofloxacine and levofloxacine), macrolides (azithromycin, erythromycin and clindamycin) and ceph- alosporins (ceftriaxone and cefepime). Multidrug Resistant (MDR) and Extensively drug resistant (XDR) E. coli, K. pneumoniae, Pseudomonas aeruginosa, A. baumannii and S. aureus were defined as follows: • E. coli and K. pneumoniae MDR: non-CRE and resistant to one third-generation cephalosporin, cip- rofloxacin and one aminoglycoside. • E. coli and K. pneumoniae XDR: carbapenem resist- ant and resistant to one third-generation cephalo- sporin, ciprofloxacin and one aminoglycoside. • P. aeruginosa MDR: resistant to three of the following four antibiotics: imipenem, ceftazidime, ciprofloxa - cin and tobramycin [17]. • A. baumannii MDR: resistant to at least one agent in Fig. 1 Fig. 1 Location, speciality, and type of the 13 participating hospitals in the VINARES 2016–2017 project three of the following four antibiotic classes: carbap- Vu et al. Antimicrob Resist Infect Control (2021) 10:78 Page 4 of 11 enems, aminoglycosides, fluoroquinolones and ceph - were mainly isolated from sputum (8,798 isolates – 21%), alosporins [18]. blood (7,118 isolates – 17%) and urine (5,202 isolates • S. aureus MDR: MRSA. – 12%); 321 isolates (1%) were from cerebrospinal fluid (CSF). AST results were obtained by disk diffusion (DD) and MIC methods. Details by laboratory, period, and bacte- Statistical analysis ria-drug combination are described in Table S1. Two lab- We analysed data for eleven pathogens: A. baumannii, oratories used 100% DD in the first period and 100% MIC P. aeruginosa, E. coli, K. pneumoniae, Enterobacter spp., in the second period, one laboratory used MIC in the first Enterococcus faecium, S. aureus, S. pneumoniae, Haemo- period and DD in second period, and others kept 100% philus influenzae, Salmonella spp. and Shigella spp. [19]. DD for MRSA screening in both periods. Among the 13 Data were de-duplicated, so that one isolate represents hospitals participating in the two periods, the number of one patient. Only the first isolate per patient, per path - hospitals that used MIC for ESBL testing increased from ogen, per reporting period, per stratification level (hos - 6 to 12. As a result, we observed an increase in the num- pital) was included. This also minimized bias associated ber of ESBL-producing E. coli and K. pneumoniae tests with reporting of repeat cultures [20]. Local specimen (1659 in the first period and 9911 in the second period). types were converted into specimen types compatible This increase might also be because more hospitals with WHONET. switched from manual to automated systems, and ESBL An analysis of antibiotic resistance by hospital type were tested for all samples and not just to confirm third- was carried out. Three hospital types were considered: generation cephalosporins resistance. Two laboratories national and provincial level general and specialised hos- nd in the 2 period of VINARES used MIC for imipenem- pitals, as shown in Fig.  1. Among the 16 hospitals par- st resistance testing versus only one in the 1 period. Pen- ticipating in VINARES 2012–2013, three (one national icillin-susceptibility of S. pneumoniae were tested using and two specialised, all in the northern region) did not MIC by three laboratories with 86/344 (25%) tests in the participate in 2016–2017 period. Data from each hospi- st 1 period, while they were tested using MIC in six labo- tal type were pooled and analysed. This analysis served nd ratories with 694/1,136 (61%) tests in the 2 period. to compare susceptibility between hospital types. Only the pathogen-antimicrobial combinations with the high- est sample numbers were selected, including imipenem- Antibiotic susceptibility testing results of Gram‑positive resistant A. baumannii, E. coli, ESBL-producing E. coli bacteria and MRSA. Antimicrobial susceptibility testing results of bacteria Resistant proportions of pathogen-antimicrobial com- from all specimens and from invasive infections or stool binations between two periods of VINARES were com- are shown in Tables  1, 2, 3, 4, respectively. Additional pared using Chi-square test (significance level = 0.05). file 1: Table 2a and 2b shows AST results from ICUs. Since not all isolates were tested for all listed antibi- Results otics, the denominator of each susceptible proportion Distribution of bacteria and antibiotics test was different and smaller than the total number of Between May 2016 and April 2017, hospitals submit- isolates collected. There were 4,833 S. aureus isolates, ted results from 75,051 specimens. Among them, 22,752 including 715 (15%) from blood and CSF. 690 isolates records were unknown or reported no growth, 48,084 (14%) were from ICU. 73% (3,302/4,515 isolates) of S. were from Gram-negative and Gram-positive bacteria, aureus were MRSA, 71% of S. aureus (476/674) from 882 were fungi, 1454 were anaerobes, 1,864 were myco- blood and CSF were MRSA. Among the isolates from bacteria and 15 were parasites. ICU, the proportion of MRSA was 75% (478/640). The After removal of negative cultures, fungi, anaerobes, proportion reported as non-susceptible to vancomycin mycobacteria and parasites and deduplication, results was low (2% (45/2,680) in all specimens and 1% (7/565) in from 42,553 isolates were included in the analysis; blood and CSF). No confirmatory testing for vancomycin including from 30,222 (71%) Gram-negative and 12,331 resistance was reported. The proportion resistant to mac - (29%) Gram-positive bacteria. Among all isolates, 8,793 rolides was 83% (38,61/4,661) in all specimens. (21%) were from ICUs and 7,439 (18%) were from inva- E. faecium was isolated from 296 specimens; among sive infections. which 51 (17%) were blood and CSF and 65 (22%) were E. coli and S. aureus were the most frequently iso- from ICU. 34/46 tested isolates (74%) were high level lated species with 9,092 (21%) and 4,833 isolates (11%), aminoglycoside-resistant, 7/9 isolated from blood and respectively; followed by K. pneumoniae (3,870 isolates CSF. 99/290 isolates (34%) of E. faecium were resistant to – 9%) and A. baumannii (3,710 isolates – 9%). Bacteria vancomycin (VRE) (19% of VRE tests were done by MIC Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 5 of 11 Table 1 Antimicrobial susceptibility testing results of three gram-positive bacteria in all specimens of 13 hospitals in VINARES 2016–2017 project. Denominators and numerators are the numbers of tested resistant isolates respectively. Corresponding resistant percentages are in brackets S. aureus (N = 4833) S. pneumoniae (N = 1367) E. faecium (N = 296) Aminoglycosides 1674/4090 (41%) 34/46 (74%) Fluoroquinolones 1720/4618 (37%) 31/1117 (3%) Macrolides 3861/4661 (83%) 1234/1317 (94%) 249/262 (95%) Penicillin 2347/2400 (98%) 663/1136 (58%) 111/124 (90%) SXT 1021/4158 (25%) 886/1069 (83%) 73/77 (95%) Ampicillin 57/64 (89%) 2/21 (10%) 228/253 (90%) Vancomycin 45/2680 (2%)* 16/1229 (1%) 91/290 (31%) SXT: Trimethoprim/Sulfamethoxazole Table 2 Antimicrobial susceptibility testing results of eight gram-negative bacteria in all specimens of 13 hospitals in VINARES 2016– 2017 project. Denominators and numerators are the numbers of tested and resistant isolates respectively. Corresponding resistant percentages are in brackets E. coli K. A. baumannii P. aeruginosa Enterobacter H. influenzae Salmonella Shigella spp. (N = 9092) pneumoniae (N = 3710) (N = 3461) spp. (N = 1085) spp. (N = 277) (N = 53) (N = 3870) (N = 1322) Carbapenem 961/8830 1049/3816 2855/3622 1514/3376 376/1298 0/1065 (0%) 1/195 (1%) 1/19 (5%) (11%) (27%) (79%) (45%) (29%) Aminoglyco- 4188/8785 1756/3780 2686/3641 1457/3389 637/1297 48/78 (62%) 4/5 (80%) sides (48%) (46%) (74%) (43%) (49%) Fluoroquinolo- 5813/8682 1593/3619 2929/3589 1435/3357 484/1271 7/909 (1%) 18/253 (7%) 7/46 (15%) nes (67%) (44%) (82%) (43%) (38%) Cephalospor- 5441/8195 1995/3732 2969/3549 1392/3058 675/1192 18/664 (3%) 20/217 (9%) 8/26 (31%) ins (66%) (53%) (84%) (46%) (57%) Macrolides 25/29 (86%) 4/1015 (0%) 53/137 (39%) 2/3 (67%) SXT 5704/7843 1753/3348 1329/1388 467/929 (50%) 429/470 (91%) 39/237 (16%) 44/50 (88%) (73%) (52%) (96%) AMC 1476/3251 1080/1999 461/604 (76%) 271/358 (76%) (45%) (54%) Ampicillin 5547/5938 2563/2622 476/510 (93%) 804/911 (88%) 104/252 (41%) 35/46 (76%) (93%) (98%) TCC 1317/2947 863/1449 1097/2160 297/671 (44%) (45%) (60%) (51%) SXT: Trimethoprim/Sulfamethoxazole; AMC: amoxicillin clavulanic acid; TCC: Ticarcillin/Clavulanic Acid; *: Resistant and Intermediate Table 3 Antimicrobial susceptibility testing results in blood and CSF of three gram-positive bacteria of 13 hospitals in VINARES 2016– 2017 project. Denominators and numerators are the numbers of tested and resistant isolates respectively. Corresponding resistant percentages are in brackets S. aureus (N = 715) S. pneumoniae (N = 160) E. faecium (N = 51) Aminoglycosides 294/637 (46%) 7/9 (78%) Fluoroquinolones 297/689 (43%) 2/143 (1%) Macrolides 545/693 (79%) 140/152 (92%) 46/48 (96%) Penicillin 490/504 (97%) 42/114 (37%) 19/22 (86%) SXT 233/661 (35%) 107/134 (80%) 20/20 (100%) Ampicillin 37/40 (92%) Vancomycin 7/565 (1%) 4/148 (3%) 13/51 (25%) SXT: Trimethoprim/Sulfamethoxazole Vu et al. Antimicrob Resist Infect Control (2021) 10:78 Page 6 of 11 Table 4 Antimicrobial susceptibility testing results in blood and CSF of A. baumannii, P. aeruginosa, E. coli, K. pneumoniae, Enterobacter spp., E. faecium, S. aureus, S. pneumoniae and H. influenzae; in stool for Salmonella spp. and Shigella spp. of 13 hospitals in VINARES 2016– 2017 project. Denominators and numerators are the numbers of tested and resistant isolates respectively. Corresponding resistant percentages are in brackets E. coli K. A. baumannii P. aeruginosa Enterobacter Shigella spp. Salmonella H. influenzae (N = 1535) pneumoniae (N = 187) (N = 142) spp. (N = 77) (N = 37)** spp. (N = 12) (N = 482) (N = 32)** Carbapenem 116/1483 (8%) 109/476 (23%) 110/183 (60%) 54/139 (39%) 20/77 (26%) 1/14 (7%) 0/19 (0%) 0/11 (0%) Aminoglyco- 637/1471 (43%) 195/470 (41%) 107/185 (58%) 48/138 (35%) 35/75 (47%) sides Fluoroquinolo- 953/1475 (65%) 177/459 (39%) 96/182 (53%) 37/138 (27%) 24/76 (32%) 4/31 (13%) 3/27 (11%) 0/9 (0%) nes Cephalosporins 931/1402 (66%) 221/471 (47%) 118/178 (66%) 47/120 (39%) 37/66 (56%) 7/21 (33%) 4/28 (14%) 0/11 (0%) Macrolides 1/1 (100%) 2/3 (67%) 1/5 (20%) 0/4 (0%) SXT 935/1377 (68%) 215/454 (47%) 74/82 (90%) 29/57 (51%) 31/34 (91%) 6/30 (20%) 5/8 (62%) AMC 180/577 (31%) 112/285 (39%) 26/32 (81%) 1/5 (20%) Ampicillin 928/1028 (90%) 278/287 (97%) 21/23 (91%) 23/33 (70%) 14/31 (45%) 7/8 (88%) TCC 169/356 (47%) 115/195 (59%) 46/110 (42%) 14/45 (31%) SXT: Trimethoprim/Sulfamethoxazole; AMC: amoxicillin clavulanic acid; TCC: Ticarcillin/Clavulanic Acid; *: Resistant and Intermediate method). 22 of 64 isolates (36%) from ICU were reported coli and K. pneumoniae were 29% (2,015/6,956) and 14% as vancomycin-resistant. (428/3,141), respectively. There were 514/6,956 (7%) of E. 1,367 S. pneumoniae were isolated among which 160 coli and 722/3,141 (23%) of K. pneumoniae classified as (12%) were from blood and CSF and 184 isolates (13%) XDR. were from ICU. The penicillin-resistant S. pneumoniae The number of isolates of A. baumannii and P. aer- proportion was 58% (663/1,136) in all specimens, and uginosa were similar (3,710 and 3,461, respectively). 187 lower in blood and CSF (37%, 42/114 isolates) and among (5%) isolates of A. baumannii and 482 (13%) of P. aer- isolates from specimens collected in ICU (29%, 42/146 uginosa were isolated from blood and CSF. A high pro- isolates). 691/794 (87%) of penicillin susceptibility tests portion of A. baumannii and P. aeruginosa isolates were were done by MIC method. 58/356 (16%) S. pneumoniae from ICU (32% (1,176/3,710) and 33% (1,158/3,461), isolates were cephalosporin-resistant; this proportion respectively). Ceftazidime-resistant proportions of A. was lower among ICU isolates (11%, 10/94). Two isolates baumannii in all specimens and in ICU were 2,743/3,298 (0.2%) were recorded as resistant to vancomycin, none of (83%) and 866/958 (90%). These resistant proportions them were from blood/CSF or ICU. in P. aeruginosa were 1,378/3,231 (43%) and 574/1,062 (54%). Carbapenem-resistant proportions of A. bauman- Antibiotic susceptibility testing results of Gram‑negative nii and P. aeruginosa were 79% (2,855/3,622) and 45% bacteria (1,514/3,376), respectively. Out of 1,566 P. aeruginosa The numbers of K. pneumoniae, E.  coli and Enterobac - tested with the four selected antibiotics, 660 isolates ter spp. were 3,870, 9,092 and 1,322, respectively. In (42%) were MDR. 2,781/3,442 (81%) of the tested A. bau- blood and CSF, these proportions were 12% (482/3,870), mannii isolates were MDR. 17% (1,535/9,092) and 6% (77/1,322) in same order. The Of 1,085 H. influenzae isolates submitted, 146 were proportions of K. pneumoniae, E. coli and Enterobac- from ICU and 12 were from blood and CSF. The pro - ter spp. isolated from ICUs were 28% (1,069/3,870), portion of ampicillin-resistant H. influenzae was 88% 11% (1,016/9,092 isolates) and 17% (230/1,322), respec- (804/911) among all isolates; this proportion was higher tively. The proportion of E. coli carrying ESBL was 59% among isolates collected on ICU (92/98 isolates – 94%). (4,085/6,953) and 40% (1,186/2,958) in K. pneumo- Three percent (18/664) of H. influenzae isolates were niae. Carbapenem-resistance among K. pneumoniae, cephalosporins-resistant, while none were found resist- E. coli and Enterobacter spp. was 29% (376/1,298), 11% ant to carbapenems. (961/8,830) and 27% (1,049/3,816), respectively. Tri- Salmonella spp. and Shigella spp. susceptibility were methoprim/sulfamethoxazole-resistance ranged from investigated in all specimens and in stool. Among 277 iso- 47% (215/454) of K. pneumoniae in blood and CSF to lates of Salmonella spp., there were 32 isolates from stool 76% (700/925) of E. coli in ICU. MDR proportions of E. and 18 isolates from ICU. Fluoroquinolones-resistant Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 7 of 11 Comparison with data from VINARES 2012–2013 Salmonella spp. in all specimens and in stool were 7% We compared the susceptibility of bacteria-antimicro- (18/253 and 11% (3/27), respectively. Among 53 Shigella bial combinations between the two periods of VINARES spp. isolates, 70% came from stool. 7/46 (15%) of Shigella (2012–2013 versus 2016–2017). Laboratories used simi- spp. were fluoroquinolones-resistant. lar protocols in the two periods, including antimicro- bial susceptibility testing methods using translated CLSI guidelines and data collection procedures. Laboratories Susceptibility by hospital type were enrolled in the UK-NEQAS external quality assess- Carbapenem-resistant A. baumannii, ESBL positive E. ment programme during both data collection periods. coli and MRSA in national, provincial general and spe- Since the VINARES 2016–2017 had 13 hospitals, we cialised hospitals were compared, as the number of these calculated the antimicrobial susceptibility result of VIN- pathogen-antimicrobial combinations were high enough ARES 2012–2013 in whole dataset and in a subset of 13 for reliable comparison. Details are shown in Additional hospitals. Table 5 shows resistant proportions of priority file  1: Table  3. A. baumannii had the highest carbape- pathogen-antimicrobial combinations between the two nem resistant proportion in national level hospitals, fol- periods. lowed by specialised and provincial level hospitals (82% The total number of isolates submitted in the 2016– (1,979/2,413), 77% (444/577) and 68% (432/632), respec- 2017 period was twice as high as in the 2012–2013 tively). E. coli showed a different ESBL positive propor - period; for some pathogen-antimicrobial combinations tion between national and provincial level hospitals (58% the number of isolates was up to fourfold (eg. fourfold (2,145/3,726) and 65% (1,541/2,371), respectively). MRSA for in ESBL, threefold for MRSA). Overall, antimicro- proportions were lower in provincial (71% (1,499/2,115)) bial resistant proportions were higher in 2016–2017 and specialised hospitals (72% (688/960)) than in national for almost all pathogen-antimicrobial combinations of level hospitals (77% (1,115/1,440)). Table 5 Resistance proportion of priority bacteria-antimicrobial combinations in all specimens and in blood and CSF, in 2012 and 2016. Denominators and numerators are the numbers of tested and resistant isolates respectively. Corresponding resistant percentages are in brackets Bacteria All specimens Blood and CSF (stool for Salmonella spp. and Shigella spp.) 2012 (16 2012 (13 2016 2012 (16 2012 (13 2016 hospitals) hospitals) hospitals) hospitals) ESBL E. coli 1337/1928 (69%) 626/844 (74%) 4085/6953 (59%) 126/183 (69%) 59/81 (73%) 655/1107 (59%) Imipenem E. coli 180/2 977 (6%) 145/2111 (7%) 687/8438 (8%) 15/403 (4%) 9/309 (3%) 92/1410 (7%) Ceftriaxone E. coli 2342/4 192 (56%) 776/1472 (53%) 5051/7049 (72%) 240/514 (47%) 114/234 (49%) 912/1324 (69%) MDR E. coli 453/1828 (25%) 441/1639 (27%) 2015/6956 (29%) 24/125 (19%) 24/125 (19%) 336/1204 (28%) XDR E. coli 71/1828 (4%) 63/1639 (4%) 514/6956 (7%) 2/125 (2%) 2/125 (2%) 65/1204 (5%) ESBL K. pneumoniae 887/1400 (63%) 555/815 (68%) 1186/2958 (40%) 91/172 (53%) 34/61 (56%) 128/365 (35%) Imipenem K. pneumoniae 393/2 294 (17%) 259/1697 (15%) 891/3647 (24%) 64/361 (18%) 26/233 (11%) 91/454 (20%) Ceftriaxone K. pneumoniae 1479/2 227 (66%) 626/1380 (45%) 1912/3436 (56%) 101/190 (53%) 63/175 (36%) 214/435 (49%) MDR K. pneumoniae 318/1553 (20%) 294/1315 (22%) 428/3141 (14%) 17/112 (15%) 17/112 (15%) 53/403 (13%) XDR K. pneumoniae 205/1553 (13%) 171/1315 (13%) 722/3141 (23%) 12/112 (11%) 12/112 (11%) 81/403 (20%) Imipenem A. baumannii 1495/2138 (70%) 1056/1584 (67%) 2769/3551 (78%) 110/244 (45%) 85/205 (41%) 100/178 (56%) MDR A. baumannii 897/1334 (67%) 897/1282 (70%) 2781/3442 (81%) 27/44 (61%) 27/44 (61%) 101/171 (59%) Imipenem P. aeruginosa 578/1 765 (33%) 322/996 (32%) 1403/3220 (44%) 36/129 (28%) 22/88 (25%) 49/135 (36%) MDR P. aeruginosa 178/576 (31%) 144/392 (37%) 660/1566 (42%) 4/25 (16%) 4/17 (24%) 17/70 (24%) MRSA S. aureus 1 098/1 580 (69%) 950/1303 (73%) 3302/4515 (73%) 145/197 (74%) 130/171 (76%) 476/674 (71%) Vancomycin S. aureus 28/823 (3.4%) 10/372 (2%) 45/2680 (2%) 5/135 (3.7%) 0/65 (0%) 7/565 (1%) ** ** ** ** Penicillin S. pneumoniae 115/344 (33%) 115/341 (34%) 663/1136(58%) 7/30 (23%) 7/30 (23%) 42/114 (37%) Ceftriaxone S. pneumoniae 90/358 (25%) 31/299 (10.4%) 57/352 (16%) 9/52 (17%) 4/47 (8.5%) 17/125 (14%) Vancomycin E. faecium 20/79 (25%) 20/79 (25%) 91/290 (31%) 2/14 (14%) 2/14 (14%) 13/51 (25%) Ampicillin H. influenzae 160/226 (71%) 1/1 (100%) 804/911 (88%) 3/5 (60%) 1/1 (100%) 7/8 (88%) ESBL: extended-spectrum β-lactamase; *: Intermediate and Resistant; **: Combination result of oxacillin screening and penicillin MIC test; MDR: Multi-drug resistant; XDR: Extensively drug resistant; MRSA: Methicillin-resistant Staphylococcus aureus Vu et al. Antimicrob Resist Infect Control (2021) 10:78 Page 8 of 11 interest including carbapenem-resistant A. bauman- ceftriaxone-resistant Enterobacterales and MRSA were nii, P. aeruginosa and Enterobacterales. All Chi-square seen. The number of AST tests of A. baumannii, K. pneu- test returned p-value < 0.0001 that highlight the differ - moniae and E. coli remained unchanged in blood and ence between the two periods; except the combinations CSF of 16 or 13 hospitals in VINARES 2012–2013. with only few isolates (eg. comparison of ceftriaxone- The MDR proportion of E. coli, A. baumannii, P. aerug- resistant S. pneumoniae, vancomycin-resistant E. faecium inosa in all specimens were higher in the second period. and ampicillin-resistant H. influenzae proportions from K. pneumoniae MDR went down from 22% (294/1,315 blood and CSF between two periods of VINARES). of 13 hospitals) in first period to 14% (428/3,141) in the Resistant proportions for 13 pathogen-antimicrobial second. XDR proportions of E. coli in all specimens and combinations of 13 hospitals that participated in both in blood & CSF were 7% (514/6,956) and 5% (65/1,204), periods (2012–13, 2016–17) are shown in Fig.  2. Most respectively. K. pneumoniae had a higher XDR than MDR hospitals had higher imipenem-resistant A. baumannii, proportion (23% (722/3,141) in second period compared K. pneumoniae, P. aeruginosa and penicillin non-suscep- to 14% of first period of VINARES. tible S. pneumoniae proportions in the second period. ESBL positive Enterobacterales were lower in the second period. No trends for vancomycin-resistant E. faecium, Fig. 2 Resistant proportions in 2016–2017 as a function of resistance proportions in 2012–2013 for 13 pathogen-antimicrobial combinations (one per subplot). Each dot corresponds to one of the 13 hospitals that participated in the two VINARES periods. The line is the first diagonal, showing equal proportions of resistance in the 2 periods. IPM: imipenem, CRO: ceftriaxone, VAN: vancomycin, PEN: penicillin Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 9 of 11 Discussion methods. This difference, looking at the denominators We described the identification and antimicrobial sus - for testing between 2012 and 2016, is more likely an arte- ceptibility testing results from 13 laboratories within the fact of increased ESBL testing using VITEK2 or other VINARES network in 2016–2017. Overall, we found high automated systems than that they signal a true decrease proportions of resistance among all tested priority bac- of ESBL circulation. In 2012–13 ESBL confirmation was teria and these proportions were generally higher than only done on a proportion of ceftriaxone resistant iso- those reported in 2012–2013. lates in most sites, whereas in 2016–17 a number of sites Proportions of carbapenem-resistant Gram-negative had switched to using automated systems and almost all pathogens increased gradually in the VINARES hos- isolates were screened for ESBL production. pitals. Carbapenem-resistant A. baumannii increased: According to the GLASS 2020 report, ESBL car- 40% reported from the GARP report in 2009 [11]; 70% riage among E. coli in Asian and African countries was (1,495/2,138) from VINARES 2012–2013 and 79% in the 30–70%, on par with 59% from VINARES 2016–2017. 2016–2017 period. A similar observation can be made for ESBL carriage among K. pneumoniae in VINARES 2016– carbapenem-resistant P. aeruginosa (30%, 33% and 45%, 2017 was 35%, lower in comparison with other countries respectively). (38% in Cambodia to 77% in Nigeria) [21]. In the 2012 point prevalence survey in 15 hospitals’ An increasing trend of penicillin non-susceptible S. ICU in Viet Nam, Phu et  al. reported that the two most pneumoniae could not be described properly for the common pathogens of hospital acquired infections (HAI) period between 2012–2013 and 2016–2017 period as were A. baumannii (24%) and P. aeruginosa (14%) [9]. different methods were used for assessment. There was This report showed carbapenem resistance in patients a change from oxacillin disk diffusion screening in 2012 having HAI was most common in A. baumannii (89% to penicillin susceptibility test in 2016 across sites. The [149/167]) and P. aeruginosa (56% [49/88])[9], similar to ANSORP study from 2000 to 2001 reported 91% of peni- our VINARES 2016 data. cillin non-susceptible S. pneumoniae [23] in Viet Nam, In order to understand the situation in Viet Nam from but it may not represent the true prevalence of the entire a global perspective, we compared resistant proportions country because samples were taken in only one hospital of VINARES 2016–2017 with national AMR surveillance in Ho Chi Minh city. data from LMICs which were submitted to GLASS in Results from the SOAR study (2009–2011) in 11 cen- 2018 and were published in the GLASS 2020 report [21]. tres in Viet Nam reported that 51% (100/195) of H. influ - Blood isolates of three countries in Asia (Laos, Cambodia enzae were resistant to ampicillin [24]. In the VINARES and Myanmar) and two in Africa (Nigeria and Tunisia) 2016–17 data, ampicillin resistant proportions increased were selected for comparison (Additional file  1:  Table 4). further from 71% in 2012–2013 to 88% in 2016–2017. Imipenem-resistant A. baumannii in blood isolates from Despite the number of hospitals participating in the Asian countries ranged from 33% (7/21) in Cambodia to surveillance network being lower in the second period, 59% (17/29) in Myanmar [21]. In Tunisia, this resistant the number of isolates submitted was significantly higher. proportion was 82% (173/210), while data of Nigeria was Proportions of AMR were also higher for a number of not available [6]. This proportion was 60% in the VIN - bug-drug combinations, reflecting the possibly true ARES data. The proportions of MRSA remained around increases in resistance] over time, the increasing labo- 70% in both data periods in VINARES, but was higher ratory capacity and the increasing use of microbiology than reported from GARP in 2009 (from 17 to 63% in testing as part of diagnostic and antibiotic stewardship hospitals) [11] and from the Antimicrobial Sensitivity programmes. We found a decrease of K. pneumoniae Testing Study in 2006 (42%) [22]. MDR in VINARES 2016–2017. This decrease might not MRSA proportions ranged from 11% (4/35) in Laos to reflect an actual trend, but could be explained by the defi - 74% (117/158) in Myanmar [21] in selected Asian LMICs nition of MDR as a K. pneumoniae MDR isolate is sus- and were 66% (146/222) and 21% (102/483) in Nigeria ceptible to carbapenems by definition and. carbapenem and Tunisia, respectively [21]. VINARES 2016–2017 had resistance increased. similar MRSA proportions as Myanmar and Nigeria. Our results document a higher proportion of resist- Vancomycin-intermediate and resistant S. aureus ance in national than in provincial level hospitals. Pre- remained stable (2%) over the two time periods of VIN- vious studies [25–27] have shown that the proportion ARES with no trend observed. Vancomycin-resistance of patients with hospital-acquired infection is higher in among S. aureus was not confirmed molecularly and we national hospitals. As bacteria associated with hospi- are unsure of the significance of these findings. tal acquired infections are usually more resistant, this The decrease in ESBL detection among Enterobac - may partially explain our observation. Furthermore, terales was mostly due to changes in use of detection in accordance with national health recommendations, Vu et al. Antimicrob Resist Infect Control (2021) 10:78 Page 10 of 11 patients with resistant bacterial infections or patients Supplementary Information unresponsive to therapy because of resistance are gen- The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13756- 021- 00937-4. erally transferred from provincial to national level hos- pitals, which could further explain the higher levels of Additional file 1. Supplementary materials. resistance in national level hospitals. Acknowledgements Limitations The authors would like to thank the hospitals in the VINARES project for providing their laboratory routine AST results: National Hospital for Tropical VINARES collected isolate-based data (surveillance Disease, Saint-Paul Hospital, Uong Bi Hospital, Viet Tiep Hospital, Hue Central approaches based solely on laboratory data), without General Hospital, Da Nang General Hospital, Binh Dinh General Hospital, Dak epidemiological, clinical, and population-level data. Cur- Lak General Hospital, Khanh Hoa Provincial Hospital, Cho Ray Hospital, Chil- dren’s Hospital 1, Hospital for Tropical Disease and Can Tho Central Hospital. rently, GLASS accepts both isolate-based and sample- We also appreciate the support from Prof. Nguyen Viet Tien, Prof. Luong Ngoc based data, but it encourages countries to collect and Khue, Dr. Nguyen Trong Khoa and MSc. Ngo Thi Bich Ha at the Ministry of report sample-based data, which can provide strati- Health of Viet Nam in setting up and managing the VINARES network. The VINARES consortium: National Hospital for Tropical Diseases, Saint-Paul fied and therefore more useful information [6]. Current Hospital, Uong Bi Hospital, Viet Tiep Hospital, Hue Central General Hospital, Da data collected in VINARES do not allow to differenti - Nang General Hospital, Binh Dinh General Hospital, Dak Lak General Hospital, ate between hospital or community acquired infections. Khanh Hoa Provincial Hospital, Cho Ray Hospital, Children’s Hospital 1, Hospital for Tropical Diseases and Can Tho Central Hospital. Therefore, resistant proportions may be inflated when trying to use data to inform empiric treatment for com- Authors’ contributions munity acquired infections. Sample- or case-based data HRvD, HFLW, NVT, NVK conceived the idea and designed the study. VINARES consortium and DTTN acquired the data. NVMH, JIC and LTH supported the collection may provide potential solutions for this issue. microbiological testing. VTVD performed data analysis and interpretation, and AST data were collected from isolates cultured from prepared the manuscript. The manuscript was reviewed and approved by samples sent in for routine diagnostics as part of stand- HFLW, HRvD and MC. All authors read and approved the final manuscript. ard of care. While our study did not make any selection Funding of samples and included all laboratory results, it is known This work was funded by the Department of Health and Social Care (UK) that microbiology is underused in many LMICs for vari- through the Newton Fund, grant 172698142 and the Fleming Fund Viet Nam Pilot grant. ous reasons [28] which may lead to bias, usually towards overestimating resistant proportions. Availability of data and materials A standardized sampling and data collection strategy The data sharing agreements are in place for aggregated data with Resist- anceMap (www. resis tance map. org) and in preparation for individual level data across the whole surveillance network is important to with the the Global Burden of Disease / GRAM project on AMR. We have no minimize sampling biases, enhance representativeness agreement from the hospitals to make individual level data publicly available. and interpretation of the results, and allow inference of the results to the country representativeness [6]. The Declarations change in the participation of hospitals had impact on Ethical approval the overall resistant proportions. Not required. Competing interests Conclusions None declared. We show the results from a successful continuation of Author details a large AMR surveillance network in Viet Nam. The data Oxford University Clinical Research Unit, National Hospital for Tropical Dis- show alarmingly high and increasing resistant proportions eases, 78 Giai Phong, Dong Da, Hanoi, Viet Nam. National Hospital for Tropical Diseases, Hanoi, Viet Nam. Centre for Tropical Medicine and Global Health, in important organisms causing infections in Viet Nam. Nuffield Department of Medicine, University of Oxford, Oxford, UK. Depar t- However, AMR proportions varied across hospital types ment of Medical Microbiology, Radboudumc Center for Infectious Diseases, in the network. The results may not reflect the true AMR Radboudumc, Nijmegen, Netherlands. prevalence in Viet Nam as there may be sampling biases Received: 10 June 2020 Accepted: 21 April 2021 and data on whether isolates were from hospital- or com- munity-acquired were not included. Affordable and scal - able ways to adopt a sample-or case-based approach across the network should be explored. Clinical data should also References be included in the reports from the hospitals to help pro- 1. Cassini A, et al. Attributable deaths and disability-adjusted life-years vide more informative interpretations of the surveillance caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. data. Lancet Infect Dis. Jan. 2019;19(1):56–66. https:// doi. org/ 10. 1016/ S1473- 3099(18) 30605-4. Vu  et al. Antimicrob Resist Infect Control (2021) 10:78 Page 11 of 11 2. Lim C, et al. Epidemiology and burden of multidrug-resistant bacterial 18. Magiorakos A-P, et al. Multidrug-resistant, extensively drug-resistant and infection in a developing country. eLife. 2016;5:e18082. https:// doi. org/ pandrug-resistant bacteria: an international expert proposal for interim 10. 7554/ eLife. 18082. standard definitions for acquired resistance. Clin Microbiol Infect. Mar. 3. Rossolini GM, Arena F, Pecile P, Pollini S. Update on the antibiotic resist- 2012;18(3):268–81. https:// doi. org/ 10. 1111/j. 1469- 0691. 2011. 03570.x. ance crisis. Curr Opin Pharmacol. Oct. 2014;18:56–60. https:// doi. org/ 10. 19. Tacconelli E, et al. Discovery, research, and development of new antibiot- 1016/j. coph. 2014. 09. 006. ics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. 4. World Health Organization. Global Action Plan on Antimicrobial Resist- Lancet Infect Dis. 2018;18(3):318–27. https:// doi. org/ 10. 1016/ S1473- ance. Geneva: Switzerland; 2015.3099(17) 30753-3. 5. WHO, Antimicrobial resistance: global report on surveillance. Geneva, Swit- 20. World Health Organization, Global antimicrobial resistance surveillance zerland: World Health Organization, 2014. system (GLASS) report: early implementation 2016–2017, Geneva, 2017. 6. World Health Organization. Global antimicrobial resistance surveillance 21. World Health Organization. Global antimicrobial resistance and use system: manual for early implementation. Geneva: Switzerland; 2015. surveillance system (GLASS) report: Early implementation 2020. Geneva: 7. Vu Tien Viet D, et al. Antimicrobial susceptibility testing and antibiotic World Health Organization; 2020. consumption results from 16 hospitals in Viet Nam- the VINARES project, 22. H. Phan Quốc, ‘ Vi khuẩn kháng thuốc kháng sinh: Thực trạng đáng báo 2012–2013. J Glob Antimicrob Resist. 2019. https:// doi. org/ 10. 1016/j. jgar. động (AMR: an alarming situation)’, Ministry of Health. http:// amr. moh. 2019. 06. 002.gov. vn/ vi- khuan- khang- thuoc- khang- sinh- thuc- trang- dang- bao- dong/ 8. Wertheim HFL, et al. Providing impetus, tools, and guidance to (accessed Dec. 26, 2019). strengthen national capacity for antimicrobial stewardship in Viet Nam. 23. Song J-H, et al. High prevalence of antimicrobial resistance among PLOS Med. 2013. https:// doi. org/ 10. 1371/ journ al. pmed. 10014 29. clinical streptococcus pneumoniae isolates in Asia (an ANSORP study). 9. Phu VD, et al. Burden of hospital acquired infections and antimicrobial Antimicrob Agents Chemother. Jun. 2004;48(6):2101–7. https:// doi. org/ use in vietnamese adult intensive care units. PLoS ONE. 2016. https:// doi. 10. 1128/ AAC. 48.6. 2101- 2107. 2004. org/ 10. 1371/ journ al. pone. 01475 44. 24. Van PH, Binh PT, Minh NHL, Morrissey I, Torumkuney D. Results from the 10. Kinh NV, et al. Developing an antimicrobial resistance reference labora- survey of antibiotic resistance (SOAR) 2009–11 in Vietnam. J Antimicrob tory and surveillance programme in Vietnam. Lancet Glob Health. Dec. Chemother. May 2016;71(Suppl 1):i93–102. https:// doi. org/ 10. 1093/ jac/ 2017;5(12):e1186–7. https:// doi. org/ 10. 1016/ S2214- 109X(17) 30370-4. dkw069. 11. Van Kinh Nguyen et al., First report on antibiotic use and resistance in 25. Labi A-K, et al. Multi-centre point-prevalence survey of hospital-acquired Vietnam hospitals in 2008–2009, 2012. infections in Ghana. J Hosp Infect. Jan. 2019;101(1):60–8. https:// doi. org/ 12. Song J-H, et al. Spread of methicillin-resistant Staphylococcus aureus 10. 1016/j. jhin. 2018. 04. 019. between the community and the hospitals in Asian countries: an 26. Wang J, et al. The prevalence of healthcare-associated infections in main- ANSORP study. J Antimicrob Chemother. 2011;66(5):1061–9. https:// doi. land China: a systematic review and meta-analysis. Infect Control Hosp org/ 10. 1093/ jac/ dkr024. Epidemiol. 2018;39(6):701–9. https:// doi. org/ 10. 1017/ ice. 2018. 60. 13. Ministry of Health of Viet Nam, National action plan on combatting Drug 27. Manosuthi W, Thientong V, Moolasart V, Rongrungrueng Y, Sangsajja C, resistance in the period from 2013–2020. 2013. Danchaivijitr S. Healthcare-associated infections at selected hospitals in 14. O’Brien TF, Stelling JM. WHONET: an information system for monitoring Thailand, Southeast Asian. J Trop Med Public Health. 2017;48(1):204–12. antimicrobial resistance. Emerg Infect Dis. 1995;1(2):66. 28. van Doorn HR, Ashley EA, Turner P. Case-based surveillance of antimicro- 15. J. Stelling, M. Kulldor, and Br ff ien, WHONET and baclink: software tools bial resistance in the ACORN (A Clinically Oriented Antimicrobial Resist- for laboratory-based surveillance of infectious diseases and antimicrobial ance Surveillance Network) study. JAC Antimicrob Resist. 2020. https:// resistance, 2007.doi. org/ 10. 1093/ jacamr/ dlaa0 18. 16. ‘R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL Publisher’s Note https:// www.R- proje ct. org.’ 2018. Springer Nature remains neutral with regard to jurisdictional claims in pub- 17. Hirsch EB, Tam VH. Impact of multidrug-resistant Pseudomonas lished maps and institutional affiliations. aeruginosa infection on patient outcomes. Expert Rev Pharmacoecon Outcomes Res. Aug. 2010;10(4):441–51. https:// doi. org/ 10. 1586/ erp. 10. 49. Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : fast, convenient online submission thorough peer review by experienced researchers in your field rapid publication on acceptance support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions

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