Reporting antimicrobial susceptibilities and resistance phenotypes in Acinetobacter spp: a nationwide proficiency study

Reporting antimicrobial susceptibilities and resistance phenotypes in Acinetobacter spp: a... Abstract Objectives To evaluate the proficiency of Spanish microbiology laboratories with respect to the antimicrobial susceptibility testing (AST) of Acinetobacter spp. Methods Eight Acinetobacter spp. with different resistance mechanisms were sent to 48 Spanish centres which were asked to report: (i) the AST system used; (ii) MICs; (iii) breakpoints used (CLSI versus EUCAST); (iv) clinical category; and (v) resistance mechanisms inferred. Minor, major and very major errors (mE, ME and VME, respectively) were determined. Results The greatest percentages of discrepancies were: (i) by AST method: 18.5% Etest, 14.3% Vitek 2 and Sensititre; (ii) by breakpoints: 20.5% (CLSI) and 10.8% (EUCAST); and (iii) by antimicrobial agent: ampicillin/sulbactam (56.2% CLSI), minocycline (40.7% CLSI), tobramycin (38.7% CLSI, 16.8% EUCAST), imipenem (27.8% CLSI, 30.0% EUCAST) and meropenem (25.4% CLSI, 20.8% EUCAST). Categorical error rates: (i) by AST method ranged from 30.0% (Phoenix) to 100% (Sensititre and disc diffusion) for mE, 0.0% (Etest, Sensititre, disc diffusion) to 40% (Phoenix) for ME, and 0.0% (Sensititre and disc diffusion) to 30% (Phoenix) for VME; (ii) by breakpoints: mE (80.1% CLSI, 58.4% EUCAST), ME (3.5% CLSI, 12.4% EUCAST) and VME (16.4% CLSI, 29.2% EUCAST); and (iii) by antimicrobial agent: mE (100% levofloxacin/CLSI, 100% levofloxacin and meropenem/EUCAST), ME (35.3% colistin/CLSI, 25.0% colistin/EUCAST) and VME (64.7% colistin/CLSI, 86.7% gentamicin/EUCAST). Conclusions Clinical microbiology laboratories must improve their ability to determine antimicrobial susceptibilities of Acinetobacter spp. isolates. Higher discrepancies using CLSI when compared with EUCAST are mainly due to mE and to a much lesser extent to ME or VME. Introduction The genus Acinetobacter includes more than 30 genomic species. The most important one from a clinical and epidemiological point of view is Acinetobacter baumannii owing to its ability to acquire MDR and to survive and cause nosocomial outbreaks.1 Genomic species other than A. baumannii, such as Acinetobacter pittii, are increasingly being recognized as nosocomial pathogens.2 The prevalence of MDR Acinetobacter spp. has increased in recent years, limiting the treatment options for patients infected with these isolates to some broad-spectrum antimicrobials such as carbapenems. Nevertheless, resistance to carbapenems has also increased in recent years. Data from Europe reported by the European Antimicrobial Resistance Surveillance Network (EARS-Net) showed that in 2015 ≥50% of isolates were carbapenem resistant.3 One of the best options for the treatment of patients infected with carbapenem-resistant Acinetobacter spp. isolates is colistin, but resistance to this antimicrobial is also increasing.3 Antimicrobial susceptibility testing (AST) of Acinetobacter spp. may be difficult because there are several factors that can affect the antimicrobial susceptibility results. These factors have been related to the antimicrobial (i.e. the stability of carbapenems, the effect of cation concentrations on the activity of colistin and tigecycline, the type and the age of the medium), the microorganism (i.e. type of intrinsic resistance, type of acquired resistance mechanism, heterogeneity of resistance to colistin or carbapenems) or the methodology (i.e. broth microdilution versus agar disc diffusion).4–9 The interpretation criteria employed (i.e. CLSI versus EUCAST) are also factors that affect the interpretation of results.10 Inferring mechanisms of resistance to antimicrobials forms part of the interpretative reading of the antibiogram. In A. baumannii this is very difficult to do because: (i) resistance is frequently multifactorial; and (ii) the mechanisms of resistance to some antimicrobials are still poorly characterized (i.e. penicillin-binding proteins, heteroresistance). These limitations led us to perform the present study with the aim of identifying the most frequent problems for AST of Acinetobacter spp. in Spanish clinical microbiology laboratories. Materials and methods Bacterial isolates, genomic species identification and antimicrobial susceptibility testing Eight MDR isolates of Acinetobacter spp. with different mechanisms of antimicrobial resistance were selected for this study (Table 1). Seven isolates of A. baumannii were coded as CC-01 and CC-03 to CC-08, and one isolate of A. pitti was coded as CC-02. Table 1. MICs (mg/L) of antimicrobials for Acinetobacter spp. isolates Isolate  Antimicrobial resistance mechanism  MICa   Ref  AMK  GEN  TOB  TZP  SAM  CAZ  FEP  IPM  MEM  CIP  LVX  MIN  CST  CC-01  blaCTX-M-15  8  1  8  4  16  32  >256  0.5  1  32  4  4  0.5  11  CC-02  blaOXA-58  2  0.5  0.5  >512  16  16  4  32  16  32  8  0.125  0.125  12  CC-03  blaIMP  256  128  8  >512  16  >128  >256  >64  32  >64  16  2  0.5  13  CC-04  blaOXA-24  64  4  128  >512  32  >128  32  32  16  >64  16  32  0.5  14  CC-05  blaOXA-24b  256  >128  128  >512  16  32  16  16  16  64  16  0.5  0.5  15  CC-06  blaOXA-24 + pmrAB mutation  >256  >128  8  >512  128  >128  >256  >64  >64  >64  16  16  8  16  CC-07  blaOXA-24 + adeABC hyp  16  >128  4  >512  8  64  32  32  8  >64  32  16  0.5  14  CC-08  blaOXA-51 hyp + omp33-36 def  >256  >128  16  >512  32  128  128  16  >64  >64  32  16  0.125  17  Isolate  Antimicrobial resistance mechanism  MICa   Ref  AMK  GEN  TOB  TZP  SAM  CAZ  FEP  IPM  MEM  CIP  LVX  MIN  CST  CC-01  blaCTX-M-15  8  1  8  4  16  32  >256  0.5  1  32  4  4  0.5  11  CC-02  blaOXA-58  2  0.5  0.5  >512  16  16  4  32  16  32  8  0.125  0.125  12  CC-03  blaIMP  256  128  8  >512  16  >128  >256  >64  32  >64  16  2  0.5  13  CC-04  blaOXA-24  64  4  128  >512  32  >128  32  32  16  >64  16  32  0.5  14  CC-05  blaOXA-24b  256  >128  128  >512  16  32  16  16  16  64  16  0.5  0.5  15  CC-06  blaOXA-24 + pmrAB mutation  >256  >128  8  >512  128  >128  >256  >64  >64  >64  16  16  8  16  CC-07  blaOXA-24 + adeABC hyp  16  >128  4  >512  8  64  32  32  8  >64  32  16  0.5  14  CC-08  blaOXA-51 hyp + omp33-36 def  >256  >128  16  >512  32  128  128  16  >64  >64  32  16  0.125  17  AMK, amikacin; GEN, gentamicin; TOB, tobramycin; TZP, piperacillin/tazobactam; SAM, ampicillin/sulbactam; CAZ, ceftazidime; FEP, cefepime; IPM, imipenem; MEM, meropenem; CIP, ciprofloxacin; LVX, levofloxacin; MIN, minocycline; CST, colistin. a MICs shown in bold correspond to the resistant clinical category using CLSI breakpoints; underlined MICs correspond to the intermediate category; and MICs in normal type correspond to the susceptible category. b Carbapenem heteroresistant. Bacterial identification, antimicrobial susceptibility and confirmation of the mechanisms of resistance were verified independently at Spain’s two clinical microbiology reference laboratories: Hospital Universitario Virgen Macarena (Seville), and Complejo Hospitalario Universitario de A Coruña (A Coruña). Identification was performed by partial DNA sequencing of the rpoB gene and MALDI-TOF.18,19 The antimicrobials tested were piperacillin/tazobactam, ampicillin/sulbactam, cefepime, ceftazidime, imipenem, meropenem, ciprofloxacin, levofloxacin, tobramycin, gentamicin, amikacin, colistin and minocycline. The antimicrobials were tested in duplicate at each reference centre by disc diffusion and broth microdilution, according to CLSI guidelines.20 The 2014 CLSI and EUCAST breakpoints were used to interpret clinical categories.20,21 Study design Isolates were sent in Amies transport medium to 48 participating hospitals in May 2014. The instructions specified that isolates should be treated as blood culture isolates. Participating laboratories were asked to fill in an electronic form for each isolate, which included: (i) the laboratory system or method used for AST; (ii) the antimicrobial susceptibility results [inhibition zone diameters or MIC values, and clinical categories: susceptible (S), intermediate (I) and resistant (R)]; (iii) the breakpoints used (CLSI or EUCAST); and (iv) inferred mechanism(s) that might be responsible for the observed phenotype of resistance to carbapenems and colistin. Data analysis The analysis of results consisted of: (i) descriptive analysis of AST methods, breakpoints applied, clinical category assigned and discrepancies between centres deriving from these items; (ii) analysis of categorical error rates [minor errors (mEs), major errors (MEs) and very major (VMEs)]; and (iii) the ability of participating laboratories to accurately infer possible underlying resistance mechanisms. Results Type of AST system The laboratories used the following AST systems or methods: 59.4% MicroScan WalkAway (Dade MicroScan Inc., West Sacramento, CA, USA), 18.5% Vitek 2 (bioMérieux, Marcy-l’Étoile, France), 11.0% Wider I (Francisco Soria Melguizo, Madrid, Spain), 3.7% disc diffusion, 3.6% Etest, 3.5% Phoenix (BD Biosciences, Sparks, MD, USA) and 0.4% Sensititre (Trek Diagnostic systems, Westlake, OH, USA). Regarding the type of method used for AST, the discrepancy rates related to clinical interpretation were 18.5% (Etest), 14.3% (Vitek 2 and Sensititre), 10.7% (Wider I), 9.8% (MicroScan WA) and 3.1% (Phoenix). The discrepancies using disc diffusion were 4.1%. Table 2 shows the distribution of categorical error rates according to type of AST system. Table 2. Distribution of discrepancies and categorical error rates by AST system     Errors (%)   AST system  Discrepancies (%)a  mE  ME  VME  Etest  18.5b  75.0  0.0  25.0  Vitek 2  14.3  79.4  0.5  20.1  Sensititre  14.3  100  0.0  0.0  Wider I  10.7  71.3  5.9  22.8  MicroScan WA  9.8  79.0  6.9  14.1  Disk diffusion  4.1  100  0.0  0.0  Phoenix  3.1  30.0  40.0  30.0      Errors (%)   AST system  Discrepancies (%)a  mE  ME  VME  Etest  18.5b  75.0  0.0  25.0  Vitek 2  14.3  79.4  0.5  20.1  Sensititre  14.3  100  0.0  0.0  Wider I  10.7  71.3  5.9  22.8  MicroScan WA  9.8  79.0  6.9  14.1  Disk diffusion  4.1  100  0.0  0.0  Phoenix  3.1  30.0  40.0  30.0  mE, minor error; ME, major error; VME, very major error. a The percentages of discrepancies were determined as follows: number of discrepant results in the clinical category (susceptible, intermediate and resistant)/total number of results reported. b The highest percentages are shown in bold. Breakpoints applied and type of antimicrobial agent The participating laboratories registered 4693 (94.5%) results in the database. Some of the antimicrobials most frequently not tested were ampicillin/sulbactam (26.4%), minocycline (25.3%), levofloxacin (19.0%) and piperacillin/tazobactam (15.4%). Sixty-five percent of the 48 participating laboratories exclusively applied CLSI breakpoints, whereas 19% applied only EUCAST breakpoints and 16% applied CLSI or EUCAST breakpoints depending on the antimicrobial. For all antimicrobials tested the overall discrepancy rates in clinical category due exclusively to the differential use of breakpoints were 20.5% by applying CLSI breakpoints, and 10.8% by applying EUCAST breakpoints (Table 3). By using CLSI breakpoints high percentages of discrepancies were observed for ampicillin/sulbactam (56.2%), minocycline (40.7%), tobramycin (38.7%), imipenem (27.8%) and meropenem (25.4%). In contrast, using EUCAST breakpoints the greatest discrepancies were obtained for imipenem (30.0%), meropenem (20.8%) and tobramycin (16.8%). Table 3. Distribution of discrepancies and categorical error rates by antimicrobial agent and breakpoints used (CLSI versus EUCAST) Antimicrobial agent  Discrepancies (%)a   mE (%)   ME (%)   VME (%)   CLSI  EUCAST  CLSI  EUCAST  CLSI  EUCAST  CLSI  EUCAST  Piperacillin/tazobactam  2.6  0.0  85.7  0.0  0.0  0.0  14.3  0.0  Ampicillin/sulbactam  56.2b  0.0  89.3  0.0  0.0  0.0  10.7  0.0  Cefepime  23.1  0.0  94.2  0.0  4.3  0.0  1.4  0.0  Ceftazidime  19.5  0.0  58.6  0.0  6.9  0.0  34.5  0.0  Imipenem  27.8  30.0  61.4  94.9  0.0  0.0  38.6  5.1  Meropenem  25.4  20.8  86.9  100  0.0  0.0  13.1  0.0  Ciprofloxacin  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  Levofloxacin  4.4  7.9  100  100  0.0  0.0  0.0  0.0  Tobramycin  38.7  16.8  87.9  0.0  1.0  19.0  11.1  85.0  Gentamicin  9.1  11.5  60.9  13.3  34.8  13.3  4.3  86.7  Amikacin  8.1  14.5  63.2  77.8  10.5  11.1  26.3  11.1  Colistin  6.6  6.5  0.0  25.0  35.3  25.0  64.7  75.0  Minocycline  40.7  0.0  88.6  0.0  0.0  0.0  11.4  0.0  Overall  20.5  10.8  80.1  58.4  3.5  12.4  16.4  29.2  Antimicrobial agent  Discrepancies (%)a   mE (%)   ME (%)   VME (%)   CLSI  EUCAST  CLSI  EUCAST  CLSI  EUCAST  CLSI  EUCAST  Piperacillin/tazobactam  2.6  0.0  85.7  0.0  0.0  0.0  14.3  0.0  Ampicillin/sulbactam  56.2b  0.0  89.3  0.0  0.0  0.0  10.7  0.0  Cefepime  23.1  0.0  94.2  0.0  4.3  0.0  1.4  0.0  Ceftazidime  19.5  0.0  58.6  0.0  6.9  0.0  34.5  0.0  Imipenem  27.8  30.0  61.4  94.9  0.0  0.0  38.6  5.1  Meropenem  25.4  20.8  86.9  100  0.0  0.0  13.1  0.0  Ciprofloxacin  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  Levofloxacin  4.4  7.9  100  100  0.0  0.0  0.0  0.0  Tobramycin  38.7  16.8  87.9  0.0  1.0  19.0  11.1  85.0  Gentamicin  9.1  11.5  60.9  13.3  34.8  13.3  4.3  86.7  Amikacin  8.1  14.5  63.2  77.8  10.5  11.1  26.3  11.1  Colistin  6.6  6.5  0.0  25.0  35.3  25.0  64.7  75.0  Minocycline  40.7  0.0  88.6  0.0  0.0  0.0  11.4  0.0  Overall  20.5  10.8  80.1  58.4  3.5  12.4  16.4  29.2  a The percentages of discrepancies were determined as follows: number of discrepant results in the clinical category (susceptible, intermediate and resistant)/total number of results reported. b The highest percentages are shown in bold. There was a high proportion of mE (80.1% using CLSI breakpoints and 58.4% using EUCAST breakpoints), particularly for levofloxacin, cefepime, ampicillin/sulbactam, minocycline, tobramycin and meropenem using CLSI breakpoints, and for levofloxacin, meropenem and imipenem using EUCAST breakpoints (Table 3). The percentages of MEs (false resistance) were 3.5% using CLSI breakpoints, and 12.4% using EUCAST breakpoints. The highest ME rates were observed for colistin, gentamicin and amikacin using CLSI breakpoints, and for colistin, tobramycin, gentamicin and amikacin using EUCAST breakpoints. The percentages of VME (false susceptibility) were 16.4% using CLSI breakpoints and 29.2% using EUCAST breakpoints. The highest VME rates were observed for colistin, imipenem, ceftazidime and amikacin using CLSI breakpoints, and for gentamicin, tobramycin, colistin and amikacin using EUCAST breakpoints. Type of reference isolate Discrepancies in the MICs by type of reference isolate (Table 4) were >50% and ranged from 51.0% (CC-06) to 56.5% (CC-03), whereas categorical error rates ranged from 7.4% (CC-03) to 16.8% (CC-04) for mE, 0.0% (CC-03) to 4.4% (CC-04) for ME and 1.0% (CC-03) to 5.2% (CC-06) for VME. Table 4. Distribution of discrepancies and categorical error rates by tested strain Reference isolate (resistance mechanism)  Discrepancies (%)a  Errors (%)   mE  ME  VME  CC-01 (blaCTX-M-15)  53.1  14.9  1.0  1.3  CC-02 (blaOXA-58)  52.9  12.6  1.5  1.2  CC-03 (blaIMP)  56.5b  7.4  0.0  1.0  CC-04 (blaOXA-24)  52.3  16.8  4.4  2.0  CC-05 (blaOXA-24/HR)  56.4  14.9  1.5  4.9  CC-06 (blaOXA-24 and pmrAB mutation)  51.0  14.2  0.2  5.2  CC-07 (blaOXA-24 + adeABC hyp)  54.5  11.7  1.5  2.1  CC-08 (blaOXA-51 hyp + omp 33-36 def)  53.3  12.6  0.7  5.1  Reference isolate (resistance mechanism)  Discrepancies (%)a  Errors (%)   mE  ME  VME  CC-01 (blaCTX-M-15)  53.1  14.9  1.0  1.3  CC-02 (blaOXA-58)  52.9  12.6  1.5  1.2  CC-03 (blaIMP)  56.5b  7.4  0.0  1.0  CC-04 (blaOXA-24)  52.3  16.8  4.4  2.0  CC-05 (blaOXA-24/HR)  56.4  14.9  1.5  4.9  CC-06 (blaOXA-24 and pmrAB mutation)  51.0  14.2  0.2  5.2  CC-07 (blaOXA-24 + adeABC hyp)  54.5  11.7  1.5  2.1  CC-08 (blaOXA-51 hyp + omp 33-36 def)  53.3  12.6  0.7  5.1  a The percentages of discrepancies were determined as follows: number of discrepant results in the clinical category (susceptible, intermediate and resistant)/total number of results reported. b The highest percentages are shown in bold. The highest percentages of mE and ME were observed with the CC-04 isolate (blaOXA-24), whereas the highest percentages of VME were observed with the CC-06 (blaOXA-24 and resistant to colistin by pmrAB mutation) and CC-05 isolates (blaOXA-24 and heteroresistant to carbapenems). Ability of centres to infer resistance mechanisms The analysis of inferred mechanisms of resistance focused only on β-lactams, particularly carbapenems, and colistin owing to the heterogeneous information reported by the participating laboratories. Only one centre inferred ESBL production in the blaCTX-M-15-producing isolate (CC-01). With respect to the blaOXA-58-producing isolate (CC-02), 11 laboratories indicated carbapenemase production, but only 3 specified that it was a class D carbapenemase. Of the 19 centres that reported the blaIMP-producing isolate (CC-03) as a carbapenemase producer, only 4 specified production of a class B carbapenemase. Only 1 centre reported that the blaOXA-24-producing isolate (CC-04) was a carbapenemase producer. With respect to the blaOXA-24-producing isolate heteroresistant to carbapenems (CC-05), 9 centres inferred carbapenemase production but none reported the carbapenem-heteroresistant phenotype. For the blaOXA-24-producing isolate resistant to colistin (CC-06), 14 centres reported this isolate to be a colistin-resistant pmrAB mutant. Carbapenemase production in the blaOXA-24-producing isolate overexpressing adeABC (CC-07) was inferred by 14 participating centres, but overexpression of adeABC was not mentioned. Finally, for the blaOXA-51-hyperproducing isolate deficient in omp33-36 (CC-08), 16 centres reported carbapenemase production, but none inferred porin loss. Discussion Most laboratories use either automated and/or manual systems or methods with different performances for AST. Erroneous antimicrobial susceptibility results have been described in Acinetobacter spp. and have been associated with several factors. In our study, the discrepancies observed in antimicrobial susceptibility results provided by participating centres were related to the AST system used, the differential application of CLSI or EUCAST breakpoints, the antimicrobial and the type of isolate. The type of assay or method used for AST was a possible contributory factor to the discrepancies observed in this study. The Etest was the least reliable method for AST with 18.5% of discrepancies in the MICs. Among automated AST systems, the least reliable ones were Vitek 2 and Sensititre. All the AST methods, except Phoenix, showed too many mEs (≥75%). The highest percentages of ME and VME were obtained using the Phoenix system, suggesting that this AST system performs poorly. Nevertheless, these results should be interpreted with caution as only two centres used the Phoenix method for AST. VMEs were also elevated, particularly using Etest, but not when using Sensititre or disc diffusion with which no VMEs were detected. In the study by Kulah et al.,7 whose objective was to detect imipenem resistance in A. baumannii using three automated systems (BD Phoenix, MicroScan WalkAway, Vitek 2), it was observed that the highest error rates (25% VME and 44.6% mE) and the worst performance in susceptibility testing occurred with the MicroScan WA system. The study by Akers et al.22 showed that disc diffusion and Etest tended to be more accurate than the Vitek 2, Phoenix and MicroScan automated systems, with VMEs of 5.6% and 13.1% for tobramycin and amikacin, respectively, using Vitek 2. Discrepancies in the clinical category derived from the breakpoints or guidelines applied may have no effect on the clinical category or may result in a high categorical error rate. In the present study higher discrepancies using CLSI breakpoints when compared with those of EUCAST breakpoints are mainly due to mE and to a much lesser extent to ME or VME, suggesting that the breakpoint used is an important factor contributing to antimicrobial susceptibility discrepancies and errors in clinical category. With respect to the categorical error rates, the overall percentages of mE using CLSI breakpoints were higher than those obtained using EUCAST breakpoints. In contrast, the percentages of ME and VME were higher using EUCAST breakpoints. Categorical error rates (mE and VME) for ampicillin/sulbactam and minocycline were associated particularly with the use of CLSI breakpoints. For imipenem and meropenem, elevated mEs were observed independent of the breakpoints used (CLSI or EUCAST), whereas VMEs were much more frequent using CLSI breakpoints. For tobramycin the errors were associated with the use of CLSI breakpoints (mE) and EUCAST breakpoints (ME and VME). Type of antimicrobial was another factor contributing to the discrepancies and errors observed. The antimicrobials that showed the highest discrepancies in susceptibility associated with high categorical error rates were levofloxacin, ampicillin/sulbactam, imipenem, meropenem, tobramycin and minocycline, which coincides with some previous studies.7,21–25 Susceptibility to β-lactams may be difficult to test in the laboratory for several reasons. When the assay is performed by broth microdilution, subtle growth patterns (i.e. granular, small button or ‘starry’ growth) are frequently observed, making the results difficult to interpret and leading to elevated MEs.23 Using disc diffusion methods, problems of interpretation have also been observed related to the presence of colonies growing inside the inhibition halo. The stability of β-lactams is another factor that could lead to discrepancies in susceptibility. This is particularly relevant for carbapenems, which are very labile, and for which false resistance (VME) is frequently detected, particularly using some automated devices. The number of laboratories able to infer some type of mechanism of resistance was very low, with >50% reporting discrepancies in clinical categories, reflecting the difficulty of inferring resistance mechanisms in Acinetobacter, particularly with antimicrobials such as carbapenems. As mentioned before, this kind of inference is complicated by the presence of various resistance mechanisms (namely, hyperexpression of the blaOXA-51 carbapenemase and some of the acquired carbapenem-hydrolysing oxacillinases described in A. baumannii) and by the fact that mechanisms of resistance to some antimicrobials in Acinetobacter spp. have not been studied to the extent of Enterobacteriaceae or Pseudomonas aeruginosa. In our study, a high degree of disagreement was found when inferring resistance mechanisms, particularly for most MDR or carbapenem-resistant isolates with acquired mechanisms of carbapenem resistance, for which the percentage of mE and VME was unacceptably high. An analysis of factors that contribute to antimicrobial susceptibility results should be a priority for clinical laboratories. One way to address this problem is to participate in quality control programmes, which can be very helpful for detecting potential laboratory problems and enabling corrective measures to be established for optimizing the process and the quality of the reports offered to clinicians. This information is very useful for optimizing the best therapeutic strategies, improving the rational use of antimicrobials (reducing resistance rates) and facilitating the control of nosocomial infections (by reducing transmission of MDR clones) so as to avoid outbreaks.26–29 In conclusion, this study clearly shows that microbiology laboratories need to improve their ability to accurately determine the antimicrobial susceptibility of Acinetobacter spp. This is particularly relevant for ampicillin/sulbactam, carbapenems and tobramycin, and when both automated devices and CLSI breakpoints are used. The higher discrepancies using CLSI breakpoints are mainly related to mEs and in a much lesser extent to MEs or VMEs. Acknowledgements We are grateful to the 48 participating centres and the SEIMC Quality Control Program for their indispensable help in making this study possible. The complete list of the 48 participating hospitals is below. Hospital Universitario de Tarragona Joan XXIII (Tarragona, Tarragona; Angels Vilanova), Hospital General de Gran Canaria Dr Negrín (Las Palmas Gran Canaria, Las Palmas; Ana Bordes Benítez), Hospital Costa del Sol (Marbella, Málaga; Natalia Montiel Quezel-Guerraz), Hospital Universitario Miguel Servet (Zaragoza, Zaragoza; Ana Isabel López Calleja), Hospital de Cabueñes (Gijón, Asturias; Luis Otero Guerra), Hospital Doce de Octubre (Madrid, Madrid; Fernando Chaves Sánchez), Hospital Universitario Marqués de Valdecilla (Santander, Cantabria; Jorge Calvo Montes), Hospital Sierrallana (Torrelavega, Cantabria; Inés de Benito Población), Hospital Santa Barbara (Complejo Hospitalario de Soria, Soria; Angel Campos Bueno), Hospital Clínico Universitario de Valladolid (Valladolid, Valladolid; Raul Ortiz de Lejarazu Leonardo), Hospital Universitario Rio Hortega (Valladolid, Valladolid; Mónica de Frutos Serna), Complejo Asistencial de Avila (Avila, Avila; Antonio Gómez del Campo Dechado), Hospital General Universitario de Ciudad Real (Ciudad Real, Ciudad Real; Isabel Barbas Ferrera), Hospital General Universitario de Guadalajara (Guadalajara, Guadalajara; González Praetorius), Hospital Universitario de Bellvitge (L’Hospitalet de Llobregat, Barcelona; M. Angeles Domínguez Luzón), Fundación Jiménez Díaz (Madrid, Madrid; Ricardo Fernández Roblas), Complejo Hospitalario Universitario de Vigo (Vigo, Pontevedra; Maximiliano Alvarez Fernández), Complejo Hospitalario Universitario A Coruña (A Coruña, A Coruña; Begoña Fernández Pérez), Hospital Infantil Niño Jesús (Madrid, Madrid; M. Mercedes Alonso Sanz), Hospital de la Princesa (Madrid, Madrid; Laura Cardeñoso), Hospital General U. Gregorio Marañón (Madrid, Madrid; Carlos Sánchez), Hospital Clínico Universitario San Carlos (Madrid, Madrid; Juan J. Picazo de la Garza), Hospital Universitario Puerta de Hierro Majadahonda (Majadahonda, Madrid; Francisca Portero), Clínica Universidad de Navarra (Pamplona, Navarra; José Leiva León), Hospital Comarcal Marina Baixa (Villajoyosa, Valencia; Carmen Martínez Peinado), Hospital Universitario La Fe (Valencia, Valencia; José Luis López Hontangas), Hospital General Universitario de Elche (Elche, Alicante; Gloria Royo García), Hospital Universitario Puerta del Mar (Cádiz, Cádiz; Fátima Galán-Sánchez), Hospital Universitario Virgen de la Victoria (Málaga, Málaga; Encarnación Clavijo Frutos), Hospital Universitari Arnau de Vilanova (Lleida, Lleida; Mercedes García González), Hospital Lucus Augusti (Lugo, Lugo; Pilar Alonso García), Complejo Hospitalario de Pontevedra (Pontevedra, Pontevedra; María José Zamora López), Hospital Universitario La Paz (Madrid, Madrid; Julio García Rodríguez), Hospital Universitario Son Espases (Palma de Mallorca, Baleares; José L Pérez Sáenz), Hospital Ramón y Cajal (Madrid, Madrid; María Isabel Morosini), Hospital Universitario Insular de Gran Canaria (Las Palmas de Gran Canarias, Las Palmas; Antonio Manuel Martín Sánchez), Hospital de Jerez (Jerez de la Frontera, Cádiz; M. Dolores López Prieto), Hospital de la Ribera (Alzira, Valencia; Javier Colomina Rodríguez), Hospital Universitario Fundación Alcorcón (Alcorcón, Madrid; Alberto Delgado-Iribarren), Hospital San Pedro de Alcántara (Cáceres, Cáceres; Jesús Viñuelas Bayón), Hospital Universitario Vall d’Hebron (Barcelona, Barcelona; Rosa Juve Saumell), Hospital Universitario Virgen del Rocío (Sevilla, Sevilla; Javier Aznar Martín), Hospital General Universitario de Albacete (Albacete, Albacete; Eva Riquelme Bravo), Hospital Universitario de Getafe (Getafe, Madrid; David Molina Arana), Consorcio Hospital General Universitario de Valencia (Valencia, Valencia; Nuria Tormo), Hospital Universitario Severo Ochoa (Leganés, Madrid; Pilar Reyes Pecharromán), Hospital Virgen de las Nieves (Granada, Granada; Consuelo Miranda Casas), Hospital Universitario Virgen de la Arrixaca (El Palmar, Murcia; Genoveva Yagüe). Funding This work was supported by the Ministerio de Sanidad y Consumo, Instituto de Salud Carlos III (projects PI11-02046, PI10/00056 and PI12/00552) and the Consejería de Innovación Ciencia y Empresa, Junta de Andalucía (P11-CTS-7730), Spain, by the Plan Nacional de I + D+i 2008–2011 and the Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Economía y Competitividad, the Spanish Network for Research in Infectious Diseases (REIPI RD12/0015)—co-financed by European Development Regional Fund ‘A way to achieve Europe’ ERDF, and the Programa integral de prevención, control de las infecciones relacionadas con la asistencia sanitaria, y uso apropiado de los antimicrobianos (PIRASOA; Junta de Andalucía, Consejería de Salud, Junta de Andalucía). Transparency declarations None to declare. References 1 Peleg AY, Seifert H, Paterson DL. Acinetobacter baumannii: emergence of a successful pathogen. Clin Microbiol Rev  2008; 21: 538– 82. Google Scholar CrossRef Search ADS PubMed  2 Park KH, Shin JH, Lee SY et al.   The clinical characteristics, carbapenem resistance, and outcome of Acinetobacter bacteremia according to cenospecies. PLoS One  2013; 8: e65026. Google Scholar CrossRef Search ADS PubMed  3 European Centre for Disease Prevention and Control (ECDC). Carbapenem-Resistant Acinetobacter Baumannii in Healthcare Settings. 2016. https://ecdc.europa.eu/sites/portal/files/media/en/publications/Publications/8-Dec-2016-RRA-Acinetobacter baumannii-Europe.pdf. 4 Lee SY, Shin JH, Lee K et al.   Comparison of the Vitek 2, MicroScan, and Etest methods with the agar dilution method in assessing colistin susceptibility of bloodstream isolates of Acinetobacter species from a Korean university hospital. J Clin Microbiol  2013; 51: 1924– 6. Google Scholar CrossRef Search ADS PubMed  5 Girardello R, Bispo PJ, Yamanaka TM et al.   Cation concentration variability of four distinct Mueller-Hinton agar brands influences polymyxin B susceptibility results. J Clin Microbiol  2012; 50: 2414– 8. Google Scholar CrossRef Search ADS PubMed  6 Tan TY, Ng LS, Chen DM. Influence of different Mueller-Hinton agars and media age on Etest susceptibility testing of tigecycline. Diagn Microbiol Infect Dis  2010; 68: 93– 5. Google Scholar CrossRef Search ADS PubMed  7 Kulah C, Aktas E, Comert F et al.   Detecting imipenem resistance in Acinetobacter baumannii by automated systems (BD Phoenix, Microscan WalkAway, Vitek 2); high error rates with Microscan WalkAway. BMC Infect Dis  2009; 9: 30. Google Scholar CrossRef Search ADS PubMed  8 Markelz AE, Mende K, Murray CK et al.   Carbapenem susceptibility testing errors using three automated systems, disk diffusion, Etest, and broth microdilution and carbapenem resistance genes in isolates of Acinetobacter baumannii-calcoaceticus complex. Antimicrob Agents Chemother  2011; 55: 4707– 11. Google Scholar CrossRef Search ADS PubMed  9 Fernández Cuenca F, Sánchez MC, Caballero-Moyano FJ et al.   Prevalence and analysis of microbiological factors associated with phenotypic heterogeneous resistance to carbapenems in Acinetobacter baumannii. Int J Antimicrob Agents  2012; 39: 472– 7. Google Scholar CrossRef Search ADS PubMed  10 Hombach M, Bloemberg GV, Böttger EC. Effects of clinical breakpoint changes in CLSI guidelines 2010/2011 and EUCAST guidelines 2011 on antibiotic susceptibility test reporting of Gram-negative bacilli. J Antimicrob Chemother  2012; 67: 622– 32. Google Scholar CrossRef Search ADS PubMed  11 Beceiro A, Fernández-Cuenca F, Ribera A et al.   False extended-spectrum β-lactamase detection in Acinetobacter spp. due to intrinsic susceptibility to clavulanic acid. J Antimicrob Chemother  2008; 61: 301– 8. Google Scholar CrossRef Search ADS PubMed  12 Marti S, Sánchez-Céspedes J, Blasco MD et al.   Characterization of the carbapenem-hydrolyzing oxacillinase oxa-58 in an Acinetobacter genospecies 3 clinical isolate. Antimicrob Agents Chemother  2008; 52: 2955– 8. Google Scholar CrossRef Search ADS PubMed  13 Cornaglia G, Riccio ML, Mazzariol A et al.   Appearance of IMP-1 metallo-β-lactamase in Europe. Lancet  1999; 353: 899– 900. Google Scholar CrossRef Search ADS PubMed  14 Rumbo C, Gato E, López M et al.   Contribution of efflux pumps, porins, and β-lactamases to multidrug resistance in clinical isolates of Acinetobacter baumannii. Antimicrob Agents Chemother  2013; 57: 5247– 57. Google Scholar CrossRef Search ADS PubMed  15 Fernández-Cuenca F, Tomás-Carmona M, Caballero-Moyano F et al.   In vitro activity of 18 antimicrobial agents against clinical isolates of Acinetobacter spp.: multicenter national study GEIH-REIPI-Ab 2010. Enferm Infecc Microbiol Clin  2013; 31: 4– 9. Google Scholar CrossRef Search ADS PubMed  16 Beceiro A, Moreno A, Fernández N et al.   Biological cost and impact on virulence of different mechanisms of colistin resistance in Acinetobacter baumannii. Antimicrob Agents Chemother  2013; 58: 518– 26. Google Scholar CrossRef Search ADS PubMed  17 del Mar Tomás M, Beceiro A, Pérez A et al.   Cloning and functional analysis of the gene encoding the 33- to 36-kilodalton outer membrane protein associated with carbapenem resistance in Acinetobacter baumannii. Antimicrob Agents Chemother  2005; 49: 5172– 5. Google Scholar CrossRef Search ADS PubMed  18 Lee MJ, Jang SJ, Li XM et al.   Comparison of rpoB gene sequencing, 16S rRNA gene sequencing, gyrB multiplex PCR, and the VITEK2 system for identification of Acinetobacter clinical isolates. Diagn Microbiol Infect Dis  2014; 78: 29– 34. Google Scholar CrossRef Search ADS PubMed  19 Espinal P, Seifert H, Dijkshoorn L et al.   Rapid and accurate identification of genomic species from the Acinetobacter baumannii (Ab) group by MALDI-TOF MS. Clin Microbiol Infect  2012; 18: 1097– 103. Google Scholar CrossRef Search ADS PubMed  20 Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing: Twenty-Fourth Informational Supplement M100-S24 . CLSI, Wayne, PA, USA, 2014. 21 EUCAST. Clinical Breakpoints and Epidemiological Cut-Off Values. Version 4.0. 2014. http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_6.0_Breakpoint_table.pdf. 22 Akers KS, Chaney C, Barsoumian A et al.   Aminoglycoside resistance and susceptibility testing errors in Acinetobacter baumannii-calcoaceticus complex. J Clin Microbiol  2010; 48: 1132– 8. Google Scholar CrossRef Search ADS PubMed  23 Swenson JM, Killgore GE, Tenover FC. Antimicrobial susceptibility testing of Acinetobacter spp. by NCCLS broth microdilution and disk diffusion methods. J Clin Microbiol  2004; 42: 5102– 8. Google Scholar CrossRef Search ADS PubMed  24 Moodley VM, Oliver SP, Shankland I et al.   Evaluation of five susceptibility test methods for detection of tobramycin resistance in a cluster of epidemiologically related Acinetobacter baumannii isolates. J Clin Microbiol  2013; 51: 2535– 40. Google Scholar CrossRef Search ADS PubMed  25 Wang P, Bowler SL, Kantz SF et al.   Comparison of minocycline susceptibility testing methods for carbapenem-resistant Acinetobacter baumannii. J Clin Microbiol  2016; 54: 2937– 41. Google Scholar CrossRef Search ADS PubMed  26 Bronzwaer S, Buchholz U, Courvalin P et al.   Comparability of antimicrobial susceptibility test results from 22 European countries and Israel: an external quality assurance exercise of the European Antimicrobial Resistance Surveillance System (EARSS) in collaboration with the United Kingdom National External Quality Assurance Scheme (UK NEQAS). J Antimicrob Chemother  2002; 50: 953– 64. Google Scholar CrossRef Search ADS PubMed  27 Cantón R, Loza E, Del Carmen Conejo M et al.   Quality control for β-lactam susceptibility testing with a well-defined collection of Enterobacteriaceae and Pseudomonas aeruginosa strains in Spain. J Clin Microbiol  2003; 41: 1912– 8. Google Scholar CrossRef Search ADS PubMed  28 Chaitram JM, Jevitt LA, Lary S et al.   The World Health Organization’s external quality assurance system proficiency testing program has improved the accuracy of antimicrobial susceptibility testing and reporting among participating laboratories using NCCLS methods. J Clin Microbiol  2003; 41: 2372– 7. Google Scholar CrossRef Search ADS PubMed  29 Jones RN, Glick T, Sader HS et al.   Educational antimicrobial susceptibility testing as a critical component of microbiology laboratory proficiency programs: American Proficiency Institute results for 2007-2011. Diagn Microbiol Infect Dis  2013; 75: 357– 60. Google Scholar CrossRef Search ADS PubMed  © The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. 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Abstract

Abstract Objectives To evaluate the proficiency of Spanish microbiology laboratories with respect to the antimicrobial susceptibility testing (AST) of Acinetobacter spp. Methods Eight Acinetobacter spp. with different resistance mechanisms were sent to 48 Spanish centres which were asked to report: (i) the AST system used; (ii) MICs; (iii) breakpoints used (CLSI versus EUCAST); (iv) clinical category; and (v) resistance mechanisms inferred. Minor, major and very major errors (mE, ME and VME, respectively) were determined. Results The greatest percentages of discrepancies were: (i) by AST method: 18.5% Etest, 14.3% Vitek 2 and Sensititre; (ii) by breakpoints: 20.5% (CLSI) and 10.8% (EUCAST); and (iii) by antimicrobial agent: ampicillin/sulbactam (56.2% CLSI), minocycline (40.7% CLSI), tobramycin (38.7% CLSI, 16.8% EUCAST), imipenem (27.8% CLSI, 30.0% EUCAST) and meropenem (25.4% CLSI, 20.8% EUCAST). Categorical error rates: (i) by AST method ranged from 30.0% (Phoenix) to 100% (Sensititre and disc diffusion) for mE, 0.0% (Etest, Sensititre, disc diffusion) to 40% (Phoenix) for ME, and 0.0% (Sensititre and disc diffusion) to 30% (Phoenix) for VME; (ii) by breakpoints: mE (80.1% CLSI, 58.4% EUCAST), ME (3.5% CLSI, 12.4% EUCAST) and VME (16.4% CLSI, 29.2% EUCAST); and (iii) by antimicrobial agent: mE (100% levofloxacin/CLSI, 100% levofloxacin and meropenem/EUCAST), ME (35.3% colistin/CLSI, 25.0% colistin/EUCAST) and VME (64.7% colistin/CLSI, 86.7% gentamicin/EUCAST). Conclusions Clinical microbiology laboratories must improve their ability to determine antimicrobial susceptibilities of Acinetobacter spp. isolates. Higher discrepancies using CLSI when compared with EUCAST are mainly due to mE and to a much lesser extent to ME or VME. Introduction The genus Acinetobacter includes more than 30 genomic species. The most important one from a clinical and epidemiological point of view is Acinetobacter baumannii owing to its ability to acquire MDR and to survive and cause nosocomial outbreaks.1 Genomic species other than A. baumannii, such as Acinetobacter pittii, are increasingly being recognized as nosocomial pathogens.2 The prevalence of MDR Acinetobacter spp. has increased in recent years, limiting the treatment options for patients infected with these isolates to some broad-spectrum antimicrobials such as carbapenems. Nevertheless, resistance to carbapenems has also increased in recent years. Data from Europe reported by the European Antimicrobial Resistance Surveillance Network (EARS-Net) showed that in 2015 ≥50% of isolates were carbapenem resistant.3 One of the best options for the treatment of patients infected with carbapenem-resistant Acinetobacter spp. isolates is colistin, but resistance to this antimicrobial is also increasing.3 Antimicrobial susceptibility testing (AST) of Acinetobacter spp. may be difficult because there are several factors that can affect the antimicrobial susceptibility results. These factors have been related to the antimicrobial (i.e. the stability of carbapenems, the effect of cation concentrations on the activity of colistin and tigecycline, the type and the age of the medium), the microorganism (i.e. type of intrinsic resistance, type of acquired resistance mechanism, heterogeneity of resistance to colistin or carbapenems) or the methodology (i.e. broth microdilution versus agar disc diffusion).4–9 The interpretation criteria employed (i.e. CLSI versus EUCAST) are also factors that affect the interpretation of results.10 Inferring mechanisms of resistance to antimicrobials forms part of the interpretative reading of the antibiogram. In A. baumannii this is very difficult to do because: (i) resistance is frequently multifactorial; and (ii) the mechanisms of resistance to some antimicrobials are still poorly characterized (i.e. penicillin-binding proteins, heteroresistance). These limitations led us to perform the present study with the aim of identifying the most frequent problems for AST of Acinetobacter spp. in Spanish clinical microbiology laboratories. Materials and methods Bacterial isolates, genomic species identification and antimicrobial susceptibility testing Eight MDR isolates of Acinetobacter spp. with different mechanisms of antimicrobial resistance were selected for this study (Table 1). Seven isolates of A. baumannii were coded as CC-01 and CC-03 to CC-08, and one isolate of A. pitti was coded as CC-02. Table 1. MICs (mg/L) of antimicrobials for Acinetobacter spp. isolates Isolate  Antimicrobial resistance mechanism  MICa   Ref  AMK  GEN  TOB  TZP  SAM  CAZ  FEP  IPM  MEM  CIP  LVX  MIN  CST  CC-01  blaCTX-M-15  8  1  8  4  16  32  >256  0.5  1  32  4  4  0.5  11  CC-02  blaOXA-58  2  0.5  0.5  >512  16  16  4  32  16  32  8  0.125  0.125  12  CC-03  blaIMP  256  128  8  >512  16  >128  >256  >64  32  >64  16  2  0.5  13  CC-04  blaOXA-24  64  4  128  >512  32  >128  32  32  16  >64  16  32  0.5  14  CC-05  blaOXA-24b  256  >128  128  >512  16  32  16  16  16  64  16  0.5  0.5  15  CC-06  blaOXA-24 + pmrAB mutation  >256  >128  8  >512  128  >128  >256  >64  >64  >64  16  16  8  16  CC-07  blaOXA-24 + adeABC hyp  16  >128  4  >512  8  64  32  32  8  >64  32  16  0.5  14  CC-08  blaOXA-51 hyp + omp33-36 def  >256  >128  16  >512  32  128  128  16  >64  >64  32  16  0.125  17  Isolate  Antimicrobial resistance mechanism  MICa   Ref  AMK  GEN  TOB  TZP  SAM  CAZ  FEP  IPM  MEM  CIP  LVX  MIN  CST  CC-01  blaCTX-M-15  8  1  8  4  16  32  >256  0.5  1  32  4  4  0.5  11  CC-02  blaOXA-58  2  0.5  0.5  >512  16  16  4  32  16  32  8  0.125  0.125  12  CC-03  blaIMP  256  128  8  >512  16  >128  >256  >64  32  >64  16  2  0.5  13  CC-04  blaOXA-24  64  4  128  >512  32  >128  32  32  16  >64  16  32  0.5  14  CC-05  blaOXA-24b  256  >128  128  >512  16  32  16  16  16  64  16  0.5  0.5  15  CC-06  blaOXA-24 + pmrAB mutation  >256  >128  8  >512  128  >128  >256  >64  >64  >64  16  16  8  16  CC-07  blaOXA-24 + adeABC hyp  16  >128  4  >512  8  64  32  32  8  >64  32  16  0.5  14  CC-08  blaOXA-51 hyp + omp33-36 def  >256  >128  16  >512  32  128  128  16  >64  >64  32  16  0.125  17  AMK, amikacin; GEN, gentamicin; TOB, tobramycin; TZP, piperacillin/tazobactam; SAM, ampicillin/sulbactam; CAZ, ceftazidime; FEP, cefepime; IPM, imipenem; MEM, meropenem; CIP, ciprofloxacin; LVX, levofloxacin; MIN, minocycline; CST, colistin. a MICs shown in bold correspond to the resistant clinical category using CLSI breakpoints; underlined MICs correspond to the intermediate category; and MICs in normal type correspond to the susceptible category. b Carbapenem heteroresistant. Bacterial identification, antimicrobial susceptibility and confirmation of the mechanisms of resistance were verified independently at Spain’s two clinical microbiology reference laboratories: Hospital Universitario Virgen Macarena (Seville), and Complejo Hospitalario Universitario de A Coruña (A Coruña). Identification was performed by partial DNA sequencing of the rpoB gene and MALDI-TOF.18,19 The antimicrobials tested were piperacillin/tazobactam, ampicillin/sulbactam, cefepime, ceftazidime, imipenem, meropenem, ciprofloxacin, levofloxacin, tobramycin, gentamicin, amikacin, colistin and minocycline. The antimicrobials were tested in duplicate at each reference centre by disc diffusion and broth microdilution, according to CLSI guidelines.20 The 2014 CLSI and EUCAST breakpoints were used to interpret clinical categories.20,21 Study design Isolates were sent in Amies transport medium to 48 participating hospitals in May 2014. The instructions specified that isolates should be treated as blood culture isolates. Participating laboratories were asked to fill in an electronic form for each isolate, which included: (i) the laboratory system or method used for AST; (ii) the antimicrobial susceptibility results [inhibition zone diameters or MIC values, and clinical categories: susceptible (S), intermediate (I) and resistant (R)]; (iii) the breakpoints used (CLSI or EUCAST); and (iv) inferred mechanism(s) that might be responsible for the observed phenotype of resistance to carbapenems and colistin. Data analysis The analysis of results consisted of: (i) descriptive analysis of AST methods, breakpoints applied, clinical category assigned and discrepancies between centres deriving from these items; (ii) analysis of categorical error rates [minor errors (mEs), major errors (MEs) and very major (VMEs)]; and (iii) the ability of participating laboratories to accurately infer possible underlying resistance mechanisms. Results Type of AST system The laboratories used the following AST systems or methods: 59.4% MicroScan WalkAway (Dade MicroScan Inc., West Sacramento, CA, USA), 18.5% Vitek 2 (bioMérieux, Marcy-l’Étoile, France), 11.0% Wider I (Francisco Soria Melguizo, Madrid, Spain), 3.7% disc diffusion, 3.6% Etest, 3.5% Phoenix (BD Biosciences, Sparks, MD, USA) and 0.4% Sensititre (Trek Diagnostic systems, Westlake, OH, USA). Regarding the type of method used for AST, the discrepancy rates related to clinical interpretation were 18.5% (Etest), 14.3% (Vitek 2 and Sensititre), 10.7% (Wider I), 9.8% (MicroScan WA) and 3.1% (Phoenix). The discrepancies using disc diffusion were 4.1%. Table 2 shows the distribution of categorical error rates according to type of AST system. Table 2. Distribution of discrepancies and categorical error rates by AST system     Errors (%)   AST system  Discrepancies (%)a  mE  ME  VME  Etest  18.5b  75.0  0.0  25.0  Vitek 2  14.3  79.4  0.5  20.1  Sensititre  14.3  100  0.0  0.0  Wider I  10.7  71.3  5.9  22.8  MicroScan WA  9.8  79.0  6.9  14.1  Disk diffusion  4.1  100  0.0  0.0  Phoenix  3.1  30.0  40.0  30.0      Errors (%)   AST system  Discrepancies (%)a  mE  ME  VME  Etest  18.5b  75.0  0.0  25.0  Vitek 2  14.3  79.4  0.5  20.1  Sensititre  14.3  100  0.0  0.0  Wider I  10.7  71.3  5.9  22.8  MicroScan WA  9.8  79.0  6.9  14.1  Disk diffusion  4.1  100  0.0  0.0  Phoenix  3.1  30.0  40.0  30.0  mE, minor error; ME, major error; VME, very major error. a The percentages of discrepancies were determined as follows: number of discrepant results in the clinical category (susceptible, intermediate and resistant)/total number of results reported. b The highest percentages are shown in bold. Breakpoints applied and type of antimicrobial agent The participating laboratories registered 4693 (94.5%) results in the database. Some of the antimicrobials most frequently not tested were ampicillin/sulbactam (26.4%), minocycline (25.3%), levofloxacin (19.0%) and piperacillin/tazobactam (15.4%). Sixty-five percent of the 48 participating laboratories exclusively applied CLSI breakpoints, whereas 19% applied only EUCAST breakpoints and 16% applied CLSI or EUCAST breakpoints depending on the antimicrobial. For all antimicrobials tested the overall discrepancy rates in clinical category due exclusively to the differential use of breakpoints were 20.5% by applying CLSI breakpoints, and 10.8% by applying EUCAST breakpoints (Table 3). By using CLSI breakpoints high percentages of discrepancies were observed for ampicillin/sulbactam (56.2%), minocycline (40.7%), tobramycin (38.7%), imipenem (27.8%) and meropenem (25.4%). In contrast, using EUCAST breakpoints the greatest discrepancies were obtained for imipenem (30.0%), meropenem (20.8%) and tobramycin (16.8%). Table 3. Distribution of discrepancies and categorical error rates by antimicrobial agent and breakpoints used (CLSI versus EUCAST) Antimicrobial agent  Discrepancies (%)a   mE (%)   ME (%)   VME (%)   CLSI  EUCAST  CLSI  EUCAST  CLSI  EUCAST  CLSI  EUCAST  Piperacillin/tazobactam  2.6  0.0  85.7  0.0  0.0  0.0  14.3  0.0  Ampicillin/sulbactam  56.2b  0.0  89.3  0.0  0.0  0.0  10.7  0.0  Cefepime  23.1  0.0  94.2  0.0  4.3  0.0  1.4  0.0  Ceftazidime  19.5  0.0  58.6  0.0  6.9  0.0  34.5  0.0  Imipenem  27.8  30.0  61.4  94.9  0.0  0.0  38.6  5.1  Meropenem  25.4  20.8  86.9  100  0.0  0.0  13.1  0.0  Ciprofloxacin  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  Levofloxacin  4.4  7.9  100  100  0.0  0.0  0.0  0.0  Tobramycin  38.7  16.8  87.9  0.0  1.0  19.0  11.1  85.0  Gentamicin  9.1  11.5  60.9  13.3  34.8  13.3  4.3  86.7  Amikacin  8.1  14.5  63.2  77.8  10.5  11.1  26.3  11.1  Colistin  6.6  6.5  0.0  25.0  35.3  25.0  64.7  75.0  Minocycline  40.7  0.0  88.6  0.0  0.0  0.0  11.4  0.0  Overall  20.5  10.8  80.1  58.4  3.5  12.4  16.4  29.2  Antimicrobial agent  Discrepancies (%)a   mE (%)   ME (%)   VME (%)   CLSI  EUCAST  CLSI  EUCAST  CLSI  EUCAST  CLSI  EUCAST  Piperacillin/tazobactam  2.6  0.0  85.7  0.0  0.0  0.0  14.3  0.0  Ampicillin/sulbactam  56.2b  0.0  89.3  0.0  0.0  0.0  10.7  0.0  Cefepime  23.1  0.0  94.2  0.0  4.3  0.0  1.4  0.0  Ceftazidime  19.5  0.0  58.6  0.0  6.9  0.0  34.5  0.0  Imipenem  27.8  30.0  61.4  94.9  0.0  0.0  38.6  5.1  Meropenem  25.4  20.8  86.9  100  0.0  0.0  13.1  0.0  Ciprofloxacin  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  Levofloxacin  4.4  7.9  100  100  0.0  0.0  0.0  0.0  Tobramycin  38.7  16.8  87.9  0.0  1.0  19.0  11.1  85.0  Gentamicin  9.1  11.5  60.9  13.3  34.8  13.3  4.3  86.7  Amikacin  8.1  14.5  63.2  77.8  10.5  11.1  26.3  11.1  Colistin  6.6  6.5  0.0  25.0  35.3  25.0  64.7  75.0  Minocycline  40.7  0.0  88.6  0.0  0.0  0.0  11.4  0.0  Overall  20.5  10.8  80.1  58.4  3.5  12.4  16.4  29.2  a The percentages of discrepancies were determined as follows: number of discrepant results in the clinical category (susceptible, intermediate and resistant)/total number of results reported. b The highest percentages are shown in bold. There was a high proportion of mE (80.1% using CLSI breakpoints and 58.4% using EUCAST breakpoints), particularly for levofloxacin, cefepime, ampicillin/sulbactam, minocycline, tobramycin and meropenem using CLSI breakpoints, and for levofloxacin, meropenem and imipenem using EUCAST breakpoints (Table 3). The percentages of MEs (false resistance) were 3.5% using CLSI breakpoints, and 12.4% using EUCAST breakpoints. The highest ME rates were observed for colistin, gentamicin and amikacin using CLSI breakpoints, and for colistin, tobramycin, gentamicin and amikacin using EUCAST breakpoints. The percentages of VME (false susceptibility) were 16.4% using CLSI breakpoints and 29.2% using EUCAST breakpoints. The highest VME rates were observed for colistin, imipenem, ceftazidime and amikacin using CLSI breakpoints, and for gentamicin, tobramycin, colistin and amikacin using EUCAST breakpoints. Type of reference isolate Discrepancies in the MICs by type of reference isolate (Table 4) were >50% and ranged from 51.0% (CC-06) to 56.5% (CC-03), whereas categorical error rates ranged from 7.4% (CC-03) to 16.8% (CC-04) for mE, 0.0% (CC-03) to 4.4% (CC-04) for ME and 1.0% (CC-03) to 5.2% (CC-06) for VME. Table 4. Distribution of discrepancies and categorical error rates by tested strain Reference isolate (resistance mechanism)  Discrepancies (%)a  Errors (%)   mE  ME  VME  CC-01 (blaCTX-M-15)  53.1  14.9  1.0  1.3  CC-02 (blaOXA-58)  52.9  12.6  1.5  1.2  CC-03 (blaIMP)  56.5b  7.4  0.0  1.0  CC-04 (blaOXA-24)  52.3  16.8  4.4  2.0  CC-05 (blaOXA-24/HR)  56.4  14.9  1.5  4.9  CC-06 (blaOXA-24 and pmrAB mutation)  51.0  14.2  0.2  5.2  CC-07 (blaOXA-24 + adeABC hyp)  54.5  11.7  1.5  2.1  CC-08 (blaOXA-51 hyp + omp 33-36 def)  53.3  12.6  0.7  5.1  Reference isolate (resistance mechanism)  Discrepancies (%)a  Errors (%)   mE  ME  VME  CC-01 (blaCTX-M-15)  53.1  14.9  1.0  1.3  CC-02 (blaOXA-58)  52.9  12.6  1.5  1.2  CC-03 (blaIMP)  56.5b  7.4  0.0  1.0  CC-04 (blaOXA-24)  52.3  16.8  4.4  2.0  CC-05 (blaOXA-24/HR)  56.4  14.9  1.5  4.9  CC-06 (blaOXA-24 and pmrAB mutation)  51.0  14.2  0.2  5.2  CC-07 (blaOXA-24 + adeABC hyp)  54.5  11.7  1.5  2.1  CC-08 (blaOXA-51 hyp + omp 33-36 def)  53.3  12.6  0.7  5.1  a The percentages of discrepancies were determined as follows: number of discrepant results in the clinical category (susceptible, intermediate and resistant)/total number of results reported. b The highest percentages are shown in bold. The highest percentages of mE and ME were observed with the CC-04 isolate (blaOXA-24), whereas the highest percentages of VME were observed with the CC-06 (blaOXA-24 and resistant to colistin by pmrAB mutation) and CC-05 isolates (blaOXA-24 and heteroresistant to carbapenems). Ability of centres to infer resistance mechanisms The analysis of inferred mechanisms of resistance focused only on β-lactams, particularly carbapenems, and colistin owing to the heterogeneous information reported by the participating laboratories. Only one centre inferred ESBL production in the blaCTX-M-15-producing isolate (CC-01). With respect to the blaOXA-58-producing isolate (CC-02), 11 laboratories indicated carbapenemase production, but only 3 specified that it was a class D carbapenemase. Of the 19 centres that reported the blaIMP-producing isolate (CC-03) as a carbapenemase producer, only 4 specified production of a class B carbapenemase. Only 1 centre reported that the blaOXA-24-producing isolate (CC-04) was a carbapenemase producer. With respect to the blaOXA-24-producing isolate heteroresistant to carbapenems (CC-05), 9 centres inferred carbapenemase production but none reported the carbapenem-heteroresistant phenotype. For the blaOXA-24-producing isolate resistant to colistin (CC-06), 14 centres reported this isolate to be a colistin-resistant pmrAB mutant. Carbapenemase production in the blaOXA-24-producing isolate overexpressing adeABC (CC-07) was inferred by 14 participating centres, but overexpression of adeABC was not mentioned. Finally, for the blaOXA-51-hyperproducing isolate deficient in omp33-36 (CC-08), 16 centres reported carbapenemase production, but none inferred porin loss. Discussion Most laboratories use either automated and/or manual systems or methods with different performances for AST. Erroneous antimicrobial susceptibility results have been described in Acinetobacter spp. and have been associated with several factors. In our study, the discrepancies observed in antimicrobial susceptibility results provided by participating centres were related to the AST system used, the differential application of CLSI or EUCAST breakpoints, the antimicrobial and the type of isolate. The type of assay or method used for AST was a possible contributory factor to the discrepancies observed in this study. The Etest was the least reliable method for AST with 18.5% of discrepancies in the MICs. Among automated AST systems, the least reliable ones were Vitek 2 and Sensititre. All the AST methods, except Phoenix, showed too many mEs (≥75%). The highest percentages of ME and VME were obtained using the Phoenix system, suggesting that this AST system performs poorly. Nevertheless, these results should be interpreted with caution as only two centres used the Phoenix method for AST. VMEs were also elevated, particularly using Etest, but not when using Sensititre or disc diffusion with which no VMEs were detected. In the study by Kulah et al.,7 whose objective was to detect imipenem resistance in A. baumannii using three automated systems (BD Phoenix, MicroScan WalkAway, Vitek 2), it was observed that the highest error rates (25% VME and 44.6% mE) and the worst performance in susceptibility testing occurred with the MicroScan WA system. The study by Akers et al.22 showed that disc diffusion and Etest tended to be more accurate than the Vitek 2, Phoenix and MicroScan automated systems, with VMEs of 5.6% and 13.1% for tobramycin and amikacin, respectively, using Vitek 2. Discrepancies in the clinical category derived from the breakpoints or guidelines applied may have no effect on the clinical category or may result in a high categorical error rate. In the present study higher discrepancies using CLSI breakpoints when compared with those of EUCAST breakpoints are mainly due to mE and to a much lesser extent to ME or VME, suggesting that the breakpoint used is an important factor contributing to antimicrobial susceptibility discrepancies and errors in clinical category. With respect to the categorical error rates, the overall percentages of mE using CLSI breakpoints were higher than those obtained using EUCAST breakpoints. In contrast, the percentages of ME and VME were higher using EUCAST breakpoints. Categorical error rates (mE and VME) for ampicillin/sulbactam and minocycline were associated particularly with the use of CLSI breakpoints. For imipenem and meropenem, elevated mEs were observed independent of the breakpoints used (CLSI or EUCAST), whereas VMEs were much more frequent using CLSI breakpoints. For tobramycin the errors were associated with the use of CLSI breakpoints (mE) and EUCAST breakpoints (ME and VME). Type of antimicrobial was another factor contributing to the discrepancies and errors observed. The antimicrobials that showed the highest discrepancies in susceptibility associated with high categorical error rates were levofloxacin, ampicillin/sulbactam, imipenem, meropenem, tobramycin and minocycline, which coincides with some previous studies.7,21–25 Susceptibility to β-lactams may be difficult to test in the laboratory for several reasons. When the assay is performed by broth microdilution, subtle growth patterns (i.e. granular, small button or ‘starry’ growth) are frequently observed, making the results difficult to interpret and leading to elevated MEs.23 Using disc diffusion methods, problems of interpretation have also been observed related to the presence of colonies growing inside the inhibition halo. The stability of β-lactams is another factor that could lead to discrepancies in susceptibility. This is particularly relevant for carbapenems, which are very labile, and for which false resistance (VME) is frequently detected, particularly using some automated devices. The number of laboratories able to infer some type of mechanism of resistance was very low, with >50% reporting discrepancies in clinical categories, reflecting the difficulty of inferring resistance mechanisms in Acinetobacter, particularly with antimicrobials such as carbapenems. As mentioned before, this kind of inference is complicated by the presence of various resistance mechanisms (namely, hyperexpression of the blaOXA-51 carbapenemase and some of the acquired carbapenem-hydrolysing oxacillinases described in A. baumannii) and by the fact that mechanisms of resistance to some antimicrobials in Acinetobacter spp. have not been studied to the extent of Enterobacteriaceae or Pseudomonas aeruginosa. In our study, a high degree of disagreement was found when inferring resistance mechanisms, particularly for most MDR or carbapenem-resistant isolates with acquired mechanisms of carbapenem resistance, for which the percentage of mE and VME was unacceptably high. An analysis of factors that contribute to antimicrobial susceptibility results should be a priority for clinical laboratories. One way to address this problem is to participate in quality control programmes, which can be very helpful for detecting potential laboratory problems and enabling corrective measures to be established for optimizing the process and the quality of the reports offered to clinicians. This information is very useful for optimizing the best therapeutic strategies, improving the rational use of antimicrobials (reducing resistance rates) and facilitating the control of nosocomial infections (by reducing transmission of MDR clones) so as to avoid outbreaks.26–29 In conclusion, this study clearly shows that microbiology laboratories need to improve their ability to accurately determine the antimicrobial susceptibility of Acinetobacter spp. This is particularly relevant for ampicillin/sulbactam, carbapenems and tobramycin, and when both automated devices and CLSI breakpoints are used. The higher discrepancies using CLSI breakpoints are mainly related to mEs and in a much lesser extent to MEs or VMEs. Acknowledgements We are grateful to the 48 participating centres and the SEIMC Quality Control Program for their indispensable help in making this study possible. The complete list of the 48 participating hospitals is below. Hospital Universitario de Tarragona Joan XXIII (Tarragona, Tarragona; Angels Vilanova), Hospital General de Gran Canaria Dr Negrín (Las Palmas Gran Canaria, Las Palmas; Ana Bordes Benítez), Hospital Costa del Sol (Marbella, Málaga; Natalia Montiel Quezel-Guerraz), Hospital Universitario Miguel Servet (Zaragoza, Zaragoza; Ana Isabel López Calleja), Hospital de Cabueñes (Gijón, Asturias; Luis Otero Guerra), Hospital Doce de Octubre (Madrid, Madrid; Fernando Chaves Sánchez), Hospital Universitario Marqués de Valdecilla (Santander, Cantabria; Jorge Calvo Montes), Hospital Sierrallana (Torrelavega, Cantabria; Inés de Benito Población), Hospital Santa Barbara (Complejo Hospitalario de Soria, Soria; Angel Campos Bueno), Hospital Clínico Universitario de Valladolid (Valladolid, Valladolid; Raul Ortiz de Lejarazu Leonardo), Hospital Universitario Rio Hortega (Valladolid, Valladolid; Mónica de Frutos Serna), Complejo Asistencial de Avila (Avila, Avila; Antonio Gómez del Campo Dechado), Hospital General Universitario de Ciudad Real (Ciudad Real, Ciudad Real; Isabel Barbas Ferrera), Hospital General Universitario de Guadalajara (Guadalajara, Guadalajara; González Praetorius), Hospital Universitario de Bellvitge (L’Hospitalet de Llobregat, Barcelona; M. Angeles Domínguez Luzón), Fundación Jiménez Díaz (Madrid, Madrid; Ricardo Fernández Roblas), Complejo Hospitalario Universitario de Vigo (Vigo, Pontevedra; Maximiliano Alvarez Fernández), Complejo Hospitalario Universitario A Coruña (A Coruña, A Coruña; Begoña Fernández Pérez), Hospital Infantil Niño Jesús (Madrid, Madrid; M. Mercedes Alonso Sanz), Hospital de la Princesa (Madrid, Madrid; Laura Cardeñoso), Hospital General U. Gregorio Marañón (Madrid, Madrid; Carlos Sánchez), Hospital Clínico Universitario San Carlos (Madrid, Madrid; Juan J. Picazo de la Garza), Hospital Universitario Puerta de Hierro Majadahonda (Majadahonda, Madrid; Francisca Portero), Clínica Universidad de Navarra (Pamplona, Navarra; José Leiva León), Hospital Comarcal Marina Baixa (Villajoyosa, Valencia; Carmen Martínez Peinado), Hospital Universitario La Fe (Valencia, Valencia; José Luis López Hontangas), Hospital General Universitario de Elche (Elche, Alicante; Gloria Royo García), Hospital Universitario Puerta del Mar (Cádiz, Cádiz; Fátima Galán-Sánchez), Hospital Universitario Virgen de la Victoria (Málaga, Málaga; Encarnación Clavijo Frutos), Hospital Universitari Arnau de Vilanova (Lleida, Lleida; Mercedes García González), Hospital Lucus Augusti (Lugo, Lugo; Pilar Alonso García), Complejo Hospitalario de Pontevedra (Pontevedra, Pontevedra; María José Zamora López), Hospital Universitario La Paz (Madrid, Madrid; Julio García Rodríguez), Hospital Universitario Son Espases (Palma de Mallorca, Baleares; José L Pérez Sáenz), Hospital Ramón y Cajal (Madrid, Madrid; María Isabel Morosini), Hospital Universitario Insular de Gran Canaria (Las Palmas de Gran Canarias, Las Palmas; Antonio Manuel Martín Sánchez), Hospital de Jerez (Jerez de la Frontera, Cádiz; M. Dolores López Prieto), Hospital de la Ribera (Alzira, Valencia; Javier Colomina Rodríguez), Hospital Universitario Fundación Alcorcón (Alcorcón, Madrid; Alberto Delgado-Iribarren), Hospital San Pedro de Alcántara (Cáceres, Cáceres; Jesús Viñuelas Bayón), Hospital Universitario Vall d’Hebron (Barcelona, Barcelona; Rosa Juve Saumell), Hospital Universitario Virgen del Rocío (Sevilla, Sevilla; Javier Aznar Martín), Hospital General Universitario de Albacete (Albacete, Albacete; Eva Riquelme Bravo), Hospital Universitario de Getafe (Getafe, Madrid; David Molina Arana), Consorcio Hospital General Universitario de Valencia (Valencia, Valencia; Nuria Tormo), Hospital Universitario Severo Ochoa (Leganés, Madrid; Pilar Reyes Pecharromán), Hospital Virgen de las Nieves (Granada, Granada; Consuelo Miranda Casas), Hospital Universitario Virgen de la Arrixaca (El Palmar, Murcia; Genoveva Yagüe). Funding This work was supported by the Ministerio de Sanidad y Consumo, Instituto de Salud Carlos III (projects PI11-02046, PI10/00056 and PI12/00552) and the Consejería de Innovación Ciencia y Empresa, Junta de Andalucía (P11-CTS-7730), Spain, by the Plan Nacional de I + D+i 2008–2011 and the Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Economía y Competitividad, the Spanish Network for Research in Infectious Diseases (REIPI RD12/0015)—co-financed by European Development Regional Fund ‘A way to achieve Europe’ ERDF, and the Programa integral de prevención, control de las infecciones relacionadas con la asistencia sanitaria, y uso apropiado de los antimicrobianos (PIRASOA; Junta de Andalucía, Consejería de Salud, Junta de Andalucía). Transparency declarations None to declare. References 1 Peleg AY, Seifert H, Paterson DL. Acinetobacter baumannii: emergence of a successful pathogen. 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Journal of Antimicrobial ChemotherapyOxford University Press

Published: Mar 1, 2018

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