Rapid disc diffusion antibiotic susceptibility testing for Pseudomonas aeruginosa, Acinetobacter baumannii and Enterococcus spp.

Rapid disc diffusion antibiotic susceptibility testing for Pseudomonas aeruginosa, Acinetobacter... Abstract Background We investigated the feasibility of rapid disc diffusion antibiotic susceptibility testing (rAST) with reading of inhibition zones after 6 and/or 8 h of incubation for Enterococcus faecalis, Enterococcus faecium, Pseudomonas aeruginosa and Acinetobacter baumannii. In addition, we evaluated discrimination of resistant populations from the WT populations at early timepoints and the requirement for clinical breakpoint adaptations for proper interpretation of rAST data. Methods In total, 815 clinical strains [E. faecalis (n = 135), E. faecium (n = 227), P. aeruginosa (n = 295) and A. baumannii (n = 158)] were included in this study. Disc diffusion plates were streaked, incubated and imaged using the WASPLabTM automation system. WT populations and non-WT populations were defined using epidemiological cut-offs. Results and conclusions rAST at 6 and 8 h was possible for A. baumannii and enterococci with readability of inhibition zones >90%. Overall categorical agreement of rAST at 6 h with AST at 18 h was 97.2%, 97.4% and 95.3% for E. faecalis, E. faecium and A. baumannii, respectively. With few exceptions, major categorization error rates were <1% for A. baumannii, and vancomycin-resistant E. faecium were clearly separated from the WT at 6 h. For P. aeruginosa the average readability of inhibition zones was 68.9% at 8 h and we found an overall categorical agreement of 94.8%. Adaptations of clinical breakpoints and/or introduction of technical buffer zones, preferably based on aggregated population data from various epidemiological settings, are required for proper interpretation of rAST. Introduction Owing to increasing antibiotic resistance, drug susceptibility patterns of bacterial pathogens become more and more unpredictable and empirical first-line therapy often turns out to be inadequate.1,2 Rapid disc diffusion antibiotic susceptibility testing (rAST) would be beneficial for adequate patient care and proper installation of targeted antimicrobial therapy, significantly improving clinical outcome and reducing mortality.3–6 Automated antimicrobial susceptibility test devices such as VITEK-II or Phoenix™ in principle are able to deliver test results within 8–16 h, but for some species/drug combinations significant error rates have been demonstrated, e.g. for Pseudomonas aeruginosa and β-lactams such as cefepime, ceftazidime, carbapenems and piperacillin/tazobactam.7,8 Another weakness of automated microdilution is a poor sensitivity for important resistance phenotypes such as VRE or carbapenem-resistant Acinetobacter baumannii.9,10 Molecular detection of resistance determinants delivers rapid results but is hampered by the high number of genes to be covered for adequate sensitivity and by the lack of validated phenotype/genotype knowledge databases, particularly for P. aeruginosa and enterococci.11,12 Maintenance of an accurate coverage is a complex task taking into account the geographical differences in the prevalence of resistance-mediating genes. In addition, the mere presence of a gene encoding for antibiotic resistance does not necessarily correlate with its phenotypic expression.11 Disc diffusion is an established, accurate and standardized procedure, which can be adapted to a diagnostic laboratory’s needs. EUCAST and CLSI recommend an incubation time of 16–18 h for most species/drug combinations.13,14 In a proof-of-principle study, we recently demonstrated that automated rAST is feasible.15 We here evaluated whether early reading of disc diffusion is possible for clinical pathogens with a high diversity of intrinsic resistance mechanisms, namely enterococci, P. aeruginosa and A. baumannii. The aims of this study were to examine the feasibility of rAST to discriminate important resistance phenotypes from WT populations and to identify species/drug combinations that need adaptation of clinical breakpoints (CBPs). Methods Clinical isolates Study isolates were selected covering a range of inhibition zone diameters from 6 to 40 mm for each species/drug combination tested (Figure S1, available as Supplementary data at JAC Online). In particular, isolates close to the CBPs were included. All non-duplicate clinical strains included in this study were isolated over a 3 year period from 2013 until 2016 in the clinical microbiology laboratory of the Institute of Medical Microbiology, University of Zurich. Isolates of the same species were considered duplicate(s) if they (i) originated from the same patient, and (ii) showed one major and two minor differences in AST interpretation at maximum. The following numbers of clinical isolates were tested: Enterococcus faecalis (n = 135), Enterococcus faecium (n = 227), P. aeruginosa (n = 295) and A. baumannii (n = 158). Quality control strains To control for methodological precision and for calibration to EUCAST CBPs, E. faecalis ATCC 29212 and P. aeruginosa ATCC 27853 EUCAST quality control (QC) strains were tested daily from individual fresh subcultures and individually prepared 0.5 McFarland standards. Interpretation was done according to EUCAST QC tables version 6.1.16 QC ranges and targets were fulfilled during this study (data not shown). Definition of phenotypes Resistance phenotypes and the WT populations were defined using EUCAST epidemiological cut-offs (ECOFFs; Table S1).17 Prior to conducting this study drug susceptibility was assigned on the basis of independent disc diameter measurements applying the EUCAST recommended method on Mueller–Hinton II agar (Beckon-Dickinson, Franklin Lakes, NJ, USA) using antibiotic discs from i2a (Montpellier, France) and automated recording using the SirSCAN™/SirWeb™ system (i2a). We screened for vancomycin resistance in enterococci according to the EUCAST guidelines using a 24 h incubation period.18 Briefly, isolates of E. faecalis and E. faecium with vancomycin inhibition zone diameters ≥12 mm and sharp zone edges were considered vancomycin susceptible, i.e. WT. Isolates with vancomycin inhibition zones <12 mm, isolates with fuzzy zone edges (regardless of the inhibition zone diameter) or isolates with colonies within the inhibition zone were subjected to PCR assays targeting vanA and vanB to confirm the vancomycin-resistant phenotype.19 For clinical isolates of E. faecalis with an ampicillin non-WT phenotype, identification was reconfirmed from the AST plate. Automated susceptibility testing Susceptibility testing was performed as described previously according to EUCAST guidelines version 6.0, which are essentially the same as that of CLSI 2016 for the organisms of this study.13–15 In brief, bacterial suspensions were manually adjusted to a turbidity equivalent to that of a 0.5 McFarland standard and processed within 15 min. Mueller–Hinton II agar plates (Oxoid Limited, Basingstoke, UK) were processed in the fully automated WASPTM (Copan Italia, S.p.A., Brescia, Italy), i.e. plates were each inoculated with 60 μL of the bacterial suspension and streaked automatically. Antibiotic discs of a single production lot (Oxoid Limited) were placed using a standard distributor, which was handled by a WASPTM AST robot immediately after plate streaking. Subsequently, plates were automatically transported to and incubated in a WASPLabTM incubator (Copan) at 36 ± 2 °C in ambient air. Images were taken after 6, 8, 12 and 18 h of incubation under continuous temperature conditions. Diameter measurements were automatically done by the WASPLabTM reading software (Copan) and were, if necessary, adjusted on-screen by an experienced technician. Statistical analyses All statistical analyses were performed using R, version 3.2.3.20 The R package pROC, version 1.8, was used to calculate areas under the receiver operating characteristic curve and associated confidence intervals.21 To quantify the separability of the WT and non-WT populations, the maximal accuracy (i.e. the maximal fraction of true predictions) achievable by a cut-off was calculated based on the prevalences in our dataset. Results Readability Readability was defined as the percentage of data points for which a diameter measurement could reliably be determined for a given species/drug combination. Enterococci For E. faecalis and E. faecium the average readability was >90% at 6 h, >95% at 8 h and >99% at 12 h (Table 1). Table 1. Readability and categorical agreement of early zone reading after 6, 8 and 12 h as compared with standard incubation at 18–24 h   Zone diameter measurements and related classification parameters (all values in %)   6 versus 18 h   8 versus 18 h   12 versus 18 h   readability  categorical agreement  vME  ME  mE  readability  categorical agreement  vME  ME  mE  readability  categorical agreement  vME  ME  mE  E. faecalis, n = 135   ampicillin  92.6  99.2  0.0  0.0  0.8  97.8  98.5  0.0  0.0  1.5  99.3  98.5  0.0  0.0  1.5   gentamicin  93.3  91.3  8.7  0.0  0.0  97.8  93.2  6.8  0.0  0.0  99.3  100.0  0.0  0.0  0.0   norfloxacin  94.1  98.4  1.6  0.0  0.0  98.5  98.5  1.5  0.0  0.0  100.0  100.0  0.0  0.0  0.0   vancomycin  93.3  100.0  0.0  0.0  0.0  97.8  100.0  0.0  0.0  0.0  99.3  100.0  0.0  0.0  0.0   averageb  93.3  97.2  2.6  0.0  0.2  98.0  97.6  2.1  0.0  0.4  99.5  99.6  0.0  0.0  0.5  E. faecium, n = 227   ampicillin  100.0  99.1  0.0  0.0  0.9  100.0  99.1  0.0  0.0  0.9  100.0  99.1  0.0  0.0  0.9   gentamicin  83.3  93.1  6.9  0.0  0.0  98.7  85.7  14.3  0.0  0.0  100.0  98.2  1.8  0.0  0.0   norfloxacin  93.4  100.0  0.0  0.0  0.0  99.6  99.1  0.9  0.0  0.0  100.0  100.0  0.0  0.0  0.0   vancomycin  89.4  97.5  2.5  0.0  0.0  98.2  97.8  2.2  0.0  0.0  100.0  99.1  0.9  0.0  0.0   average  91.5  97.4  2.4  0.0  0.2  99.1  95.4  4.3  0.0  0.2  100.0  99.1  0.7  0.0  0.2  P. aeruginosa, n = 295a   piperacillin/tazobactam            68.5  95.5  2.5  2.0  0.0  92.9  96.7  3.3  0.0  0.0   cefepime            68.5  95.5  2.5  2.0  0.0  93.6  97.5  2.5  0.0  0.0   ceftazidime            68.8  95.1  2.5  2.5  0.0  93.2  95.6  3.6  0.7  0.0   imipenem            67.5  93.5  1.5  0.0  5.0  93.2  97.1  0.7  0.0  2.2   meropenem            69.2  89.2  0.0  0.0  10.8  92.9  95.6  0.4  0.0  4.0   gentamicin            69.8  99.0  0.5  0.5  0.0  93.6  99.3  0.7  0.0  0.0   tobramycin            69.8  99.0  1.0  0.0  0.0  93.6  98.9  1.1  0.0  0.0   amikacin            67.8  96.5  0.0  0.0  3.5  93.6  98.2  0.0  0.0  1.8   ciprofloxacin            69.8  90.3  0.0  9.7  0.0  93.6  96.7  0.0  3.3  0.0   average            68.9  94.8  1.2  1.9  2.1  93.4  97.3  1.4  0.4  0.9  A. baumannii, n = 158   imipenem  99.4  98.7  0.6  0.0  0.6  100.0  99.4  0.0  0.0  0.6  100.0  99.4  0.0  0.0  0.6   meropenem  99.4  91.7  0.6  0.0  7.6  100.0  100.0  0.0  0.0  0.0  100.0  99.4  0.0  0.0  0.6   gentamicin  99.4  99.4  0.0  0.6  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   tobramycin  99.4  96.8  0.0  3.2  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   amikacin  98.7  87.8  0.0  0.6  11.5  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   ciprofloxacin  98.7  98.1  0.0  1.9  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   levofloxacin  99.4  94.9  0.0  0.6  4.5  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   average  99.2  95.3  0.2  1.0  3.5  100.0  99.9  0.0  0.0  0.1  100.0  99.8  0.0  0.0  0.2    Zone diameter measurements and related classification parameters (all values in %)   6 versus 18 h   8 versus 18 h   12 versus 18 h   readability  categorical agreement  vME  ME  mE  readability  categorical agreement  vME  ME  mE  readability  categorical agreement  vME  ME  mE  E. faecalis, n = 135   ampicillin  92.6  99.2  0.0  0.0  0.8  97.8  98.5  0.0  0.0  1.5  99.3  98.5  0.0  0.0  1.5   gentamicin  93.3  91.3  8.7  0.0  0.0  97.8  93.2  6.8  0.0  0.0  99.3  100.0  0.0  0.0  0.0   norfloxacin  94.1  98.4  1.6  0.0  0.0  98.5  98.5  1.5  0.0  0.0  100.0  100.0  0.0  0.0  0.0   vancomycin  93.3  100.0  0.0  0.0  0.0  97.8  100.0  0.0  0.0  0.0  99.3  100.0  0.0  0.0  0.0   averageb  93.3  97.2  2.6  0.0  0.2  98.0  97.6  2.1  0.0  0.4  99.5  99.6  0.0  0.0  0.5  E. faecium, n = 227   ampicillin  100.0  99.1  0.0  0.0  0.9  100.0  99.1  0.0  0.0  0.9  100.0  99.1  0.0  0.0  0.9   gentamicin  83.3  93.1  6.9  0.0  0.0  98.7  85.7  14.3  0.0  0.0  100.0  98.2  1.8  0.0  0.0   norfloxacin  93.4  100.0  0.0  0.0  0.0  99.6  99.1  0.9  0.0  0.0  100.0  100.0  0.0  0.0  0.0   vancomycin  89.4  97.5  2.5  0.0  0.0  98.2  97.8  2.2  0.0  0.0  100.0  99.1  0.9  0.0  0.0   average  91.5  97.4  2.4  0.0  0.2  99.1  95.4  4.3  0.0  0.2  100.0  99.1  0.7  0.0  0.2  P. aeruginosa, n = 295a   piperacillin/tazobactam            68.5  95.5  2.5  2.0  0.0  92.9  96.7  3.3  0.0  0.0   cefepime            68.5  95.5  2.5  2.0  0.0  93.6  97.5  2.5  0.0  0.0   ceftazidime            68.8  95.1  2.5  2.5  0.0  93.2  95.6  3.6  0.7  0.0   imipenem            67.5  93.5  1.5  0.0  5.0  93.2  97.1  0.7  0.0  2.2   meropenem            69.2  89.2  0.0  0.0  10.8  92.9  95.6  0.4  0.0  4.0   gentamicin            69.8  99.0  0.5  0.5  0.0  93.6  99.3  0.7  0.0  0.0   tobramycin            69.8  99.0  1.0  0.0  0.0  93.6  98.9  1.1  0.0  0.0   amikacin            67.8  96.5  0.0  0.0  3.5  93.6  98.2  0.0  0.0  1.8   ciprofloxacin            69.8  90.3  0.0  9.7  0.0  93.6  96.7  0.0  3.3  0.0   average            68.9  94.8  1.2  1.9  2.1  93.4  97.3  1.4  0.4  0.9  A. baumannii, n = 158   imipenem  99.4  98.7  0.6  0.0  0.6  100.0  99.4  0.0  0.0  0.6  100.0  99.4  0.0  0.0  0.6   meropenem  99.4  91.7  0.6  0.0  7.6  100.0  100.0  0.0  0.0  0.0  100.0  99.4  0.0  0.0  0.6   gentamicin  99.4  99.4  0.0  0.6  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   tobramycin  99.4  96.8  0.0  3.2  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   amikacin  98.7  87.8  0.0  0.6  11.5  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   ciprofloxacin  98.7  98.1  0.0  1.9  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   levofloxacin  99.4  94.9  0.0  0.6  4.5  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   average  99.2  95.3  0.2  1.0  3.5  100.0  99.9  0.0  0.0  0.1  100.0  99.8  0.0  0.0  0.2  mE, minor error. Readability was defined as the percentage of clinical isolates for which a zone diameter after a given incubation time could be determined; vME and ME rates >1% and mE with values >5% are shown in bold. a Categorical agreement at 6 h and error rates were not calculated due to low readability (average readability 9.2%). b Average values are shown in italics. Non-fermenters Average readability for A. baumannii was 99.2% at 6 h and 100% at 8 and 12 h. For P. aeruginosa inhibition zone diameters were not readable reliably at 6 h, but were readable for 68.9% of all isolates at 8 h and for 93.4% of all isolates at 12 h (Table 1). Categorical agreement and interpretation errors Compared with 12 h, categorical agreement at 6–8 h with interpretation at 18 h varied for different species/drug combinations. Enterococci Average agreement for E. faecalis and E. faecium was 97.2% and 97.4% at 6 h and 97.6% and 95.4% at 8 h (Table 1). For E. faecalis categorical agreement at 6 h ranged from 91.3% for gentamicin to 100% for vancomycin, and for E. faecium categorical agreement at 6 h ranged from 93.1% for gentamicin to 100% for norfloxacin (Table 1). The highest interpretation error rates in both enterococcal species were observed for gentamicin [very major error (vME) rates of up to 8.7%]. Non-fermenters For P. aeruginosa categorical agreement was 94.8% at 8 h and 97.3% at 12 h (Table 1). Average categorical agreement for A. baumannii was 95.3% at 6 h and 99.9% at 8 h. For P. aeruginosa categorical agreement at 8 h ranged from 89.2% for meropenem to 99% for tobramycin and gentamicin, and for A. baumannii categorical agreement at 6 h ranged from 87.8% for amikacin to 99.4% for gentamicin (Table 1). Except for meropenem, categorical agreement for P. aeruginosa at 8 h was >90% for β-lactams (piperacillin/tazobactam, ceftazidime, cefepime and imipenem), ciprofloxacin and the aminoglycosides (amikacin, gentamicin and tobramycin). vME and ME rates for A. baumannii were low (<1%) at 6 h for the majority of drugs and no ME and vME were observed for A. baumannii at 8 h (Table 1). Discrimination of non-WT and WT populations Enterococci WT and non-WT populations were well separated at 6 and 8 h for all examined species/drug combinations including vancomycin-resistant E. faecium (n = 62; Table 2 and Figure S1). Table 2. Maximal accuracy (in %) for the separation of non-WT and WT populations; cut-offs achieving maximal accuracy are given in parentheses and prevalences were taken from our dataset Organism  Antibiotic  Maximal accuracya (in %) for the separation of non-WT and WT populations (cut-off) (mm)   6 h  8 h  12 h  18 h  E. faecalis  ampicillin  99.2 (8)  99.2 (11)  100 (11)  100 (10)  E. faecalis  gentamicin  96.8 (13)  97.7 (11)  98.5 (8)  99.3 (9)  E. faecalis  norfloxacin  100 (14)  100 (14)  100 (13)  100 (12)  E. faecium  ampicillin  100 (11)  100 (12)  100 (12)  100 (11)  E. faecium  gentamicin  98.9 (11)  99.1 (11)  99.6 (9)  100 (8)  E. faecium  norfloxacin  100 (15)  100 (15)  100 (14)  100 (13)  E. faecium  vancomycin  98 (13)  98.2 (13)  99.1 (11)  100 (11)  P. aeruginosa  piperacillin/tazobactam  NA  96 (16)  96.7 (18)  100 (18)  P. aeruginosa  cefepime  NA  95.5 (19)  98.2 (20)  100 (19)  P. aeruginosa  ceftazidime  NA  95.6 (16)  95.6 (17)  100 (17)  P. aeruginosa  imipenem  NA  86.9 (20)  93.8 (25)  100 (25)  P. aeruginosa  meropenem  NA  95.1 (21)  96.7 (24)  100 (24)  P. aeruginosa  gentamicin  NA  99.5 (14)  99.3 (15)  100 (15)  P. aeruginosa  tobramycin  NA  100 (17)  98.9 (16)  100 (16)  P. aeruginosa  amikacin  NA  98 (15)  99.3 (18)  100 (18)  P. aeruginosa  ciprofloxacin  NA  97.6 (22)  95.3 (25)  100 (25)  A. baumannii  imipenem  98.7 (20)  100 (24)  100 (24)  100 (24)  A. baumannii  meropenem  98.7 (15)  100 (21)  100 (22)  100 (22)  A. baumannii  gentamicin  99.4 (16)  100 (16)  100 (16)  100 (16)  A. baumannii  tobramycin  99.4 (14)  100 (15)  100 (15)  100 (14)  A. baumannii  amikacin  99.4 (17)  100 (18)  100 (18)  100 (18)  A. baumannii  ciprofloxacin  100 (17)  100 (18)  100 (18)  100 (18)  A. baumannii  levofloxacin  99.4 (19)  100 (20)  100 (20)  100 (19)  Organism  Antibiotic  Maximal accuracya (in %) for the separation of non-WT and WT populations (cut-off) (mm)   6 h  8 h  12 h  18 h  E. faecalis  ampicillin  99.2 (8)  99.2 (11)  100 (11)  100 (10)  E. faecalis  gentamicin  96.8 (13)  97.7 (11)  98.5 (8)  99.3 (9)  E. faecalis  norfloxacin  100 (14)  100 (14)  100 (13)  100 (12)  E. faecium  ampicillin  100 (11)  100 (12)  100 (12)  100 (11)  E. faecium  gentamicin  98.9 (11)  99.1 (11)  99.6 (9)  100 (8)  E. faecium  norfloxacin  100 (15)  100 (15)  100 (14)  100 (13)  E. faecium  vancomycin  98 (13)  98.2 (13)  99.1 (11)  100 (11)  P. aeruginosa  piperacillin/tazobactam  NA  96 (16)  96.7 (18)  100 (18)  P. aeruginosa  cefepime  NA  95.5 (19)  98.2 (20)  100 (19)  P. aeruginosa  ceftazidime  NA  95.6 (16)  95.6 (17)  100 (17)  P. aeruginosa  imipenem  NA  86.9 (20)  93.8 (25)  100 (25)  P. aeruginosa  meropenem  NA  95.1 (21)  96.7 (24)  100 (24)  P. aeruginosa  gentamicin  NA  99.5 (14)  99.3 (15)  100 (15)  P. aeruginosa  tobramycin  NA  100 (17)  98.9 (16)  100 (16)  P. aeruginosa  amikacin  NA  98 (15)  99.3 (18)  100 (18)  P. aeruginosa  ciprofloxacin  NA  97.6 (22)  95.3 (25)  100 (25)  A. baumannii  imipenem  98.7 (20)  100 (24)  100 (24)  100 (24)  A. baumannii  meropenem  98.7 (15)  100 (21)  100 (22)  100 (22)  A. baumannii  gentamicin  99.4 (16)  100 (16)  100 (16)  100 (16)  A. baumannii  tobramycin  99.4 (14)  100 (15)  100 (15)  100 (14)  A. baumannii  amikacin  99.4 (17)  100 (18)  100 (18)  100 (18)  A. baumannii  ciprofloxacin  100 (17)  100 (18)  100 (18)  100 (18)  A. baumannii  levofloxacin  99.4 (19)  100 (20)  100 (20)  100 (19)  NA, not applicable. a Maximal accuracy as a measure for separation of two populations requires cut-offs. Using a definitory approach, cut-offs for the different incubation period/species/antibiotic combinations were set and the maximum accuracy of the separation was calculated (in %). Non-fermenters A. baumannii and P. aeruginosa WT and non-WT populations were well separated at early timepoints (>95%) for most species/drug combinations, except for P. aeruginosa and the β-lactams, in particular imipenem (Table 2 and Figure S1). Change of zone diameters and putative ECOFFs during the incubation period In general, the change of zone diameter was only minor during the incubation period. For 12 of 24 species/drug combinations the ECOFF increased over time (Figure 1). For 11 species/drug combinations, the inhibition zone diameters of the WT population did not change during the incubation period, and for one combination diameters decreased over time (E. faecium and gentamicin). Figure 1. View largeDownload slide Graphs depict the fifth percentile of the WT population. This value was used as surrogate for the ECOFF as it indicates the lower end of the WT population. Increasing values (lines with filled circles) or decreasing values (lines with asterisks) depict absolute diameter changes of > 2 mm between 6 and 18 h. Stable values (absolute diameter changes of ≤ 2 mm between 6 and 18 h) are displayed as lines with diamonds. Figure 1. View largeDownload slide Graphs depict the fifth percentile of the WT population. This value was used as surrogate for the ECOFF as it indicates the lower end of the WT population. Increasing values (lines with filled circles) or decreasing values (lines with asterisks) depict absolute diameter changes of > 2 mm between 6 and 18 h. Stable values (absolute diameter changes of ≤ 2 mm between 6 and 18 h) are displayed as lines with diamonds. Discussion We recently demonstrated in a proof-of-principle study the feasibility of rAST.15E. faecalis, E. faecium, P. aeruginosa and A. baumannii represent a significant part of microorganisms isolated from critically ill patients. We here evaluated whether early reading of disc diffusion can be applied to these pathogens. As reading times 6, 8 and 12 h were chosen in comparison with standard 18 h evaluation. Readability at early timepoints differed for the species studied: on average, zone diameters of A. baumannii and enterococci were readable for >99% and >90% of isolates after 6 h, respectively. P. aeruginosa showed an average readability of 68.9% at 8 h and 93.4% at 12 h (Table 1). Extended incubation times related to insufficient growth of P. aeruginosa have also been reported in automated antimicrobial susceptibility test devices.22 For A. baumannii, WT and non-WT populations were well separated at 6 and 8 h (Table 2 and Figure S1). When applying the 18 h EUCAST CBPs, ME rates (false-resistant results) of >1% were only seen for ciprofloxacin and tobramycin at 6 h and average vME rates (false-susceptible results) were 0.2% at 6 h and 0% at 8 h. Therefore, early reading and categorization of A. baumannii zone diameters is well feasible at 6 and 8 h applying current 18 h EUCAST CBPs. For P. aeruginosa, we in general observed a good separation of WT and non-WT populations at early 8 h reading (Table 2 and Figure S1). For piperacillin/tazobactam, ceftazidime and imipenem we observed less well separation of WT and non-WT populations, resulting in categorization error rates ranging from 0.5% to 9.7% when applying current 18 h EUCAST CBPs (Table 1). An upcoming task will be the development of species-specific CBPs for early reading timepoints. In addition, rAST of P. aeruginosa may need zones of methodological uncertainty that will cover the diameter overlap of WT and non-WT populations and that will serve as buffer zones to prevent categorization errors. Most clinical strains of P. aeruginosa display zone diameters that are outside the zone of overlap and can therefore be assigned to the susceptible/WT or the resistant/non-WT population. Ruling out resistance will be possible for a significant number of strains and facilitate proper decision-making for initial antibiotic therapy. For enterococci, non-WT and WT populations were well separated at early reading times (Table 2 and Figure S1). When current 18 h EUCAST CBPS were applied vME rates of 8.7% and 1.6% for E. faecalis and gentamicin and norfloxacin at 6 h were observed. For E. faecium, we found vME rates of 6.9% and 2.5% for gentamicin and vancomycin at 6 h, respectively (Table 1). Incubation period-adapted CBPs or zones of methodological uncertainty will improve categorization into WT and non-WT phenotypes. This is of particular relevance for VRE, as 3 of 65 VRE clinical isolates would have been missed when using the 24 h-based CBP. The current EUCAST 24 h vancomycin ≥S/<R CBP is 12 mm. If, e.g. vancomycin diameters of 12–13 mm were considered inconclusive at 6 or 8 h, the rate of vME in E. faecium would drop from 2.5% to 0%. In conclusion, this study demonstrates that rAST, with reading at 6 h, is possible for A. baumannii, E. faecalis and E. faecium. Only few CBP changes and/or buffer zones will be needed to avoid ME and vME. In addition, rAST with reading after 8 h is possible for P. aeruginosa, but it will require buffer zones, covering the overlap of WT and non-WT populations, to prevent erroneous categorizations, in particular for the β-lactams. As a further limitation, this single-centre study cannot issue generally applicable CBPs for rAST, as aggregated population data from various epidemiological settings are recommended for proper CBP determination. We conclude that rAST at 6 or 8 h is feasible for important drug classes for A. baumannii, E. faecalis and E. faecium, and with some limitations for P. aeruginosa. Acknowledgements We are grateful to the laboratory technicians of the Institute of Medical Microbiology, University of Zurich for their dedicated help and to Martina Marchesi and Chantal Quiblier for support in strain testing and data management. We would like to thank Laura Navarria and the Copan team for technical support and discussions and Pietro Quadrelli for data export and management. Funding This work was supported by the University of Zurich. Transparency declarations The Institute of Medical Microbiology, University of Zurich and Copan Italia, S.p.A. cooperate in the development of automated AST. M. H. and P. M. K. received a travel grant from Copan Italia. E. C. B. is a consultant of Copan Italia. All other authors: none to declare. Supplementary data Figure S1 and Table S1 are available as Supplementary data at JAC Online. References 1 Merli M , Lucidi C, Di Gregorio V et al.   The spread of multi drug resistant infections is leading to an increase in the empirical antibiotic treatment failure in cirrhosis: a prospective survey. PLoS One  2015; 10: e0127448. Google Scholar CrossRef Search ADS PubMed  2 Zilberberg MD , Nathanson BH, Sulham K et al.   Multidrug resistance, inappropriate empiric therapy, and hospital mortality in Acinetobacter baumannii pneumonia and sepsis. Crit Care  2016; 20: 221. Google Scholar CrossRef Search ADS PubMed  3 Buehler SS , Madison B, Snyder SR et al.   Effectiveness of practices to increase timeliness of providing targeted therapy for inpatients with bloodstream infections: a laboratory medicine best practices systematic review and meta-analysis. Clin Microbiol Rev  2016; 29: 59– 103. Google Scholar CrossRef Search ADS PubMed  4 Ferrer R , Martin-Loeches I, Phillips G et al.   Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. Crit Care Med  2014; 42: 1749– 55. Google Scholar CrossRef Search ADS PubMed  5 Kumar A , Roberts D, Wood KE et al.   Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med  2006; 34: 1589– 96. Google Scholar CrossRef Search ADS PubMed  6 Verroken A , Defourny L, le Polain de Waroux O et al.   Clinical impact of MALDI-TOF MS identification and rapid susceptibility testing on adequate antimicrobial treatment in sepsis with positive blood cultures. PLoS One  2016; 11: e0156299. Google Scholar CrossRef Search ADS PubMed  7 Hsieh WS , Sung LL, Tsai KC et al.   Evaluation of the VITEK 2 cards for identification and antimicrobial susceptibility testing of non-glucose-fermenting Gram-negative bacilli. APMIS  2009; 117: 241– 7. Google Scholar CrossRef Search ADS PubMed  8 Mazzariol A , Aldegheri M, Ligozzi M et al.   Performance of Vitek 2 in antimicrobial susceptibility testing of Pseudomonas aeruginosa isolates with different mechanisms of β-lactam resistance. J Clin Microbiol  2008; 46: 2095– 8. Google Scholar CrossRef Search ADS PubMed  9 Hegstad K , Giske CG, Haldorsen B et al.   Performance of the EUCAST disk diffusion method, the CLSI agar screen method, and the Vitek 2 automated antimicrobial susceptibility testing system for detection of clinical isolates of enterococci with low- and medium-level VanB-type vancomycin resistance: a multicenter study. J Clin Microbiol  2014; 52: 1582– 9. Google Scholar CrossRef Search ADS PubMed  10 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  11 Ellington MJ , Ekelund O, Aarestrup FM et al.   The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: report from the EUCAST Subcommittee. Clin Microbiol Infect  2017; 23: 2– 22. Google Scholar CrossRef Search ADS PubMed  12 Piddock LJ. Assess drug-resistance phenotypes, not just genotypes. Nat Microbiol  2016; 1: 16120. Google Scholar CrossRef Search ADS PubMed  13 Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing: Twenty-Sixth Informational Supplement M100-S26 . CLSI, Wayne, PA, USA, 2016. 14 EUCAST. Breakpoint Tables for Interpretation of MICs and Zone Diameters, 2016. http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_6.0_Breakpoint_table.pdf. 15 Hombach M , Jetter M, Blöchliger N et al.   Fully automated disc diffusion for rapid antibiotic susceptibility test results: a proof-of-principle study. J Antimicrob Chemother  2017; 72: 1659– 68. Google Scholar CrossRef Search ADS PubMed  16 EUCAST. Routine and Extended Internal Quality Control for MIC Determination and Disk Diffusion as Recommended by EUCAST, Version 6.1, 2016. http://www.eucast.org/ast_of_bacteria/qc_tables/. 17 EUCAST. Antimicrobial Wild Type Distributions of Microorganisms, 2016. http://www.eucast.org/zone_diameter_distributions/. 18 EUCAST. EUCAST Guidelines for Detection of Resistance Mechanisms and Specific Resistances of Clinical and/or Epidemiological Importance, Version 1, 2013. http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Resistance_mechanisms/EUCAST_detection_of_resistance_mechanisms_v1.0_20131211.pdf. 19 Werner G , Coque TM, Hammerum AM et al.   Emergence and spread of vancomycin resistance among enterococci in Europe. Euro Surveill  2008; 13: pii=19046. 20 R Core Team. R: A Language and Environment for Statistical Computing . Vienna, Austria: R Foundation for Statistical Computing, 2015. https://www.r-project.org/. 21 Robin X , Turck N, Hainard A et al.   pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics  2011; 12: 77. Google Scholar CrossRef Search ADS PubMed  22 Bobenchik AM , Deak E, Hindler JA et al.   Performance of Vitek 2 for antimicrobial susceptibility testing of Acinetobacter baumannii, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia with Vitek 2 (2009 FDA) and CLSI M100S 26th edition breakpoints. J Clin Microbiol  2017; 55: 450– 6. Google Scholar CrossRef Search ADS PubMed  © The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Antimicrobial Chemotherapy Oxford University Press

Rapid disc diffusion antibiotic susceptibility testing for Pseudomonas aeruginosa, Acinetobacter baumannii and Enterococcus spp.

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Oxford University Press
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© The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.
ISSN
0305-7453
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1460-2091
D.O.I.
10.1093/jac/dkx404
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Abstract

Abstract Background We investigated the feasibility of rapid disc diffusion antibiotic susceptibility testing (rAST) with reading of inhibition zones after 6 and/or 8 h of incubation for Enterococcus faecalis, Enterococcus faecium, Pseudomonas aeruginosa and Acinetobacter baumannii. In addition, we evaluated discrimination of resistant populations from the WT populations at early timepoints and the requirement for clinical breakpoint adaptations for proper interpretation of rAST data. Methods In total, 815 clinical strains [E. faecalis (n = 135), E. faecium (n = 227), P. aeruginosa (n = 295) and A. baumannii (n = 158)] were included in this study. Disc diffusion plates were streaked, incubated and imaged using the WASPLabTM automation system. WT populations and non-WT populations were defined using epidemiological cut-offs. Results and conclusions rAST at 6 and 8 h was possible for A. baumannii and enterococci with readability of inhibition zones >90%. Overall categorical agreement of rAST at 6 h with AST at 18 h was 97.2%, 97.4% and 95.3% for E. faecalis, E. faecium and A. baumannii, respectively. With few exceptions, major categorization error rates were <1% for A. baumannii, and vancomycin-resistant E. faecium were clearly separated from the WT at 6 h. For P. aeruginosa the average readability of inhibition zones was 68.9% at 8 h and we found an overall categorical agreement of 94.8%. Adaptations of clinical breakpoints and/or introduction of technical buffer zones, preferably based on aggregated population data from various epidemiological settings, are required for proper interpretation of rAST. Introduction Owing to increasing antibiotic resistance, drug susceptibility patterns of bacterial pathogens become more and more unpredictable and empirical first-line therapy often turns out to be inadequate.1,2 Rapid disc diffusion antibiotic susceptibility testing (rAST) would be beneficial for adequate patient care and proper installation of targeted antimicrobial therapy, significantly improving clinical outcome and reducing mortality.3–6 Automated antimicrobial susceptibility test devices such as VITEK-II or Phoenix™ in principle are able to deliver test results within 8–16 h, but for some species/drug combinations significant error rates have been demonstrated, e.g. for Pseudomonas aeruginosa and β-lactams such as cefepime, ceftazidime, carbapenems and piperacillin/tazobactam.7,8 Another weakness of automated microdilution is a poor sensitivity for important resistance phenotypes such as VRE or carbapenem-resistant Acinetobacter baumannii.9,10 Molecular detection of resistance determinants delivers rapid results but is hampered by the high number of genes to be covered for adequate sensitivity and by the lack of validated phenotype/genotype knowledge databases, particularly for P. aeruginosa and enterococci.11,12 Maintenance of an accurate coverage is a complex task taking into account the geographical differences in the prevalence of resistance-mediating genes. In addition, the mere presence of a gene encoding for antibiotic resistance does not necessarily correlate with its phenotypic expression.11 Disc diffusion is an established, accurate and standardized procedure, which can be adapted to a diagnostic laboratory’s needs. EUCAST and CLSI recommend an incubation time of 16–18 h for most species/drug combinations.13,14 In a proof-of-principle study, we recently demonstrated that automated rAST is feasible.15 We here evaluated whether early reading of disc diffusion is possible for clinical pathogens with a high diversity of intrinsic resistance mechanisms, namely enterococci, P. aeruginosa and A. baumannii. The aims of this study were to examine the feasibility of rAST to discriminate important resistance phenotypes from WT populations and to identify species/drug combinations that need adaptation of clinical breakpoints (CBPs). Methods Clinical isolates Study isolates were selected covering a range of inhibition zone diameters from 6 to 40 mm for each species/drug combination tested (Figure S1, available as Supplementary data at JAC Online). In particular, isolates close to the CBPs were included. All non-duplicate clinical strains included in this study were isolated over a 3 year period from 2013 until 2016 in the clinical microbiology laboratory of the Institute of Medical Microbiology, University of Zurich. Isolates of the same species were considered duplicate(s) if they (i) originated from the same patient, and (ii) showed one major and two minor differences in AST interpretation at maximum. The following numbers of clinical isolates were tested: Enterococcus faecalis (n = 135), Enterococcus faecium (n = 227), P. aeruginosa (n = 295) and A. baumannii (n = 158). Quality control strains To control for methodological precision and for calibration to EUCAST CBPs, E. faecalis ATCC 29212 and P. aeruginosa ATCC 27853 EUCAST quality control (QC) strains were tested daily from individual fresh subcultures and individually prepared 0.5 McFarland standards. Interpretation was done according to EUCAST QC tables version 6.1.16 QC ranges and targets were fulfilled during this study (data not shown). Definition of phenotypes Resistance phenotypes and the WT populations were defined using EUCAST epidemiological cut-offs (ECOFFs; Table S1).17 Prior to conducting this study drug susceptibility was assigned on the basis of independent disc diameter measurements applying the EUCAST recommended method on Mueller–Hinton II agar (Beckon-Dickinson, Franklin Lakes, NJ, USA) using antibiotic discs from i2a (Montpellier, France) and automated recording using the SirSCAN™/SirWeb™ system (i2a). We screened for vancomycin resistance in enterococci according to the EUCAST guidelines using a 24 h incubation period.18 Briefly, isolates of E. faecalis and E. faecium with vancomycin inhibition zone diameters ≥12 mm and sharp zone edges were considered vancomycin susceptible, i.e. WT. Isolates with vancomycin inhibition zones <12 mm, isolates with fuzzy zone edges (regardless of the inhibition zone diameter) or isolates with colonies within the inhibition zone were subjected to PCR assays targeting vanA and vanB to confirm the vancomycin-resistant phenotype.19 For clinical isolates of E. faecalis with an ampicillin non-WT phenotype, identification was reconfirmed from the AST plate. Automated susceptibility testing Susceptibility testing was performed as described previously according to EUCAST guidelines version 6.0, which are essentially the same as that of CLSI 2016 for the organisms of this study.13–15 In brief, bacterial suspensions were manually adjusted to a turbidity equivalent to that of a 0.5 McFarland standard and processed within 15 min. Mueller–Hinton II agar plates (Oxoid Limited, Basingstoke, UK) were processed in the fully automated WASPTM (Copan Italia, S.p.A., Brescia, Italy), i.e. plates were each inoculated with 60 μL of the bacterial suspension and streaked automatically. Antibiotic discs of a single production lot (Oxoid Limited) were placed using a standard distributor, which was handled by a WASPTM AST robot immediately after plate streaking. Subsequently, plates were automatically transported to and incubated in a WASPLabTM incubator (Copan) at 36 ± 2 °C in ambient air. Images were taken after 6, 8, 12 and 18 h of incubation under continuous temperature conditions. Diameter measurements were automatically done by the WASPLabTM reading software (Copan) and were, if necessary, adjusted on-screen by an experienced technician. Statistical analyses All statistical analyses were performed using R, version 3.2.3.20 The R package pROC, version 1.8, was used to calculate areas under the receiver operating characteristic curve and associated confidence intervals.21 To quantify the separability of the WT and non-WT populations, the maximal accuracy (i.e. the maximal fraction of true predictions) achievable by a cut-off was calculated based on the prevalences in our dataset. Results Readability Readability was defined as the percentage of data points for which a diameter measurement could reliably be determined for a given species/drug combination. Enterococci For E. faecalis and E. faecium the average readability was >90% at 6 h, >95% at 8 h and >99% at 12 h (Table 1). Table 1. Readability and categorical agreement of early zone reading after 6, 8 and 12 h as compared with standard incubation at 18–24 h   Zone diameter measurements and related classification parameters (all values in %)   6 versus 18 h   8 versus 18 h   12 versus 18 h   readability  categorical agreement  vME  ME  mE  readability  categorical agreement  vME  ME  mE  readability  categorical agreement  vME  ME  mE  E. faecalis, n = 135   ampicillin  92.6  99.2  0.0  0.0  0.8  97.8  98.5  0.0  0.0  1.5  99.3  98.5  0.0  0.0  1.5   gentamicin  93.3  91.3  8.7  0.0  0.0  97.8  93.2  6.8  0.0  0.0  99.3  100.0  0.0  0.0  0.0   norfloxacin  94.1  98.4  1.6  0.0  0.0  98.5  98.5  1.5  0.0  0.0  100.0  100.0  0.0  0.0  0.0   vancomycin  93.3  100.0  0.0  0.0  0.0  97.8  100.0  0.0  0.0  0.0  99.3  100.0  0.0  0.0  0.0   averageb  93.3  97.2  2.6  0.0  0.2  98.0  97.6  2.1  0.0  0.4  99.5  99.6  0.0  0.0  0.5  E. faecium, n = 227   ampicillin  100.0  99.1  0.0  0.0  0.9  100.0  99.1  0.0  0.0  0.9  100.0  99.1  0.0  0.0  0.9   gentamicin  83.3  93.1  6.9  0.0  0.0  98.7  85.7  14.3  0.0  0.0  100.0  98.2  1.8  0.0  0.0   norfloxacin  93.4  100.0  0.0  0.0  0.0  99.6  99.1  0.9  0.0  0.0  100.0  100.0  0.0  0.0  0.0   vancomycin  89.4  97.5  2.5  0.0  0.0  98.2  97.8  2.2  0.0  0.0  100.0  99.1  0.9  0.0  0.0   average  91.5  97.4  2.4  0.0  0.2  99.1  95.4  4.3  0.0  0.2  100.0  99.1  0.7  0.0  0.2  P. aeruginosa, n = 295a   piperacillin/tazobactam            68.5  95.5  2.5  2.0  0.0  92.9  96.7  3.3  0.0  0.0   cefepime            68.5  95.5  2.5  2.0  0.0  93.6  97.5  2.5  0.0  0.0   ceftazidime            68.8  95.1  2.5  2.5  0.0  93.2  95.6  3.6  0.7  0.0   imipenem            67.5  93.5  1.5  0.0  5.0  93.2  97.1  0.7  0.0  2.2   meropenem            69.2  89.2  0.0  0.0  10.8  92.9  95.6  0.4  0.0  4.0   gentamicin            69.8  99.0  0.5  0.5  0.0  93.6  99.3  0.7  0.0  0.0   tobramycin            69.8  99.0  1.0  0.0  0.0  93.6  98.9  1.1  0.0  0.0   amikacin            67.8  96.5  0.0  0.0  3.5  93.6  98.2  0.0  0.0  1.8   ciprofloxacin            69.8  90.3  0.0  9.7  0.0  93.6  96.7  0.0  3.3  0.0   average            68.9  94.8  1.2  1.9  2.1  93.4  97.3  1.4  0.4  0.9  A. baumannii, n = 158   imipenem  99.4  98.7  0.6  0.0  0.6  100.0  99.4  0.0  0.0  0.6  100.0  99.4  0.0  0.0  0.6   meropenem  99.4  91.7  0.6  0.0  7.6  100.0  100.0  0.0  0.0  0.0  100.0  99.4  0.0  0.0  0.6   gentamicin  99.4  99.4  0.0  0.6  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   tobramycin  99.4  96.8  0.0  3.2  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   amikacin  98.7  87.8  0.0  0.6  11.5  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   ciprofloxacin  98.7  98.1  0.0  1.9  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   levofloxacin  99.4  94.9  0.0  0.6  4.5  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   average  99.2  95.3  0.2  1.0  3.5  100.0  99.9  0.0  0.0  0.1  100.0  99.8  0.0  0.0  0.2    Zone diameter measurements and related classification parameters (all values in %)   6 versus 18 h   8 versus 18 h   12 versus 18 h   readability  categorical agreement  vME  ME  mE  readability  categorical agreement  vME  ME  mE  readability  categorical agreement  vME  ME  mE  E. faecalis, n = 135   ampicillin  92.6  99.2  0.0  0.0  0.8  97.8  98.5  0.0  0.0  1.5  99.3  98.5  0.0  0.0  1.5   gentamicin  93.3  91.3  8.7  0.0  0.0  97.8  93.2  6.8  0.0  0.0  99.3  100.0  0.0  0.0  0.0   norfloxacin  94.1  98.4  1.6  0.0  0.0  98.5  98.5  1.5  0.0  0.0  100.0  100.0  0.0  0.0  0.0   vancomycin  93.3  100.0  0.0  0.0  0.0  97.8  100.0  0.0  0.0  0.0  99.3  100.0  0.0  0.0  0.0   averageb  93.3  97.2  2.6  0.0  0.2  98.0  97.6  2.1  0.0  0.4  99.5  99.6  0.0  0.0  0.5  E. faecium, n = 227   ampicillin  100.0  99.1  0.0  0.0  0.9  100.0  99.1  0.0  0.0  0.9  100.0  99.1  0.0  0.0  0.9   gentamicin  83.3  93.1  6.9  0.0  0.0  98.7  85.7  14.3  0.0  0.0  100.0  98.2  1.8  0.0  0.0   norfloxacin  93.4  100.0  0.0  0.0  0.0  99.6  99.1  0.9  0.0  0.0  100.0  100.0  0.0  0.0  0.0   vancomycin  89.4  97.5  2.5  0.0  0.0  98.2  97.8  2.2  0.0  0.0  100.0  99.1  0.9  0.0  0.0   average  91.5  97.4  2.4  0.0  0.2  99.1  95.4  4.3  0.0  0.2  100.0  99.1  0.7  0.0  0.2  P. aeruginosa, n = 295a   piperacillin/tazobactam            68.5  95.5  2.5  2.0  0.0  92.9  96.7  3.3  0.0  0.0   cefepime            68.5  95.5  2.5  2.0  0.0  93.6  97.5  2.5  0.0  0.0   ceftazidime            68.8  95.1  2.5  2.5  0.0  93.2  95.6  3.6  0.7  0.0   imipenem            67.5  93.5  1.5  0.0  5.0  93.2  97.1  0.7  0.0  2.2   meropenem            69.2  89.2  0.0  0.0  10.8  92.9  95.6  0.4  0.0  4.0   gentamicin            69.8  99.0  0.5  0.5  0.0  93.6  99.3  0.7  0.0  0.0   tobramycin            69.8  99.0  1.0  0.0  0.0  93.6  98.9  1.1  0.0  0.0   amikacin            67.8  96.5  0.0  0.0  3.5  93.6  98.2  0.0  0.0  1.8   ciprofloxacin            69.8  90.3  0.0  9.7  0.0  93.6  96.7  0.0  3.3  0.0   average            68.9  94.8  1.2  1.9  2.1  93.4  97.3  1.4  0.4  0.9  A. baumannii, n = 158   imipenem  99.4  98.7  0.6  0.0  0.6  100.0  99.4  0.0  0.0  0.6  100.0  99.4  0.0  0.0  0.6   meropenem  99.4  91.7  0.6  0.0  7.6  100.0  100.0  0.0  0.0  0.0  100.0  99.4  0.0  0.0  0.6   gentamicin  99.4  99.4  0.0  0.6  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   tobramycin  99.4  96.8  0.0  3.2  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   amikacin  98.7  87.8  0.0  0.6  11.5  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   ciprofloxacin  98.7  98.1  0.0  1.9  0.0  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   levofloxacin  99.4  94.9  0.0  0.6  4.5  100.0  100.0  0.0  0.0  0.0  100.0  100.0  0.0  0.0  0.0   average  99.2  95.3  0.2  1.0  3.5  100.0  99.9  0.0  0.0  0.1  100.0  99.8  0.0  0.0  0.2  mE, minor error. Readability was defined as the percentage of clinical isolates for which a zone diameter after a given incubation time could be determined; vME and ME rates >1% and mE with values >5% are shown in bold. a Categorical agreement at 6 h and error rates were not calculated due to low readability (average readability 9.2%). b Average values are shown in italics. Non-fermenters Average readability for A. baumannii was 99.2% at 6 h and 100% at 8 and 12 h. For P. aeruginosa inhibition zone diameters were not readable reliably at 6 h, but were readable for 68.9% of all isolates at 8 h and for 93.4% of all isolates at 12 h (Table 1). Categorical agreement and interpretation errors Compared with 12 h, categorical agreement at 6–8 h with interpretation at 18 h varied for different species/drug combinations. Enterococci Average agreement for E. faecalis and E. faecium was 97.2% and 97.4% at 6 h and 97.6% and 95.4% at 8 h (Table 1). For E. faecalis categorical agreement at 6 h ranged from 91.3% for gentamicin to 100% for vancomycin, and for E. faecium categorical agreement at 6 h ranged from 93.1% for gentamicin to 100% for norfloxacin (Table 1). The highest interpretation error rates in both enterococcal species were observed for gentamicin [very major error (vME) rates of up to 8.7%]. Non-fermenters For P. aeruginosa categorical agreement was 94.8% at 8 h and 97.3% at 12 h (Table 1). Average categorical agreement for A. baumannii was 95.3% at 6 h and 99.9% at 8 h. For P. aeruginosa categorical agreement at 8 h ranged from 89.2% for meropenem to 99% for tobramycin and gentamicin, and for A. baumannii categorical agreement at 6 h ranged from 87.8% for amikacin to 99.4% for gentamicin (Table 1). Except for meropenem, categorical agreement for P. aeruginosa at 8 h was >90% for β-lactams (piperacillin/tazobactam, ceftazidime, cefepime and imipenem), ciprofloxacin and the aminoglycosides (amikacin, gentamicin and tobramycin). vME and ME rates for A. baumannii were low (<1%) at 6 h for the majority of drugs and no ME and vME were observed for A. baumannii at 8 h (Table 1). Discrimination of non-WT and WT populations Enterococci WT and non-WT populations were well separated at 6 and 8 h for all examined species/drug combinations including vancomycin-resistant E. faecium (n = 62; Table 2 and Figure S1). Table 2. Maximal accuracy (in %) for the separation of non-WT and WT populations; cut-offs achieving maximal accuracy are given in parentheses and prevalences were taken from our dataset Organism  Antibiotic  Maximal accuracya (in %) for the separation of non-WT and WT populations (cut-off) (mm)   6 h  8 h  12 h  18 h  E. faecalis  ampicillin  99.2 (8)  99.2 (11)  100 (11)  100 (10)  E. faecalis  gentamicin  96.8 (13)  97.7 (11)  98.5 (8)  99.3 (9)  E. faecalis  norfloxacin  100 (14)  100 (14)  100 (13)  100 (12)  E. faecium  ampicillin  100 (11)  100 (12)  100 (12)  100 (11)  E. faecium  gentamicin  98.9 (11)  99.1 (11)  99.6 (9)  100 (8)  E. faecium  norfloxacin  100 (15)  100 (15)  100 (14)  100 (13)  E. faecium  vancomycin  98 (13)  98.2 (13)  99.1 (11)  100 (11)  P. aeruginosa  piperacillin/tazobactam  NA  96 (16)  96.7 (18)  100 (18)  P. aeruginosa  cefepime  NA  95.5 (19)  98.2 (20)  100 (19)  P. aeruginosa  ceftazidime  NA  95.6 (16)  95.6 (17)  100 (17)  P. aeruginosa  imipenem  NA  86.9 (20)  93.8 (25)  100 (25)  P. aeruginosa  meropenem  NA  95.1 (21)  96.7 (24)  100 (24)  P. aeruginosa  gentamicin  NA  99.5 (14)  99.3 (15)  100 (15)  P. aeruginosa  tobramycin  NA  100 (17)  98.9 (16)  100 (16)  P. aeruginosa  amikacin  NA  98 (15)  99.3 (18)  100 (18)  P. aeruginosa  ciprofloxacin  NA  97.6 (22)  95.3 (25)  100 (25)  A. baumannii  imipenem  98.7 (20)  100 (24)  100 (24)  100 (24)  A. baumannii  meropenem  98.7 (15)  100 (21)  100 (22)  100 (22)  A. baumannii  gentamicin  99.4 (16)  100 (16)  100 (16)  100 (16)  A. baumannii  tobramycin  99.4 (14)  100 (15)  100 (15)  100 (14)  A. baumannii  amikacin  99.4 (17)  100 (18)  100 (18)  100 (18)  A. baumannii  ciprofloxacin  100 (17)  100 (18)  100 (18)  100 (18)  A. baumannii  levofloxacin  99.4 (19)  100 (20)  100 (20)  100 (19)  Organism  Antibiotic  Maximal accuracya (in %) for the separation of non-WT and WT populations (cut-off) (mm)   6 h  8 h  12 h  18 h  E. faecalis  ampicillin  99.2 (8)  99.2 (11)  100 (11)  100 (10)  E. faecalis  gentamicin  96.8 (13)  97.7 (11)  98.5 (8)  99.3 (9)  E. faecalis  norfloxacin  100 (14)  100 (14)  100 (13)  100 (12)  E. faecium  ampicillin  100 (11)  100 (12)  100 (12)  100 (11)  E. faecium  gentamicin  98.9 (11)  99.1 (11)  99.6 (9)  100 (8)  E. faecium  norfloxacin  100 (15)  100 (15)  100 (14)  100 (13)  E. faecium  vancomycin  98 (13)  98.2 (13)  99.1 (11)  100 (11)  P. aeruginosa  piperacillin/tazobactam  NA  96 (16)  96.7 (18)  100 (18)  P. aeruginosa  cefepime  NA  95.5 (19)  98.2 (20)  100 (19)  P. aeruginosa  ceftazidime  NA  95.6 (16)  95.6 (17)  100 (17)  P. aeruginosa  imipenem  NA  86.9 (20)  93.8 (25)  100 (25)  P. aeruginosa  meropenem  NA  95.1 (21)  96.7 (24)  100 (24)  P. aeruginosa  gentamicin  NA  99.5 (14)  99.3 (15)  100 (15)  P. aeruginosa  tobramycin  NA  100 (17)  98.9 (16)  100 (16)  P. aeruginosa  amikacin  NA  98 (15)  99.3 (18)  100 (18)  P. aeruginosa  ciprofloxacin  NA  97.6 (22)  95.3 (25)  100 (25)  A. baumannii  imipenem  98.7 (20)  100 (24)  100 (24)  100 (24)  A. baumannii  meropenem  98.7 (15)  100 (21)  100 (22)  100 (22)  A. baumannii  gentamicin  99.4 (16)  100 (16)  100 (16)  100 (16)  A. baumannii  tobramycin  99.4 (14)  100 (15)  100 (15)  100 (14)  A. baumannii  amikacin  99.4 (17)  100 (18)  100 (18)  100 (18)  A. baumannii  ciprofloxacin  100 (17)  100 (18)  100 (18)  100 (18)  A. baumannii  levofloxacin  99.4 (19)  100 (20)  100 (20)  100 (19)  NA, not applicable. a Maximal accuracy as a measure for separation of two populations requires cut-offs. Using a definitory approach, cut-offs for the different incubation period/species/antibiotic combinations were set and the maximum accuracy of the separation was calculated (in %). Non-fermenters A. baumannii and P. aeruginosa WT and non-WT populations were well separated at early timepoints (>95%) for most species/drug combinations, except for P. aeruginosa and the β-lactams, in particular imipenem (Table 2 and Figure S1). Change of zone diameters and putative ECOFFs during the incubation period In general, the change of zone diameter was only minor during the incubation period. For 12 of 24 species/drug combinations the ECOFF increased over time (Figure 1). For 11 species/drug combinations, the inhibition zone diameters of the WT population did not change during the incubation period, and for one combination diameters decreased over time (E. faecium and gentamicin). Figure 1. View largeDownload slide Graphs depict the fifth percentile of the WT population. This value was used as surrogate for the ECOFF as it indicates the lower end of the WT population. Increasing values (lines with filled circles) or decreasing values (lines with asterisks) depict absolute diameter changes of > 2 mm between 6 and 18 h. Stable values (absolute diameter changes of ≤ 2 mm between 6 and 18 h) are displayed as lines with diamonds. Figure 1. View largeDownload slide Graphs depict the fifth percentile of the WT population. This value was used as surrogate for the ECOFF as it indicates the lower end of the WT population. Increasing values (lines with filled circles) or decreasing values (lines with asterisks) depict absolute diameter changes of > 2 mm between 6 and 18 h. Stable values (absolute diameter changes of ≤ 2 mm between 6 and 18 h) are displayed as lines with diamonds. Discussion We recently demonstrated in a proof-of-principle study the feasibility of rAST.15E. faecalis, E. faecium, P. aeruginosa and A. baumannii represent a significant part of microorganisms isolated from critically ill patients. We here evaluated whether early reading of disc diffusion can be applied to these pathogens. As reading times 6, 8 and 12 h were chosen in comparison with standard 18 h evaluation. Readability at early timepoints differed for the species studied: on average, zone diameters of A. baumannii and enterococci were readable for >99% and >90% of isolates after 6 h, respectively. P. aeruginosa showed an average readability of 68.9% at 8 h and 93.4% at 12 h (Table 1). Extended incubation times related to insufficient growth of P. aeruginosa have also been reported in automated antimicrobial susceptibility test devices.22 For A. baumannii, WT and non-WT populations were well separated at 6 and 8 h (Table 2 and Figure S1). When applying the 18 h EUCAST CBPs, ME rates (false-resistant results) of >1% were only seen for ciprofloxacin and tobramycin at 6 h and average vME rates (false-susceptible results) were 0.2% at 6 h and 0% at 8 h. Therefore, early reading and categorization of A. baumannii zone diameters is well feasible at 6 and 8 h applying current 18 h EUCAST CBPs. For P. aeruginosa, we in general observed a good separation of WT and non-WT populations at early 8 h reading (Table 2 and Figure S1). For piperacillin/tazobactam, ceftazidime and imipenem we observed less well separation of WT and non-WT populations, resulting in categorization error rates ranging from 0.5% to 9.7% when applying current 18 h EUCAST CBPs (Table 1). An upcoming task will be the development of species-specific CBPs for early reading timepoints. In addition, rAST of P. aeruginosa may need zones of methodological uncertainty that will cover the diameter overlap of WT and non-WT populations and that will serve as buffer zones to prevent categorization errors. Most clinical strains of P. aeruginosa display zone diameters that are outside the zone of overlap and can therefore be assigned to the susceptible/WT or the resistant/non-WT population. Ruling out resistance will be possible for a significant number of strains and facilitate proper decision-making for initial antibiotic therapy. For enterococci, non-WT and WT populations were well separated at early reading times (Table 2 and Figure S1). When current 18 h EUCAST CBPS were applied vME rates of 8.7% and 1.6% for E. faecalis and gentamicin and norfloxacin at 6 h were observed. For E. faecium, we found vME rates of 6.9% and 2.5% for gentamicin and vancomycin at 6 h, respectively (Table 1). Incubation period-adapted CBPs or zones of methodological uncertainty will improve categorization into WT and non-WT phenotypes. This is of particular relevance for VRE, as 3 of 65 VRE clinical isolates would have been missed when using the 24 h-based CBP. The current EUCAST 24 h vancomycin ≥S/<R CBP is 12 mm. If, e.g. vancomycin diameters of 12–13 mm were considered inconclusive at 6 or 8 h, the rate of vME in E. faecium would drop from 2.5% to 0%. In conclusion, this study demonstrates that rAST, with reading at 6 h, is possible for A. baumannii, E. faecalis and E. faecium. Only few CBP changes and/or buffer zones will be needed to avoid ME and vME. In addition, rAST with reading after 8 h is possible for P. aeruginosa, but it will require buffer zones, covering the overlap of WT and non-WT populations, to prevent erroneous categorizations, in particular for the β-lactams. As a further limitation, this single-centre study cannot issue generally applicable CBPs for rAST, as aggregated population data from various epidemiological settings are recommended for proper CBP determination. We conclude that rAST at 6 or 8 h is feasible for important drug classes for A. baumannii, E. faecalis and E. faecium, and with some limitations for P. aeruginosa. Acknowledgements We are grateful to the laboratory technicians of the Institute of Medical Microbiology, University of Zurich for their dedicated help and to Martina Marchesi and Chantal Quiblier for support in strain testing and data management. We would like to thank Laura Navarria and the Copan team for technical support and discussions and Pietro Quadrelli for data export and management. Funding This work was supported by the University of Zurich. Transparency declarations The Institute of Medical Microbiology, University of Zurich and Copan Italia, S.p.A. cooperate in the development of automated AST. M. 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Journal

Journal of Antimicrobial ChemotherapyOxford University Press

Published: Feb 1, 2018

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