Normalized Resistance Interpretation as a Tool for Establishing Epidemiological MIC Susceptibility Breakpoints

0095-1137/10/$12.00 doi:10.1128/JCM.01101-10

Copyright © 2010, American Society for Microbiology. All Rights Reserved.

Normalized Resistance Interpretation as a Tool for Establishing

Epidemiological MIC Susceptibility Breakpoints

䌤

Go

¨ran Kronvall*

Department of Microbiology and Tumor Biology–MTC, Clinical Microbiology, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden

Received 1 June 2010/Returned for modification 2 September 2010/Accepted 24 September 2010

Normalized resistance interpretation (NRI) utilizes the fact that the wild-type population on the sensitive side is not affected by resistance development, and therefore a normalized reconstruction of the peak can be performed. The method was modified for MIC distributions by the introduction of helper variables, in-between

values assigned the mean of the neighboring numbers of isolates. This method was used on

Staphylo-coccus aureus and Escherichia coli MIC distributions for 27 antimicrobials each and obtained from the EUCAST (European Committee on Antimicrobial Susceptibility Testing) website (http://www.eucast.org/mic _distributions/). The number of isolates in each of the 54 distributions ranged from 40 to 124,472. NRI

produced normalized distributions in all cases. Cutoff values were calculated forⴙ2.0 and ⴙ2.5 standard

deviations (SD) above the means and then rounded up to nearest regular MIC dilution step. EUCAST also

show cutoff values, ECOFF values, which were used as the reference. The NRI generatedⴙ2.0 SD values

showed the best agreement with 26 of 27 withinⴞ1 dilution step and 17 exactly on the ECOFF values for

Staphylococcus aureus, and 25 of 27 withinⴞ1 dilution step and 14 right on the ECOFF values forEscherichia

coli. NRI offers an objective method for the reconstruction of the wild-type population in an MIC distribution

for a given bacterial species and an antimicrobial agent. This method offers a new tool in comparative susceptibility studies such as global surveillance of resistance, as well as in quality control in individual laboratories.

Traditional concepts for setting interpretive breakpoints in antimicrobial susceptibility testing were challenged by J. D. Williams in 1990 when he proposed the setting of so-called microbiological guidelines in relation to the distribution of inhibition zone diameter values (or MIC values) of the suscep-tible population, for instance two standard deviations (SD) below the mode inhibition zone size (24). This would require a species-related aspect which was not yet accepted, although some reports on this issue had been published (2, 12, 14). Earlier, O’Brien had shown that an adjustment of interpretive breakpoints in relation to the susceptible peak in zone diam-eter histograms made results from different laboratories com-parable and thereby foresaw later developments (17). In recent years, the term epidemiological breakpoints or epidemiologi-cal cutoff (ECOFF), has been proposed by the European Com-mittee on Antimicrobial Susceptibility Testing (EUCAST) to describe the upper MIC limit of the susceptible peak in an MIC distribution, the wild-type (WT) population. MIC distri-butions of WT isolates species-wise for many antimicrobials have been published on the internet by EUCAST and are publicly available (http://www.eucast.org/mic_distributions/) (5, 6).

Normalized resistance interpretation (NRI) is an objective method to define the WT population in inhibition zone diam-eter histograms (4, 8, 10). The method has been used for setting interpretive breakpoints in an 18-year resistance

sur-veillance, and also in an investigation of meropenem

suscep-tibility inPseudomonas aeruginosa(3, 22). It was recently used

extensively in marine microbiology by P. Smith et al. (19–21). NRI was also used successfully to analyze Etest MIC data to set epidemiological breakpoints for tigecycline (11). However, a direct application of NRI to regular 2-fold dilution MIC results has not been possible because the mathematical calcu-lations will be based on too few data points. The use of NRI has been extended in the present investigations to regular MIC values by the introduction of helper variables, and NRI has been evaluated as a method for the objective estimation of epidemiological breakpoints using the EUCAST MIC distri-butions as the material and as the reference (http://www.eucast .org/mic_distributions/).

MATERIALS AND METHODS

MIC distributions.The publicly available EUCAST MIC distributions include a large number of bacterial species and antimicrobials (http://www.eucast.org /mic_distributions/). For the purpose of testing the NRI method for MIC distri-butions in the present studies, results forStaphylococcus aureusandEscherichia coliwere used. EUCAST claim on their website that the “Histograms display WT organisms.” It is not clear, however, which method is used to ensure this selection in the published materials, which can include as many as⬎100,000 isolates for a single combination of antimicrobial agent and species. It is apparent from the distributions that in many cases these selections do include non-WT organisms as well.

MIC zone data from EUCAST were also obtained from the ESCMID Year-book 2009/2010 (p. 45) forEscherichia coliand cefotaxime and used for the calculation of the regression line for this combination of species and antimicro-bial (http://www.escmid.org/escmid_library/publications/escmid_yearbook/). The regression line was calculated for 2-log MIC values⫹9 on thexaxis and the squared inhibition zone diameter on theyaxis for optimal linearity (7).

NRI.The NRI method utilizes the fact that the upper part of the WT popu-lation in a zone diameter histogram or the lower part in an MIC distribution is

* Mailing address: Clinical Microbiology L2:02, Karolinska University Hospital Solna, SE-17176 Stockholm, Sweden. Phone: 46-8-51774910. Fax: 46-8-308099. E-mail: [email protected].

䌤_{Published ahead of print on 6 October 2010.}

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not affected by the occurrence of resistant isolates and therefore the WT pop-ulation can be reconstructed (4, 8, 10). The method was developed by using inhibition zone diameter histograms but has also been applied to Etest MIC distributions as described earlier (11). In the present studies the EUCAST MIC distributions used in the experiments were determined by using conventional 2-log dilution steps. In order to make such distributions possible to analyze with NRI, we used helper variables in the following way. The independent variable was expanded to include values between the regular 2-log values in the MIC distribution. The number of isolates for these intermediate MIC values were set to the mean of the numbers of isolates for the two neighboring regular MIC values, thereby creating the helper variable. This expanded distribution with more data points made the calculations mathematically possible. However, the distributions were broader, and the regular parameter settings chosen for zone diameter histograms had to be changed for MIC distributions to a slight peak adjustment toward the susceptible side (4). These fixed parameter settings were used throughout. The cutoff values were determined at 2.0 and 2.5 SD above the calculated means of the normalized WT curves.

The actual NRI calculations were performed as described earlier with MIC values converted to 2-log values⫹9 as in a traditional regression line and then to zone diameter equivalents in order to be able to use a computer program developed for inhibition zone diameter histogram analysis (NRI was used with permission from the patent holder, Bioscand AB [Ta¨by, Sweden]. The NRI method is protected under U.S. patent 7,465,559 and European patent 1383913) (11). This procedure using zone equivalents has nothing to do with any true inhibition zones but is merely an aid in the calculations. A direct calculation according to NRI principles using log MIC is also possible.

RESULTS

The NRI method was applied to 27 antimicrobial MIC

dis-tributions forS. aureusand to 27 distributions forE. coli, all of

them obtained from the public website of EUCAST. NRI with

helper variables produced normal distributions of WT isolates in all of these 54 cases, irrespective of the variation in number of isolates in the individual distributions, with numbers ranging from 40 to 124,472 (Tables 1 and 2). The introduction of helper variables therefore made NRI calculations possible also for regular MIC distributions.

Epidemiological cutoff values were calculated from the NRI

results at⫹2.0 and ⫹2.5 SD from the NRI means and are

shown in Tables 1 and 2. These cutoff values were rounded up to the nearest MIC value and compared to the corresponding

ECOFF given by EUCAST. For the 27S. aureusdistributions,

the cutoff values at the⫹2.0 SD level corresponded in 17 cases

to the EUCAST ECOFF values; in two cases the values were one step below, in seven cases they were one step above, and in one case they were two steps above the ECOFF values. At

the ⫹2.5 SD level commonly used for NRI analysis of zone

diameter histograms, the calculated cutoff values corresponded in 12 cases to the ECOFF values; in 2 cases they were one step below, in 9 cases they were one step above, and in 4 cases they

were two steps above. For the 27E. colidistributions, the cutoff

values at the⫹2.0 SD level corresponded in 14 cases to the

ECOFF values; in 1 case they were two steps below, in 5 cases they were one step below, in 6 cases they were one step above, and in 1 case they were two steps above the respective ECOFF

values. At the⫹2.5 SD level, the figures were in 11 cases right

at the ECOFF level, in 3 cases they were one step below, in 11

TABLE 1. NRI analysis of 27S. aureusMIC distributionsa

Antimicrobial

NRI (Sⱕmg/liter) EUCAST ECOFF

value

No. of isolates

⫹2.0 SD ⫹2.5 SD

Amikacin 12.3 17.7 8 6,346

Azithromycin 1.44 1.87 2 7,146

Cefotaxime 4.5 9.3 4 5,857

Cefoxitin 6.7 9.9 4 856

Cefuroxime 4.4 6.3 4 11,891

Chloramphenicol 14.2 18.2 16 1,647

Ciprofloxacin 0.99 1.4 1 42,273

Clarithromycin 0.58 0.78 0.5 7,146

Clindamycin 0.26 0.34 0.25 25,797

Doxycycline 0.29 0.39 0.5 4,869

Erythromycin 1.13 1.6 1 36,038

Fusidic acid 0.36 0.5 0.5 16,662

Gentamicin 1.18 1.6 2 45,694

Levofloxacin 0.36 0.47 0.5 27,459

Linezolid 3.8 4.9 4 62,262

Moxifloxacin 0.15 0.21 0.25 16,389

Netilmicin 0.55 0.74 1 40

Ofloxacin 0.72 0.92 1 2,828

Oxacillin 1.4 2 2 25,347

Rifampin 0.011 0.013 0.016 995

Sulfamethoxazole 38 52 128 123

Teicoplanin 1.6 2 2 69,170

Tetracycline 0.95 1.2 1 1,709

Tigecycline 0.26 0.34 0.5 1,363

Tobramycin 2.3 3.8 1 1,585

Trimethoprim-sulfamethoxazole

0.15 0.18 0.5 3,108

Vancomycin 1.95 2.5 2 87,764

a_{The cutoff values at⫹2.0 and⫹2.5 SD from the means of the calculated} normal distributions are shown, as well as the ECOFF values given by EUCAST. The numbers of isolates in the 27 distributions are also indicated. Values in agreement with EUCAST values are indicated in boldface.

TABLE 2. NRI analysis of 27E. coliMIC distributionsa

Antimicrobial

NRI (Sⱕmg/liter) EUCAST ECOFF

value

No. of isolates

⫹2.0 SD ⫹2.5 SD

Amikacin 7.8 10.8 8 10,917

Amoxicillin-clavulanic acid

16.9 24.1 8 56,847

Ampicillin 10.8 15.1 8 39,220

Cefotaxime 0.28 0.39 0.25 10,829

Cefoxitin 13 19 16 66,874

Ceftazidime 0.48 0.68 0.5 15,136

Cefuroxime 10.4 14.6 8 124,472

Chloramphenicol 21.5 29.9 16 11,725

Ciprofloxacin 0.031 0.04 0.032 17,877

Doxycycline 6.7 9.8 4 3,653

Gentamicin 2 2.8 2 43,924

Imipenem 0.49 0.69 0.5 16,667

Levofloxacin 0.082 0.1 0.25 9,144

Amdinocillin 0.49 0.68 1 1,636

Meropenem 0.05 0.066 0.125 8,005

Moxifloxacin 0.14 0.19 0.25 3,219

Nalidixic acid 8.9 11.9 16 5,190

Netilmicin 0.9 1.15 2 6,011

Nitrofurantoin 34 45 64 3,938

Norfloxacin 0.09 0.12 0.25 654

Ofloxacin 0.2 0.25 0.25 3,680

Piperacillin 4.4 5.9 8 25,906

Tetracycline 4.1 5.4 8 7,521

Tigecycline 0.52 0.68 1 4,238

Tobramycin 2.2 3 2 28,424

Trimethoprim 1.3 1.78 2 8,198

Trimethoprim-sulfamethoxazole

0.21 0.28 1 7,712

a

The cutoff values at⫹2.0 and⫹2.5 SD from the means of the calculated normal distributions are shown, as well as the ECOFF values given by EUCAST. The numbers of isolates in the 27 distributions are also indicated. Values in agreement with EUCAST values are indicated in boldface.

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cases they were one step above, and in 2 cases they were two steps above the ECOFF values. It seems that the cutoff values

set at⫹2.0 SD above the mean of the NRI generated normal

distributions corresponded best with the ECOFF values issued by EUCAST.

The deviations from the ECOFF values might be due to a number of influencing factors. One such possible factor was

the size of the material in the distributions. FifteenS. aureus

distributions had lower than 10,000 isolates included. When the degree of deviation from the ECOFF values for these 15 distributions were compared to the 12 with a higher number of

isolates there was no difference, neither for the⫹2.0 SD cutoff

nor for the⫹2.5 SD cutoff values. AmongE. colidistributions

there was a tendency toward higher cutoff values for the 13 distributions with more than 10,000 isolates compared to the

14 distributions with lower numbers, both for the⫹2.0 SD and

the⫹2.5 SD cutoff values. This was further analyzed by using

Pearson Correlation and found significant, with two-tailed sig-nificances of 0.018 and 0.021, respectively. This correlation does not explain the differences noted for several combinations of species and antimicrobial between NRI-generated cutoff values and the ECOFF values of EUCAST. Unfortunately, the method for their estimation of epidemiological cutoff values is not clear from the information on their website, so a further analysis of individual distributions will have to await clarifica-tion on this point.

Examples of NRI calculations on MIC distributions are

shown in Fig. 1 and Fig. 2. All fourS. aureusMIC distributions

in Fig. 1 were clearly bimodal, indicating that these distribu-tions do in fact contain non-WT isolates with some kind of antimicrobial resistance. The ECOFF values given for these

antimicrobials and S. aureus also testify to this fact. For

azithromycin (Fig. 1A), the NRI-derived cutoff values at⫹2.0

and⫹2.5 SD were 1.44 and 1.87 mg/liter, respectively,

suggest-ing a cutoff value (S) ofⱕ2 mg/liter, which is identical to the

EUCAST ECOFF value. ForS. aureusand erythromycin (Fig.

1B), the NRI derived cutoff values were 1.13 and 1.6 mg/liter at

⫹2.0 and⫹2.5 SD, respectively, which rounded up to 2 mg/

liter, whereas the ECOFF value was 1.0 mg/liter. ForS. aureus

and ofloxacin (Fig. 1C), the rounded up NRI cutoff values and the ECOFF value were the same: 1.0 mg/liter. When it comes toS. aureusand tetracycline (Fig. 1D), the⫹2.0 SD value 0.95 corresponded to the ECOFF value of 1.0 mg/liter, whereas the

⫹2.5 SD value of 1.2 would give a cutoff 2.0 mg/liter.

TheE. colidistribution of cefotaxime MIC values (Fig. 2A) showed a largely unimodal pattern with an ECOFF value of 0.25 mg/liter. The NRI-derived cutoff values were 0.28 and 0.39

mg/liter, which give an adjusted figure of Sⱕ0.5 mg/liter, one

step higher than the ECOFF value. For this particular combi-nation, some additional EUCAST data are available in the ESCMID YearBook 2009/2010 (p. 45) (1). From this informa-tion a regression line between MIC values and inhibiinforma-tion zone diameters could be calculated. The recommended clinical

cut-off value for susceptibility was S ⱕ 1 mg/liter, and for disc

diffusion tests it was Sⱖ21 mm. However, from the EUCAST

data regression line, their MIC breakpoint corresponded to an

inhibition zone breakpoint of Sⱖ19, whereas the NRI MIC

cutoff and the ECOFF value corresponded to Sⱖ21 mm and

Sⱖ23 mm, respectively. It is interesting that the

NRI-gener-ated MIC cutoff value of 0.5 mg/liter gave the same inhibition

zone value as the EUCAST inhibition zone breakpoint, Sⱖ

21 mm, as calculated from the regression line made from EUCAST data.

Doxycycline susceptibility is usually deduced from tetracy-cline susceptibility results, but the doxycytetracy-cline MIC

distribu-tion for E. coli was interesting (Fig. 2B). The pattern was

bimodal but with an apparent overlap. Still, NRI clearly de-fined a WT population which trailed into the 8 to 64 mg/liter region with a large number of non-WT isolates. The ECOFF is set to 4 mg/liter, but the NRI-derived normal distribution

showed cutoff values of 6.7 and 9.8 mg/liter at⫹2.0 and⫹2.5

SD, respectively. The⫹1.0 SD cutoff was 3.1 mg/liter, which is

closer to the ECOFF value.

The MIC distribution forE. coliand moxifloxacin (Fig. 2C)

showed a pattern with two major peaks and a tendency toward a third peak in between. The WT peak was clearly identified using NRI, and the resultant cutoff values corresponded

ex-actly to the ECOFF value of 0.25 mg/liter. WhenE. coliand

tetracycline (Fig. 2D) was analyzed and compared to the doxy-cycline distribution (Fig. 2B), there was a good separation of the WT peak from isolates with resistance. Also, for this anti-microbial the rounded up cutoff values were the same as the ECOFF value of 8 mg/liter.

DISCUSSION

The NRI method was modified for the analysis of MIC distributions by the introduction of helper variables and tested on 54 EUCAST distributions from their public website (http: //www.eucast.org/mic_distributions/). The 54 MIC distribu-tions consisted of from 40 to 124,472 isolates and, for all of them, NRI produced a normal distribution peak corresponding to the apparent WT population. Epidemiological breakpoints

were calculated from the NRI results at ⫹2.0 and ⫹2.5 SD

above the mean and were compared to the ECOFF values

published by EUCAST. The ⫹2.0 SD cutoff values

corre-sponded best to the ECOFF values. For the 27 S. aureus

distributions, 26 were within⫾1 step and 17 were right on the

ECOFF values. For the 27 E. coli distributions, 25 were

within⫾1 step and 14 right on the ECOFF values. NRI thus

represents an objective method for estimating the WT popu-lation in both MIC distributions and inhibition zone histo-grams. NRI was recently used in a retrospective 30-year

fol-low-up onS. aureusand E. coli resistance development at a

university hospital (9). Several changes in the methodology during these 30 years made a direct comparison of the suscep-tibility interpretations invalid, but with NRI-generated zone diameter cutoff values, such a comparison was made possible. NRI therefore shows a great potential as a tool in surveillance work. In the present studies these possibilities have been ex-tended to include MIC results. NRI is an objective method that now can be applied to both zone diameter histograms and MIC distributions and can therefore be used when an estimation of epidemiological breakpoints is of interest, for instance in com-parative studies, including different laboratories and countries. One problem in the present comparison with EUCAST ECOFF values is the lack of reference to the methods used by EUCAST for setting these values. Since there is no other golden standard available, the EUCAST values were taken as the true reference. However, from the MIC distribution shown

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in Fig. 2B it is already clear that an MIC ECOFF of 4 mg/liter is too low to include all WT isolates. The ECOFF value seems to have been adjusted in order to minimize possible errors identify-ing resistant isolates as susceptible WT. Whether more such con-siderations are included in the list of ECOFF values is not clear at present. Another question arises from the fact that on the EUCAST website for MIC distributions is stated that “histograms display WT organisms, together with EUCAST clinical break-points and epidemiological cutoff values (ECOFFs)” (http://www .eucast.org/mic_distributions/). A method for selecting WT

or-ganisms has not been established, and many distributions do in fact contain non-WT organisms. At present, the EUCAST data can be considered the best data available, and therefore the com-parisons in the present studies utilized EUCAST data as the golden standard.

The best agreement between NRI generated cutoff values

and ECOFF values were with NRI cutoff values set at⫹2.0 SD.

This would suggest that 97.7% of the WT isolates are included in the calculated WT peak. In reality, this figure might be even higher when some additional facts are taken into account.

FIG. 1.S. aureusMIC distributions for azithromycin (A), erythromycin (B), ofloxacin (C), and tetracycline (D). Diagram bars show the percent S. aureusisolates as given on the EUCAST website. The NRI-derived normal distributions of WT isolates are indicated by line graphs.

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First, the exact figure from NRI calculations is rounded up, thereby including more isolates. Second, an analysis of the WT peak in an inhibition zone diameter histogram showed that the WT population was slightly peaked and skewed toward higher zone values (13). This would also apply to MIC distributions, although the precision is lower compared to disc diffusion tests. The upper part of the WT MIC distribution thus ends more abruptly compared to a normal distribution, raising the figure for WT isolates included slightly higher.

Turnidge et al. have published another method for the

esti-mation of epidemiological cutoff values in MIC distributions (23). These researchers used nonlinear regression on cumula-tive 2-log MIC data and iterated analysis to find the optimum subset of data representing the WT population. This optimum was found when the difference between the estimated and the true number of isolates in the fitted subset was minimal. This method has the advantage of giving a more detailed statistical analysis of the MIC distributions. They published information

on the analysis of threeS. aureus distributions (vancomycin,

linezolid, fusidic acid) and fourE. colidistributions (imipenem,

FIG. 1—Continued.

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ampicillin, cefotaxime, and gentamicin) made from pooled data, and their epidemiological cutoff values corresponded to those of EUCAST for six of them (23). Five of these six distributions gave the same cutoff values using NRI

calcula-tions at the⫹2.0 SD level. ForE. coliand cefotaxime, their

cutoff value was 0.5, which was the same as the NRI-generated cutoff value obtained in the present studies. From a linear regression analysis of MIC zone data from EUCAST it was apparent that the recommended clinical inhibition zone

break-point of EUCAST, S ⱖ 21 mm, corresponded to the MIC

cutoff value of Turnidge et al., as well as the cutoff values calculated using NRI in the present studies (23).

The initiative by EUCAST to make MIC distributions avail-able on the internet represents an important step for interna-tional cooperation in clinical microbiology. The fact that all distributions are species specific also emphasizes the fact that individual bacterial species show a more or less unique re-sponse to a given antimicrobial. The trend in Europe at present is to use species-related interpretations and also in this regard EUCAST is in the forefront, issuing MIC breakpoints for

FIG. 2.E. coliMIC distributions for cefotaxime (A), doxycycline (B), moxifloxacin (C), and tetracycline (D). Diagram bars show the percent E. coliisolates as given on the EUCAST website. The NRI-derived normal distributions of WT isolates are indicated by line graphs.

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groups of species or individual species and publishing them on the internet (http://www.eucast.org/clinical_breakpoints/). The concept of species-specific interpretive breakpoints was sug-gested as early as 1974 and later (4, 8, 10, 18). Also, the extensive work on inhibition zone diameter histogram analysis of O’Brien had this species-specific concept as a basis, although it was not expressed in words (15–17). This emphasis on the importance of species-specific interpretations will also influ-ence surveillance work, global or local. An automated, objec-tive method, such as NRI, could play an important role in

making antimicrobial susceptibility results comparable in such surveillance studies.

ACKNOWLEDGMENT

The EUCAST organization, chaired by Gunnar Kahlmeter, is ac-knowledged for making their vast source of antimicrobial susceptibility test results publicly available.

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