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JAC Advance Access originally published online on March 1, 2007
Journal of Antimicrobial Chemotherapy 2007 59(5):913-918; doi:10.1093/jac/dkm040
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© The Author 2007. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Effect of excluding duplicate isolates of Escherichia coli and Staphylococcus aureus in a 14 year consecutive database

Martin Sundqvist1,2,* and Gunnar Kahlmeter1,2

1 Department of Clinical Microbiology, Central Hospital, Varendsgatan 8, Växjö, Sweden 2 Department of Medical Sciences, Uppsala University Hospital, Uppsala, Sweden

* Corresponding author. Tel: +46-470-587478; Fax: +46-470-587455; E-mail: martin.sundqvist{at}ltkronoberg.se

Received 15 November 2006; returned 12 December 2006; revised 20 January 2007; accepted 29 January 2007


   Abstract
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Objectives: It is recommended that duplicate isolates are excluded when reporting resistance rates. The rationale for this is that failing to do so will yield falsely high resistance rates. We analysed a 14 year consecutive database of Escherichia coli (n = 62 380) and Staphylococcus aureus (n = 28 178) using various cut-off algorithms to determine the importance of excluding duplicates and principal differences between the bacteria.

Methods: Susceptibility testing was performed according to the Swedish Reference Group for Antibiotics guidelines. Duplicates were excluded on the basis of species, individual and time (exclusion cut-offs of 7, 14, 30, 45, 90, 180, 270 and 365 days) from the first isolate.

Results: Although 30% of the isolates were excluded using a 365 day exclusion algorithm, the effects on resistance rates of excluding duplicates were small. Irrespective of cut-off, resistance in S. aureus decreased when duplicates were excluded. Using 7–30 days cut-offs, resistance in E. coli decreased or was not affected, whereas higher resistance rates were obtained when exclusion was based on a 365 day cut-off. Fluoroquinolone resistance was a clear exception to this rule.

Conclusions: Although the effect of exclusion of duplicates was minor, we suggest that exclusion cut-offs should match the study timeline. The data presented on E. coli, from urinary tract infections, and S. aureus, from skin and soft tissue infections, suggest that E. coli infection, >90 days after the first culture, is mainly caused by new less-resistant strains. Patients with S. aureus continue to be colonized with the same strain.

Keywords: antimicrobial resistance , surveillance , urinary tract infections , skin and soft tissue infections


   Introduction
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During recent years, several authors in this journal have addressed the issue of excluding duplicate isolates from routine susceptibility reports.1–4 Consecutive routine susceptibility data will within any studied time period include isolates that originate from the same patient. To avoid overestimation of uncommon or spectacular resistances (as these tend to be re-cultured more often than wild-type strains; failure of therapy, repeat culturing for infection control etc.) it is desirable to exclude the duplicate isolates. The exclusion algorithm is performed in the computer-based laboratory system and depends on the reliable identification of the patient, the bacterial species, various cut-off times and sometimes the susceptibility pattern.1–8 In 2000, based on studies on methicillin-resistant Staphylococcus aureus (MRSA), the CLSI (then the NCCLS) proposed that the calculation of resistance rates should be based on the results from the first patient isolate.9 This method was validated by Shannon and French,4 although one of the cut-off times for exclusion (7 days) was argued to be too short to define a single episode of infection. ESGARS (European Study Group on Antibiotic Resistance Surveillance) has also addressed the problem in the ‘European recommendations for antimicrobial resistance surveillance’,10 where the use of a cut-off time algorithm (as the CLSI proposal) or exclusion based on susceptibility pattern is recommended.

In the present study, we studied the effect on calculated resistance rates of applying eight different cut-off times on several antibiotics in two different species. We used 14 years of consecutive quantitative antimicrobial susceptibility test data stored in our database. During this time period, the same standardized susceptibility testing method was used and all data stored as quantitative results.

Hypothesis

Failure to remove duplicate isolates in antibiotic resistance surveillance leads to overestimation of resistance rates. It is reasonable that a single algorithm for removal of duplicate isolates is suitable for all epidemiological analysis. Using the exclusion algorithm will have the same principal result irrespective of which drug it is applied to and irrespective of when in time the analysis is performed. To validate the hypothesis, longitudinal data for several antimicrobials in more than one species are needed.


   Materials and methods
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Isolates and susceptibility testing

All isolates, Escherichia coli (n = 62 380), mainly from urinary tract infections, and S. aureus (n = 28 178), mainly from skin and soft tissue infections, isolated and tested for antimicrobial susceptibility from 1 January 1990 until the end of December 2003 at the Department of Clinical Microbiology, Central Hospital, Växjö, Sweden, were analysed. The reason for choosing this time period was that during this period the bacteria and the drugs suitable for the study were not subjected to changes in breakpoints or, with the exception below, changes in methodology. Växjö is the central town (~60 000 inhabitants) in a predominantly rural area with a stable population of ~180 000 inhabitants.

Susceptibility testing was performed using Iso-Sensitest agar and antibiotic discs (Oxoid, Basingstoke, UK) and S, I and R breakpoints as described by the Swedish Reference Group for Antibiotics (SRGA).11 E. coli ATCC 25922 and S. aureus ATCC 29213 were used daily to control methodology. All zone diameters were measured to the nearest millimetre with a slide gauge and stored together with all patient data in the database and retrieved for analysis in the present study. The methodology for culturing and susceptibility testing was stable over time, except for the testing of fluoroquinolone susceptibility in E. coli where the test substance used was changed over time (10 µg of norfloxacin during 1990–3, 10 µg of ciprofloxacin during 1994–7, 5 µg of ciprofloxacin during 1998–2000 and 30 µg of nalidixic acid during 2001–3).

Removal of duplicate isolates

Duplicate isolates were identified and excluded by the laboratory computer system (ADBaktTM) on the basis of bacterial species and the unique personal identification number given to all Swedes at birth. The following algorithms were investigated: 7, 14, 30, 45, 90, 180, 270 and 365 days interval between sampling dates for the first and following isolates of E. coli versus ampicillin and trimethoprim and for S. aureus versus fusidic acid and clindamycin. For other antibiotics, cut-off values of 30 and 365 days were analysed. Methicillin resistance was not included in the analysis, since less than 20 strains were available in any of the 14 years.

The following antimicrobial susceptibility test results were analysed for E. coli—ampicillin, mecillinam, cefadroxil, trimethoprim, nitrofurantoin and fluoroquinolones and for S. aureus–clindamycin and fusidic acid. Antibiotics were chosen to represent different classes of antibiotics and because they belonged to the standard routine panel of antibiotics used over the whole period.

Statistics

Exact two-sided test of proportions based on the binomial distribution was used; P < 0.001 was considered significant.


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E. coli

The 365 day algorithm excluded approximately one-third of E. coli isolates (median 29%, range 21–32% over the 14 years). The effect on resistance rates of excluding duplicate isolates was in general small but systematic over time. This was true for all investigated antibiotics as shown in Table 1 and in detail for ampicillin in Figure 1.


Figure 1
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  		Figure 1.. Effect on the ampicillin resistance rate in community isolates of E. coli of different time cut-offs for the exclusion of duplicate isolates. Bars in the following order: no exclusion (black), exclusion algorithms 7, 14, 30, 45, 90, 180 and 270 (left to right in shades of grey) and 365 days (white).

 


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  		Table 1.. Effect on resistance rates of exclusion (cut-off time 30 and 365 days) of duplicate isolates of E. coli and S. aureus

 
In E. coli, resistance rates decreased or were not affected when duplicate isolates were excluded on the basis of 7–30 days, but increased when duplicates were excluded on the basis of 90–365 days. This pattern was most pronounced in the group of isolates from the community (n = 36 401) (Figure 1), but the same trend was observed in E. coli isolated from inpatients (data not shown). For fluoroquinolones, the pattern was different (Table 1) in that resistance rates gradually decreased when duplicate isolates were excluded.

S. aureus

The 365 day algorithm excluded approximately one-third of S. aureus (median 32%, range 25–35% over the 14 years). Excluding duplicate isolates resulted in small but systematic resistance rate decreases for clindamycin and during the first 10 years for fusidic acid (Table 1). The pattern common for these drugs is shown for clindamycin in Figure 2. Fusidic acid from 1999 and onwards exhibited a different pattern as shown in Figure 3. This coincided with an epidemic, with a fusidic-acid-resistant S. aureus clone causing epidemic impetigo in children (see the Discussion section). The epidemic did not affect the elderly, and the principal pattern following the removal of duplicates did not change in this age group (Table 2). The proportion of duplicates did not change during the epidemic (Figure 4).


Figure 2
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  		Figure 2.. Effect on the clindamycin resistance rate in S. aureus of different time cut-offs for the exclusion of duplicate isolates. Bars in the following order: no exclusion (black), exclusion algorithms 7, 14, 30, 45, 90, 180 and 270 (left to right in shades of grey) and 365 days (white).

 


Figure 3
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  		Figure 3.. Effect on the fusidic acid resistance rate in S. aureus of different time cut-offs for the exclusion of duplicate isolates. Bars in the following order: no exclusion (black), exclusion algorithms 7, 14, 30, 45, 90, 180 and 270 (left to right in shades of grey) and 365 days (white).

 


Figure 4
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  		Figure 4.. Proportion of duplicate isolates in three age groups (filled circles, 0–12 years; filled squares, 13–60 years; filled triangles, >60 years) analysed for fusidic acid resistance. The epidemic of a fusidic-acid-resistant S. aureus impetigo clone began in 1997 and lasted throughout the observation time.

 


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  		Table 2.. Effect on fusidic acid resistance rates in community isolates of S. aureus from different age groups when duplicate isolates were excluded using a 365 days cut-off

 

   Discussion
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The effect on resistance rates of excluding duplicate isolates in studies on antimicrobial resistance has been discussed over the last 20 years.1–8 These studies have analysed data collected during short time periods, maximum of 6 years, and, in some of these,1,3 raw data have been pooled for the whole period prior to analysis. Most studies have focused on emerging resistances like MRSA, vancomycin-resistant enterococci and gentamicin-resistant Klebsiella pneumoniae. The effect of removal is not always impressive, but it is agreed that it is important to control the influence of duplicate isolates on resistance rates to ensure the comparability of data from different settings, geographic regions and over time.10 In most cases, it is assumed that unless repeat and/or duplicate isolates are removed, falsely high resistance rates ensue.

Our study is the first to serially analyse the effect of removing duplicates in a database of consecutive isolates collected over a long period of time, in our case 14 years. Our study did not focus only on ‘spectacular resistance’ and it compared the effects of several exclusion algorithms on two species, E. coli and S. aureus, both common targets in resistance surveillance programmes.12–14 Isolates from all clinical settings were included, even though E. coli isolates were predominantly from urinary tract infections and S. aureus isolates were predominantly from skin and soft tissue infections. Importantly, the population studied and the susceptibility testing methodology were stable over time. Antimicrobial resistance was presented as yearly rates mimicking the situation in most resistance surveillance programmes. Several exclusion algorithms were proposed in the guidelines from the ESGARS and the CLSI.9,10 We examined how several of them influenced resistance rates.

The results for S. aureus basically confirmed the hypothesis, i.e. that the systematic removal of duplicate isolates reduces resistance rates. It is known that patients once colonized with resistant S. aureus retain the same strain for long periods of time.7 Without being able to confirm this with molecular methods, our data suggest that the colonization time is at least 1 year. In the case of fusidic acid, Sweden experienced an epidemic of impetigo in children caused by a fusidic-acid-resistant S. aureus clone starting in 1997.15 The influence of the epidemic was evident in our analysis, where the described and typical pattern inverted with the advent of the epidemic (Figure 3). This switch was most pronounced in the age group clearly affected by the epidemic, i.e. 0–12 years, and was absent in the oldest age group (Table 2). Interestingly, although the total number of isolates for S. aureus in the youngest age group did increase during the epidemic, there was no change in the relative occurrence of duplicates. We do not know why this switch occurred, but one may speculate that the epidemic drew attention to this type of skin infections in the age group 0–12 years and that this caused a higher than normal willingness to culture wound infections. The rationale for taking cultures would change from ‘culturing when in therapeutic difficulties’ to ‘culturing because there is an epidemic’, causing an increase in re-culturing irrespective of chronicity or therapeutic difficulties. This would cause the unexpected shift in the year 2000 (higher resistance rates with a cut-off of 365 days than with a cut-off of 30 days).

Our analysis shows that contrary to what one may expect, the exclusion of duplicates does not always result in lower resistance rates. In E. coli, resistance rates were gradually lower or were not influenced by exclusion cut-offs of 7, 14 and 30 days, but were gradually higher with longer cut-offs. This was true over the whole observation period. This suggests that after a period longer than 90 days, ‘duplicates’ are more often representative of re-infection with a new and more susceptible E. coli. However, fluoroquinolone resistance rates in E. coli decreased whenever duplicate samples were excluded, irrespective of algorithm, indicating that this category of patients was different. Fluoroquinolone resistance is known to be associated with urinary catheters, previous quinolone treatment, male sex and age.16–19 However, when the material was stratified for the presence or absence of a urinary catheter, this did not explain the finding (data not shown). This pattern was also seen for trimethoprim resistance in K. pneumoniae, Proteus mirabilis and Enterococcus faecalis (data not shown), indicating that this might be an effect of cultures from complicated urinary tract infections. Whether this pattern for exclusion of duplicates in fluoroquinolone resistance in E. coli is mainly due to characteristics related to the host or the bacterium or the disease remains to be investigated. This would require the typing of large numbers of consecutive isolates over a long period of time, something that can be done using the PhenePlateTM system.20

In summary, the effects on resistance rates of excluding duplicates were small. None of the observed differences would affect our local treatment guidelines. Our analysis shows that it is wrong to assume that exclusion of duplicate isolates always results in lower resistance rates. The effect may be different in different pathogens, and it may be affected by ongoing clonal outbreaks. Although important to control, the effect on resistance rates of duplicate isolates should be weighed with the influence of age, sex and the geographic origin (rural versus urban) of the patient. We believe that the recommendation that the cut-off time for exclusion of duplicates should match the study timeline is reasonable for most surveillance programmes. However, our results indicate that for E. coli isolated from urine, the effects of different cut-offs are less predictable and may have to be modified in accordance with local conditions. We advocate that articles on antimicrobial resistance rates should be required to state the procedure for and the principal result of excluding duplicate samples. Thus, the algorithm (365 days for yearly rates and 30 days for monthly rates etc.) and the effect of the exclusion (as, for example, ≤X%) should be stated.


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None to declare.


   Acknowledgements
 
We thank Anna Lindgren for statistical advise. This work was in part financed by an unrestricted grant from the Research and Development Unit, Kronoberg County Council, Växjö, Sweden.


   References
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1 Magee JT. (2004) Effects of duplicate and screening isolates on surveillance of community and hospital antibiotic resistance. J Antimicrob Chemother 54:155–62.[Abstract/Free Full Text]

2 Rodriguez JC, Sirvent E, Lopez-Lozano JM, et al. (2003) Criteria of time and antibiotic susceptibility in the elimination of duplicates when calculating resistance frequencies. J Antimicrob Chemother 52:132–4.[Abstract/Free Full Text]

3 Shannon KP and French GL. (2002) Antibiotic resistance: effect of different criteria for classifying isolates as duplicates on apparent resistance frequencies. J Antimicrob Chemother 49:201–4.[Abstract/Free Full Text]

4 Shannon KP and French GL. (2002) Validation of the NCCLS proposal to use results only from the first isolate of a species per patient in the calculation of susceptibility frequencies. J Antimicrob Chemother 50:965–9.[Abstract/Free Full Text]

5 Huovinen P. (1985) Recording of antimicrobial resistance of urinary tract isolates—effect of repeat samples on resistance levels. J Antimicrob Chemother 16:443–7.[Abstract/Free Full Text]

6 Bennett WP, O'Connor ML, Wasilauskas BL. (1985) A comparison of antibiotic susceptibility profiles using single and multiple isolates per patient. Infect Control 6:157–60.[Web of Science][Medline]

7 Horvat RT, Klutman NE, Lacy MK, et al. (2003) Effect of duplicate isolates of methicillin-susceptible and methicillin-resistant Staphylococcus aureus on antibiogram data. J Clin Microbiol 41:4611–6.[Abstract/Free Full Text]

8 White RL, Friedrich LV, Burgess DS, et al. (2001) Effect of removal of duplicate isolates on cumulative susceptibility reports. Diagn Microbiol Infect Dis 39:251–6.[CrossRef][Web of Science][Medline]

9 National Committee for Clinical Laboratory Standards. (2000) Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data: Proposed Guideline M39-P(NCCLS, Wayne, PA, USA).

10 Cornaglia G, Hryniewicz W, Jarlier V, et al. (2004) European recommendations for antimicrobial resistance surveillance. Clin Microbiol Infect 10:349–83.[CrossRef][Web of Science][Medline]

11 The Swedish Reference Group for Antibiotics (SRGA). Susceptibillity Testing (SRGA) http://www.srga.org/RAFMETOD/BASMET.HTM (20 January 2007, date last accessed).

12 Veldhuijzen I, Bronzwaer SL, Degener J, et al. (2000) European Antimicrobial Resistance Surveillance System (EARSS): susceptibility testing of invasive Staphylococcus aureus. Euro Surveill 5:34–6.[Medline]

13 Schmitz FJ, Verhoef J, Fluit A. (1999) Geographical distribution of quinolone resistance among Staphylococcus aureus, Escherichia coli and Klebsiella spp. isolates from 20 European university hospitals. SENTRY Participants Group. J Antimicrob Chemother 43:431–4.[Free Full Text]

14 Kahlmeter G. (2003) Prevalence and antimicrobial susceptibility of pathogens in uncomplicated cystitis in Europe. The ECO·SENS study. Int J Antimicrob Agents 22:Suppl 2, 49–52.[CrossRef][Medline]

15 Osterlund A, Eden T, Olsson-Liljequist B, et al. (2002) Clonal spread among Swedish children of a Staphylococcus aureus strain resistant to fusidic acid. Scand J Infect Dis 34:729–34.[CrossRef][Web of Science][Medline]

16 Abelson Storby K, Osterlund A, Kahlmeter G. (2004) Antimicrobial resistance in Escherichia coli in urine samples from children and adults: a 12 year analysis. Acta Paediatr 93:487–91.[CrossRef][Web of Science][Medline]

17 Sotto A, De Boever CM, Fabbro-Peray P, et al. (2001) Risk factors for antibiotic-resistant Escherichia coli isolated from hospitalized patients with urinary tract infections: a prospective study. J Clin Microbiol 39:438–44.[Abstract/Free Full Text]

18 Garau J, Xercavins M, Rodriguez-Carballeira M., et al. (1999) Emergence and dissemination of quinolone-resistant Escherichia coli in the community. Antimicrob Agents Chemother 43:2736–41.[Abstract/Free Full Text]

19 Bruinsma N, Cornaglia G, Baquero F, et al. Fluoroquinolone resistance in invasive Escherichia coli in Europe is related to age and gender. Abstracts of the Fifteenth European Congress of Clinical Microbiology and Infectious Diseases, Copenhagen, Denmark, 2005(European Society of Clinical Microbiology and Infectious Disease, Basel, Switzerland) Abstract 1134_04_122.

20 Landgren M, Oden H, Kuhn I, et al. (2005) Diversity among 2481 Escherichia coli from women with community-acquired lower urinary tract infections in 17 countries. J Antimicrob Chemother 55:928–37.[Abstract/Free Full Text]


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