Antimicrobial usage and resistance trend relationships from the MYSTIC Programme in North America (1999–2001)

Alan H. Mutnick1,2, Paul R. Rhomberg1, Helio S. Sader1,3 and Ronald N. Jones1,4,*

1 The JONES Group/JMI Laboratories, Inc., 345 Beaver Kreek Centre, Suite A, North Liberty, IA 52317; 2 University of Iowa College of Pharmacy, Iowa City, IA; 4 Tufts University School of Medicine, Boston, MA, USA; 3 Universidade Federal de Sao Paulo, Sao Paulo, Brazil

Received 22 April 2003; returned 24 July 2003; revised 14 October 2003; accepted 29 October 2003


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Background: The MYSTIC Programme is a global, longitudinal antimicrobial surveillance network of hospitals that frequently utilize carbapenems. One aspect of the programme is the ability to capture antimicrobial consumption data from participating institutions. The current report evaluates these relationships for Enterobacteriaceae and Pseudomonas aeruginosa over the initial 3 year period of the programme in the USA.

Methods: Between 10 and 15 medical centres participated during 1999–2001, each submitting up to 200 isolates/year (7003 strains overall). Evaluations of the relationship between drug usage and antimicrobial resistance in P. aeruginosa and Enterobacteriaceae for the carbapenems (imipenem and meropenem), cefepime, ceftazidime, ciprofloxacin, gentamicin and piperacillin–tazobactam were determined. Data were analysed based on: (1) aggregate usage results; (2) medical centre-specific usage compared with resistance rates; and (3) medical centre-specific usage results compared with yearly changes in resistance rates ({Delta}R). The parameter of drug usage was the defined daily dose (DDD)/100 patient days calculated from total grams administered, using WHO definitions.

Results: Resistance (1999–2001) among Enterobacteriaceae did not change significantly for ß-lactams, but tended to increase slightly for gentamicin (+1.1%) and ciprofloxacin (+3.1%). P. aeruginosa resistance rates (1999–2001) for gentamicin (+9.0%) and ciprofloxacin (+10.2%) increased, in contrast to a significantly decreased resistance rate for meropenem (–7.7%). Formulary-use changes were noted: increased meropenem and ciprofloxacin use and decreased consumption for imipenem, aminoglycosides, ceftazidime and cefepime. Aggregate ciprofloxacin DDD/100 days rates were directly related (+3.3 DDD) to Enterobacteriaceae and P. aeruginosa resistance changes, whereas among P. aeruginosa, usage and resistance were inversely correlated for gentamicin (–3.8 DDD; +9.0% resistant). Medical centre-specific antimicrobial usage calculations did not demonstrate a correlation to rates of resistance (r = –0.38 to 0.61) or yearly changes in resistance rates (r = –0.56 to 0.43).

Conclusions: The availability of aggregate USA medical centre antimicrobial usage data enabled us to identify several important trends in the incidence of resistance among P. aeruginosa and Enterobacteriaceae: (1) increased use of ciprofloxacin associated with a higher resistance among Enterobacteriaceae; and (2) a correlation between ciprofloxacin categories of resistance and levels of resistance to other antimicrobial classes in P. aeruginosa. Medical centre-specific antimicrobial usage and resistance did not demonstrate direct statistical relationships, and require a continued search for other monitoring methods that can better identify antimicrobial/environmental factors that lead to resistance.

Keywords: defined daily doses, correlations, ciprofloxacin, carbapenems, P. aeruginosa


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The relationship between antimicrobial use and resistance rates has been evaluated in many studies and by numerous analytical methods.16 Although intuitively one would expect that reduction in the use of an antimicrobial agent/class would be followed by a reduction in the prevalence of resistance, this has been infrequently documented. Thus, it is clear that multiple interactive factors influence changes in bacterial resistance. Furthermore, the capture of accurate antimicrobial use data is not always possible, and thus, in many cases, relating antimicrobial consumption to the development of resistance relies on ‘best-guess’ estimates of the extent of clinical antimicrobial use.5,7

The Meropenem Yearly Susceptibility Test Information Collection (MYSTIC) Programme is a global surveillance network of hospitals utilizing carbapenems, especially meropenem.8,9 Institutions have been monitored since 1997 internationally (1999 in the USA), using reference susceptibility methods of the NCCLS, to detect emerging resistances to carbapenems and comparator broad-spectrum antimicrobial agents.10,11 The defining aspect of this programme has been the intent to capture antimicrobial consumption data from participating institutions, leading to the evaluation of usage patterns, individually or in aggregate, compared with emerging resistance patterns of carbapenems and other classes of antimicrobials.11

In this report, we describe the 4 year (1999–2002) trends of microbiology susceptibility testing data, as well as the 3 year (1999–2001) antimicrobial consumption results from the MYSTIC Programme (USA) focusing on key Gram-negative pathogens, Pseudomonas aeruginosa and all Enterobacteriaceae.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Microbiology study design

During 1999–2002, 10–15 medical centres participated in the MYSTIC Programme (USA). These institutions were distributed geographically across the USA, and all actively utilized meropenem for the treatment of infections in seriously ill hospitalized patients. These medical centres included 12 university hospitals, one Veterans Administration medical centre, one cancer treatment centre, two paediatric hospitals and one cystic fibrosis reference hospital (several medical centres comprised more than one type of unit). The study design and susceptibility testing methods used throughout the MYSTIC Programme have been described previously8,9,11 and will not be repeated in detail.

Organisms

Each institution submitted to the central monitoring laboratory (JMI Laboratories, North Liberty, IA, USA) up to 100 aerobic Gram-negative and 100 aerobic Gram-positive isolates from serious infections in hospitalized patients. Only organisms known to be intrinsically resistant to carbapenems (oxacillin-resistant staphylococci, Enterococcus faecium and Stenotrophomonas maltophilia) were excluded. All isolates were sent to the monitoring laboratory for identification confirmation by colony morphology, biochemical tests and Vitek ID cards (Hazelwood, MO, USA), when necessary.

Susceptibility testing

Reference MIC determinations for aztreonam, cefepime, ceftazidime, ceftizoxime, ceftriaxone, ciprofloxacin, gentamicin, imipenem, meropenem, piperacillin–tazobactam and tobramycin were determined using the NCCLS broth microdilution method.12 Interpretive criteria for susceptibility and resistance for each antimicrobial agent followed those recommended by the NCCLS.12,13

Antimicrobial usage and statistical analysis

Antimicrobial usage data for 1999–2001 were obtained from a yearly survey provided to all participating centres. Survey data, received from 6–10 sites per year, included bed capacity, occupancy rates and gram usage for select antimicrobials. Drug usage was expressed as defined daily dose (DDD)/100 patient days, calculated from total grams administered, using WHO definitions.14 Evaluations of the relationship between drug usage (DDD/100 patient days) and antimicrobial resistance (% resistance and % change in resistance) in P. aeruginosa and Enterobacteriaceae for the carbapenems (imipenem and meropenem), cefepime, ceftazidime, ciprofloxacin, gentamicin and piperacillin–tazobactam were determined in three different ways, as described below.

Aggregate antimicrobial usage and resistance rate comparisons. DDD/100 patient days was calculated for each antimicrobial agent by totalling overall gram usage for all of the medical centres (aggregate total) providing usage data each year, and the result was compared with the overall P. aeruginosa and Enterobacteriaceae resistance rates (aggregate resistance rate for the same sites) for the listed agents for each of the 3 years (1999, 2000 and 2001).

Medical centre-specific antimicrobial usage and resistance rate comparisons. At each medical centre, DDD/100 patient days and resistance rates for P. aeruginosa and Enterobacteriaceae for each of the 3 years (1999, 2000 and 2001) were calculated for each antimicrobial agent. A total of 23 or 24 data points were generated and plotted as a scattergram comparing DDD/100 patient days with resistance rates (separate scattergram for P. aeruginosa and Enterobacteriaceae) for each medical centre during the 3 year study period.

Medical centre-specific antimicrobial usage compared with changes in resistance rates ({Delta}R). To minimize the influence of endemic resistance rates existing prior to the study interval, an analysis was directed only at variations or changes in resistance. DDD/100 patient days was calculated for each antimicrobial agent at each medical centre providing >=2 consecutive years of antimicrobial usage data and microbiological data. Antimicrobial usage during the first year was compared with the variation in P. aeruginosa and Enterobacteriaceae resistance rates ({Delta}R) from year 1 to year 2. A total of 11 data sets (six for 1999–2000, five for 2000–2001) were included in scattergrams comparing DDD/100 patient days (during the second year) with {Delta}R (between year 1 and year 2) for P. aeruginosa and Enterobacteriaceae.

For each of these methods, regression line equations and corresponding correlation coefficients were calculated from linear regression analysis to identify any relationships between antimicrobial usage and resistance rates. The types of relationships were further defined by the slope (positive or negative) of the regression line, and included the following associations:

1. Positive slope where increased drug usage was associated with increased resistance, or decreased drug usage was associated with decreased resistance.

2. Negative slope where increased drug usage was associated with decreased resistance, or decreased drug usage was associated with increased resistance.


    Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Trends in antimicrobial resistance (1999–2002)

A total of 5036 isolates (3925 Enterobacteriaceae and 1111 P. aeruginosa) were identified, confirmed and tested for susceptibility during 1999–2002.

Table 1 provides the annual number of isolates processed, and the percent susceptibility and resistance for selected drugs against Gram-negative pathogens. Against P. aeruginosa, the carbapenems exhibited a trend towards increasing susceptibility rates and declining levels of resistance throughout the study period. For example, meropenem susceptibility rates of P. aeruginosa increased from 78.2% in 1999 to 93.1% in 2002, whereas resistance to this agent dropped from 16.1% to 4.4% over the same period. Although over the total 4 year period tobramycin demonstrated the highest overall P. aeruginosa susceptibility rate (91.9% overall) of the agents tested, it also showed a trend towards increasing P. aeruginosa resistance rates, from 5.7% in 1999 to 9.1% in 2001 (+7.6% overall). In contrast, cefepime trended towards increased susceptibility of P. aeruginosa, from 79.3% in 1999 to 87.9% in 2002, and decreasing resistance over the course of the study. Only three of the agents tested inhibited >90% of P. aeruginosa within the susceptible MIC ranges:12,13 meropenem (93.1%), piperacillin–tazobactam (91.5%) and tobramycin (92.2%).


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Table 1. Antimicrobial spectrum of activity for selected drugs tested in the MYSTIC Programme (USA; 1999–2002) against Gram-negative pathogens
 
All of the selected broad-spectrum antimicrobials provided excellent activity and susceptibility patterns against Enterobacteriaceae isolates, with the carbapenems and cefepime demonstrating the highest overall susceptibility rates (98.8%–99.8%) and the lowest levels of resistance (0.1%–1.0%). Enterobacteriaceae susceptibility was lowest for ciprofloxacin, declining from 95.3% in 1999 to 91.7% in 2002 (Table 1).

Relationship between antimicrobial consumption and P. aeruginosa and Enterobacteriaceae resistance rates (1999–2001)

Table 2 and Figure 1(a and b) provide comparisons between the aggregate antimicrobial usage results (all medical centres combined) for selected agents and the aggregate resistance rate by year (all centres combined) for P. aeruginosa and Enterobacteriaceae during the 3 year period. Among P. aeruginosa isolates, high correlations were discovered between the use of meropenem (r = 0.98), ciprofloxacin (r = 0.92) and ceftazidime (r = 0.83), and the resistance to these agents. For the carbapenems, the positive slope of the line demonstrated that during the 3 year period, a slight decrease in the use of meropenem (–0.1 DDD/100 patient days; –7.7%) was accompanied by a more dramatic decrease in the P. aeruginosa resistance rate, from 16.1% to 8.4% (47.8% reduction in resistance rate; Table 2, Figure 1a). This is in contrast to ciprofloxacin where the positive slope reflected a doubling in drug usage during the 3 year period (3.1–6.4 DDD/100 patient days; +106.5%), occurring with an associated doubling of the P. aeruginosa resistance rate (11.9%–22.1%; Figure 1a). Ceftazidime also had a positive slope, as evidenced by an initial increase in both usage and resistance (10.9%–13.0%), followed by a decrease in both usage and P. aeruginosa resistance rates (13.0%–10.1%; Table 2).


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Table 2. Trends in aggregate antimicrobial usage versus aggregate resistance rates for P. aeruginosa and Enterobacteriaceae in the MYSTIC Programme (USA; 1999–2001)
 


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Figure 1. Ciprofloxacin and meropenem usage [defined daily dose (DDD)/100 patient days] compared with (a) P. aeruginosa and (b) Enterobacteriaceae resistance (R) rates (USA; 1999–2001). Diamond and solid line, meropenem usage; circle and solid line, meropenem resistance; diamond and broken line, ciprofloxacin usage; circle and broken line, ciprofloxacin resistance.

 
Among the Enterobacteriaceae, ciprofloxacin and piperacillin–tazobactam were the only antimicrobial agents that demonstrated a high relationship between drug usage and resistance rates, the positive slopes (r = 0.97 and 0.96, respectively) describing the association between increased drug usage and increased incidence of resistance among Enterobacteriaceae isolates (Table 2). In Figure 1(b), the two trend lines for ciprofloxacin nearly overlap, demonstrating the direct relationship that existed by this type of analysis between the increase in ciprofloxacin usage and a parallel increase in the occurrence of Enterobacteriaceae resistance. Again, a more than two-fold increase in the usage of ciprofloxacin was accompanied by an 83.8% increase in Enterobacteriaceae resistance. In contrast, no relationship was observed between meropenem usage and resistance among Enterobacteriaceae.

Figure 2(a) illustrates the relationship between usage of ciprofloxacin and P. aeruginosa resistance to this compound at each medical centre during each year (total of 23 or 24 data sets). Corresponding data for carbapenems and P. aeruginosa are shown in Figure 2(b), whereas Figure 2(c) presents medical centre-specific data examining the association between usage of ciprofloxacin and its resistance rate among Enterobacteriaceae during the 3 year evaluation period. In stark contrast to the aggregate data (r = +0.92 to 0.98) for all medical centres (Table 2), Figure 2(a–c) demonstrates essentially no correlation (r = –0.23 to 0.26) between antimicrobial usage of carbapenems or ciprofloxacin and resistance rates for these compounds at the individual medical centre level. Corresponding correlation coefficients for the relationships between medical centre-specific antimicrobial usage and resistance rates for the remaining test agents were also low, ranging from –0.23 to +0.61 for P. aeruginosa and –0.38 to +0.37 for Enterobacteriaceae (data not shown). For P. aeruginosa, gentamicin had the highest r value (0.61) when medical centre-specific results were calculated.



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Figure 2. (a) Medical centre-specific data on ciprofloxacin usage compared with P. aeruginosa resistance (R) rates for ciprofloxacin (MYSTIC Programme USA; 1999–2001). (b) Medical centre-specific data on carbapenem usage compared with P. aeruginosa R rates for carbapenems (MYSTIC Programme USA; 1999–2001). (c) Medical centre-specific ciprofloxacin usage compared with Enterobacteriaceae R rates for ciprofloxacin (MYSTIC Programme USA; 1999–2001).

 
Figure 3 presents medical centre-specific data examining the relationship between usage of carbapenems and the year-to-year change in resistance rates ({Delta}R) for this agent among P. aeruginosa. Evaluation of Figure 3 reveals no significant relationships between antimicrobial usage and {Delta}R. To allow for evaluation of {Delta}R, only those institutions providing antimicrobial usage and microbiological isolates during >=2 consecutive years were included in the analysis. Correlation coefficients for the relationship between antimicrobial usage and {Delta}R for P. aeruginosa ranged from –0.56 (ciprofloxacin) to +0.36 (piperacillin–tazobactam), with an average r value of –0.24. Of the antimicrobial agents evaluated, ciprofloxacin provided the highest causal relationship between the two parameters (r = –0.56). Similarly, no correlation between antimicrobial usage and {Delta}R could be demonstrated for Enterobacteriaceae: correlation coefficients ranged from –0.34 (ceftazidime) to +0.43 (piperacillin–tazobactam), with an average r value of only 0.09.



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Figure 3. Medical centre-specific carbapenem usage compared with year-to-year change at the individual medical centres in P. aeruginosa resistance (R) rates for carbapenems ({Delta}R).

 

    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Antimicrobial drug use is one of the documented risk factors for the development of antimicrobial resistance. Studies evaluating the relationship between antimicrobial usage and antimicrobial resistance require careful collection and analysis of data from various types of institutions. Additionally, due to the wide assortment of interventions taking place within monitored institutions, data obtained do not always reflect usage/resistance relationships consistently when comparing aggregate data to specific data obtained from each medical centre.

The objective of the present analysis was to assess possible relationships between consumption of broad-spectrum antimicrobials and rates of antimicrobial resistance among P. aeruginosa and Enterobacteriaceae isolates collected during 1999–2001 from USA medical centres participating in the MYSTIC Programme. The results from the MYSTIC Programme (USA) review have demonstrated several of the problems associated with attempts to calculate the relationships between drug usage and resistance. The initial analysis of aggregate usage data (all participants), led to the perception that a direct relationship existed between rates of P. aeruginosa and/or Enterobacteriaceae resistance for meropenem, ceftazidime, ciprofloxacin and piperacillin/tazobactam, and usage of these agents. However, as the data were assessed more critically by medical centre-specific parameters, it became apparent that direct causal relationships between usage and resistance could not be demonstrated clearly. Other factors, in addition to antimicrobial consumption, such as therapeutic or infection control interventions that might have taken place at some institutions during the evaluation period, could have resulted in significant changes within individual medical centres, without having a noticeable impact on the incidence of resistance as a group. Consequently, we need to look for other monitoring issues that might better allow evaluations of usage and resistance, as well as other confounding factors.

Antimicrobial usage analysis is therefore a complex task, and the results of each study may vary depending on the manner in which it is quantified.5,15 In our analyses, defined by the WHO Collaborating Centre for Drug Statistics Methodology14 to assess antimicrobial usage, DDD has been generally considered the preferred technical measurement for assessing consumption patterns in health care environments. However, the DDD has been related to different denominators, such as DDD per 100 occupied bed days, DDD per 100 patient days, DDD per 1000 patient days and DDD per 1000 inhabitants per day for the use of antimicrobials in the community. One criticism of drug usage studies has been whether or not the studies contain appropriate descriptions of the methods used to evaluate such usage.15 The DDD method was developed for statistical purposes, and its limitations have been recognized by those who developed it.14 The main limitation resides in its very definition: ‘the DDD is the assumed average dose per day for a drug used on its main indication in adults’. However, the recommended dose may vary widely among patients or clinicians, and could be expressed as a range. In reality, the appropriate dose for an individual patient cannot be determined without several adjustments that address the patient’s specific clinical needs and the specific pharmacological properties of each antimicrobial agent. Additionally, the DDD of a given antimicrobial may vary geographically (country to country) due to differences in medical practice or administrative/regulatory philosophy.

Many studies have evaluated the relationship between antimicrobial use and resistance in clinical isolates, and distinctly different results have been reported.16 Differing results can be explained simply by the lack of consistencies in the types of patient populations studied (entire nations, hospitalized or non-hospitalized patients, intensive care units, etc.), and the definitions of antimicrobial usage (quantitative, lag times, etc.). As an example of such a study, German workers reported research that evaluated the consumption of imipenem and other ß-lactams and correlations with ß-lactam resistance among P. aeruginosa isolates in a 600-bed community hospital.4 In the study, DDD/month were used as the marker for antimicrobial consumption, and the conversion from gram amounts of antimicrobials to DDD was performed by using the most frequently administered doses used in the hospital, and did not take into account the number of hospitalized patients/month. Study results showed monthly resistance rates of imipenem, piperacillin–tazobactam and ceftazidime were associated with imipenem prescription rates in the same or the preceding month, and that the monthly use of ceftazidime or piperacillin–tazobactam had no apparent association with resistance.4 Researchers in the USA recently evaluated the degree of antimicrobial resistance among Gram-negative bacilli and antimicrobial use during a 5 year period in a Veterans Affairs medical center medical intensive care units (ICU).3 Annual antimicrobial use data were collected retrospectively for the 5 year evaluation period, and the DDD was determined according to the methods described previously in the Intensive Care Antimicrobial Resistance Epidemiology Project (ICARE).7 The results showed that for several antimicrobials (fluoroquinolones, ceftazidime), significant changes in susceptibilities inversely corresponded with a significant change in the use of the specific antimicrobial agent or category. However, imipenem demonstrated different effects between the two types of ICU patient organism populations. P. aeruginosa susceptibility rates to imipenem increased in both units during the 5 year period, but imipenem usage decreased by more than 30% in the medical ICU, while increasing 85% in the surgical ICU.3

The MYSTIC Programme is one of the most published and cited longitudinal, comprehensive global surveillance networks.811,16 The commitment to well-organized surveillance, timely and effective data distribution make it valuable as only one component of a global effort to minimize the spread and proliferation of antimicrobial resistance.16 The continued success of the MYSTIC Programme is dependent on improving the collection of the antimicrobial usage data and subsequent inclusion of more detailed patient and institutional demographic profiling to complement the comprehensive in vitro surveillance results.16

From this first MYSTIC Programme (USA) antimicrobial consumption report, we have failed to demonstrate clearly a correlation between antimicrobial usage as a single parameter and the rates of resistance. Regardless of this, the results suggest emerging resistance problems are concurrent with increased usage of fluoroquinolones and aminoglycosides, whereas the carbapenems were less associated with quantifiable resistance problems. Currently, the broad-spectrum carbapenems (specifically imipenem and meropenem) demonstrate a stable spread of antimicrobial activity against MYSTIC Programme isolates in centres where these agents have been used frequently. Future reports from this programme will provide continued trend analysis in an effort to follow the impact of the recently released carbapenem, ertapenem, and to expand the number of monitored parameters that may significantly impact resistance rates.


    Acknowledgements
 
We thank K. Meyer for assistance in the preparation of this manuscript and the support by an educational/research grant from AstraZeneca. The MYSTIC Programme (USA) participant sites were: Arkansas Children’s Hospital (T. Beavers-May/R. Jacobs), Little Rock, AR; Christiana Care Health Service (L. Steele-Moore), Wilmington, DE; Columbia Presbyterian Medical Center (P. Della-Latta/L. Lee), New York, NY; Creighton University, St. Joseph Hospital (S. Cavalieri), Omaha, NE; Denver Health Medical Center (M. Wilson), Denver, CO; New York University Medical Center (P. Tierno), New York, NY; Northwestern Memorial Medical Center (L. Peterson), Chicago, IL; Ochsner Clinic Foundation (G. Pankey), New Orleans, LA; University Hospitals of Cleveland/Case Western Reserve (M. Jacobs), Cleveland, OH; University of Iowa Healthcare (M.A. Pfaller), Iowa City, IA; University of Texas, MD Anderson Cancer Center (K. Rolston), Houston, TX; University of Utah, ARUP Laboratories, Inc. (K. Carroll), Salt Lake City, UT; Veterans’ Affairs Medical Center, (D. Sewell), Portland, OR; Winthrop University Hospital (P. Schoch), Mineola, NY; Spectrum Health (T. Cappo), Grand Rapids, MI; Children’s Hospital of San Diego (J. Bradley), San Diego, CA; Penrose Hospital (M. Reynolds), Colorado Springs, CO; and Emory University (F. Nolte), Atlanta, GA.


    Footnotes
 
* Corresponding author. Tel: +1-319-665-3370; Fax +1-319-665-3371; E-mail: ronald-jones@jmilabs.com Back


    References
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
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