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JAC Advance Access originally published online on June 26, 2007
Journal of Antimicrobial Chemotherapy 2007 60(3):605-612; doi:10.1093/jac/dkm228
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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

What are the most appropriate antibiotics for the treatment of acute exacerbation of chronic obstructive pulmonary disease? A therapeutic outcomes model

Andrés Canut1,*, Jose E. Martín-Herrero2, Alicia Labora1 and Hiart Maortua1

1 Department of Clinical Microbiology, Hospital Santiago Apóstol, Vitoria, Spain 2 Medical Department, GlaxoSmithKline S.A., Tres Cantos, Spain

* Corresponding author. Tel: +34-945007874; E-mail: acanut{at}hsan.osakidetza.net

Received 10 January 2007; returned 25 March 2007; revised 24 May 2007; accepted 26 May 2007


   Abstract
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 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 Funding
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 References
 
Objectives: To predict the clinical efficacy of several antimicrobials in the treatment of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

Methods: A probability model (therapeutic outcomes model) was used to predict the likelihood of clinical success with particular antimicrobial agents in the treatment of patients with AECOPD, both in those clinically diagnosed (total patients with an AECOPD diagnosis regardless of the cause) and in those with bacterial AECOPD. The model took into account the following variables: (i) the proportion of patients with a clinical diagnosis of AECOPD and non-bacterial disease; (ii) likelihood of spontaneous resolution of a non-bacterial infection; (iii) prevalence of subcauses (different bacterial species) in bacterial AECOPD; (iv) rates of spontaneous resolution of bacterial AECOPD; and (v) antimicrobial efficacy of each antibiotic against each bacterial species (susceptibility based on PK/PD breakpoints).

Results: Fluoroquinolones (levofloxacin, ciprofloxacin and moxifloxacin), a new third-generation oral cephalosporin (cefditoren) and high doses of amoxicillin/clavulanate were the antimicrobials with the highest predicted clinical efficacy both in mild–moderate AECOPD and in severe AECOPD (rates of 89.2% to 90.5% and 80.3% to 88.1%, respectively), whereas cefaclor, azithromycin, erythromycin and clarithromycin had the lowest predicted clinical efficacy (rates of 79.1% to 81.3% and 51.8% to 55.6% for mild–moderate and severe AECOPD, respectively), which was not much higher than that predicted for placebo (73.6% and 45.5%, respectively).

Conclusions: According to our model, fluoroquinolones (levofloxacin, ciprofloxacin and moxifloxacin), cefditoren and amoxicillin/clavulanate are the most appropriate antibiotics for the treatment of patients with AECOPD in terms of predicted clinical efficacy, with wide differences with respect to other antibiotics commonly used in the treatment of these patients, such as clarithromycin and azithromycin.

Keywords: mathematical models , AECOPD , resistance


   Introduction
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 Abstract
 Introduction
Materials and methods
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Acute exacerbations, mainly an infectious aetiology, are a frequent cause of morbidity in chronic obstructive pulmonary disease (COPD) with an average of two episodes per year requiring medical attention in patients with moderate-to-severe COPD. Acute exacerbations are the most common cause of death among COPD patients.1,2

Acute exacerbation of COPD (AECOPD) accounted for 280 000 hospital admissions and 10 million outpatient visits in the USA (1999). As inpatient management of AECOPD is more than 70 times as expensive as outpatient care, any therapy that allows more patients to be treated in the outpatient setting and to reduce the risk of failure and the need for hospitalization is likely to generate significant savings.3

Initial antimicrobial treatment for patients with AECOPD is usually selected empirically and should provide appropriate coverage against the most common causative organisms, including resistant strains. The choice of antibiotics for empirical treatment should be based on the spectrum of pathogens causing AECOPD, the local resistance situation and the severity of illness.4

Several methods are used to assess the activity and efficacy of antibiotics in AECOPD. The usual method used is to determine clinical outcome in a non-inferiority clinical trial. However, it must be taken into account that patients with AECOPD have high rates of spontaneous resolution, and a non-inferiority study design will not differ between the arms of the study. Superiority outcome clinical studies would require considerably larger sample sizes than non-inferiority studies. Therefore, discrimination between ‘adequate’ and ‘non-adequate’ antimicrobials based on clinical outcome alone is not easy,5 because of the difficulty in having enough numbers of patients in clinical trials infected with resistant strains. This limitation of the clinical studies to show differences between antibiotics can be reduced by the application of PK/PD parameters that predict bacterial eradication, efficacy and prevention of resistance emergence. Application of these parameters should benefit therapeutic outcomes,6 as they allow integration of in vitro antibiotic susceptibility with in vivo response.7,8

Integrating PK/PD parameters into a probability model would make it possible to know the potential impact of antimicrobial resistance on clinical outcomes. The aim of the present study was to predict and calculate the clinical efficacy of several antimicrobials in the treatment of patients with AECOPD, using a therapeutic outcomes model (TOM) proposed by Poole.9


   Materials and methods
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 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 Funding
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 References
 
A previously described probability model (TOM) was used to predict the likelihood of clinical success with particular antimicrobial agents.9 The TOM was designed to predict the treatment outcomes in those disease states where the clinical diagnosis is not clear (difficulty to differentiate viral causes from bacterial causes from non-infectious causes), where there are multiple subcauses (several bacterial species) with different clinical courses and where there are various treatment options (antimicrobials) with a variable effect on the different bacterial causes involved. This model was used to calculate the clinical efficacy of different antimicrobials in patients with acute bacterial rhinosinusitis (ABRS). The model was the base for the antimicrobial recommendations from Sinus and Allergy Health Partnership's antimicrobial guidelines ABRS.10

The TOM carried out for AECOPD takes into account the following variables: (i) the proportion of patients with a clinical diagnosis of AECOPD and non-bacterial disease; (ii) likelihood of spontaneous resolution of a non-bacterial infection; (iii) prevalence of subcauses (different bacterial species) in AECOPD; (iv) rates of spontaneous resolution of bacterial AECOPD; and (v) antimicrobial efficacy of each antibiotic against each bacterial species (susceptibility based on PK/PD breakpoints).

The TOM organizes the variables into formulae, as described previously by Poole.9 For this study, a computer spreadsheet (Microsoft Excel®) was used. Values of the variables were entered as fractions of one, and the results were automatically calculated.

Two primary outcome measures were evaluated: (i) proportion of all bacterial cases spontaneously resolved or cured with each antibiotic; and (ii) the overall clinical efficacy in all patients considered. These measures were calculated for patients with mild–moderate AECOPD and severe AECOPD.

Proportion of patients with a clinical diagnosis of AECOPD and non-bacterial disease

Among patients with AECOPD, bacterial infection may be responsible for ~50% of exacerbations.11 Viruses may account for 25% of exacerbations, particularly rhinovirus, influenza and parainfluenza viruses and adenoviruses.11–13 In a recent study, Papi et al.12 reported the presence of rhinovirus in around half of virus-positive cases in patients with severe exacerbation. Other non-infectious factors such as heart failure, low temperature and air pollution may cause up to 25% of exacerbations.11,14 For this study, it was considered that non-bacterial causes were responsible for up to 55% and 28% of exacerbations in patients with mild–moderate and severe AECOPD, respectively.15

Rate of spontaneous resolution of a non-bacterial infection

The term ‘spontaneous resolution’ refers to any acute exacerbation that is resolved in the absence of effective treatment. The rate of spontaneous resolution varies according to the cause, but it is very difficult to estimate because there are no studies designed to investigate the rate of spontaneous resolution of the different causes of AECOPD. According to the study carried out by Stockley et al.,16 the spontaneous resolution rate among patients with AECOPD and a mucoid sputum on presentation suggestive of a non-bacterial episode was 85%, so globally we assumed this rate for the model.

Prevalence of subcauses in AECOPD

The prevalence for each bacterial subcause in AECOPD for the present model was based on a study carried out by Miravitlles et al.15 In this study, potentially pathogenic microorganisms were only regarded as significant if they reached a growth of 106 cfu, except for Streptococcus pneumoniae where growth of 105 cfu was deemed sufficient, in samples with less than 10 epithelial cells and greater than 25 leucocytes per low magnification field (100x). Bacterial agents were classified according to the degree of functional impairment, as measured by the FEV1 (<50% or >50% predicted).

For the purpose of this model, we considered mild–moderate COPD if FEV1 was higher than 50% and severe COPD if FEV1 was lower than 50%. Table 1 shows the distribution of pathogens according to the FEV1 considered for the present model.


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  		Table 1. Values for distribution of pathogens according to the FEV1 in patients with AECOPD used in the model

 
Rates of spontaneous resolution for patients with mild–moderate or severe bacterial AECOPD

We did not find any studies assessing the rate of spontaneous resolution of each bacterial species in AECOPD, so it was assumed that this rate is the same for different microorganisms involved in AECOPD. The calculation of the rate of spontaneous resolution in patients with AECOPD has been estimated from a study comparing an active antimicrobial with placebo.17 In this study, the rate of spontaneous resolution was analysed on the basis of severity of baseline lung function of the patients with AECOPD. The authors classified patients into three different clusters: cluster 1 (104 patients) mean screening FEV1 32.67% ± 6.83 (SD); cluster 2 (109 patients) mean screening FEV1 54.12% ± 5.56; cluster 3 (122 patients) mean screening FEV1 71.54% ± 5.51. Cluster 1 was considered to have severe COPD, whereas patients belonging to clusters 2 and 3 were considered to have mild–moderate COPD. For this model, rates of spontaneous resolution for patients with mild–moderate and severe COPD were considered to be 59.4% and 30.2%, respectively.

Antibacterial efficacy of each antibiotic against each bacterial species

Antibacterial efficacy of each antibiotic against the different pathogens involved in AECOPD was calculated by using current susceptibility data for each organism at PK/PD breakpoints (maximum MIC value complying with the adequate value for the predictive PD parameter),18–21 because PK/PD susceptibility breakpoints for given dosing regimens have been shown to correlate with bacteriological cure rates.7,8,18 In order to estimate the rates of antimicrobial susceptibility to amoxicillin, amoxicillin/clavulanate, cefuroxime axetil, cefaclor, ciprofloxacin, clarithromycin, erythromycin and azithromycin among S. pneumoniae and Haemophilus influenzae, we used data from a multicentre susceptibility surveillance (The SAUCE Project) carried out in Spain.22 As levofloxacin, moxifloxacin, telithromycin and cefditoren were not tested in the SAUCE Project, we obtained their rates of antimicrobial susceptibility from other multicentre susceptibility surveillance studies.23–27 Regarding Moraxella catarrhalis, we used data for all antimicrobials except cefditoren from multinational susceptibility surveillance (The Alexander Project).19 The rate of susceptibility of M. catarrhalis to cefditoren was obtained from the Arise Project.24 As regards respiratory isolates of Pseudomonas aeruginosa and Enterobacteriaceae (Escherichia coli, Proteus mirabilis and Klebsiella pneumoniae), the susceptibility rates to all antibiotics studied were obtained from recent surveillance studies and international databases.28–33

Table 2 shows the PK/PD susceptibility rates among respiratory pathogens involved in AECOPD to the antimicrobials in the study.


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  		Table 2. Susceptibility ratesa among respiratory pathogens based on PK/PD breakpointsb

 

   Results
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Discussion
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In patients with mild–moderate AECOPD caused by bacterial infection, according to our model, antimicrobials could be placed into the following relative rank order of predicted clinical efficacy: 97.0% for levofloxacin, 96.1% for moxifloxacin, 95.2% for cefditoren, 95% for ciprofloxacin, 94.2% to 93.6% for amoxicillin/clavulanate 2000/125 mg twice a day and 875/125 mg three times a day, respectively, 86.4% for telithromycin, 85.7% for cefuroxime axetil, 79.9% for amoxicillin, 76.7% for clarithromycin, 76.6% for erythromycin, 76.2% for azithromycin and 71.8% for cefaclor. If we consider all the patients clinically diagnosed (total number of patients with an AECOPD diagnosis), these figures were slightly lower for levofloxacin, moxifloxacin, cefditoren, ciprofloxacin, amoxicillin/clavulanate, telithromycin and cefuroxime axetil and slightly higher for amoxicillin, clarithromycin, erythromycin, azithromycin and cefaclor. The predicted spontaneous resolution rate for placebo in patients with bacterial infection and in those with clinically diagnosed AECOPD was as high as 59.5% and 73.6%, respectively (Table 3).


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  		Table 3. Calculated clinical efficacy in AECOPD (mild–moderate COPD)

 
Regarding predicted clinical efficacy for patients with severe AECOPD, levofloxacin, ciprofloxacin, moxifloxacin, cefditoren and amoxicillin/clavulanate were again the antimicrobials with the highest resolution rates both in patients with bacterial infection and in patients with clinically diagnosed AECOPD. Likewise, macrolides and cefaclor showed the lowest figures. The predicted spontaneous resolution rates for placebo (30.2% and 45.5% for patients with bacterial infection and patients with clinically diagnosed AECOPD, respectively) were lower in this group than in the mild–moderate AECOPD group (Table 4).


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  		Table 4. Calculated clinical efficacy in AECOPD (severe COPD)

 
Resolution rates for the bacterially infected group and the clinically diagnosed AECOPD are shown in Figures 1 and 2.


Figure 1
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  		Figure 1. Mild–moderate COPD. Resolution rates for patients with bacterial infection and those with clinically diagnosed AECOPD.

 


Figure 2
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  		Figure 2. Severe COPD. Resolution rates for patients with bacterial infection and those with clinically diagnosed AECOPD.

 

   Discussion
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 Introduction
 Materials and methods
 Results
 Discussion
Funding
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The present model allows us to perform an approach for the ranking of different antimicrobials in the treatment of patients with AECOPD, based on the coverage of the antimicrobials against bacterial pathogens causing AECOPD. Fluoroquinolones such as levofloxacin, ciprofloxacin and moxifloxacin, the new third-generation oral cephalosporin, cefditoren, and high doses of amoxicillin/clavulanate were the antimicrobials with the highest predicted clinical efficacy in mild–moderate AECOPD and severe AECOPD (rates of 88.9% to 90.5% and 79.3% to 88.1%, respectively), whereas cefaclor, azithromycin, erythromycin and clarithromycin were those with the lowest predicted clinical efficacy (rates of 79.1% to 81.3% and 51.8% to 55.6%, respectively), which was expected to be not much more than that predicted with placebo (73.6% and 45.5%, respectively).

For this model, we have used PK/PD susceptibility breakpoints because their ability to predict therapeutic efficacy is higher than the Clinical and Laboratory Standards Institute (CLSI) susceptibility breakpoints. For S. pneumoniae, there are small differences depending on whether CLSI or PK/PD susceptibility breakpoints are considered, whereas for H. influenzae, there are large discrepancies between CLSI and PK/PD interpretative categories at least for certain antibiotics. So, for instance, susceptibility to clarithromycin shifts from 72.3% to 1.2%, to azithromycin from 100% to 2.2% and to cefaclor from 82% to 1.4% when PK/PD breakpoints are used.22 In the case of macrolides, this can be explained as a result of the presence of efflux pumps in virtually all H. influenzae strains. In fact, there is evidence to confirm that in acute otitis media, H. influenzae behaves clinically as a macrolide-resistant organism, despite the in vitro susceptibility claimed by CLSI, because bacteriological failures occur in patients infected with H. influenzae independently of the MIC values.34

The principal objective in prescribing antimicrobials for patients with AECOPD is to eradicate the pathogen and to shorten the duration of symptoms and minimize failure rate. In addition, in patients with severe COPD, where patients' pulmonary reserves are limited, antibiotic therapy is also given with the aim of preventing further disability and hospitalization, whereas in patients with the most advanced forms of disease, antibiotics may increase the likelihood of survival.35 Several placebo-controlled studies have documented the benefit of antibiotics for the treatment of AECOPD,36–40 particularly in patients who have severe COPD, probably because, in this group of patients, the spontaneous resolution rate is lower than in patients with mild-to-moderate COPD. A meta-analysis on nine randomized, placebo-controlled studies demonstrated significant improvements in outcome measurements for patients with AECOPD treated with antibiotics, especially in those with low baseline flow rates.41 In our model, the predicted spontaneous resolution rate in patients with mild–moderate AECOPD was higher than that of patients with severe AECOPD (73.6% versus 45.5%).

Spontaneous clinical recovery may very often mask differences in bacteriological effectiveness of antibiotics. Thus, antibiotics with poor bacteriological efficacy can appear clinically almost as good as those with optimal efficacy.5 Although sometimes these small differences could be considered irrelevant, when they are translated to significant number of bacteriological failures in large populations, they could lead to prolongation of morbidity, risk of resistance emergence and dramatic cost consequences.6 In addition, in some diseases such as AECOPD, where the clinical diagnosis may be uncertain and it may be difficult to differentiate between bacterial and viral or non-infectious causes, the differences between adequate and non-adequate antibiotics can be masked even more. In our model, if only bacterial cases of AECOPD are considered, wide differences between the different antimicrobials are observed. However, these differences are much less evident when all the cases of clinically diagnosed AECOPD (bacterial and non-bacterial cases) are considered. Thus, in mild–moderate AECOPD, although the difference between levofloxacin and cefaclor was up to 25.2% (97% versus 71.8%) for patients with bacterial infection, it was just 11.4% when patients with bacterial and non-bacterial infection were considered.

Our model has several limitations. First, this is a theoretical model in which it is assumed that PK/PD resistance led to clinical failure. The resolution rates based on in vitro microbiological efficacy do not always guarantee clinical outcome. However, in the absence of better evidence, the TOM could be regarded as the best method to predict clinical outcomes and to investigate differences between antimicrobials. Secondly, some variables such as patient compliance with antibiotic treatment or previous antibiotic use were not included in the model. We considered a 100% compliance rate for all the antibiotics included in the model as well as the lack of previous antibiotic use, as our main objective was to predict the clinical efficacy for each antibiotic based on its antimicrobial characteristics, regardless of other factors such as the patient compliance with the treatment or history of previous antibiotic consumption. Thirdly, another potential limitation of this study is the paucity of data regarding the spontaneous resolution rates of the several subcauses of AECOPD. We assumed a similar spontaneous resolution rate for all the bacteria involved in AECOPD because of the lack of data, as placebo-controlled studies assessing this issue have not been carried out.

According to the data generated for the model, a possible strategy for the treatment of patients with exacerbation of COPD would be to give priority to adequate ß-lactams for patients with mild–moderate and severe AECOPD without risk factors for P. aeruginosa and to fluoroquinolones for patients with severe AECOPD and risk factors for P. aeruginosa (recent hospitalization, frequent or recent administration of antibiotic, severe disease—FEV1 < 30%—and previous isolation of P. aeruginosa during exacerbation or patient colonized by P. aeruginosa) because of their activity against this microorganism.42,43

When comparing the model-generated rates of the different antibiotics with those reported in published clinical trials, it is observed that both are generally comparable, although some discrepancies can also be found. In the only prospective clinical trial in which patients with exacerbation of COPD have been stratified by degree of underlying illness, Martinez et al.44 reported a clinical success in outpatients with uncomplicated exacerbation of COPD (FEV1 > 50% in more than 98% of patients) of 93% and 90% for levofloxacin and azithromycin, respectively. For microbiologically evaluable patients, clinical response was slightly higher for levofloxacin (96.3%) and slightly lower for azithromycin (87.4%). For patients with complicated exacerbation of COPD (FEV1 < 50% in 52% of patients), clinical success was observed in 79.0% and 82% of patients treated with levofloxacin and amoxicillin/clavulanate, respectively. For microbiologically evaluable patients, clinical response was 81% for both levofloxacin and amoxicillin/clavulanate. Wilson et al.45 reported a clinical success rate for patients treated with moxifloxacin of 83% in those with FEV1 < 50% and 91% in those with FEV ≥ 50%. These figures were slightly lower (81% and 86%) for patients treated with other antibiotics (amoxicillin, clarithromycin and cefuroxime). Although resolution rates are concordant with those observed in clinical trials, differences between antimicrobials in these studies were not so wide as in our model. Several reasons could explain this: (i) trials routinely exclude patients with suspected resistant pathogens and with severe disease; (ii) these trials were undertaken in places with different patterns of resistance to those used in the model; (iii) studies were carried out in different geographical areas that is why the aetiological pattern could also be different; (iv) patient selection and clinical assessment criteria could modify the final cure rates; and (v) use of steroids in these studies.

The methodology used for this model is applicable to other disease processes where multiple causes and subcauses can be involved, with each having a variable clinical course (e.g. spontaneous resolution rate), and where different treatment options with different efficacy can be administered. Some examples would be other respiratory tract infections such as acute tonsillopharyngitis, acute otitis media, community-acquired pneumonia and odontogenic infections.

In summary, the data from this mathematical model suggest that fluoroquinolones (levofloxacin, ciprofloxacin and moxifloxacin), cefditoren and amoxicillin/clavulanate are the most appropriate antibiotics for the treatment of patients with AECOPD, with wide differences with respect to other antibiotics commonly used in the treatment of these patients, such as clarithromycin and azithromycin. Obviously, these differences were higher when only patients with bacterial infection were considered. As optimal therapy depends on the selection of agents that are reliably able to kill the pathogens implicated, even in an environment in which antibiotic-resistant strains are common, macrolides should not be considered as first-line antibiotics for the treatment of patients with AECOPD because of their poor activity against H. influenzae, the high prevalence of macrolide-resistant pneumococci and their lack of activity against enterobacteria. In conclusion, outpatient antibiotic treatment of AECOPD should maximize the likelihood of cure, using for it those antibiotics that have the highest predicted clinical efficacy.


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This study was supported in part by an unrestricted grant from GlaxoSmithKline S.A., Madrid, Spain.


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J. E. Martin-Herrero is an employee of GSK without other financial interests in the company.


   Acknowledgements
 
We gratefully acknowledge Dr Michael D. Poole (Mercer School of Medicine, Georgia Ear Institute, Savannah, GA, USA) for his permission to use the TOM.


   References
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1 Miravitlles M, Mayordomo C, Artes M, et al. Treatment of chronic obstructive pulmonary disease and its exacerbations in general practice. EOLO Group. Estudio Observacional de la Limitacion Obstructiva al Flujo Aereo. Respir Med (1999) 93:173–9.[CrossRef][Web of Science][Medline]

2 Burrows B, Earle RH. Course and prognosis of chronic obstructive lung disease. A prospective study of 200 patients. N Engl J Med (1969) 280:397–404.[Web of Science][Medline]

3 Niederman MS, McCombs JS, Unger AN, et al. Treatment cost of acute exacerbations of chronic bronchitis. Clin Ther (1999) 21:576–91.[CrossRef][Web of Science][Medline]

4 Ortqvist A. Treatment of community-acquired lower respiratory tract infections in adults. Eur Respir J Suppl (2002) 36:40–53.

5 Dagan R, Klugman KP, Craig WA, et al. Evidence to support the rationale that bacterial eradication in respiratory tract infection is an important aim of antimicrobial therapy. J Antimicrob Chemother (2002) 47:129–40.[CrossRef][Web of Science]

6 Ball P, Baquero F, Cars O, et al. Antibiotic therapy of community respiratory tract infections: strategies for optimal outcomes and minimized resistance emergence. J Antimicrob Chemother (2002) 49:31–40.[Abstract/Free Full Text]

7 Calbo E, Garau J. Application of pharmacokinetics and pharmacodynamics to antimicrobial therapy of community-acquired respiratory tract infections. Respiration (2005) 72:561–71.[Web of Science][Medline]

8 Craig WA. Pharmacokinetic/pharmacodynamic parameters: rationale for antibacterial dosing of mice and men. Clin Infect Dis (1998) 26:1–10.[Web of Science][Medline]

9 Poole MD. A mathematical therapeutic outcomes model for sinusitis. Otolaryngol Head Neck Surg (2004) 130:46–50.[CrossRef][Web of Science][Medline]

10 Anon JB, Jacobs MR, Poole MD, et al. Antimicrobial treatment guidelines for acute bacterial rhinosinusitis. Otolaryngol Head Neck Surg (2004) 130:1–45.[Web of Science][Medline]

11 Miravitlles M. Exacerbations of chronic obstructive pulmonary disease: when are bacteria important? Eur Respir J Suppl (2002) 36:9s–19s.

12 Papi A, Bellettato M, Braccioni F, et al. Infections and airway inflammation in chronic obstructive pulmonary disease severe exacerbations. Am J Respir Crit Care Med (2006) 173:1114–21.[Abstract/Free Full Text]

13 Seemungal TA, Harper-Owen R, Bhowmik A, et al. Detection of rhinovirus in induced sputum at exacerbation of chronic obstructive pulmonary disease. Eur Respir J (2000) 16:677–83.[Abstract]

14 Alvarez F, Bouza E, Garcia-Rodriguez JA, et al. Second consensus report on the use of antimicrobial agents in exacerbations of chronic obstructive pulmonary disease. Arch Bronconeumol (2003) 39:274–82.[CrossRef][Web of Science][Medline]

15 Miravitlles M, Espinosa C, Fernandez-Laso E, et al. Relationship between bacterial flora in sputum and functional impairment in patients with acute exacerbations of COPD. Study Group of Bacterial Infection in COPD. Chest (1999) 116:40–6.[CrossRef][Web of Science][Medline]

16 Stockley RA, O'Brien C, Pye A, et al. Relationship of sputum color to nature and outpatient management of acute exacerbations of COPD. Chest (2000) 117:1638–45.[CrossRef][Web of Science][Medline]

17 Allegra L, Blasi F, de Bernardi B, et al. Antibiotic treatment and baseline severity of disease in acute exacerbations of chronic bronchitis: a re-evaluation of previously published data of a placebo-controlled randomized study. Pulm Pharmacol Ther (2001) 14:149–55.[CrossRef][Web of Science][Medline]

18 Jacobs MR. Optimisation of antimicrobial therapy using pharmacokinetic and pharmacodynamic parameters. Clin Microbiol Infect (2001) 7:589–96.[CrossRef][Web of Science][Medline]

19 Jacobs MR, Felmingham D, Appelbaum PC, et al. The Alexander Project 1998–2000: susceptibility of pathogens isolated from community-acquired respiratory tract infection to commonly used antimicrobial agents. J Antimicrob Chemother (2003) 52:229–46.[Abstract/Free Full Text]

20 Jones RN, Pfaller MA, Jacobs MR, et al. Cefditoren in vitro activity and spectrum: a review of international studies using reference methods. Diagn Microbiol Infect Dis (2001) 41:1–14.[CrossRef][Web of Science][Medline]

21 Lodise TP, Preston S, Bhargava V, et al. Pharmacodynamics of an 800-mg dose of telithromycin in patients with community-acquired pneumonia caused by extracellular pathogens. Diagn Microbiol Infect Dis (2005) 52:45–52.[CrossRef][Web of Science][Medline]

22 Perez-Trallero E, Garcia-de-la-Fuente C, Garcia-Rey C, et al. Geographical and ecological analysis of resistance, coresistance, and coupled resistance to antimicrobials in respiratory pathogenic bacteria in Spain. Antimicrob Agents Chemother (2005) 49:1965–72.[Abstract/Free Full Text]

23 Soriano F, Granizo JJ, Fenoll A, et al. Antimicrobial resistance among clinical isolates of Streptococcus pneumoniae isolated in four southern European countries (ARISE Project) from adult patients: results from the cefditoren surveillance program. J Chemother (2003) 15:107–12.[Web of Science][Medline]

24 Soriano F, Granizo JJ, Coronel P, et al. Antimicrobial susceptibility of Haemophilus influenzae, Haemophilus parainfluenzae and Moraxella catarrhalis isolated from adult patients with respiratory tract infections in four southern European countries. The ARISE Project. Int J Antimicrob Agents (2004) 23:296–9.[Web of Science][Medline]

25 Alós JI, Oteo J, Aracil B, et al. Comparative in vitro study of the activity of moxifloxacin and other antibiotics against 150 strains of penicillin non-susceptible Streptococcus pneumoniae and against 110 strains of ampicillin-resistant Haemophilus influenzae isolated in 1999–2000 in Spain. J Antimicrob Chemother (2001) 48:141–56.[Free Full Text]

26 Querol-Ribelles JM, Molina J, Naberan K, et al. Discrepancy between antibiotics administered in acute exacerbations of chronic bronchitis and susceptibility of isolated pathogens in respiratory samples: multicentre study in the primary care setting. Int J Antimicrob Agents (2006) 28:472–6.[CrossRef][Web of Science][Medline]

27 Schito GC, Felmingham D. Susceptibility of Streptococcus pneumoniae to penicillin, azithromycin and telithromycin (PROTEKT 1999–2003). Int J Antimicrob Agents (2005) 26:479–85.[CrossRef][Web of Science][Medline]

28 Drusano GL, Preston SL, Fowler C, et al. Relationship between fluoroquinolone area under the curve: minimum inhibitory concentration ratio and the probability of eradication of the infecting pathogen, in patients with nosocomial pneumonia. J Infect Dis (2004) 189:1590–7.[CrossRef][Web of Science][Medline]

29 Karlowsky JA, Kelly LJ, Thornsberry C, et al. Susceptibility to fluoroquinolones among commonly isolated Gram-negative bacilli in 2000: TRUST and TSN data for the United States. Tracking Resistance in the United States Today. The Surveillance Network. Int J Antimicrob Agents (2002) 19:21–31.[CrossRef][Web of Science][Medline]

30 Sader HS, Biedenbach DJ, Jones RN. Global patterns of susceptibility for 21 commonly utilized antimicrobial agents tested against 48,440 Enterobacteriaceae in the SENTRY Antimicrobial Surveillance Program (1997–2001). Diagn Microbiol Infect Dis (2003) 47:361–4.[CrossRef][Web of Science][Medline]

31 Fluit AC, Verhoef J, Schmitz FJ. Frequency of isolation and antimicrobial resistance of gram-negative and gram-positive bacteria from patients in intensive care units of 25 European university hospitals participating in the European arm of the SENTRY Antimicrobial Surveillance Program 1997–1998. Eur J Clin Microbiol Infect Dis (2001) 20:617–25.[Web of Science][Medline]

32 Soussy CJ, Nguyen J, Goldstein F, et al. In vitro antibacterial activity of moxifloxacin against hospital isolates: a multicentre study. Clin Microbiol Infect (2003) 9:997–1005.[CrossRef][Web of Science][Medline]

33 Hoban DJ, Bouchillon SK, Johnson JL, et al. Comparative in vitro surveillance of amoxicillin–clavulanic acid and four oral comparators against 21232 clinical isolates from Europe. Eur J Clin Microbiol Infect Dis (2003) 22:261–7.[Web of Science][Medline]

34 Dagan R, Leibovitz E. Bacterial eradication in the treatment of otitis media. Lancet Infect Dis (2002) 2:593–604.[CrossRef][Web of Science][Medline]

35 Pechere JC, Lacey L. Optimizing economic outcomes in antibiotic therapy of patients with acute bacterial exacerbations of chronic bronchitis. J Antimicrob Chemother (2000) 45:19–24.[Abstract]

36 Anthonisen P, Riis P, Sogaard-Andersen T. The value of lung physiotherapy in the treatment of acute exacerbations in chronic bronchitis. Acta Med Scand (1964) 175:715–9.[Web of Science][Medline]

37 Petersen ES, Esmann V, Honcke P, et al. A controlled study of the effect of treatment on chronic bronchitis. An evaluation using pulmonary function tests. Acta Med Scand (1967) 182:293–305.[Web of Science][Medline]

38 Elmes PC, King TK, Langlands JH, et al. Value of ampicillin in the hospital treatment of exacerbations of chronic bronchitis. Br Med J (1965) 2:904–8.[Free Full Text]

39 Nicotra MB, Rivera M, Awe RJ. Antibiotic therapy of acute exacerbations of chronic bronchitis. A controlled study using tetracycline. Ann Intern Med (1982) 97:18–21.[Abstract/Free Full Text]

40 Jorgensen AF, Coolidge J, Pedersen PA, et al. Amoxicillin in treatment of acute uncomplicated exacerbations of chronic bronchitis. A double-blind, placebo-controlled multicentre study in general practice. Scand J Prim Health Care (1992) 10:7–11.[Medline]

41 Saint S, Bent S, Vittinghoff E, et al. Antibiotics in chronic obstructive pulmonary disease exacerbations. A meta-analysis. JAMA (1995) 273:957–60.[Abstract/Free Full Text]

42 Woodhead M, Blasi F, Ewig S, et al. Guidelines for the management of adult lower respiratory tract infections. Eur Respir J (2005) 26:1138–80.[Abstract/Free Full Text]

43 Karlowsky JA, Jones ME, Thornsberry C, et al. Stable antimicrobial susceptibility rates for clinical isolates of Pseudomonas aeruginosa from the 2001–2003 tracking resistance in the United States today surveillance studies. Clin Infect Dis (2005) 40(Suppl 2):89–98.[CrossRef][Web of Science][Medline]

44 Martinez FJ, Grossman RF, Zadeikis N, et al. Patient stratification in the management of acute bacterial exacerbation of chronic bronchitis: the role of levofloxacin 750 mg. Eur Respir J (2005) 25:1001–10.[Abstract/Free Full Text]

45 Wilson R, Allegra L, Huchon G, et al. Short-term and long-term outcomes of moxifloxacin compared to standard antibiotic treatment in acute exacerbations of chronic bronchitis. Chest (2004) 125:953–64.[CrossRef][Web of Science][Medline]


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