Skip to main content
Download PDF

Microorganisms resistant to conventional antimicrobials in acute exacerbations of chronic obstructive pulmonary disease

Respiratory Research volume 19, Article number: 119 (2018) Cite this article

Abstract

Background

Antimicrobial treatment for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) remains controversial. In some cases AECOPD are caused by microorganisms that are resistant to treatments recommended by guidelines. Our aims were: 1) identify the risk factors associated with infection by microorganisms resistant to conventional treatment (MRCT), 2) Compare the clinical characteristics and outcomes of patients with AECOPD resulting from MRCT against those with AECOPD from other causes.

Methods

We prospective analysed a cohort of patients admitted with severe AECOPD (2009 to 2015) who were assigned to three groups: patients with MRCT (those patients with germs resistant to antibiotics recommended in guidelines), patients with microorganisms sensitive to conventional antimicrobial treatment (MSCT), and patients with negative microbiology results who had not previously received antibiotics. Multinomial logistic regression analyses were used to examine the associations between microbial aetiology groups and risk factors. The association between LOS and risk factors was also tested in simple and multiple analyses, and similar inclusion criteria were applied for the linear regression analysis.

Results

Of the 451 patients admitted, 195 patients (43%) were included. Respiratory cultures were positive in 86(44%) and negative in 109(56%). MRCT were isolated in 34 cases (40%) and MSCT in 52 (60%). Patients with MRCT had more AECOPD in the previous year, received more antibiotic treatment in the previous three months, had more severe disease, higher dyspnoea and a positive respiratory culture in the previous year (mainly for Pseudomonas aeruginosa). The following conditions were independent factors for MRCT isolation: non-current smoker (odds ratio [OR] 4.19 [95% confidence interval [CI] 1.29–13.67], p = 0.017), ≥ 2 AECOPD or ≥ 1 admission for AECOPD in the previous year (OR 4.13 [95% CI 1.52–11.17], p = 0.005), C-reactive protein < 5 mg/dL; (OR 3.58 [95% CI 1.41–9.07], p = 0.007). Mortality rates were comparable at 30-days, one year and 3 years; however, patients in the MRCT group had longer hospital stays.

Conclusion

In conclusion, there are risk factors for resistant germs in AECOPD; however, the presence of these germs does not increase mortality. Patients with isolation of MRCT had longer length of stay.

Background

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) negatively affect hospitalisation, readmission, disease progression and mortality rates in patients with COPD [1]. Severe AECOPD are mainly triggered by bacterial infection, viral infection or environmental agents, with the most common causes of bacterial infection being Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis [2,3,4]. Thus, current recommendations for antimicrobial treatment are aminopenicillin with or without clavulanic acid, a macrolide or a tetracycline [5, 6]. AECOPD are infrequently caused by microorganisms—such as Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus (MRSA), Stenotrophomonas maltophilia or enterobacteria—that are resistant to these treatments. Guidelines and previous studies of severe AECOPD suggest that these patients have increased frequencies of exacerbations, previous antibiotic use, previous hospital admissions and more severe airflow limitations [2, 5, 7, 8].

At least 30% of COPD patients are colonised by a potential pathogen when in a stable phase of their disease; however only 0.5% are colonised by Enterobacteriaceae, P. aeruginosa or S. maltophilia [9]. Also, AECOPD are associated with the overgrowth of potential pathogens and with the occurrence of P. aeruginosa in the lower airway [10]. Knowing the risk factors to microorganisms resistant to conventional antibiotic treatment (MRCT) in AECOPD could lead to improved prophylaxis and empirical antimicrobial treatment.

We hypothesised that specific factors predict the presence of MRCT. Our primary aim was to identify the risk factors associated with infection by MRCT. Our secondary aim was to compare the clinical characteristics and outcomes of patients with AECOPD resulting from MRCT against those with AECOPD from other causes.

Methods

Study design and patients selection

This observational cohort study was performed between January 2009 and December 2015, and included all patients admitted with a diagnosis of AECOPD to the Respiratory Department of the Hospital Clinic, Barcelona, Spain. COPD was defined according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines [5], with spirometry performed in a stable disease phase and at least six months prior to hospital admission. Patients with a smoking history of 10 pack-years were considered positive smokers. A worsening of respiratory symptoms compared with the preceding days, and which required a change in home care medication, was used as a clinical definition for AECOPD [5, 11]. Exacerbation severity was based on the respiratory symptoms/signs and the presence of potential indications for hospitalisation [5]. The exclusion criteria were: 1) documented history of asthma or bronchiectasis as the predominant illness and 2) clinical pneumonia or acute heart failure identified at admission.

Ethics approval and consent to participate

The study protocol was approved by the Hospital Research and Ethics Committee (CEIC 2008/4106) and the study was conducted in accordance with good clinical practice guidelines and the declaration of Helsinki. Written informed consent was obtained from all enrolled patients.

Microbiological evaluation

Sputum samples were obtained at admission for bacterial culture, before starting antibiotic therapy. Routine antimicrobial susceptibility testing included the disc diffusion method or E-test for P. aeruginosa. The results of susceptibility testing were interpreted according to the European Committee on Antimicrobial Susceptibility Testing guidelines [12]. Multidrug-resistant (MDR), extensively drug resistant (XDR) and pan-drug resistant bacteria were categorised according to criteria set out by Magiorakos et al. [13]. The quality of sputum samples was assessed using the Murray and Washington scoring system. Patients with poor quality sputum samples (> 10 epithelial cells or < 25 leucocytes) were excluded from analysis [14]. Patients with mycobacterial, fungal isolation (e.g., Aspergillus or Candida) or Nocardia spp. were also excluded.

Patients were classified into 3 groups: 1) patients with the isolation of microorganism sensitive to conventional treatment (MSCT) according to GOLD guidelines (i.e., aminopenicillin with clavulanic acid, a macrolide or a tetracycline); 2) patients with MRCT isolation, (i.e., P. aeruginosa, MRSA, S. maltophilia, Enterobacteriaceae producer of extended spectrum of beta lactamase and Acinetobacter baumannii); and 3) patients with negative microbiology results who did not receive antibiotics in the 7 days previous at admission.

Previous antibiotic treatment was not considered as inclusion/exclusion criteria in MRCT or MSCT groups. Nobody patient used macrolide as chronic treatment, thus it was not considered as variable.

Clinical measurements and outcomes

Demographic variables, body mass index (BMI), smoking history (former smoker was considered as those patients who quit smoke more than 12 months), presence of co-morbidities measured by Charlson index [15], baseline dyspnoea grade based in modified medical research council (mMRC), COPD severity score measured by a questionnaire (COPDSS) [16] and BODEx index (i.e., BMI, airflow obstruction, dyspnoea and exacerbations) [17], use of long-term oxygen therapy (LTOT) and use of domiciliary medications (i.e., inhaled bronchodilators, such as short-acting β2 agonist [SABA], long-acting β2 agonist [LABA], anticholinergics or inhaled corticosteroids) were recorded at hospital admission. Characteristics of any exacerbations during the previous year, any previous antibiotic treatment (3 months before admission) and any microorganism isolated in the previous year were also recorded. Vital signs (body temperature, respiratory rate, heart rate and blood pressure) were assessed at admission. Arterial blood gases and laboratory parameters (i.e., leukocytes, haematocrit, haemoglobin, C-reactive protein, glucose and creatinine) were recorded at admission and at day 3.

Variables relating to clinical progression included length of hospital stay (LOS), use of non-invasive mechanical ventilation (NIMV), use of invasive mechanical ventilation (IMV) and intensive care unit (ICU) admission during the initial hospitalisation. Data on prognosis (cumulative number of deaths for all-causes and time to death) were recorded at 30 days, 1 year and 3 years.

Statistical analysis

We report the number and percentage of patients for categorical variables and the median and interquartile range (IQR) for continuous variables. Categorical variables were compared using the chi-square test, and continuous variables were compared by one-way analysis of variance or the nonparametric Kruskal–Wallis test. Post-hoc pairwise comparisons were carried out via the Bonferroni method to control for the experiment-wise error rate. Survival curves were obtained using the Kaplan–Meier method and compared using the Gehan-Breslow-Wilcoxon test. Patients lost to follow-up were censored in the survival analysis.

Multinomial logistic regression analyses were used to examine the associations between microbial aetiology groups (i.e., MRCT or MSCT relative to unknown aetiology) and risk factors (i.e., baseline characteristics and clinical presentation). Variables were included in the multivariate model when univariate comparisons yielded a level of significance of p < 0.05 due the limited number of patients in the MRCT and MSCT groups and in order to exclude bias related to overestimation or underestimation of regression coefficient variance. The final multivariate model was calculated in a stepwise forward selection procedure (pin = 0.05, pout = 0.10). To identify the problem of collinearity, we calculated the r coefficient of 2 variables; that is, if 2 independent variables were highly correlated (r > | ± 0.30|), the variable with the largest variance was excluded from the multivariate analysis [18]. The association between LOS and risk factors was also tested in simple and multiple analyses, and similar inclusion criteria were applied for the linear regression analysis (p < 0.05). The odds ratios (ORs) or beta coefficients (βs) and their 95% confidence intervals (CIs) were estimated. The Cox and Snell R2 and the Nagelkerke R2 were calculated to assess the overall fit of the multinomial logistic regression model and the R2 for the linear regression model. The area under the receiver operating characteristic (ROC) curve of the multivariate model to predict MRCT was calculated. Internal validation of the prediction models was conducted using ordinary nonparametric bootstrapping with 1000 bootstrap samples and bias-corrected, accelerated, 95% CIs [19]. The same logistic regression analyses for microbial aetiology groups were also performed but using a multinomial logistic regression model for MRCT with only P. aeruginosa or MSCT relative to negative microbiology.

We investigated the missing data patterns for covariates, assumed missing at random [20], and used multiple imputation [21] to generate 5 datasets to evaluate the prediction performance for the microbial aetiology group. The model for multiple imputation included all covariates of the risk models as well as the microbial aetiology group. For simplicity, in the evaluation of the performance we filled in missing values with the first set of imputed values from the multiple imputation.

The level of significance was set at 0.05 (two-tailed). All analyses were performed with IBM SPSS Statistics 23.0 (Armonk, New York).

Results

Patient characteristics

Of the 451 patients admitted with an AECOPD during the observation period, 256 (57%) were excluded. The study population therefore comprised 195 patients (43%), of which 86 (44%) had positive respiratory cultures and 109 (56%) had negative microbiology and no history of previous antibiotic therapy (Fig. 1). AECOPD was associated with MRCT isolation in 34 cases (40%), and other pathogens were isolated in the remaining 52 cases (60%).

Fig. 1
figure 1

Flowchart

Full size image

Compared with the other groups, patients with MRCT tended to be non-current smokers, have more AECOPD episodes in the previous year, have more hospital admissions in the previous year, receive more antibiotic treatments in the previous 3 months and have more severe disease (higher mMRC dyspnoea grades, higher BODEx indexes and higher COPDSS values) (Table 1). No differences were observed at baseline in purulent sputum, Anthonisen AECOPD classification or pulmonary gas exchange. A higher percentage of patients with MRCT had a positive respiratory culture in the previous year, mainly for P. aeruginosa.

Table 1 Patient characteristics
Full size table

Microbiological findings

In the group of patients with MRCT, the most frequent pathogen was P. aeruginosa (25 patients [74%]). Two patients had methicillin-resistant S. aureus, one patient S. maltophilia, another had A. baumannii and 5 had polymicrobial aetiology (Fig. 2 and Additional file 1: Table S1). In total, 9 patients (50%) with previous P. aeruginosa isolation had received effective treatment. Among the patients with MSCT, S. pneumoniae and H. influenzae were the most common pathogens (35 and 31%, respectively), but 2 patients had Enterobacteriaceae (Klebsiella spp. and Serratia spp.) that were sensitive to aminopenicillins (Fig. 2). P. aeruginosa isolates were categorised as MDR in 10 cases (40%) and XDR in 3 cases (12%).

Fig. 2
figure 2

Microbial aetiology. Panel a Microorganisms Resistant to Conventional Treatment (n = 34). Panel b Patients with Microorganisms Sensitive to Conventional Treatment (n = 52). Polymicrobial Isolation includes: 3 Pseudomonas aeruginosa, 2 Stenotrophomonas maltophilia, 1 Staphylococcus aureus, 3 Streptococcus pneumoniae, 1 other. Others isolation include: 3 Corynebacterium spp., 2 Pasteurella spp., 1 Capnocytophaga spp.

Full size image

Antibiotic treatment

Data on antibiotic treatment were available for 163 patients (88%). The most frequent regimen was fluoroquinolone monotherapy (n = 82; 50%), penicillin monotherapy (n = 27; 17%) and antibiotic combination therapy (n = 44; 27%). The group with MRCT received more combination therapy (p = 0.003) and less fluoroquinolone monotherapy (p = 0.012) compared with the other 2 groups. The most frequent used combinations were β-lactam plus macrolide in the MSCT group and fluoroquinolones based combinations in the MRCT group. Empirical antimicrobial treatment was inadequate in 20 cases with positive microbiology (24%), of which 15 (44%) and 5 (10%) were among patients with MRCT and MSCT, respectively (p < 0.001).

Risk factors for MRCT and MSCT

The following risk factors showed significant associations with the microbial aetiology groups in individual multinomial logistic regression, and were thus used for the initial multivariate model: smoker status, ≥2 AECOPD episode or ≥ 1 AECOPD admission in the previous year, bronchiectasis, LTOT, BODEx index and C-reactive protein (data not shown). The results of the multivariate model are displayed in Table 2. The model shows that the OR for MRCT isolation was significantly increased if the patients was a non-current smoker, had ≥2 AECOPD episodes or ≥ 1 AECOPD admission in the previous year and had a low systemic inflammatory response. The OR for MSCT isolation, however, was strongly decreased with a high BODEx index (4th quartile) (Table 3). The AUC was 0.80 (95% CI, 0.72–0.87) for the model predictive of MRCT isolation (Fig. 3). The data for internal validation of the logistic regression model (using bootstrapping with 1000 samples) are presented in Additional file 1: Table S2. The variables included in the model demonstrated robust results, with small 95% CIs around the original coefficients. When differentiating MRCT with and without P. aeruginosa, previous isolation of P. aeruginosa was the most important predictor of P. aeruginosa isolation (Additional file 1: Table S3, Table S4 and Figure S1).

Table 2 Multinomial logistic regression model for microorganisms resistant to conventional treatment or microorganisms sensitive to conventional treatment relative to negative microbiology
Full size table
Table 3 Clinical outcomes
Full size table
Fig. 3
figure 3

Receiver operating characteristic curve for multinomial logistic regression model to predict MRCT isolation. Abbreviations: AUC indicates area under the curve; CI, confidence interval

Full size image

Outcomes

Patients with MRCT had longer median hospital stays than the other 2 groups (9 days [7,8,9,10,11,12,13,14] vs. 8 days [6,7,8,9,10] in both cases, respectively; p = 0.026) (Table 3). No differences were observed in ICU admission, IMV or NIMV rates among the groups. Mortality rates were comparable at 30-days, one year and 3 years, and the number of AECOPD after discharge did not differ among the groups. The Kaplan-Meier survival curves depicting the 3-year mortality rates as a function of the 3 microbial aetiology groups are shown in Fig. 4. No differences were observed when comparing patients with adequate and inadequate empiric antibiotic treatment in each group (MRCT and MSCT) or in overall population (Additional file 1: Table S5).

Fig. 4
figure 4

Kaplan–Meier analysis of the effect of microbial aetiology groups on time to death

Full size image

The simple linear regression analysis revealed several variables significantly associated with length of hospital stay (Table 4). BODeX index, CRP levels, requirement of mechanical ventilation (invasive or non-invasive) and isolation of MRCT were those independently associated with length of hospital stay in the multiple analyses. The data for internal validation of the linear regression model (using bootstrapping with 1000 samples) are presented in Additional file 1: Table S8. The variables included in the model demonstrated robust results, with small 95% CIs around the original coefficients.

Table 4 Significant simple and multiple linear regression analyses of associations of risk of length of hospital stay
Full size table

Patients with P. aeruginosa isolation in the MRCT group had higher hospital stays than patients without P. aeruginosa in the same group. Patients with isolation of MDR or XDR P. aeruginosa had the longest stays, but there was no difference in mortality (Additional file 1: Table S3, Table S4, Figure S1). The 3-year mortality among patients with < 2 AECOPD episodes and no admissions for AECOPD in the previous year differed significantly between groups in the Kaplan-Meier analysis (p = 0.019; Additional file 1: Figure S2).

Discussion

In our study, we analysed 3 well-characterised groups, comparing patients with MRCTs against controls groups of patients with MSCTs and patients with negative microbiology and no previous antibiotic use. Our analyses revealed that not currently smoking, ≥ 2 AECOPD episodes or ≥ 1 admission for AECOPD in the last year, and a low systemic inflammatory response at admission were independent risk factors for AECOPD caused by an MRCT. However, although patients with MRCT had longer hospital stays, they did not have higher mortality or more severe AECOPD than the control groups. At baseline, patients with MRCT had more severe disease, as measured by the dyspnoea scale, COPDSS scale, BODEx index and history of previous AECOPD. There were no differences in symptoms or pulmonary gas exchange features at admission.

AECOPD are events that mark disease progression, and as taken into account by the GOLD guidelines [5], are as important as airflow limitation. Indeed, it is evident that there are patients who are susceptible to frequent exacerbations, and in these, the most important predictor of future episodes is the history of AECOPD [22]; however, the association with microbiologic data has been poorly analysed to date.

A low systemic inflammatory response was a risk factor for MRCT isolation in this study, which could be due to lower virulence or reduced ability to produce acute phase reactants in the presence of these microorganisms. Similar results were observed in patients with community acquired pneumonia or ventilator associated pneumonia in whom P. aeruginosa was isolated [23, 24].

There was also an association between smoking status and MRCT isolation, specifically in favour of non-current smoking status. It is known that smoke increases upper respiratory tract colonisation of S. pneumoniae, H. influenzae, M. catarrhalis and Streptococcus pyogenes [25], and that smoking facilitates colonisation of the lung with these bacteria [9]. This is probably related to the decreased phagocytic ability of alveolar macrophages and the decreased cytokine response associated with smoking [26, 27]. The association between smoking status and MRCT isolation in this study should not, therefore, weaken the recommendation for smoking cessation for all patients. Other explanations to this point maybe those individuals who develop respiratory symptoms due to more severe disease being more likely to quit smoking.

Previous studies have produced controversial data about the presence of P. aeruginosa in isolates, though they have tended to show that sensitive P. aeruginosa had higher mortality [28,29,30,31]. We found no differences in mortality between patients with P. aeruginosa, including those with MDR strains. In other respiratory diseases, such as cystic fibrosis or non-cystic fibrosis bronchiectasis, microbiologic isolation of P. aeruginosa and MRSA has been shown to have an important role in disease progression [32,33,34,35,36]. Although there is evidence that eradication with antibiotic treatment would be beneficial in these diseases, there is no such evidence that similar benefits would exist for patients with COPD.

The role of antimicrobial treatment remains controversial in AECOPD. With the exception of patients who require mechanical ventilation and ICU admission, the benefits of antibiotic treatment are limited, and are mainly observed in patients with purulent sputum or in those with AECOPD graded as type I by the Anthonisen classification [37,38,39,40,41]. The effect of inadequate antibiotic treatment is poorly understood in patients with AECOPD. In this study, we did not observe any differences in outcomes between patients with inadequate and adequate empiric antibiotic treatment.

The predictive factors identified in this study represent the first step in the development of a prediction model. To move forward, the potential model will need to undergo external validation with larger patient cohorts from multiple centres. We could also apply the results of internal validation techniques to understand how likely this model will be replicable to future studies and to studies at other centres. Bootstrapping techniques were applied to our data, and the results indicated that the coefficients obtained from the prediction model were quite robust. Notably, previous P. aeruginosa isolation was the one factor that the bootstrap analysis indicated might have limited repeatability in future work. Thus, we opted to remove previous P. aeruginosa isolation from the overall model and include it in a specific multivariate analysis for P. aeruginosa. In the real-world clinical setting where this prediction model could be used, previous P. aeruginosa isolation remains an important clinical characteristic that can play a substantial role in decision making.

We did not observe differences in the majority of outcomes when comparing MRCT vs non-MRCT exacerbations. However; length of stay was longer in the MRCT group. This is an important outcome to be taken into account to make efforts in predicting and treating these microorganisms in AECOPD.

Our study has some limitations that should be acknowledged. First, the study was carried out at only one centre in Spain. Second, the small sample limited the analysis of specific factors per bacterium. There is limited information about MRCT isolation in patients with AECOPD, and where there is, it is mainly for bacteria other than P. aeruginosa. A confirmation of our results in a large and well balanced, international cohort of AECOPD is therefore desirable. Finally, other limitation of this study was the use of sputum cultures and the potential difficulty to distinguish between colonization and infection. However, we only accepted samples of good quality and we did not culture those of low quality. In addition this is the usual way to diagnose lower airway infection in AECOPD in the majority of studies, given that performing bronchoscopy in these patients is extremely difficult. Moreover we validated sputum cultures some years ago in comparison with bronchoscopic samples [42].

Conclusions

In conclusion, non-current smoking status, ≥2 AECOPD episodes or ≥ 1 admissions for AECOPD in the previous year, and low systemic inflammatory response are independent risk factors to have an AECOPD caused by a MRCT. Length of stay was significantly longer in AECOPD caused by MRCT microorganisms.

Abbreviations

AECOPD:

Acute exacerbations of chronic obstructive pulmonary disease

BMI:

Body mass index

CI:

Confidence intervals

COPD:

Chronic obstructive pulmonary disease

COPDSS:

COPD severity score

GOLD:

Global Initiative for Chronic Obstructive Lung Disease

LTOT:

Long-term oxygen therapy

MDR:

Multidrug-resistant

mMRC:

Modified medical research council

MRCT:

Microorganism resistant to conventional treatment

MRSA:

Methicillin-resistant Staphylococcus aureus

MSCT:

Microorganisms sensitive to conventional antimicrobial treatment

OR:

Odds ratios

ROC:

Receiver operating characteristic

XDR:

Extensively drug resistant

References

  1. Soler-Cataluña JJ, Martínez-García MA, Román Sánchez P, Salcedo E, Navarro M, Ochando R. Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease. Thorax. 2005;60:925–31.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Soler N, Torres A, Ewig S, Gonzalez J, Celis R, El-Ebiary M, et al. Bronchial microbial patterns in severe exacerbations of chronic obstructive pulmonary disease (COPD) requiring mechanical ventilation. Am J Respir Crit Care Med. 1998;157:1498–505.

    Article  PubMed  CAS  Google Scholar 

  3. Wedzicha JA, Seemungal TAR. COPD exacerbations: defining their cause and prevention. Lancet Lond Engl. 2007;370:786–96.

    Article  Google Scholar 

  4. White AJ, Gompertz S, Stockley RA. Chronic obstructive pulmonary disease. 6: the aetiology of exacerbations of chronic obstructive pulmonary disease. Thorax. 2003;58:73–80.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. From the Global Strategy for the Diagnosis, Management and Prevention of COPD Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017. Available http://www.goldcopd.org/. Accessed 1 Nov 2018.

  6. Woodhead M, Blasi F, Ewig S, Huchon G, Leven M, Ortqvist A, et al. Guidelines for the management of adult lower respiratory tract infections. Eur Respir J. 2005;26:1138–80.

    Article  PubMed  CAS  Google Scholar 

  7. Miravitlles M, Espinosa C, Fernández-Laso E, Martos JA, Maldonado JA, Gallego M. 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.

    Article  PubMed  CAS  Google Scholar 

  8. Eller J, Ede A, Schaberg T, Niederman MS, Mauch H, Lode H. Infective exacerbations of chronic bronchitis: relation between bacteriologic etiology and lung function. Chest. 1998;113:1542–8.

    Article  PubMed  CAS  Google Scholar 

  9. Soler N, Ewig S, Torres A, Filella X, Gonzalez J, Zaubet A. Airway inflammation and bronchial microbial patterns in patients with stable chronic obstructive pulmonary disease. Eur Respir J. 1999;14:1015–22.

    Article  PubMed  CAS  Google Scholar 

  10. Rosell A, Monsó E, Soler N, Torres F, Angrill J, Riise G, et al. Microbiologic determinants of exacerbation in chronic obstructive pulmonary disease. Arch Intern Med. 2005;165:891–7.

    Article  PubMed  Google Scholar 

  11. Celli BR, Barnes PJ. Exacerbations of chronic obstructive pulmonary disease. Eur Respir J. 2007;29:1224–38.

    Article  PubMed  CAS  Google Scholar 

  12. Leclercq R, Cantón R, Brown DFJ, Giske CG, Heisig P, MacGowan AP, et al. EUCAST expert rules in antimicrobial susceptibility testing. Clin Microbiol Infect Off Publ Eur Soc Clin Microbiol Infect Dis. 2013;19:141–60.

    CAS  Google Scholar 

  13. Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect Off Publ Eur Soc Clin Microbiol Infect Dis. 2012;18:268–81.

    CAS  Google Scholar 

  14. Murray PR, Washington JA. Microscopic and baceriologic analysis of expectorated sputum. Mayo Clin Proc. 1975;50:339–44.

    PubMed  CAS  Google Scholar 

  15. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.

    Article  PubMed  CAS  Google Scholar 

  16. Miravitlles M, Llor C, de Castellar R, Izquierdo I, Baró E, Donado E. Validation of the COPD severity score for use in primary care: the NEREA study. Eur Respir J. 2009;33:519–27.

    Article  PubMed  CAS  Google Scholar 

  17. Soler-Cataluña JJ, Martínez-García MA, Sánchez LS, Tordera MP, Sánchez PR. Severe exacerbations and BODE index: two independent risk factors for death in male COPD patients. Respir Med. 2009;103:692–9.

    Article  PubMed  Google Scholar 

  18. Healey JF. Statistics: a tool for social research. 9th ed: Wadsworth: Cengage Learning; 2011.

  19. Efron B, Tibshirani RJ. An introduction to the bootstrap: CRC Press; 1994.

  20. Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Steyerberg E. Clinical prediction models: a practical Aapproach to development, validation, and updating. Springer: New York, 2009.

    Book  Google Scholar 

  22. Hurst JR, Vestbo J, Anzueto A, Locantore N, Müllerova H, Tal-Singer R, et al. Susceptibility to exacerbation in chronic obstructive pulmonary disease. N Engl J Med. 2010;363:1128–38.

    Article  PubMed  CAS  Google Scholar 

  23. Cillóniz C, Gabarrús A, Ferrer M, Puig de la Bellacasa J, Rinaudo M, Mensa J, et al. Community-acquired pneumonia due to multidrug and non-multidrug resistant Pseudomonas aeruginosa. Chest 2016.

  24. Fernández-Barat L, Ferrer M, De Rosa F, Gabarrús A, Esperatti M, Terraneo S, et al. Intensive care unit-acquired pneumonia due to Pseudomonas aeruginosa with and without multidrug resistance. J Inf Secur. 2017;74:142–52.

    Google Scholar 

  25. Brook I, Gober AE. Recovery of potential pathogens and interfering bacteria in the nasopharynx of smokers and nonsmokers. Chest. 2005;127:2072–5.

    Article  PubMed  Google Scholar 

  26. Berenson CS, Garlipp MA, Grove LJ, Maloney J, Sethi S. Impaired phagocytosis of nontypeable Haemophilus influenzae by human alveolar macrophages in chronic obstructive pulmonary disease. J Infect Dis. 2006;194:1375–84.

    Article  PubMed  Google Scholar 

  27. Berenson CS, Wrona CT, Grove LJ, Maloney J, Garlipp MA, Wallace PK, et al. Impaired alveolar macrophage response to Haemophilus antigens in chronic obstructive lung disease. Am J Respir Crit Care Med. 2006;174:31–40.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Renom F, Yáñez A, Garau M, Rubí M, Centeno M-J, Gorriz M-T, et al. Prognosis of COPD patients requiring frequent hospitalization: role of airway infection. Respir Med. 2010;104:840–8.

    Article  PubMed  Google Scholar 

  29. Boutou AK, Raste Y, Reid J, Alshafi K, Polkey MI, Hopkinson NS. Does a single Pseudomonas aeruginosa isolation predict COPD mortality? Eur Respir J. 2014;44:794–7.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Garcia-Vidal C, Almagro P, Romaní V, Rodríguez-Carballeira M, Cuchi E, Canales L, et al. Pseudomonas aeruginosa in patients hospitalised for COPD exacerbation: a prospective study. Eur Respir J. 2009;34:1072–8.

    Article  PubMed  CAS  Google Scholar 

  31. Rodrigo-Troyano A, Suarez-Cuartin G, Peiró M, Barril S, Castillo D, Sanchez-Reus F, et al. Pseudomonas aeruginosa resistance patterns and clinical outcomes in hospitalized exacerbations of COPD. Respirol Carlton Vic. 2016;21:1235–42.

    Article  Google Scholar 

  32. Finch S, McDonnell MJ, Abo-Leyah H, Aliberti S, Chalmers JD. A comprehensive analysis of the impact of Pseudomonas aeruginosa colonization on prognosis in adult bronchiectasis. Ann Am Thorac Soc. 2015;12:1602–11.

    PubMed  Google Scholar 

  33. Wilson R, Aksamit T, Aliberti S, De Soyza A, Elborn JS, Goeminne P, et al. Challenges in managing Pseudomonas aeruginosa in non-cystic fibrosis bronchiectasis. Respir Med. 2016;117:179–89.

    Article  PubMed  Google Scholar 

  34. Wilson CB, Jones PW, O’Leary CJ, Hansell DM, Cole PJ, Wilson R. Effect of sputum bacteriology on the quality of life of patients with bronchiectasis. Eur Respir J. 1997;10:1754–60.

    Article  PubMed  CAS  Google Scholar 

  35. Sagel SD, Gibson RL, Emerson J, McNamara S, Burns JL, Wagener JS, et al. Impact of pseudomonas and staphylococcus infection on inflammation and clinical status in young children with cystic fibrosis. J Pediatr. 2009;154:183–8.

    Article  PubMed  Google Scholar 

  36. Emerson J, Rosenfeld M, McNamara S, Ramsey B, Gibson RL. Pseudomonas aeruginosa and other predictors of mortality and morbidity in young children with cystic fibrosis. Pediatr Pulmonol. 2002;34:91–100.

    Article  PubMed  Google Scholar 

  37. Vollenweider DJ, Jarrett H, Steurer-Stey CA, Garcia-Aymerich J, Puhan MA. Antibiotics for exacerbations of chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2012;12:CD010257.

    PubMed  Google Scholar 

  38. Nouira S, Marghli S, Belghith M, Besbes L, Elatrous S, Abroug F. Once daily oral ofloxacin in chronic obstructive pulmonary disease exacerbation requiring mechanical ventilation: a randomised placebo-controlled trial. Lancet Lond Engl. 2001;358:2020–5.

    Article  CAS  Google Scholar 

  39. Saint S, Bent S, Vittinghoff E, Grady D. Antibiotics in chronic obstructive pulmonary disease exacerbations. A meta-analysis. JAMA. 1995;273:957–60.

    Article  PubMed  CAS  Google Scholar 

  40. Wilson R, Jones P, Schaberg T, Arvis P, Duprat-Lomon I, Sagnier PP, et al. Antibiotic treatment and factors influencing short and long term outcomes of acute exacerbations of chronic bronchitis. Thorax. 2006;61:337–42.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Soler N, Esperatti M, Ewig S, Huerta A, Agustí C, Torres A. Sputum purulence-guided antibiotic use in hospitalised patients with exacerbations of COPD. Eur Respir J. 2012;40:1344–53.

    Article  PubMed  CAS  Google Scholar 

  42. Soler N, Agustí C, Angrill J, Puig De la Bellacasa J, Torres A. Bronchoscopic validation of the significance of sputum purulence in severe exacerbations of chronic obstructive pulmonary disease. Thorax. 2007;62:29–35.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We are indebted to all medical and nursing colleagues for their assistance and cooperation in this study. We thanks to Michael Maudsley by his assistance in language review.

Funding

AC is recipient of ERS Long Term Fellowship. C.C. is recipient of ERS Short Term Fellowship and Postdoctoral Grant “Strategic plan for research and innovation in health-PERIS 2016-2020”.

The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Author information

Author notes

  1. Cristina Estirado and Adrian Ceccato contributed equally to this work.

Authors and Affiliations

  1. Pulmonology Department, Hospital del Mar-IMIM. CEXS, Universitat Pompeu Fabra, CIBERES, ISCiii, Barcelona, Spain

    Cristina Estirado & Joaquim Gea

  2. Pneumology Department, Respiratory Institute (ICR), Hospital Clinic of Barcelona - Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona (UB), SGR 911- Ciber de Enfermedades Respiratorias (CIBERES), ICREA Academia, Barcelona, Spain

    Adrian Ceccato, Monica Guerrero, Arturo Huerta, Catia Cilloniz, Olivia Vilaró, Albert Gabarrús, Nestor Soler & Antoni Torres

  3. Respiratory and Lung Function Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy

    Ernesto Crisafulli

  4. Department of Pneumology, Hospital Clinic of Barcelona, Villarroel 140, 08036, Barcelona, Spain

    Antoni Torres

Authors
  1. Cristina Estirado
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adrian Ceccato
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monica Guerrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arturo Huerta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Catia Cilloniz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Olivia Vilaró
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Albert Gabarrús
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Joaquim Gea
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ernesto Crisafulli
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nestor Soler
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Antoni Torres
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study concept and design: AT, CE, AC; data collection: CE, AC, MG, CC, AH, OV, EC, NS; statistical analysis: AG, analysis and interpretation of data: CE, AC, MG, AH, EC, JG, NS, AT.; drafting of the manuscript: AC, CE; critical revision of the manuscript for important intellectual content: JG, EC, NS, AT; and study supervision: AT. AT had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Antoni Torres.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Hospital Research and Ethics Committee (CEIC 2008/4106) and the study was conducted in accordance with good clinical practice guidelines and the declaration of Helsinki. Written informed consent was obtained from all enrolled patients.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional file

Additional file 1:

Table S1. Microbiological Isolations. Table S2. Internal Validation of the Multivariate Logistic Regression Model using Bootstrap Method. Table S3. Multinomial Logistic Regression Model for Microorganisms Resistant to Conventional Treatment (with Pseudomonas aeruginosa) or Microorganisms Sensitive to Conventional Treatment Relative to Negative Microbiology. Table S4. Internal Validation of the Multinomial Logistic Regression Model for Microorganisms Resistant to Conventional Treatment (with Pseudomonas aeruginosa) or Microorganisms Sensitive to Conventional Treatment using Bootstrap Method. Table S5. Outcomes according to Appropriateness of Empiric Treatment. Table S6. Comparison of Outcomes between Patients with Pseudomonas Aeruginosa and Patients without Pseudomonas Aeruginosa in Microorganisms Resistant to Conventional Treatment Group. Table S7. Comparison between Pseudomonas Aeruginosa MDR/XDR Isolation with other Microorganism Isolated in Microorganisms Resistant to Conventional Treatment Group. Table S8. Internal Validation of Risk of Length of Hospital Stay Using Bootstrap Technique. Figure S1. Receiver Operating Characteristic Curve for Multinomial Logistic Regression Model to Pseudomonas aeruginosa. Figure S2. Kaplan–Meier Analysis of the Effect of Microbial Aetiology Groups on Time to Death. Figure S3. Kaplan–Meier Analysis of the Effect of Microbial Aetiology Groups on Time to Death. A) Patients with < 2 AECOPD and none admission by AECOPD in the previous year; B) Patients with ≥ 2 AECOPD or 1 admission by AECOPD in the previous year. (DOC 265 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Estirado, C., Ceccato, A., Guerrero, M. et al. Microorganisms resistant to conventional antimicrobials in acute exacerbations of chronic obstructive pulmonary disease. Respir Res 19, 119 (2018). https://doi.org/10.1186/s12931-018-0820-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12931-018-0820-1

Keywords

Respiratory Research

ISSN: 1465-993X

Contact us