Introduction
============

Patients with COPD frequently experience worsening of symptoms, including increased sputum and dyspnea. Acute exacerbation of COPD (AECOPD) is defined as a sudden worsening of COPD symptoms that requires additional treatment.[@b1-copd-13-3077] Hospitalization is sometimes required depending on AECOPD severity and some patients are hospitalized repeatedly.[@b2-copd-13-3077] AECOPD impacts quality of life, accelerates the decline in pulmonary function, and increases mortality.[@b3-copd-13-3077]--[@b5-copd-13-3077] Appropriate treatment is needed to prevent frequent acute exacerbation and hospitalization.

Causes of AECOPD include bacterial and respiratory viral infections and irritants such as air pollutants.[@b6-copd-13-3077],[@b7-copd-13-3077] In many cases, the exact cause of AECOPD is unknown. It also remains unclear how causes of AECOPD are related to its prognosis. For example, it is unknown whether the prognosis for severe AECOPD differs depending on the bacterium or virus identified as a cause of infection. Many studies have been conducted on the relationship between the prognosis of COPD and the bacterial or respiratory viral pathogens. And *Pseudomonas aeruginosa* has been suggested to be associated with a poor prognosis.[@b8-copd-13-3077],[@b9-copd-13-3077] The purpose of this study is to analyze AECOPD readmission events and determine whether the prognosis varies with the bacterium or virus identified.

Methods
=======

Data recruitment
----------------

We retrospectively found 736 patients diagnosed with severe AECOPD in Korea University Guro Hospital from January 2011 to May 2017 ([Figure 1](#f1-copd-13-3077){ref-type="fig"}). Thirty-two AECOPD (4.4%) died during hospitalization. We analyzed 704 patients with AECOPD who were discharged after treatment. Because many other studies of AECOPD had used 30 days as a standard of readmission, we did likewise.[@b10-copd-13-3077],[@b11-copd-13-3077] Events were divided into two groups, one in which the patient was readmitted within 30 days after discharge and the other with no readmission within 30 days. This study was approved by the Institutional Review Board of the Korea University Guro Hospital (KUGH16131-002). This study was a retrospective study, so patient consent was not necessary, and we maintained patient confidentiality.

AECOPD was defined as "worsening of a patient's respiratory symptoms beyond normal day-to-day variation." Severe AECOPD was defined as AECOPD requiring hospitalization.[@b12-copd-13-3077] Events were included if the following criteria were met: 1) the patient had a previous spirometry that showed airway obstruction (a ratio of forced expiratory volume in the first second to forced vital capacity of \<70% in postbronchodilator spirometry);[@b1-copd-13-3077] 2) the patient was diagnosed with severe AECOPD; 3) the patient was discharged after treatment and continuously followed up; and 4) the patient was \>40 years old.

Medical records were reviewed and analyzed for the following data: age, gender, smoking history, comorbidities, Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage, inhaler use, pulmonary oral medication use, use of home oxygen therapy, and culture and polymerase chain reaction (PCR) assay data for identification of the bacterium or virus. A real-time PCR can detect influenza virus, respiratory syncytial virus, parainfluenza virus, coronavirus, rhinovirus, enterovirus, adenovirus, bocavirus, and metapneumovirus. The three most frequently identified bacteria and viruses were analyzed in the study. All cultures and PCR assays were performed within 24 hours of admission.

Statistical analysis
--------------------

Data were analyzed using SPSS 20 software (SPSS for windows, IBM Corporation, Armonk, NY, USA). Continuous variables were reported as mean ± SD and categorical variables as number and percentage of each group. Variables were analyzed by comparison between the two groups (readmission and no readmission). Continuous variables were compared using a Student's *t*-test or Mann--Whitney test. Categorical variables were compared using a chi-squared or Fisher's exact test; Fisher's exact test was used when the expected number of events was \<5.

To compensate for bias and differences in baseline characteristics between the two groups, we performed propensity score matching. Propensity scores were calculated for each patient using multivariable logistic regression based on the covariates (all variables in [Tables 1](#t1-copd-13-3077){ref-type="table"} and [2](#t2-copd-13-3077){ref-type="table"}). Matching was performed using the nearest neighbor method to select for the most similar propensity scores. We performed 1:1 matching and reported a standardized mean difference (*d*) effect size to express the suitability of matching.

After propensity score matching, we performed multivariate analysis using logistic regression. Logistic regression analysis was assessed using the Hosmer--Lemeshow test. In multivariate analysis, we analyzed factors that showed meaningful values in univariate analysis after propensity score matching. A *P*\<0.05 was considered statistically significant. We estimated odds ratios (ORs) with 95% confidence intervals (CIs).

Results
=======

Baseline characteristics
------------------------

Of the 704 severe AECOPD events, 65 led to readmission within 30 days after discharge. After propensity score matching, the number of events in each group was 52. The mean age was \>70 years in all groups. The proportion of males was higher than that of females in all groups. The majority of patients were at GOLD stage II or III. All variables related to baseline characteristics showed no statistically significant difference between the two groups. [Table 1](#t1-copd-13-3077){ref-type="table"} shows detailed baseline characteristics for the two groups before and after propensity score matching.

Pulmonary medication or treatment
---------------------------------

We analyzed pulmonary medication use or treatment before admission. Most patients (80%) were using inhalers. Triple therapy was the most commonly used type of inhaler in all groups. Half of the patients were taking mucolytic agents. Oral steroids were used in 5% of patients. Oxygen therapy at home was used by 20% of patients. Before propensity score matching, oral medication use and home oxygen therapy were greater in the readmission group; after propensity score matching, there was no significant difference. [Table 2](#t2-copd-13-3077){ref-type="table"} shows detailed pulmonary medication or treatment data for the two groups before and after propensity score matching.

Microbiological analysis
------------------------

We classified AECOPD events as only bacterial pathogen identification, only viral pathogen identification, bacterial--viral coidentification, and no pathogen identification. A bacterial or viral infection was identified in 60% of events. Before propensity score matching, the only bacterial pathogen identification rate was significantly greater in the readmission group (*P*=0.003); after matching, the difference was not statistically significant (*P*=0.063). There were no significant differences for the other variables.

We also analyzed the most frequently identified infectious bacteria and viruses in severe AECOPD. The three most commonly identified bacteria were *P. aeruginosa*, *Streptococcus pneumoniae*, and *Haemophilus influenzae*; the three most commonly identified viruses were influenza virus, rhinovirus, and parainfluenza virus. Before propensity score matching, the *P. aeruginosa* identification rate was significantly greater in the readmission group than in the no-readmission group (*P*=0.007); there were no significant differences in identification rates for the other bacteria or viruses. After matching, the *P. aeruginosa* identification rate remained significantly greater in the readmission group (*P*=0.030). [Table 3](#t3-copd-13-3077){ref-type="table"} shows detailed microbiological data for the two groups before and after propensity score matching.

Multivariate analysis
---------------------

We performed multivariate analysis of *P. aeruginosa* identification rates. First, we adjusted the propensity score and variables associated with prognosis. The *P. aeruginosa* identification rate was higher in the readmission groups compared to the no-readmission group (OR, 3.600; 95% CI, 1.077--12.035; *P*=0.038). Second, we adjusted the propensity score and all variables in [Tables 1](#t1-copd-13-3077){ref-type="table"} and [2](#t2-copd-13-3077){ref-type="table"}. The *P. aeruginosa* identification rate was again higher in the readmission groups compared to the no-readmission group (OR, 4.749; 95% CI, 1.296--17.041; *P*=0.019) ([Table 4](#t4-copd-13-3077){ref-type="table"}).

Discussion
==========

In this study, we analyzed the effect of bacterial or viral identification on readmission of patients with severe AECOPD. A previous study based in London showed that 10.2% of severe AECOPD patients were readmitted within 30 days after discharge and 17.8% were readmitted within 90 days.[@b13-copd-13-3077] Other studies of readmission in severe AECOPD have focused on age, comorbidity, inhaler use, and psychological disorders.[@b11-copd-13-3077],[@b14-copd-13-3077]--[@b16-copd-13-3077] There is a lack of data on readmission focused on the bacterial or viral identification causing exacerbation. This study is the first to demonstrate that identification of *P. aeruginosa* correlates with readmission rate in severe AECOPD.

*P. aeruginosa* is a Gram-negative rod bacterium that can cause opportunistic infections. It is the causative agent of infections mainly in immunocompromised or chronic lung disease patients, including patients with cystic fibrosis or COPD. It has become an important pathogen with increases in immunosuppressive treatments, chemotherapy, and use of intensive care units. *P. aeruginosa* is currently the most common causative agent of nosocomial infection and the second most common causative agent of ventilator-associated pneumonia in the US.[@b17-copd-13-3077]

*P. aeruginosa* infections are difficult to treat. First, *P. aeruginosa* has innate resistance to many of the antimicrobial agents commonly used in the treatment of pneumonia. *P. aeruginosa* has several broadly specific multidrug efflux systems that provide this innate resistance.[@b18-copd-13-3077] Second, *P. aeruginosa* easily acquires resistance compared to other bacteria.[@b19-copd-13-3077] *P. aeruginosa* strains possess large genomes (∼5--7 Mbp), can produce multiple secondary metabolites and polymers, and has better quorum sensing than other bacteria, which allows spreading of acquired resistance between bacteria.[@b20-copd-13-3077] Third, *P. aeruginosa* secretes virulence factors and impairs the immune system. For example, *P. aeruginosa* secretes elastase B and escapes phagocytosis.[@b21-copd-13-3077] *P. aeruginosa* also secretes exoenzyme S (ExoS), a bifunctional toxin encoded by the *exoS* gene, which disrupts the pulmonary vascular barrier, resulting in bacteremia.[@b22-copd-13-3077] The identification of *P. aeruginosa* means that treatment and complete eradication are difficult. Compared to other bacteria, antibiotics are used for longer times, and the duration of hospitalization is greater, often leading to secondary hospital infections and antibiotic side effects.

In our study, *P. aeruginosa* is the most commonly identified pathogen. However, in a previous study, the most commonly identified bacteria in AECOPD are *S. pneumoniae*, *H. influenzae*, and *Moraxella catarrhalis*.[@b23-copd-13-3077] There are some reasons for this discrepancy. First, the COPD grade in our study is high. *P. aeruginosa* is most commonly identified in patients at GOLD stages III and IV.[@b24-copd-13-3077] Meta-analysis has shown that *P. aeruginosa* identification is statistically higher in COPD patients with bronchiectasis.[@b25-copd-13-3077] The identification of *P. aeruginosa* means that the host belongs to the high-risk group. Second, it is a regional characteristic. Unlike the Western study, some studies in Korea and Asia show that *P. aeruginosa* is most commonly identified.[@b26-copd-13-3077],[@b27-copd-13-3077]

There are two clinical features when *P. aeruginosa* is identified in AECOPD patients.[@b28-copd-13-3077] The most common feature is carriage of *P. aeruginosa* for a short time followed by clearance (\<1 month). The other feature is persistent colonization with *P. aeruginosa*. There is a debate regarding the prognosis and mortality of stable COPD patients who are colonized by *P. aeruginosa*.[@b29-copd-13-3077] There is no clear evidence that antibiotics should be used in this condition. A prospective study showed that *P. aeruginosa* identification in patients with severe AECOPD was associated with a higher 3-year mortality rate.[@b30-copd-13-3077] Chronic *P. aeruginosa* infection has also been shown to increase the mutation rate and antibiotic resistance of proteases and reduce their production.[@b31-copd-13-3077] Although there is controversy regarding *P. aeruginosa* infections in patients with stable COPD, the identification of *P. aeruginosa* in cases of AECOPD means a poor prognosis.

Our study has some limitations. First, this is a retrospective study, so there were limitations in obtaining data. For example, some patients lacked chest computed tomography data, thus limiting analysis of associations with bronchiectasis. And sputum culture assay was not conducted before and after admission. Second, colonization and contamination could not be distinguished in our study. Although sputum results of grade four or five were used to analyze culture results and collection of all specimens was done by trained physicians, additional data to analyze colonization and contamination were lacking. Third, the sample size was small after propensity score matching. So some of the comparisons in this analysis suggest that there may be insufficient statistical power. For example, only the bacterial pathogen identification rate is 38.5% in the readmission group and 26.9% in the no-readmission group. But this difference is not statistically significant. Although this study was a retrospective and single-center study, we analyzed various factors in a large-scale group. An additional large-scale, multicenter, randomized control study is required to confirm our results.

Conclusion
==========

*P. aeruginosa* infections in severe AECOPD are difficult to treat, and secondary problems often arise. *P. aeruginosa* infections occur mainly in high-risk patients. In severe AECOPD, *P. aeruginosa* infections mean poor prognosis and an increased rate of readmission.

This study was supported by a grant from Korea University, Seoul, Korea (K1719301).

**Author contributions**

All authors contributed toward data analysis, drafting, and revising this paper and agree to be accountable for all aspects of the work.

**Disclosure**

The authors report no conflicts of interest in this work.

![Study design.\
**Abbreviations:** AECOPD, acute exacerbations of COPD; PCR, polymerase chain reaction.](copd-13-3077Fig1){#f1-copd-13-3077}

###### 

Baseline characteristics in readmission and no-readmission groups before and after propensity score matching

                                                                               Before matching   After matching                                                      
  ---------------------------------------------------------------------------- ----------------- ---------------- ------- -------- ------------ ------------ ------- ----------
  Age (years)[a](#tfn1-copd-13-3077){ref-type="table-fn"}                      73.9±9.6          71.8±9.3         0.078   0.2251   72.9±9.2     73.1±7.6     0.908   0.0237
  Gender                                                                                                                                                             
   Male[b](#tfn2-copd-13-3077){ref-type="table-fn"}                            48 (73.8%)        444 (69.5%)      0.465   0.1186   40 (76.9%)   38 (73.1%)   0.651   0.1133
   Female[b](#tfn2-copd-13-3077){ref-type="table-fn"}                          17 (26.2%)        195 (30.5%)                       12 (23.1%)   14 (26.9%)           
  Smoking history                                                                                                                                                    
   Current smoker[b](#tfn2-copd-13-3077){ref-type="table-fn"}                  4 (6.2%)          102 (16.0%)      0.499   0.1695   4 (7.7%)     4 (7.7%)     0.898   0.1072
   Ex smoker[b](#tfn2-copd-13-3077){ref-type="table-fn"}                       43 (66.2%)        367 (57.4%)                       37 (71.2%)   34 (65.4%)           
   Nonsmoker[b](#tfn2-copd-13-3077){ref-type="table-fn"}                       18 (27.7%)        170 (26.6%)                       11 (21.2%)   14 (26.9%)           
  Comorbidities                                                                                                                                                      
   Hypertension[b](#tfn2-copd-13-3077){ref-type="table-fn"}                    31 (47.7%)        324 (50.7%)      0.644   0.0665   26 (50.0%)   29 (55.8%)   0.556   0.1278
   Diabetes[b](#tfn2-copd-13-3077){ref-type="table-fn"}                        15 (23.1%)        149 (23.3%)      0.965   0.1175   11 (21.2%)   17 (32.7%)   0.185   0.3272
   Coronary artery disease[b](#tfn2-copd-13-3077){ref-type="table-fn"}         13 (20.0%)        99 (15.5%)       0.344   0.1710   11 (21.2%)   11 (21.2%)   1.000   0.0000
   Cerebrovascular accident[b](#tfn2-copd-13-3077){ref-type="table-fn"}        2 (3.1%)          31 (4.9%)        0.760   0.2612   2 (3.8%)     3 (5.8%)     1.000   0.2347
  Severity of COPD                                                                                                                                                   
   GOLD I[b](#tfn2-copd-13-3077){ref-type="table-fn"}                          5 (7.7%)          50 (7.8%)        0.434   0.1020   4 (7.7%)     3 (5.8%)     0.798   0.1159
   GOLD II[b](#tfn2-copd-13-3077){ref-type="table-fn"}                         25 (38.5%)        246 (38.5%)                       18 (34.6%)   22 (42.3%)           
   GOLD III[b](#tfn2-copd-13-3077){ref-type="table-fn"}                        22 (33.8%)        266 (41.6%)                       20 (38.5%)   20 (38.5%)           
   GOLD IV[b](#tfn2-copd-13-3077){ref-type="table-fn"}                         13 (20.0%)        77 (12.1%)                        10 (19.2%)   7 (13.5%)            
  Length of hospital stay (days)[a](#tfn1-copd-13-3077){ref-type="table-fn"}   14.0±10.4         9.6±8.1          0.001   0.5278   12.2±8.0     10.5±8.2     0.307   0.2099
  ICU admission                                                                10 (15.4%)        63 (9.9%)        0.164   0.2802   7 (13.5%)    8 (15.4%)    0.780   0.0860
  Use of mechanical ventilation                                                8 (13.8%)         38 (5.9%)        0.031   0.4396   6 (11.5%)    8 (15.4%)    0.566   0.1831
  Use of NIV                                                                   1 (1.5%)          10 (1.6%)        1.000   0.0096   1 (1.9%)     0 (0.0%)     1.000   Infinity

**Notes:**

Data are presented as mean ± SD.

Data are presented as number of patients (%).

**Abbreviations:** *d*, standardized mean difference; GOLD, Global Initiative for Chronic Obstructive Lung Disease; ICU, intensive care unit; NIV, noninvasive ventilation.

###### 

Pulmonary medication or treatment in readmission and no-readmission groups before and after propensity score matching

                                        Before matching   After matching                                                        
  ------------------------------------- ----------------- ---------------- --------- -------- ------------ ------------ ------- --------
  Inhaler use before admission                                                                                                  
   LABAs                                3 (4.6%)          4 (0.6%)         0.277     0.3522   0 (0.0%)     2 (3.8%)     0.730   0.0348
   LAMAs                                13 (20.0%)        75 (11.7%)                          10 (19.2%)   6 (11.5%)            
   LABAs + LAMAs                        16 (24.6%)        97 (15.2%)                          12 (23.1%)   15 (28.8%)           
   ICS/LABAs                            3 (4.6%)          51 (8.0%)                           3 (5.8%)     3 (5.8%)             
   Triple therapy (ICS/LABAs + LAMAs)   24 (36.9%)        245 (38.3%)                         21 (40.4%)   20 (38.5%)           
  Oral medication use                                                                                                           
   β~2~ Adrenoreceptor agonist          11 (16.9%)        78 (12.2%)       0.276     0.2106   7 (13.5%)    6 (11.5%)    0.767   0.0971
   N-acetylcysteine                     11 (16.9%)        50 (7.8%)        0.013     0.4826   6 (11.5%)    12 (23.1%)   0.120   0.4592
   Roflumilast                          9 (13.8%)         43 (6.7%)        0.046     0.4416   6 (11.5%)    3 (5.8%)     0.488   0.4170
   Mucolytic agent                      44 (67.7%)        339 (53.1%)      0.024     0.3404   36 (69.2%)   32 (61.5%)   0.410   0.1880
   Oral steroid                         10 (15.4%)        32 (5.0%)        0.003     0.6826   6 (11.5%)    4 (7.7%)     0.506   0.2470
   Oral antibiotics                     8 (12.3%)         27 (4.2%)        0.011     0.6380   4 (7.7%)     2 (3.8%)     0.678   0.4047
   Home oxygen therapy                  25 (38.5%)        126 (19.7%)      \<0.001   0.5149   16 (30.8%)   16 (30.8%)   1.000   0.0000

**Note:** Data are presented as number of patients (%).

**Abbreviations:** *d*, standardized mean difference; LABAs, long-acting β-agonists; LAMAs, long-acting antimuscarinic antagonists; ICS, inhaled corticosteroids.

###### 

Identified pathogen in readmission events and no-readmission groups before and after propensity score matching

                                            Before matching   After matching                                     
  ----------------------------------------- ----------------- ---------------- ------- ------------ ------------ -------
  Identification of bacteria or virus                                                                            
   Only bacterial pathogen identification   27 (41.5%)        158 (24.7%)      0.003   20 (38.5%)   14 (26.9%)   0.210
   Only viral pathogen identification       9 (13.8%)         149 (23.3%)      0.081   8 (15.4%)    13 (25.0%)   0.222
   Bacterial--viral coidentification        8 (12.3%)         91 (14.2%)       0.669   8 (15.4%)    2 (3.82%)    0.092
   No pathogen identification               21 (32.3%)        241 (37.7%)      0.390   16 (30.8%)   23 (44.2%)   0.156
  Analysis of major pathogen                                                                                     
   *Pseudomonas aeruginosa*                 15 (23.1%)        73 (11.4%)       0.007   12 (23.1%)   4 (7.7%)     0.030
   *Streptococcus pneumoniae*               6 (9.2%)          76 (11.9%)       0.524   4 (7.7%)     3 (5.8%)     1.000
   *Haemophilus influenza*                  3 (4.6%)          34 (5.3%)        1.000   3 (5.8%)     2 (3.8%)     1.000
   Influenza virus                          5 (7.7%)          83 (13.0%)       0.219   5 (9.6%)     5 (9.6%)     1.000
   Rhinovirus                               3 (4.6%)          62 (9.7%)        0.177   2 (3.8%)     4 (7.7%)     0.678
   Parainfluenza virus                      4 (6.2%)          33 (5.2%)        0.768   4 (7.7%)     2 (3.8%)     0.678

**Note:** Data are shown as number of patients (%).

###### 

Multivariate analysis of *Pseudomonas aeruginosa* identification rate after propensity score matching

  Parameter                                                                                    Odds ratio   95% CI          *P*-value
  -------------------------------------------------------------------------------------------- ------------ --------------- -----------
  Adjusted for propensity and selected variables[a](#tfn7-copd-13-3077){ref-type="table-fn"}   3.600        1.077--12.035   0.038
  Adjusted for propensity and all variables[b](#tfn8-copd-13-3077){ref-type="table-fn"}        4.749        1.296--17.401   0.019

**Notes:**

Selected variables included variables associated with prognosis such as age, comorbidity (hypertension, diabetes, coronary artery disease, cerebrovascular accident), COPD severity, smoking history, length of hospital stay, intensive care unit admission, and use of mechanical ventilation.

All variables in [Tables 1](#t1-copd-13-3077){ref-type="table"} and [2](#t2-copd-13-3077){ref-type="table"}.
