Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Risk of exacerbation in chronic obstructive pulmonary disease: a primary care retrospective cohort study

  • Josep Montserrat-Capdevila1, 2, 3Email author,
  • Pere Godoy1, 2, 4,
  • Josep Ramon Marsal5, 6,
  • Ferran Barbé1, 3, 4, 7, 8 and
  • Leonardo Galván9
BMC Family Practice201516:173

https://doi.org/10.1186/s12875-015-0387-6

Received: 21 March 2015

Accepted: 25 November 2015

Published: 8 December 2015

Abstract

Background

The risk of exacerbation in chronic obstructive pulmonary disease (COPD) depends on the severity of disease and other less well known factors. Predictive models of exacerbation are more accurate than the forced expiratory volume in one second (FEV1). The objective was to design a model that predicts the risk of exacerbation in COPD.

Methods

Retrospective cohort study with data from the electronic medical records of patients diagnosed with COPD in the province of Lleida (Spain). A total of 2501 patients were followed during 3 years. The dependent variable was acute exacerbation; independent variables were: clinical parameters, spirometry results, severity of disease, influenza and 23-valent pneumococcal immunisation, comorbidities, smoking and history of exacerbation. The association of these variables with disease exacerbation was measured by the adjusted odds ratio using a logistic regression model.

Results

Mean age at the start of the study was 68.38 years (SD = 11.60) and 74.97 % patients were men; severity of disease was considered mild in 50.82 % of patients, moderate in 35.31 %, severe in 9.44 % and very severe in 4.44 %. During the three year study period up to 83.17 % of patients experienced at least one exacerbation. Predictive factors in the model were age, gender, previous exacerbations, influenza and 23-valent pneumococcal immunisations, number of previous visits to the General Practice and severity (GOLD), with an area under the ROC curve (AUROC) of 0.70.

Conclusions

This model can identify patients at high risk of acute exacerbation. Preventive measures and modification of treatment in these high-risk patients would improve survival.

Keywords

Chronic obstructive pulmonary disease (COPD) Risk factors Acute disease

Background

The global burden of chronic obstructive pulmonary disease (COPD) is high [1]. Some patients with COPD experience three episodes of acute exacerbation per year on average [2]. Most acute exacerbation episodes require medical attention, either in primary care or in hospital [3].

Predictive factors of exacerbation are still not completely understood [49]. However, acute exacerbations are known to accelerate the severity of disease, [3] contribute to irreversible decline of pulmonary function [10], have a negative impact on quality of life [11] and decrease survival [12]. The proportion of treatment failure in patients with acute exacerbations is high (20–40 %) [13] and it is associated with elevated health costs [14].

Traditionally, a decrease in forced expiratory volume in one second (FEV1) has been considered one of the main risk factors of exacerbation. However, FEV1 does not discriminate between stable periods of disease and acute exacerbations, and is less effective in the late stages of the disease [3]. Indeed, some patients with low FEV1 have never experienced acute exacerbations, and a higher FEV1 does not rule out the risk of exacerbation [15].

It is essential to consider COPD as a systemic disease rather than just pulmonary, since it is associated with symptoms in other organs and with comorbidities [16, 17] that can determine the risk of acute exacerbation. Consequently, new multifactorial predictive models for exacerbation are required [18]. The BODE index, which comprises Body Mass Index, airflow Obstruction, Dyspnoea and Exercise (distance walked in 6 min) is a predictive factor of mortality in COPD and a better predictive factor for acute exacerbation than FEV1 [19]. The study of Marin et al. showed that the BODE index was a better predictive method than FEV1 for acute exacerbation in patients with COPD [19]. However, the six minute walk test is not easily implemented in primary care. Other models have been proposed [2022]. Nonetheless, new models with variables that can be obtained easily and quickly are needed.

Factors related to acute exacerbation are still poorly understood [5]. Some variables have been identified with risk of exacerbation. However, further studies are needed to prove the association of these factors with acute exacerbation and to validate models of prediction [23].

Factors such as the characteristics of the population, prevalence of risk factors, severity of COPD and aspects of the health system [3] result in a geographical variation in the incidence of exacerbation [24]. In consequence, predictive models should be adjusted to different settings.

The objective of this study was to determine predictive factors associated with exacerbation of COPD in a primary care cohort of patients in Catalonia (Spain) with a high percentage of mild and moderate severity during a three year follow up.

Methods

A retrospective cohort study was carried out with 2501 patients with a COPD diagnosis from seven primary care centres of the Lleida Health Region (catchment population: 172,950). The criteria of inclusion were over 40 years of age with a diagnosis of COPD in 2010 in primary care electronic medical records and having a spirometry test in the last two years. GOLD criteria were followed for the diagnosis of COPD: [25] a post bronchodilator FEV1/FVC <0.7 when the patient is in the stable phase of the disease. For a patient to be included in the study, the electronic medical records must also contain the result of a spirometry test from the previous 2 years. The dependent variable was acute exacerbation of COPD, defined as an increase in dyspnoea, amount and purulence of sputum [26] that require treatment with systemic steroids and/or antibiotics (moderate exacerbations) or that require hospitalization (severe exacerbations) [27]. The referral hospitals were the University Hospital Arnau de Vilanova and the Hospital Santa Maria de Lleida. These were the two hospitals where the patients that required admission were referred. Exacerbations between November 2010 and October 2013 were computed. Data sources were the primary care electronic medical records (e-CAP) and the Health Department Pharmacy Unit Register. The following independent variables were collected at the beginning of the study: age, gender, spirometry results (FEV1/CVF, FVC, FEV1), comorbidities (heart failure, ischemic heart disease, diabetes, chronic kidney failure, atrial fibrillation and anaemia), history of smoking, 23-valent pneumococcal and influenza immunisations for the 2009/10 season, years since COPD was diagnosed, number of visits to the health centre and number of acute exacerbations in the year prior to the start of the study, and COPD severity according to the GOLD Guidelines.

This study was approved by the Clinical Research Ethics Committee of the Primary Care Research Institute (IDIAP) Jordi Gol of Barcelona (P14/022). This ethics committee determined that individual patient consent was not necessary due to the retrospective nature of the study. We also obtained the consent by management of all the seven primary care centres participating in the study (Lleida, Pla d’Urgell, Les Borges Blanques, Bellpuig, Tàrrega, Agramunt and Cervera) as well as the approval by the management of Primary Care of the Healthcare Region of Lleida. The Primary Care Management of the Catalan Institute of Health authorized permission to access the clinical records of the patients included in the study with the ultimate aim of gathering information concerning the variables of interest.

Statistical analysis

A descriptive analysis of the data was performed. Quantitative variables were described using the mean and standard deviation and categorical variables using the absolute and relative frequencies. Hypothesis testing to evaluate the association of independent variables with the clinical outcome (COPD exacerbation) was performed using the Chi-square test for categorical variables and Student’s t test or Mann-Whitney’s U test for continuous variables. The crude odds ratio (OR) was calculated for each independent variable included in the model.

A derivation cohort (70 %) and a validation cohort (30 %) were randomly generated. The score was adjusted in the derivation cohort and the predicted probability, power and calibration were estimated from the 30 % included in the validation cohort. The score included all variables associated with an outcome with a P value <0.2. The risk score was adjusted for the 3-year period with a logistic regression analysis using automatic (forward and backward) variable selection algorithms. Selected variables were included in the model if the P value of the effect was <0.1. The characteristics of the calibration were evaluated by the Hosmer-Lemeshow test and the discriminatory power using the c- statistic of the area under the curve (AUC).

Statistical Interaction and quadratic effect were tested for all quantitative variables. Statistical significance was considered if P-value <0.05. All analyses were performed using the statistical package SPSS v15.0.

Results

Mean age of the 2501 patients at the start of the study was 68.38 years (SD = 11.60) and 75 % were men. Severity of disease (GOLD) was mild in 50.82 % patients, moderate in 35.31 %, severe in 9.44 % and very severe in 4.44 %. The year prior to the start of the study a total of 20.23 % patients had experienced at least one episode of acute exacerbation and the average number of visits to the physician or nurse in primary care was 25. With regard to immunisation, 70.45 % had received the 23-valent pneumococcal vaccine and 71.41 % the influenza vaccine during the 2009/10 season (season prior to the start of the study).

Table 1 shows the characteristics of the patients in relation to acute exacerbations. Patients who experienced acute exacerbations were older, had attended the primary care centre more often during 2009, presented more comorbidities (heart failure, ischemic heart disease, diabetes, chronic kidney failure, atrial fibrillation and anaemia) and had experienced more acute exacerbations during the year prior to the start of the study. Up to 83.17 % of patients experienced at least one episode of acute exacerbation during the 3-year follow up. Patients with a higher severity of disease (GOLD grades III and IV) experienced higher rates of acute exacerbation (Table 2), and had higher rates of immunisation with the 23-valent pneumococcal and influenza vaccines during the 2009/2010 season (p <0.001).
Table 1

Characteristics of patients at the beginning of the study and description of variables associated with acute exacerbation during the 3-year follow-up

 

Exacerbation (treatment during next 3y)

All (n = 2501)

 

No (n = 421)

Yes (n = 2080)

p

Agea

65.62 (11.90)

68.94 (11.50)

0.001

68.38 (11.60)

COPD History (years)a

2.86 (3.50)

3.69 (4.40)

0.001

3.55 (4.30)

Num. of visits (2009)a

18.01 (14.30)

25.96 (18.70)

0.001

24.63 (18.30)

FEV1

66.38 (23.20)

62.80 (24.40)

0.004

63.38 (24.30)

Gender (female)b

79 (18.76 %)

547 (26.30 %)

0.001

626 (25.03 %)

23-valent Pneumococcalb

242 (57.48 %)

1520 (73.08 %)

0.001

1762 (70.45 %)

2009 influenza vaccineb

242 (57.48 %)

1544 (74.23 %)

0.001

1786 (71.41 %)

Exacerbation 2009b

20 (4.75 %)

486 (23.37 %)

0.001

506 (20.23 %)

Number of comorbiditiesc

0.34 (0.70)

0.45 (0.70)

0.002

0.43 (0.70)

Smokingb

178 (42.28 %)

666 (32.02 %)

0.001

844 (33.75 %)

Gold (Severity)b

  

0.062

 

Mild COPD

223 (52.97 %)

1048 (50.38 %)

 

1271 (50.82 %)

Moderate COPD

157 (37.29 %)

726 (34.90 %)

 

883 (35.31 %)

Severe COPD

27 (6.41 %)

209 (10.05 %)

 

236 (9.44 %)

Very Severe COPD

14 (3.33 %)

97 (4.66 %)

 

111 (4.44 %)

Hospital Admissionb

62 (14.73 %)

750 (36.06 %)

0.001

812 (32.47 %)

Deathb

39 (9.26 %)

275 (13.22 %)

0,025

314 (12.55 %)

amean (SD): Mann–Whitney U test

babsolute and relative frequency. Chi-square test

cNumber of comorbidities: Ischemic Heart Disease, Heart Failure, Diabetes, Anaemia, Atrial Fibrillation and Chronic Renal Failure

The statistical significance is fixed for p-value <0.05

Table 2

Immunisation and acute exacerbation in relation to severity of COPD

 

Mild

Moderate

Severe

Very Severe

All

p

(n = 1271)

(n = 883)

(n = 236)

(n = 111)

(n = 2501)

23-valent Pneumococcala

866 (68.14 %)

628 (71.12 %)

185 (78.39 %)

83 (74.77 %)

1762 (70.45 %)

0.002

2009

influenza vaccinea

880 (69.24 %)

636 (72.03 %)

183 (77.54 %)

87 (78.38 %)

1786 (71.41 %)

0.002

Exacerbation (treatment during next 3y or hospital admission)a

1048 (82.45 %)

726 (82.22 %)

209 (88.56 %)

97 (87.39 %)

2080 (83.17 %)

0.043

aAbsolute and relative frequency. Chi-square test

The multivariate analysis (Table 3) shows that the following factors are significantly associated with COPD exacerbation: age (mean 68 years (aOR = 1.01), gender (woman) (aOR = 1.46), gender (woman) x age (mean 68 years (aOR = 0.96), previous exacerbations (aOR = 7.44), severe or very severe disease (GOLD), (aOR = 1.57), number of visits to the primary care centre (from 25 to 50 visits (aOR = 1.64); over 50 visits (aOR = 2.95) in relation to the standard (<25 visits), influenza vaccine (aOR = 2.00), 23-valent pneumococcal vaccine (aOR = 1.82), influenza x 23-valent pneumococcal vaccine (aOR = 0.46). Patients immunised with the pneumococcal and influenza vaccines had a 54 % lower risk of exacerbation. All these variables were significantly associated with COPD exacerbation, with a discriminatory power of 0.71. Fig. 1 shows the calibration and discriminatory power of the predictive model. The predictive factor more strongly associated with disease exacerbation was acute exacerbation during the year prior to the start of the study, which increased the risk of experiencing acute exacerbation by 7.44 during the following 3 years.
Table 3

Predictive factors of COPD exacerbation during the 3-year follow-up period

 

Exacerbation (treatment in next 3y)

OR

95 % CI

p

Num. Of visits 2009 (Standard < 25v)

  

0.001

 25–50

1.64

(1.17 – 2.29)

0.004

 > 50

2.95

(1.25 – 6.94)

0.013

Gender (female)

1.46

(1.04 – 2.04)

0.028

Exacerbation

7.44

(3.77 – 14.70)

0.001

INT: Female * Age (mean 68y)

0.96

(0.93 – 0.99)

0.003

Age (mean 68y)

1.01

(1,00 – 1.03)

0.087

Gold severity grade (severe or very severe)

1.57

(1.01 – 2.45)

0.046

2009 influenza vaccine

2.00

(1.12 – 3.58)

0.019

23-valent Pneumococcal vaccine

1.82

(1.00 – 3.29)

0.049

INT: 2009 influenza vaccine * Pneumococcal

0.46

(0.20 - 1.02)

0.055

Derivation

 Hosmer & Lemeshow

1.10

 

0.997

 AUC ROC

0.71

(0.68 – 0.74)

< 0.001

Validation

 Hosmer & Lemeshow

41.00

 

<0.001

 AUC ROC

0.69

(0.64 – 0.74)

<0.001

INT Interaction

OR odds ratio

OR was adjusted for: Num. of visits, gender, exacerbation, GOLD severity grade, influenza vaccine, 23-valent pneumococcal vaccine

Fig. 1

Exacerbation ROC curve after 3 years of follow up (validation and derivation cohorts)

Discussion

In this study, patients with at least one episode of exacerbation during the 3-year follow-up were older and more often men; they had a higher COPD severity grade (GOLD III and IV), they had experienced more acute exacerbation episodes during the year prior to the study and had attended the primary care centre more often due to a higher prevalence of comorbidities. Older patients experienced a higher rate of acute exacerbation episodes, probably because pulmonary function decreases with time and because of the accumulative effect of risk factors such as smoking. On the other hand, this study confirms the effectiveness of the influenza and 23-valent pneumococcal vaccines. Indeed, immunisation reduces risk practically by half. This model could be used in primary care visits to identify patients with a higher risk of exacerbation.

Interpretation of results based on the literature

Other authors have researched similar predictive models for exacerbation, with slight differences. Miravitlles et al. [28] designed a model for exacerbation with the following variables: age, FEV1 (GOLD severity) and chronic sputum production, and obtained an area under the ROC curve (AUROC) of 0.601 . Our model also includes age and FEV1, though it achieves a higher discriminatory power. Motegi et al. [29] compared three predictive models of COPD exacerbation: the DOSE (dyspnoea, degree of airflow limitation, smoking and exacerbations); BODE (body mass index, degree of airflow limitation, dyspnoea and exacerbations); and ADO (age, dyspnoea, degree of airflow limitation) indices. These predictive models had a discriminatory power (ROC curve) of 0.75, 0.65 and 0.64, respectively. The authors concluded that the best model to predict future exacerbations in COPD patients was the DOSE index. Our model has a higher AUROC than those found by Motegi et al. for the BODE and ADO indices, and it also contains risk factors common to the three models: age, degree of airflow limitation and history of exacerbation. Make et al. [20] developed a score (SCOPEX) that included variables such as gender, number of COPD maintenance medications, number of exacerbations in the previous year, FEV1/FVC and number of daily short-acting bronchodilators for clinicians to predict exacerbations. Moberg et al. [21] used measures of exercise capacity and dyspnoea scores to develop the iBODE index as a predictor. While Schembri et al. [22] also developed a predictive model based on data collected from clinic visits on demography, spirometry, smoking history, dyspnoea score, BMI and other variables.

In our model, a history of exacerbation is the best predictive factor for exacerbation, followed by an age over 68. The study by Hurst et al. also concluded that a history of exacerbation during the previous year was the best predictive factor of acute exacerbation [27].

As in other studies, the male sex was a risk factor of exacerbation due to the smoking habits. However, other studies have reported a higher risk of exacerbation in females [30].

Our study further supports the evidence on the effectiveness of influenza and 23-valent pneumococcal immunisations in significantly reducing the risk of acute exacerbation [31, 32]. This study contributes a new predictive model of COPD exacerbation with variables that are easily and swiftly collected. The high discriminatory power makes this model adequate for use in primary care.

Strengths and limitations of the study

The main strengths of the study are the length of the follow-up period (3 years) and characteristics of the cohort (the sample consists of a high percentage of patients with mild and moderate severity, unlike other studies, thus, our sample is more representative of the spectrum of the disease in the community. The primary care electronic medical records include all patients with a diagnosis of COPD in our region. In contrast, hospitals registers only include patients with the most severe forms of the disease. The study has also some limitations. The information was obtained from primary care electronic medical records, where some variables such as smoking and influenza and pneumococcal immunisations can be underreported. Some series like the UPLIFT studies found a prevalence of 29 % of smokers, [33] whereas another reports a prevalence as low as 4.4 % [6], much less than our study.

The study included patients with a diagnosis of COPD and with a spirometry result from 2 years prior to the start of the study. These inclusion criteria ensure that all patients were correctly diagnosed, but might exclude patients with COPD either because of underreporting or because they did not have a spirometry result in their electronic medical records. Such patients would probably be those with a mild disease. Also, patients were considered immunised against influenza or pneumococcal disease only when the immunisation was registered in the eCAP. When patients are immunised in private clinics it might not be adequately recorded. Some patients may give up smoking after the study and this change was not registered. Consequently, the effects of smoking and the protective effect of immunization might be underestimated. With regard to influenza immunisation, we only considered the 2009/10 seasonal immunisation. However, immunised patients tend to attend immunisations every year [34] and thus those immunised during the 2009/10 season were probably also immunised during the following seasons. Some variables studied in other models, such as weight/BMI, exercise/functional capacity or dyspnoea, were not investigated due to a lack of comprehensive information for all patients. Lastly, we must highlight that the predictors objectified in this study may vary depending on the geographical area of the study and the characteristics of health systems specific for each one, which is why more studies are needed in different health regions.

Implications for future research and practice

Some risk factors might vary in different settings. Other variables should be investigated in other future models and in other places, like the number of medication [20], seasonality [35, 36], sleep disturbance[37] and exposure to pollutants [38, 39]. Our study reports risk factors of COPD exacerbation in a specific region of Spain. External validation of this predictive model is required before implementing it in other cohorts of patients with COPD.

Conclusions

This predictive model of acute exacerbation in COPD can be easily and quickly implemented in primary care. Patients at high risk could be identified for preventive action, such as immunisation and for intensification of treatment. As a consequence, quality of life and survival rates in patients with COPD would improve.

Declarations

Acknowledgements

We want to thank the IDIAP Jordi Gol for the translation of this manuscript into English.

Non-financial competing interests

There are not any non-financial competing interests (political, personal, religious, ideological, academic, intellectual, commercial or any other) to declare in relation to this manuscript.

Open AccessThis 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.

Authors’ Affiliations

(1)
Biomedical Research Institute (IRB) of Lleida
(2)
Health Department, Public Health Agency of Catalonia
(3)
Catalan Institute of Health (ICS)
(4)
Faculty of Medicine, University of Lleida
(5)
Lleida Research Support Unit, Primary Care Research Institute (IDIAP) Jordi Gol. Autonomous University of Barcelona
(6)
Cardiovascular Department, Epidemiology Unit, University Hospital Vall d’Hebron
(7)
Pneumology Unit, University Hospital Arnau de Vilanova
(8)
Biomedical Research Centre Network for Respiratory Diseases (CIBERES)
(9)
Pharmacy Unit. Catalan Health Service

References

  1. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study. Lancet. 1997;349(9064):1498–504.View ArticlePubMedGoogle Scholar
  2. Wedzicha JA, Donaldson GC. Exacerbations of chronic obstructive pulmonary disease. Respir Care. 2003;48(12):1204–13. discussion 1213–5.PubMedGoogle Scholar
  3. Franciosi LG, Page CP, Celli BR, Cazzola M, Walker MJ, Danhof M, et al. Markers of exacerbation severity in chronic obstructive pulmonary disease. Respir Res. 2006;7:74.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Fabbri L, Beghé B, Caramori G, Papi A, Saetta M. Similarities and discrepancies between exacerbations of asthma and chronic obstructive pulmonary disease. Thorax. 1998;53(9):803–8.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Holgate ST. Priorities for respiratory research in the UK. Thorax. 2007;62(1):5–7.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Langsetmo L, Platt RW, Ernst P, Bourbeau J. Underreporting exacerbation of chronic obstructive pulmonary disease in a longitudinal cohort. Am J Respir Crit Care Med. 2008;177(4):396–401.View ArticlePubMedGoogle Scholar
  7. Baker CL, Zou KH, Su J. Risk assessment of readmissions following an initial COPD-related hospitalization. Int J Chron Obstruct Pulmon Dis. 2013;8:551–9.PubMedPubMed CentralGoogle Scholar
  8. Bertens LCM, Reitsma JB, Moons KGM, van Mourik Y, Lammers JWJ, Broekhuizen BDL, et al. Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2013;8:493–9.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Makita C, Nakamura T, Takada A, Takayama K, Suzuki M, Ishikawa Y, et al. Clinical outcomes and toxicity of proton beam therapy for advanced cholangiocarcinoma. Radiat Oncol. 2014;9:26.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Seemungal TA, Donaldson GC, Bhowmik A, Jeffries DJ, Wedzicha JA. Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2000;161(5):1608–13.View ArticlePubMedGoogle Scholar
  11. Seemungal TA, Donaldson GC, Paul E a, Bestall JC, Jeffries DJ, Wedzicha JA. Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 1998;157(5 Pt 1):1418–22.View ArticlePubMedGoogle Scholar
  12. Burge S, Wedzicha JA. COPD exacerbations: definitions and classifications. Eur Respir J Suppl 2003;41:46 s - 53 s.Google Scholar
  13. Donaldson GC, Seemungal T a R, Bhowmik a, Wedzicha J a. Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease. Thorax. 2002;57(10):847–52.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Heart N. Lung and Blood Institute. Morbidity and mortality chartbook on cardiovascular, lung and blood diseases. Bethesda, Maryland: US Department of Health and Human Services, Public Health Services, National Institutes of Health; 2004.Google Scholar
  15. Mador MJ, Sethi S. Systemic inflammation in predicting COPD exacerbations. JAMA. 2013;309(22):2390–1.View ArticlePubMedGoogle Scholar
  16. Oga T, Tsukino M, Hajiro T, Ikeda A, Nishimura K. Predictive properties of different multidimensional staging systems in patients with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2011;6:521–6.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Wouters EFM. Chronic obstructive pulmonary disease. 5: systemic effects of COPD. Thorax. 2002;57(12):1067–70.View ArticlePubMedPubMed CentralGoogle Scholar
  18. van Dijk WD, van den Bemt L, van den Haak-Rongen S, Bischoff E, van Weel C, Veen JCCM, et al. Multidimensional prognostic indices for use in COPD patient care. A systematic review Respir Res. 2011;12:151.View ArticlePubMedGoogle Scholar
  19. Marin JM, Carrizo SJ, Casanova C, Martinez-Camblor P, Soriano JB, Agusti AGN, et al. Prediction of risk of COPD exacerbations by the BODE index. Respir Med. 2009;103(3):373–8.View ArticlePubMedGoogle Scholar
  20. Make BJ, Eriksson G, Calverley PM, Jenkins CR, Postma DS, Peterson S, et al. A score to predict short-term risk of COPD exacerbations (SCOPEX). Int J Chron Obstruct Pulmon Dis. 2015;10:201–9.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Moberg M, Vestbo J, Martinez G, Williams JE a, Ladelund S, Lange P, et al. Validation of the i-BODE Index as a Predictor of hospitalization and Mortality in Patients with COPD Participating in Pulmonary Rehabilitation. COPD. 2013;1–7.Google Scholar
  22. Schembri S, Anderson W, Morant S, Winter J, Thompson P, Pettitt D, et al. A predictive model of hospitalisation and death from chronic obstructive pulmonary disease. Respir Med. 2009;103(10):1461–7.View ArticlePubMedGoogle Scholar
  23. Al-ani S, Spigt M, Hofset P, Melbye H. Predictors of exacerbations of asthma and COPD during one year in primary care. Fam Pract. 2013;30(6):621–8.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Otero González I, Blanco Aparicio M, Montero Martínez C, Valiño López P, Verea Hernando H. The epidemiology of COPD and asthma exacerbations in a general hospital. Arch Bronconeumol. 2002;38(6):256–62.View ArticlePubMedGoogle Scholar
  25. Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, Calverley P, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med. 2007;176(6):532–55.View ArticlePubMedGoogle Scholar
  26. Sapey E, Stockley R a. COPD exacerbations. 2: aetiology. Thorax. 2006;61(3):250–8.View ArticlePubMedPubMed CentralGoogle Scholar
  27. 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(12):1128–38.View ArticlePubMedGoogle Scholar
  28. Miravitlles M, Guerrero T, Mayordomo C, Sánchez-Agudo L, Nicolau F, Segú JL. Factors associated with increased risk of exacerbation and hospital admission in a cohort of ambulatory COPD patients: a multiple logistic regression analysis. The EOLO Study Group Respiration. 2000;67(5):495–501.Google Scholar
  29. Motegi T, Jones RC, Ishii T, Hattori K, Kusunoki Y, Furutate R, et al. A comparison of three multidimensional indices of COPD severity as predictors of future exacerbations. Int J Chron Obstruct Pulmon Dis. 2013;8:259–71.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Kilic H, Kokturk N, Sari G, Cakır M. Do females behave differently in COPD exacerbation? Int J Chron Obstruct Pulmon Dis. 2015;10:823–30.PubMedPubMed CentralGoogle Scholar
  31. Poole PJ, Chacko E, Wood-Baker RWB, Cates CJ. Influenza vaccine for patients with chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2006;1:CD002733.PubMedGoogle Scholar
  32. Viejo JLB. Vaccination against pneumococcal infection in adults. Rev Esp Quimioter. 2009;22 Suppl 1:26–8.PubMedGoogle Scholar
  33. Soler-Cataluña JJ, García MAM. Impact of efficacy and mortality studies (TORCH and UPLIFT) in bronchodilator treatment of chronic obstructive pulmonary disease. Arch Bronconeumol. 2009;45 Suppl 5:14–20.View ArticlePubMedGoogle Scholar
  34. Montserrat-Capdevila J, Godoy P, Marsal J-R, Cruz I, Solanes M. Effectiveness of influenza vaccination in preventing hospital admission due to exacerbations of chronic obstructive pulmonary disease. Enferm Infecc Microbiol Clin. 2014;32(2):70–5.View ArticlePubMedGoogle Scholar
  35. Jenkins CR, Celli B, Anderson JA, Ferguson GT, Jones PW, Vestbo J, et al. Seasonality and determinants of moderate and severe COPD exacerbations in the TORCH study. Eur Respir J. 2012;38–45.Google Scholar
  36. Donaldson GC, Goldring JJ, Wedzicha JA. Influence of season on exacerbation characteristics in patients with COPD. Chest. 2012;141(1):94–100.View ArticlePubMedGoogle Scholar
  37. Terada K, Muro S, Sato S, Ohara T, Haruna A, Marumo S, et al. Impact of gastro-oesophageal reflux disease symptoms on COPD exacerbation. Thorax. 2008;63(11):951–5.View ArticlePubMedGoogle Scholar
  38. Ling SH, van Eeden SF. Particulate matter air pollution exposure: role in the development and exacerbation of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2009;233–43.Google Scholar
  39. Song Q, Christiani DC, Xiaorong W, Ren J. The global contribution of outdoor air pollution to the incidence, prevalence, mortality and hospital admission for chronic obstructive pulmonary disease: a systematic review and meta-analysis. Int J Environ Res Public Health. 2014;11(11):11822–32.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© Montserrat-Capdevila et al. 2015

Advertisement