RESEARCH ARTICLE
Predicting Quality of Life in Chronic Obstructive Pulmonary Disease Patients Living in the Rural Area of Chiang Mai Province, Thailand
Tassawan Kantatong1
Article Information
Identifiers and Pagination:
Year: 2020Volume: 13
First Page: 357
Last Page: 364
Publisher ID: TOPHJ-13-357
DOI: 10.2174/1874944502013010357
Article History:
Received Date: 5/4/2020Revision Received Date: 27/5/2020
Acceptance Date: 31/5/2020
Electronic publication date: 30/07/2020
Collection year: 2020
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Chronic Obstructive Pulmonary Disease (COPD) becomes burdensome and reduces the quality of life in COPD patients and their families. Understanding the factors affecting the quality of life could improve the process of care and treatment of these patients. Therefore, this study aimed to investigate the factors that influence and can predict the quality of life in COPD patients.
Methods:
A total of 281 COPD patients were included and analyzed by spirometry measurement, BODE index and its components (body mass index; BMI, forced expiratory volume in one second % predicted; FEV1%predicted, dyspnea score of the modified Medical Research Council; mMRC, and distance in the six-minute walking test; 6MWT).Quality of life was assessed by the St. George’s Respiratory Questionnaire (SGRQ).
Results:
The total SGRQ score was 42.1±17.4. The mMRC dyspnea score, ABCD group classification of COPD and BODE index moderately correlated with the total SGRQ score. Weak correlations were found between BMI, FEV1%predicted and 6MWT. Multiple regression analysis showed that the mMRC dyspnea score, BODE index, age, and COPD group D were important predictors of quality of life in COPD patients with R2 of 0.467.
Conclusion:
The mMRC dyspnea score, BODE index, age, and COPD group D could predict the quality of life in the COPD patients in this study.
1. INTRODUCTION
Chronic Obstructive Pulmonary Disease (COPD) is one of the most important diseases, which affects more than 200 million people worldwide [1]. The World Health Organization predicts that COPD will be 3.8% of total Disability-adjusted Life Years (DALYs) [2] and the third-leading cause of global death by 2030 [3]. COPD is a preventable and treatable disease characterized by persistent respiratory symptoms and airflow limitation [4]. These characteristics lead to deconditioning, poor functional capacity and poor quality of life [5, 6]. Hence, COPD treatments aim to control symptoms, improve physical function, exercise tolerance and health status, and decrease the psychological impact of COPD and hospital re-admission, with the ultimate goal of improving quality of life [4, 7-10].
Quality of life is important in measuring the impact of chronic diseases [11]. The objectives of measuring the quality of life for the patients are to improve the process and planning of care and assist physicians and patients in deciding the appropriate treatment [12]. Evaluation of quality of life is necessary in COPD patients because the association between measures of airway obstruction and exercise impairment were poor, and the cause of breathlessness could not be summarized by only the lung function test [11]. The St. George’s Respiratory Questionnaire (SGRQ) is an instrument for measuring quality of life, and was specially designed for COPD patients. It shows the impact of disease on everyday life [13, 14] and includes 3 components; symptoms, activity and impact [15-18]. The SGRQ score represents the association with health resource utilities and decreased survival [19, 20]. Moreover, it responds to either pharmacological or non-pharmacological intervention [21, 22]. Previous studies have investigated the correlation and impact between quality of life and many variables, including the BODE index, Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade classification, and ABCD group classification of COPD [23-26]. Sarkar et al. [24] found that the BODE index, including its individual components, strongly correlated with quality of life, and it was shown to be better than the GOLD grade classification in predicting the health status in stable COPD patients. Nonato et al. [23] reported that COPD patients in early stages, with a BODE index score of 0 could have impaired quality of life. Martin et al. [27] revealed that quality of life correlated better with the BODE index than with forced expiratory volume in one second % predicted (FEV1 %predicted), while no correlation with body mass index (BMI) was found. Boland et al. [26] found that the ABCD group classification associated more closely with quality of life than the GOLD grade classification. Nevertheless, findings in the ABCD group and GOLD grade classification have been inconclusive, as shown in previous studies [26, 28, 29]. Thus, it was unknown whether the BODE index, GOLD grade, or ABCD group could predict the quality of life in COPD patients.
Chiang Dao district has an area of approximately 1,882 km2, in the north of Chiang Mai province, Thailand, and is surrounded by high mountains. The prevalence of COPD in Chiang Dao district was higher at 6.8% than that in municipal areas of Chiang Mai (3.7%) [30], which leads to major health problems [4]. Previous study found that outdoor air pollution affects quality of life in COPD patients in Chiang Dao [31], but other factors are inconsistent. It is important to identify the factors that influence and can predict quality of life. Therefore, this study aimed to investigate the variables that influence and can predict the quality of life in COPD patients, which could improve their process of care and treatment.
2. MATERIALS AND METHODS
This cross-sectional study was approved by the Research Ethics Committee of the Faculty of Medicine, Chiang Mai University (No. 497/2015). A total of 281 COPD patients, who attended the COPD clinic at Chiang Dao Hospital (60-bed community hospital), Chiang Mai, Thailand between December 2015 and November 2016 were recruited into this study. The inclusion criteria comprised patients clinically diagnosed with COPD according to the GOLD report [4], aged 40 years or older and able to walk independently. The exclusion criteria consisted of acute exacerbation within 4 weeks before the assessment, tuberculosis, asthma, cognitive impairment, and psychological and musculoskeletal disorders.
All of the patients completed the SGRQ - Thai version, which was permitted by Professor Paul Jones, Professor of Respiratory Medicine, St. George’s University of London. The SGRQ - Thai version is considered a gold standardized questionnaire for measuring impaired health and perceiving quality of life in chronic respiratory diseases. It contains 50 items with 76 weighted responses that are divided into three domains: symptoms - distress due to respiratory symptoms, activity - disturbances of physical activity, and impact - overall impact on daily life and well-being. Items were scored from zero (no impairment) to 100 (maximum impairment) [14].
The BODE index is a multidimensional scoring system and capacity index used for assessing COPD patients. It is calculated by the scores obtained from BMI, measurement of airflow obstruction using spirometry (FEV1%predicted), exercise capacity by distances in the six-minute walking test (6MWT) and dyspnea scale from the modified Medical Research Council (mMRC). Scoring from 0 to 10 points in further quartiles was as follows: quartile 1 (0 to 2 points), quartile 2 (3 to 4 points), quartile 3 (5 to 6 points), and quartile 4 (7 to 10 points) [32].
The severity of COPD in the patients was classified according to the GOLD report [4] by the level of airflow limitation (post-bronchodilator FEV1 %predicted) into GOLD grades 1-4: GOLD 1 (mild COPD): FEV1> 80%predicted, GOLD 2 (moderate COPD): FEV1 50-79%predicted, GOLD 3 (severe COPD): FEV1 30-49%predicted and GOLD 4 (very severe COPD): FEV1< 30%predicted. The symptoms (mMRC dyspnea scale or COPD assessment test; CAT) and the risk of future exacerbation (prior history of moderate to severe COPD exacerbation) were put into the GOLD groups A-D group: Group A: low risk, fewer symptoms, Group B: low risk, more symptoms, Group C: high risk, fewer symptoms and Group D: high risk, more symptoms.
2.1. Statistical Analysis
The results were analyzed as mean ± standard deviations or percentages. Pearson’s correlation coefficient and Spearman’s correlation coefficient were used to screening the correlation between variables. Multiple regression analysis was used to analyze all variables for predicting. The statistical model was validated using the coefficient of determination (R2). A p-value of <0.05 (2-sided) was set as the level of statistical significance.
3. RESULTS
A total of 281 COPD patients were included for analyses. All of the patients were smokers with a mean smoking history of 39.0±18.1 years; 50.9% of them were male with a mean age of 68.9±10.2 years and mean income of approximately 139.9±218.2 USD (US dollar) per month; and 48.8% had an occupation; 87.5% stayed with a caregiver; and 43.1% had comorbidity. The mean post-bronchodilator FEV1 was 63.5±28.3%predicted. Mean SGRQ scores were as follows: total score 42.1±17.4, symptom score 39.9±21.5, activity score 49.8±25.6 and impact score 38.2±16.3. Demographic data and clinical characteristics of the participants are presented in Table 1.
| Variables (N=281) | n (%) or mean±SD |
|---|---|
| Sex | |
| male | 143 (50.9) |
| Age (years) | 68.9±10.2 |
| Income (USD/month) | 139.9±218.2 |
| Smoking (years) | 39.0±18.1 |
| Occupation Yes |
137 (48.8) |
| Caregiver Yes |
246 (87.5) |
| Comorbidity Yes |
121 (43.1) |
| BODE Index (score of 10) | 3.3±2.1 |
| BMI (kg/m2) | 19.1±3.9 |
| mMRC (score) | 1.6±1.1 |
| FEV1% predicted | 63.5±28.3 |
| 6MWT (meters) | 341.7±102.1 |
| GOLD stage | |
| GOLD 1 | 66 (23.5) |
| GOLD 2 | 109 (38.8) |
| GOLD 3 | 83 (29.5) |
| GOLD 4 | 23 (8.2) |
| ABCD assessment groups | |
| Group A | 133 (47.3) |
| Group B | 107 (38.1) |
| Group C | 14 (5.0) |
| Group D | 27 (9.6) |
Table 2 shows the SGRQ score of each component with each variable. Table 3 shows the correlation between the SGRQ score and each factor.
The total SGRQ score was at a moderate level for both males and females, with no significant difference between genders when compared with the mean difference.
Age was associated with the symptom (R = -0.16; p < 0.05) and activity score (R = 0.14; p < 0.05), but not with the impact or total score. Having occupation was associated with activity score (R = -0.28; p < 0.001), impact score (R = -0.10; p < 0.001), and total score (R = -0.18; p < 0.001), but not with the symptom score.
| Variables | n | SGRQ score (mean±SD) | ||||
|---|---|---|---|---|---|---|
| Symptoms score | Activity score | Impact score | Total scores |
|||
| Overall | 281 | 39.9±21.5 | 49.8±25.6 | 38.2±16.3 | 42.1±17.4 | |
| BMI (kg/m2) | ||||||
| >21 | 201 | 40.5±21.0 | 51.6±25.0 | 39.2±15.1 | 43.3±16.3 | |
| ≤21 | 80 | 38.3±22.6 | 45.4±26.7 | 35.5±18.7 | 39.1±19.5 | |
| p value | 0.231 | 0.051 | 0.030 | 0.027 | ||
| FEV1 %predicted | ||||||
| ≥ 65 | 110 | 38.7±21.9 | 43.3±23.6 | 34.3±17.4 | 37.8±17.4 | |
| 50-65 | 65 | 40.6±23.9 | 54.8±27.0 | 41.2±15.2 | 45.4±18.0 | |
| 36-49 | 66 | 40.2±19.0 | 49.2±25.6 | 38.5±13.9 | 42.2±15.7 | |
| ≤35 | 40 | 41.1±20.7 | 60.6±24.2 | 43.4±16.2 | 48.5±16.4 | |
| p value | 0.906 | 0.001 | 0.005 | 0.002 | ||
| mMRC grade | ||||||
| Grade 0-1 | 147 | 33.6±21.6 | 34.5±19.7 | 30.2±14.0 | 32.1±14.0 | |
| Grade 2 | 60 | 43.5±17.9 | 57.1±17.5 | 42.6±11.8 | 47.3±11.5 | |
| Grade 3 | 71 | 49.6±20.3 | 74.4±19.1 | 50.4±14.6 | 57.8±13.8 | |
| Grade 4 | 3 | 42.7±5.6 | 72.9±19.5 | 48.3±21.9 | 55.1±17.1 | |
| p value | <0.001 | <0.001 | <0.001 | <0.001 | ||
| 6MWT (meters) | ||||||
| ≥350 | 157 | 39.3±22.4 | 42.2±24.3 | 36.3±16.5 | 38.6±17.4 | |
| 250-349 | 74 | 38.4±18.7 | 54.1±23.2 | 39.2±15.6 | 43.7±15.5 | |
| 150-249 | 34 | 42.5±22.5 | 63.6±23.1 | 40.9±16.4 | 48.2±17.4 | |
| <150 | 16 | 46.2±21.8 | 75.5±21.3 | 46.5±13.8 | 55.5±15.9 | |
| p value | 0.505 | <0.001 | 0.055 | <0.001 | ||
| Quartile BODE index | ||||||
| Quartile 1 | 101 | 36.4±21.6 | 36.4±21.8 | 31.6±16.4 | 33.9±16.5 | |
| Quartile 2 | 89 | 39.6±21.0 | 48.3±23.8 | 37.9±14.5 | 41.4±15.6 | |
| Quartile 3 | 58 | 43.5±19.6 | 68.1±20.0 | 45.9±14.6 | 52.5±14.2 | |
| Quartile 4 | 21 | 51.4±20.4 | 76.1±19.0 | 49.9±13.8 | 58.3±13.5 | |
| p value | <0.001 | <0.001 | <0.001 | <0.001 | ||
| ABCD group | ||||||
| Group A | 133 | 33.5±21.3 | 34.1±19.4 | 30.5±13.9 | 32.2±13.9 | |
| Group B | 107 | 45.7±18.9 | 64.7±19.8 | 45.9±13.6 | 51.8±13.4 | |
| Group C | 14 | 33.9±24.8 | 37.7±22.4 | 27.8±14.9 | 31.9±15.9 | |
| Group D | 27 | 50.9±20.0 | 74.4±20.3 | 50.8±15.1 | 58.2±14.2 | |
| p value | <0.001 | <0.001 | <0.001 | <0.001 | ||
SD: Standarddeviation, SGRQ: St George’s respiratory questionnaire, COPD: Chronic obstructive pulmonary disease, BMI: Body mass index, FEV1: Forced expiratory volume inonesecond, mMRC:modified medical research council dyspnea score, 6MWT: six-minute walking test.
| Factors | Symptoms | Activity | Impact | Total |
|---|---|---|---|---|
| Sexb | -0.05 (p = 0.393) | 0.07 (p = 0.234) | 0.06 (p = 0.333) | 0.06 (p = 0.290) |
| Agea | -0.16 (p = 0.006)* | 0.14 (p = 0.020)* | -0.08 (p = 0.155) | -0.01 (p = 0.880) |
| Incomea | -0.03 (p = 0.605) | -0.09 (p = 0.139) | -0.06 (p = 0.360) | -0.07 (p = 0.224) |
| Smokinga | -0.05 (p = 0.531) | 0.07 (p = 0.299) | 0.03 (p = 0.597) | 0.04 (p = 0.535) |
| Occupationalb | -0.01 (p = 0.847) | -0.28 (p <0.001)** | -0.10 (p <0.001)** | -0.18 (p = 0.002)* |
| Caregiverb | -0.00 (p = 0.954) | -0.03 (p = 0.623) | -0.02 (p = 0.788) | -0.02 (p = 0.696) |
| Comorbidityb | 0.03 (p = 0.665) | -0.02 (p = 0.798) | -0.01 (p = 0.902) | 0.00 (p = 0.545) |
| BODE indexa | 0.21 (p <0.001)** | 0.58 (p <0.001)** | 0.42 (p <0.001)** | 0.52 (p <0.001)** |
| BMIa | -0.04 (p = 0.491) | -0.18 (p = 0.002)* | -0.12 (p = 0.042)* | -0.15 (p = 0.010)* |
| mMRCa | 0.30 (p <0.001)** | 0.72 (p <0.001)** | 0.54 (p <0.001)** | 0.66 (p <0.001)** |
| FEV1%predicteda | -0.09 (0.143) | -0.25 (p <0.001)** | -0.22 (p <0.001)** | -0.24 (p <0.001)** |
| 6MWTa | -0.03 (0.587) | -0.36 (p <0.001)** | -0.12 (p <0.041)* | -0.23 (p <0.001)** |
| Quartile BODE indexb | 0.19 (0.002)* | 0.52 (p <0.001)** | 0.35 (p <0.001)** | 0.45 (p <0.001)** |
| ABCD groupb | 0.28 (p <0.001)* | 0.54 (p <0.001)* | 0.42 (p <0.001)* | 0.52 (p <0.001)** |
SGRQ: StGeorge’s respiratoryquestionnaire, BMI: Body massindex, mMRC:modified medical research council dyspnea score, FEV1: Forced expiratory volumeinonesecond, 6MWT: six-minute walking test.
The total SGRQ score in patients with BMI <21 kg/m2 and ≥21 kg/m2 was 43.3±16.3 and 39.1±19.5, respectively. The mean difference between the two groups was statistically significant (p<0.05). Even if BMI associated with total SGRQ scores, the correlation was weak (R = -0.15; p < 0.05).
FEV1%predicted and the total SGRQ score of at least one pair were statically significant (p<0.05). The total SGRQ score was highest in patients with FEV1 ≤35%predicted (48.5±16.4) and lowest in those with FEV1 ≥65%predicted (37.8±17.4). The correlation between FEV1%predicted and total SGRQ score was weak (R = -0.24; p< 0.001).
The mMRC dyspnea score and total SGRQ score of at least one pair were statistically significant (p<0.001). The total SGRQ score was highest in patients with mMRC grade 3 (57.8±13.8) and lowest in those with mMRC grade 0-1 (32.1±14.0). The mMRC dyspnea score showed strong correlation with the activity (R = 0.72; p < 0.001) and total score (R = 0.66; p < 0.001), but moderate and mild correlation with the impact (R = 0.54; p < 0.001) and symptom score (P = 0.30; p < 0.001), respectively.
The 6MWT and total SGRQ score of at least one pair were statistically significant (p<0.001). The total SGRQ score was highest in patients with a walking distance <150 meters (55.5±15.9) and lowest in those with a walking distance ≥350 meters (38.6±17.4). Mild negative correlations were found in the activity score (P = -0.36; p < 0.001), impact score (P = -0.12; p < 0.001), and total SGRQ score (P = -0.23; p < 0.001), but no correlation was shown in the symptom score.
The Quartile BODE index and total SGRQ score of at least one pair were statistically significant (p<0.001). The mean total SGRQ score gradually increased with increasing quartile BODE index. The highest total SGRQ score was in quartile 4 (58.3±13.5) and lowest in quartile 1 (33.9±16.5). The quartile BODE index showed moderate correlation with the activity (R = 0.52; p < 0.001) and total score (R = 0.515; p < 0.001), but mild correlation with the symptom (R = 0.19; p < 0.01) and impact score (P = 0.35; p < 0.01).
The ABCD group classification and total SGRQ score of at least one pair were statistically significant (p<0.001). The total SGRQ score was highest in group D (58.2±14.2) and lowest in group C (31.9±15.9). The ABCD group showed moderate correlation with the activity (R = 0.54; p < 0.01) and total score (R = 0.52; p < 0.01), but mild correlation with the symptom (R = 0.28; p < 0.01) and impact score (R = 0.42; p < 0.01).
Multiple regression analysis was applied to identify independent variables that could predict quality of life in COPD patients (Table 4). The coefficient of determination (R2) was 0.467, which showed that this model was capable of predicting the total SGRQ score of 46.7%, while the mMRC dyspnea and BODE index score, age, and COPD group D were important predictors of degenerating quality of life, as assessed by the total SGRQ score.
The equation of multiple regression models is as follows:
YSGRQ = 38.194 + 8.935 (mMRC) + 1.160 (BODE index) – 0.214 (Age) + 6.175 (Group D)
When the mMRC dyspnea score increases 1, the total SGRQ score increases 8.935. When the BODE index score increases 1, the total SGRQ score increases 1.160. When the age increases 1 year, the total SGRQ score decreases 0.214. COPD group D had a greater SGRQ score than the other groups, with a score of 6.175.
4. DISCUSSION
Quality of life for the rural COPD patients in this study was at a moderate level, which was better than that of patients living in the urban and rural areas in a previous study [33]. Equation multiple regression models could predict quality of life at 46.7%, and it was found that the mMRC dyspnea score, BODE index, age and COPD group D could predict quality of life in COPD patients. Although occupation, FEV1%predicted, 6MWT, and BMI had significant correlations with the SGRQ, these variables could not predict quality of life in the model of this study.
This study achieved several important findings. First, it showed a strong correlation between the ABCD group classification and total SGRQ score. Second, it confirmed that quality of life in COPD group D was worse than that in group A, B and C. Third, a negative correlation was found between age and the total SGRQ score. Finally, this study confirmed the correlation between BODE index and the SGRQ score.
The ABCD classification correlated with the total SGRQ score. Due to ABCD classification being classified by exacerbating history and dyspnea symptoms [34], a moderate to strong correlation was found between dyspnea score and total SGRQ score (R=0.66). This confirms the results of previous studies that showed a significant association between ABCD classification and quality of life in COPD patients [26, 28]. Moreover, this study found that group D of the ABCD classification could predict quality of life in COPD patients. Group D had a higher total SGRQ score than the other groups, which indicated the worst quality of life. This study found that quality of life in group C (high risk, fewer symptoms) and group A (low risk, fewer symptoms) was similar. Either group A or C had a better quality of life than group B (low risk, more symptoms) and D (high risk, more symptoms). This confirmed that group B had a lower survival rate and worse quality of life than group C in a previous study [26, 35]. Therefore, the authors assumed that the quality of life in COPD patients depended on symptoms more than risk.
Table 4. Factors predicting the SGRQ score in COPD patients by multiple regression analysis.
| Variables | B | SE | t | 95% CI |
|---|---|---|---|---|
| Constant | 38.194 | 5.255 | 7.269 | 27.85, 48.54 |
| mMRC | 8.935 | 1.008 | 8.868 | 6.95, 10.92 |
| BODE index | 1.160 | 0.502 | 2.308 | 0.17, 1.15 |
| Age | -0.214 | 0.078 | -2.757 | -0.37, -0.06 |
| ABCD groups | ||||
| Group D | 6.175 | 2.720 | 2.270 | 0.82, 11.53 |
mMRC:modified medical research council dyspnea score.
This study found a negative correlation between age and symptoms, and positive correlation between age and activity in the SGRQ score, but no correlation between impact and total SGRQ score. It showed that older COPD patients had a better quality of life than younger ones in aspects of symptoms. This result supported Martinez et al. [36], who found that older COPD patients reported better quality of life than younger ones. Whereas, previous studies revealed that age could decrease the level of quality of life [33]. The BODE index correlated significantly with the SGRQ score. This finding confirmed the results from the previous studies [23, 24, 27, 37, 38], which showed modest to strong correlation between them. Among components of the BODE index, the higher mMRC score, and lower BMI, FEV1%predicted and 6MWT, correlated with worsening quality of life in COPD patients [24]. The mMRC score was the only component of the BODE index that could predict the total SGRQ score in the equation model.
The mMRC score, which measured dyspneic symptoms, had good correlation with the total SGRQ score (R = 0.66; p < 0.01), and was an important predictor of quality of life in COPD patients. The pathology of COPD causes airway obstruction [39] that leads to air trapping, hyperinflation [40] and subsequently dyspneic symptoms. Dyspnea is associated with daily activities. COPD patients were deconditioned, due to inactivity from dyspnea, causing decreased functional capacity [5, 6] that leads to poor quality of life in the future. This study also demonstrated the correlation between mMRC score and SGRQ score as published [23, 24],and showed that perception of dyspnea could explain 25% to 54% of the quality of life score in COPD patients [41].Thus, the evaluation of quality of life can be used for the assessing dyspnea, which is simple and clear, easy to apply, and practical, particularly in the rural areas of Thailand, where the SGRQ is complex and the communication difficult, especially with minority groups and elderly people.
This study found that the mMRC dyspnea score, BODE index, age and COPD group D could predict quality of life in rural COPD patients.Hence, combined center-and home-based pulmonary rehabilitation programme, which includes exercise training and breathing exercise, can alleviate the dyspnea, increase functional capacity and improve quality of life in rural COPD patients [42].
The strengths of this study include the large sample size and number of COPD patients that was equal between men and women, which helped to decrease bias. However, the limitations in this study include only 8.2% of COPD patients at a very severe GOLD stage level, which made it impossible to generalize results from that stage. However, this seemed to be a general format, which has been seen in several other studies of COPD [24, 26, 33, 35, 43]. This study did not evaluate psychological health, which is known to affect quality of life in COPD patients.
ETHICAL APPROVAL AND CONSENT TO PARTICIPATE
The study protocol was approved by the Research Ethics Committee, Faculty of Medicine, Chiang Mai University, Thailand (Ethics approval number: 497/2015).
HUMAN AND ANIMAL RIGHTS
No animals were used in this study. All human research procedures were followed in accordance with the ethical standards of the committee responsible for human experimentation (institutional and national), and with the Helsinki Declaration of 1975, as revised in 2013.
CONSENT FOR PUBLICATION
Informed consent was obtained from all the participants.
AVAILABILITY OF DATA AND MATERIALS
The datasets generated and/or analyzed during the current study are available at Baqiyatallah University of Medical Sciences.
FUNDING
This study was supported by the Faculty of Medicine, Chiang Mai University, Grant no. 047/2559.
CONFLICT OF INTEREST
The author declares no conflict of interest, financial or otherwise.
ACKNOWLEDGEMENTS
The authors are grateful to Professor Paul Jones, Professor of Respiratory Medicine, St.George’s University of London for permission to use the Thai version of the St. George’s Respiratory Questionnaire (SGRQ). They also thank the director and working team of Chiang Dao Hospital for providing the necessary support for this study. Thanks also go to all of the patients who participated in this study.
REFERENCES
| [1] | Rycroft CE, Heyes A, Lanza L, Becker K. Epidemiology of chronic obstructive pulmonary disease: A literature review. Int J Chron Obstruct Pulmon Dis 2012; 7: 457-94. |
| [2] | World Health Organization. The Global Burden of Disease 2004. Available from: https://apps.who.int/ iris/bitstream/handle/10665/ 43942/978924156371 0_eng.pdf |
| [3] | Burden of COPD. Available from: http://www.who.int/respiratory /copd/burden/en/ |
| [4] | The Global Initiative for Chronic Obstructive Lung Disease (GOLD). 2019. Available from: http://www.goldcopd.org |
| [5] | Redelmeier DA, Goldstein RS, Min ST, Hyland RH. Spirometry and dyspnea in patients with COPD. When small differences mean little. Chest 1996; 109(5): 1163-8. |
| [6] | Orozco-Levi M. Structure and function of the respiratory muscles in patients with COPD: impairment or adaptation? Eur Respir J Suppl 2003; 46(Suppl. 46): 41s-51s. |
| [7] | O’Donnell DE, Sciurba F, Celli B, et al. Effect of fluticasone propionate/salmeterol on lung hyperinflation and exercise endurance in COPD. Chest 2006; 130(3): 647-56. |
| [8] | Agrawal R, Moghtader S, Ayyala U, Bandi V, Sharafkhaneh A. Update on management of stable chronic obstructive pulmonary disease. J Thorac Dis 2019; 11(Suppl. 14): S1800-9. |
| [9] | McCarthy B, Casey D, Devane D, Murphy K, Murphy E, Lacasse Y. Pulmonary rehabilitation for chronic obstructive pulmonary disease. Cochrane Database Syst Rev 2015; 2(2) CD003793. |
| [10] | Puhan MA, Gimeno-Santos E, Cates CJ, Troosters T. Pulmonary rehabilitation following exacerbations of chronic obstructive pulmonary disease. Cochrane Database Syst Rev 2016; 2016(12) CD005305. |
| [11] | Zamzam MA, Azab NY, El Wahsh RA, Ragab AZ, Allam EM. Quality of life in COPD patients. Egypt J Chest Dis Tuberc 2012; 61(4): 281-9. |
| [12] | Donald A. What is QOL? 2nd ed.. 2009. |
| [13] | Jones PW. Health status measurement in chronic obstructive pulmonary disease. Thorax 2001; 56(11): 880-7. |
| [14] | Jones PWSt. St. George’s Respiratory Questionnaire: MCID. COPD 2005; 2(1): 75-9. |
| [15] | Puhan MA, Guyatt GH, Goldstein R, et al. Relative responsiveness of the Chronic Respiratory Questionnaire, St. Georges Respiratory Questionnaire and four other health-related quality of life instruments for patients with chronic lung disease. Respir Med 2007; 101(2): 308-16. |
| [16] | Reardon JZ, Lareau SC, ZuWallack R. Functional status and quality of life in chronic obstructive pulmonary disease. Am J Med 2006; 119(10)(Suppl. 1): 32-7. |
| [17] | American Thoracic Society. St George's Respiratory Questionnaire (SGRQ) Available from: http://www.thoracic.org/assemblies/srn/ questionaires/sgrq.php |
| [18] | Kruis AL, Smidt N, Assendelft WJ, et al. Integrated disease management interventions for patients with chronic obstructive pulmonary disease (Review). Cochrane Database Syst Rev 2013; 10(10) CD009437. |
| [19] | Fan VS, Curtis JR, Tu SP, McDonell MB, Fihn SD. Using quality of life to predict hospitalization and mortality in patients with obstructive lung diseases. Chest 2002; 122(2): 429-36. |
| [20] | Domingo-Salvany A, Lamarca R, Ferrer M, et al. Health-related quality of life and mortality in male patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2002; 166(5): 680-5. |
| [21] | Fishman A, Martinez F, Naunheim K, et al. A randomized trial comparing lung-volume-reduction surgery with medical therapy for severe emphysema. N Engl J Med 2003; 348(21): 2059-73. |
| [22] | Mahler DA, Wire P, Horstman D, et al. Effectiveness of fluticasone propionate and salmeterol combination delivered via the Diskus device in the treatment of chronic obstructive pulmonary disease Am J Respir Crit Care Med 2002; 166(8): 1084-91. |
| [23] | Nonato NL, Díaz O, Nascimento OA, Dreyse J, Jardim JR, Lisboa C. Behavior of quality of life (SGRQ) in COPD patients according to BODE scores. Arch Bronconeumol 2015; 51(7): 315-21. [English Edition]. |
| [24] | Sarkar SK, Basuthakur S, Das SK, et al. Evaluation of correlation of BODE index with health-related quality of life among patients with stable COPD attending a tertiary care hospital. Lung India 2015; 32(1): 24-8. |
| [25] | Ong K-C, Lu S-J, Soh CS-C. Does the multidimensional grading system (BODE) correspond to differences in health status of patients with COPD? Int J Chron Obstruct Pulmon Dis 2006; 1(1): 91-6. |
| [26] | Boland MR, Tsiachristas A, Kruis AL, Chavannes NH, Rutten-van Mölken MP. Are GOLD ABCD groups better associated with health status and costs than GOLD 1234 grades? A cross-sectional study. Prim Care Respir J 2014; 23(1): 30-7. |
| [27] | Marin JM, Cote CG, Diaz O, et al. Prognostic assessment in COPD: health related quality of life and the BODE index. Respir Med 2011; 105(6): 916-21. |
| [28] | Nurwidhiyasari D, Rachmi SF, Indracahyani A, Nuraini T. Relationship between severity and quality of life in chronic obstructive pulmonary disease patients at hospitals’ outpatient units in Jakarta. Enferm Clin 2019; 29(Suppl. 2): 159-65. |
| [29] | Weldam SW, Lammers J-WJ, Heijmans MJ, Schuurmans MJ. Perceived quality of life in chronic obstructive pulmonary disease patients: a cross-sectional study in primary care on the role of illness perceptions. BMC Fam Pract 2014; 15: 140. |
| [30] | Pothirat C, Chaiwong W, Phetsuk N, Pisalthanapuna S, Chetsadaphan N, Inchai J. A comparative study of COPD burden between urban vs rural communities in northern Thailand. Int J Chron Obstruct Pulmon Dis 2015; 10: 1035-42. |
| [31] | Pothirat C, Chaiwong W, Liwsrisakun C, et al. Influence of particulate matter during seasonal smog on quality of life and lung function in patients with chronic obstructive pulmonary disease. Int J Environ Res Public Health 2019; 16(1) E106. |
| [32] | Celli BR, Cote CG, Marin JM, Casanova C, Montes de Oca M, Mendez RA, et al. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004; 350(10): 1005-12. |
| [33] | Rosińczuk J, Przyszlak M, Uchmanowicz I. Sociodemographic and clinical factors affecting the quality of life of patients with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2018; 13: 2869-82. |
| [34] | Jones PW, Harding G, Berry P, Wiklund I, Chen WH, Kline Leidy N. Development and first validation of the COPD Assessment Test. Eur Respir J 2009; 34(3): 648-54. |
| [35] | Lange P, Marott JL, Vestbo J, et al. Prediction of the clinical course of chronic obstructive pulmonary disease, using the new GOLD classification: a study of the general population. Am J Respir Crit Care Med 2012; 186(10): 975-81. |
| [36] | Martinez CH, Diaz AA, Parulekar AD, et al. Age-related differences in health-related quality of life in COPD: an analysis of the COPD gene and SPIROMICS cohorts. Chest 2016; 149(4): 927-35. |
| [37] | Corlateanu A, Botnaru V, Covantev S, Dumitru S, Siafakas N. Predicting health-related quality of life in patients with chronic obstructive pulmonary disease: the impact of age. Respiration; international review of thoracic diseases 2016; 92(4): 229-34. |
| [38] | Araujo ZT, Holanda G. Does the BODE index correlate with quality of life in patients with COPD? J Bras Pneumol 2010; 36(4): 447-52. |
| [39] | Hogg JC, Chu F, Utokaparch S, et al. The nature of small-airway obstruction in chronic obstructive pulmonary disease. N Engl J Med 2004; 350(26): 2645-53. |
| [40] | Vestbo J, Hurd SS, Agustí AG, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 2013; 187(4): 347-65. |
| [41] | Katsura H, Yamada K, Kida K. Both generic and disease specific health-related quality of life are deteriorated in patients with underweight COPD. Respir Med 2005; 99(5): 624-30. |
| [42] | Kantatong T, Panpanich R, Deesomchok A, Sungkarat S, Siviroj P. Effects of the tai chi qigong programme on functional capacity, and lung function in chronic obstructive pulmonary disease patients: a randomised controlled trial. Journal of Traditional and Complementary Medicine 2019. Available from: https://www.sciencedirect.com/ science/article/pii/S222541101830748X |
| [43] | Ahmed MS, Neyaz A, Aslami AN. Health-related quality of life of chronic obstructive pulmonary disease patients: Results from a community based cross-sectional study in Aligarh, Uttar Pradesh, India. Lung India 2016; 33(2): 148-53. |
