QJM Advance Access originally published online on October 9, 2006
QJM 2006 99(11):751-759; doi:10.1093/qjmed/hcl110
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A simple score for assessing stable chronic obstructive pulmonary disease
From the Pneumology Service and 1Research Unit, Hospital de Galdakao, Galdakao, Bizkaia, Spain
Address correspondence to Dr C. Esteban, Pneumology Service, Hospital de Galdakao, Barrio Labeaga s/n 48960, Galdakao, Bizkaia, Spain. email: cristobal_esteban{at}yahoo.es
Received 26 March 2006 and in revised form 5 June 2006
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Summary |
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Background: Chronic obstructive pulmonary disease (COPD) is usually assessed using FEV1 to establish the diagnosis and the severity of the disease. However, COPD is now considered a systemic disease.
Aim: To evaluate the utility of the Health-Activity-Dyspnoea-Obstruction (HADO) score for classifying the severity of COPD and predicting outcomes.
Design: Prospective longitudinal clinical study.
Methods: We studied 611 consecutive patients with stable COPD in five out-patient clinics of a teaching hospital. We measured dyspnoea degree, pulmonary function (by spirometry), self-reported level of daily physical activity and overall health condition. Outcome measures included health-related quality of life (HRQoL) parameters (as measured by the generic SF-36 Health Survey and by two specific questionnaires, the St George Respiratory Questionnaire and the Chronic Respiratory Questionnaire) and mortality at 3 years follow-up.
Results: Based on the HADO score, COPD was classified as mild in 26.7% of patients, moderate in 53.3%, and severe in 20%. There were statistically significant correlations between these three levels of severity and HRQoL parameters and vital status. After adjustment for relevant covariates, the HADO score reliably predicted survival and vital status.
Discussion: The HADO score can be easily obtained in an out-patient clinic, and distinguishes groups of COPD patients by their disease severity. The HADO score is better than FEV1% alone for predicting mortality at 3 years.
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Introduction |
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Chronic obstructive pulmonary disease (COPD) is a common disease1,2 that presents a major public health problem. In the US, the number of physician visits for COPD and related conditions was 16 million in 1995,3 and similarly high in other countries.4 The natural history of COPD is characterized by the progressive decline of pulmonary function, as well as weakness, diminished functional capacity, depression, and anxiety.5–7 COPD can be thought of as a systemic disease,8 and its severity should not be assessed exclusively in terms of forced expiratory volume in 1 s (FEV1).
Other measurements, such as exercise capacity, functional status, and standardized health-related quality of life (HRQoL) questionnaires, can be used to assess patients with COPD. However, these measurements are difficult to obtain during a standard office visit, and thus are not routinely used for patients with COPD.
Our goal was to devise a system for categorizing COPD severity based on variables easily obtained during a standard medical visit, and to assess its ability to predict mortality.
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Methods |
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From February 1998 to February 1999, we surveyed all patients previously diagnosed with COPD who were aged <80 years and who regularly visited out-patient clinics affiliated with a teaching hospital in the interior district of Bizkaia, Spain (with a catchment area of 300 000 rural and urban inhabitants).
Consecutive patients were included in our study if they had been diagnosed with COPD for at least 6 months, had been under treatment in the hospital's outpatient facilities for at least 6 months, and had been clinically stable (no increase in respiratory symptoms or changes in treatment) for the 6 weeks prior to inclusion. Other inclusion criteria were FEV1 <80% of predicted value, with FEV1/FVC <70% and negative bronchodilation test, with a change in FEV1 <200 ml and <15% of the baseline value. Patients were not eligible for the study if they were aged >80 years, had been diagnosed with asthma, had old or ongoing concomitant pulmonary tuberculosis, or neoplastic processes, were suffering from psychiatric or neurological problems that could prevent effective collaboration, or had hearing or other problems that precluded communication.
All patients had been previously informed about basic aspects related to their illness, such as the advisability of exercise and the need to give up smoking, as well as instruction on the correct use of inhaler devices.
The variables used for the development of the Health-Activity-Dyspnea-Obstruction (HADO) score index were: dyspnoea, FEV1 as a percentage of the predicted value (FEV1%), level of physical activity, and overall health status. Each of the four variables was rated from 0 to 3, with 0 indicating the most severe rating and 3 the mildest.
Patients were interviewed regarding their level of dyspnoea using a 4-degree scale (from 0 to 3) adapted from Fletcher.9 Degree 0 was ‘dyspnoea after walking slowly for 100 m’, or ‘dyspnoea when resting or after slight effort, such as getting dressed’, which subsumed Fletcher's degrees IV and V, degree 1 ‘capable of walking on the level at my own speed without dyspnoea, but incapable of walking at the same pace as persons my own age’, degree 2 ‘capable of walking at the same pace as other people my age on the level’ and degree 3 ‘dyspnoea only with intense and strenuous exercise’,
Spirometry used American Thoracic Society (ATS)10 criteria, with a Master-Scope-PC spirometer (Erich Jaeger GmbH & Co). Theoretical values were those prescribed by the European Community for Steel and Coal.12 COPD severity was defined following the three levels established by the ATS13 based on FEV1%, from 0 (FEV1% <35%) to 3 (FEV1% >65%).
Levels of physical activity were assessed on a scale from 0 to 3: 0, ‘doesn't leave the house, life is limited to the bed or armchair, or to doing some domestic chores’, or ‘leaves the house, but walks less than 100 m’; 1, ‘leaves the house and walks a few hundred metres, runs errands, but does not walk regularly’; 2, ‘engages in physical activity in the vegetable garden’, or ‘takes walks for up to 8 km, no less than 5 days a week’; and 3, ‘takes walks regularly for >8 km, no less than 5 days a week’, or ‘practices sports’.
Overall health was assessed by means of the question ‘In general, how would you characterize your health?’ The four possible answers were ‘bad’ (0), ‘fair’ (1), ‘good’ (2) and ‘very good or excellent’ (3).
The HADO score is estimated by adding the values for each of the four variables described above, therefore ranging from 0 points for the most severe category to 12 points for the mildest, a higher score indicating better overall clinical condition. Patients were classified into three categories according to disease severity: severe (HADO score up to 4 points), moderate (HADO score 5–7), and mild (HADO score 8–12).
HRQoL was assessed using three instruments: the generic SF-36 Health Survey (SF-36)14 and two questionnaires: the St George's Respiratory Questionnaire (SGRQ)15 and the Chronic Respiratory Questionnaire (CRQ).16
The SF-36 includes eight dimensions and two summary scales. Each dimension receives a score between 0 and 100 (with 100 representing the best health condition). For this study, we used the SF-36's physical component summary scale (PCSS). We used the SF-36 version validated in a Spanish population.17
The SGRQ includes three dimensions: symptoms, impact and activity, as well as a global score. Each area receives a score between 0 and 100, with 0 representing a complete lack of deterioration. We used the version validated in Spanish.18 We used the total score for the SGRQ.
The CRQ includes four of dimensions: dyspnoea, fatigue, emotional function, and mastery. In each area, scores are obtained by adding the scores for the items that make up each category and dividing it up by the number of items.16 This questionnaire has also been validated for a Spanish population.19 We used the total score for the CRQ.20
All participants provided verbal informed consent to take part in the study. Life status of all patients as were followed up for a period of up to 3 years, by visit or telephone. Mortality was determined via examination of the hospital database, the local mortality register, and by telephone contact with the families or primary-care physicians.
Statistical analysis
Data are means and SD for continuous variables, and frequencies and percentages for categorical variables. We used the 
2 and Fisher's exact tests for the associations between categorical variables.
To study the associations between the four categories on each of the four index variables with the HRQoL outcome parameters selected (the SF-36 PCSS, total score in the SGRQ, and total score in the CRQ), we performed one-way analyses of variance in the univariate analysis. We also studied the relationship of those variables to vital status by means of the 2 test. To evaluate the contribution of each of the four variables of the HADO index score, we used multivariate linear regression models for the continuous outcome variables (HRQoL parameters) and Cox proportional-hazards regression models for survival time. In all cases, the most severe category (group 1) for all variables was considered as the reference group. We provide beta coefficients for the linear regression models and hazard ratios for the Cox proportional-hazards regression models.
We also compared the three severity categories of our score for relevant sociodemographic and clinical variables using a one-way analysis of variance, with the Scheffe test for multiple comparisons. The non-parametric Kruskal-Wallis test was also used.
To assess the capacity of our score index to predict mortality, we performed Cox proportional-hazards regression analyses. We estimated the hazard ratio, 95%CI, and p value for our score after adjustment for co-existing conditions, BMI, and other relevant covariates such as age or packs of cigarettes smoked per year. Only age and cigarette smoking were statistically significant and kept in the final models. We repeated these analyses using vital status as the dependent variable using logistic regression models.
Finally, to determine how our score compared to FEV1% alone for predicting mortality, we computed the C statistics, by logistic regression models, for a model containing FEV1% or our score as the sole independent variable. In these analyses, the C statistic is a mathematical function of the sensitivity and specificity of our score in classifying patients by means of a logistic regression model as either dying or surviving. The null value for the C statistic is 0.5, with a maximum value of 1.0 (higher values are better). We also compared the FEV1% vs. the HADO score for predicting the SF-36 PCSS score, the total SGRQ score and the total CRQ score. In these cases we performed linear regression analysis and compared the results by means of the R2 obtained in each model.
All significance was taken to be p < 0.05 unless otherwise noted. Data were entered using Epi Info version 6.4; all statistical analyses used SAS for Windows, version 8.02 (SAS).
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Results |
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We studied a total of 611 patients, with a wide range of COPD severities. Their mean age was 67.2 years, and 97.7% were male. At 3 years follow-up, 94 patients had died (15.4%). Twenty-six patients were lost to follow-up; they were not significantly different with respect to age, FEV1% or degree of dyspnoea at the start of the study than those who completed follow-up. Demographic and clinical characteristics of the population are detailed in Table 1.
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The variables selected for our score index were analysed individually in relation to the four outcome measures: PCSS from the SF-36, total SGRQ score, total CRQ score, and vital status. There was a correlation between higher degrees of severity as measured by each of the variables separately and the four previous outcome measures, showing statistically significant differences in all cases (Table 2).
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Accordingly, we included all four variables in a multivariate model, to determine the contribution of each to the outcomes under study. Patients with the most severe disease were used as a reference group for all variables (Table 3). FEV1% was significantly associated with three of the four outcomes measures (survival time, PCSS in SF-36, and total SGRQ) only in categories 3 and 4; it was not associated with total CRQ score in any disease category. Dyspnoea was significantly associated with survival time in category 4, with SF-36 PCSS and total SGRQ in all four categories, and with total CRQ score in categories 3 and 4. Physical activity was significantly associated with SF-36 PCSS in all three categories, but not with any of the other outcome measures in any category. General health was significantly associated with three outcomes (SF-36 PCSS, total SGRQ, and total CRQ) in all three categories, but was not associated with survival time in any category.
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A classification of the patients’ severity according to the three score index categories from the HADO score showed that 163 (26.7%) were rated as having mild COPD, while 326 (53.3%) were rated as moderate and 122 (20%) as severe. There were statistically significant differences between the three levels of severity in our score index as regards age, number of hospitalizations in the previous 2 years, number of medications used for COPD treatment, and death rate, which varied between 8% and 34% and increased across the board as the index score increased. As for HRQoL, there were statistically significant differences between the three levels of severity in the score index for all areas of the various questionnaires, both the specific (SGRQ and CRQ) and the general (SF-36) HRQoL measures (Table 4).
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We studied the influence of body mass index (BMI) but we did not observe differences in relation to HADO severity levels (Table 4). Also, mortality in this cohort did not differ according to BMI, a variable for which we used a cut-off point of 21 kg/m2 for underweight.
In the multivariate analysis, we observed statistically significant differences between the three HADO severity levels, adjusted for the variables that have an influence in the univariate analysis (age and cigarette packs smoked/year), and the outcomes studied (Table 5).
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The potential influences of other factors, such as BMI, number of comorbidities, or most relevant comorbidities, were also studied. None significantly influenced the model, or were included in the final model.
The C statistic for the ability of our score index to predict the risk of death was 0.682, as compared with a value of 0.647 when using FEV1% alone. The R2 statistic of our score to explain HRQoL outcomes was 0.31 for the SF-36 PCSS, 0.37 for the total SGRQ, and 0.21 for the total CRQ. The equivalent values for FEV1% alone were 0.08 for the SF-36 PCSS, 0.10 for the total SGRQ, and 0.03 for the total CRQ.
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Discussion |
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The HADO score index, which can be readily and easily obtained in an out-patient clinic, can reliably categorize the severity of COPD in an unselected patient population and is more useful than parameters such as FEV1% in predicting mortality at 3 years.
The severity of COPD has traditionally been staged using FEV1%. In fact, all clinical guidelines for COPD refer to FEV1 as a key parameter for diagnosis, and rely on it to establish the severity of disease.12,21–23 However, factors such as dyspnoea and pulmonary function can also independently characterize the severity of COPD,24 as can muscular strength and other measurements.5 As a result, some authors have proposed establishing a multidimensional assessment of COPD severity, to evaluate not only pulmonary function but also the consequences of systemic effects.24–27
In building our score index, we provided a model that takes into account physiological factors (FEV1), symptoms (dyspnoea), physical function (activity level) and general health (overall health impression).28 We wanted this score to be easy to assemble and quick and simple to administer. We also wanted it to summarize comprehensively the information that physicians gather in evaluating COPD patients, and to have predictive and discriminative power (and thus be able to distinguish between the different levels of disease severity).
Regarding the discriminative properties of the variables included in the HADO index in relation to HRQoL outcomes, in several studies of COPD patients,9,29–31 the degree of dyspnoea showed excellent correlation with HRQoL parameters using different questionnaires.
With regard to physical activity, in studies of self-reported physical activity level, a high level of regular physical activity has been related to a lower re-admission rate in COPD patients.32 Assessing exercise capability in out-patient clinics, based on either the exertion test or the 6-minute-walk test, would be problematic, given the number of patients evaluated daily in any out-patient clinic.
Measuring HRQoL is now considered an essential outcome in most COPD studies, and thus offers an appropriate way to measure severity. However, the HRQoL is not routinely used in daily clinical practice.
Our score avoids the problem of using time-consuming tools such as HRQoL questionnaires or the walking test, by using some variables that classify the severity of COPD (FEV1% and dyspnoea) and others that evaluate the general health status and level of activity.
With respect to the second main outcome, predicting mortality, of all the score-related factors, FEV1 is the primary factor related to COPD mortality,33,34 a finding corroborated in our study. Some studies have indicated that dyspnoea is more predictive than FEV1 in evaluating survival after 5 years in a COPD patient cohort.35 On the other hand, Oga et al. demonstrated a relationship between exercise capacity and COPD mortality, independent of FEV1.36 In a study of patients with chronic respiratory disease undergoing rehabilitation, the pulmonary functional status scale, assessed by means of the 6-min walk test and a questionnaire, was a better predictor of survival than FEV1 and BMI.37 Results of the 12-min walk test have been related to 3-year survival rates in a cohort of COPD patients undergoing rehabilitation.38 Finally, HRQoL, as measured both by specific (SGRQ) and generic (SF-36) questionnaires, has been related both to overall mortality and to respiratory-related mortality.39
A recently published grading system for COPD patients demonstrated that an index that includes BMI, airflow obstruction, dyspnoea, and exercise capacity (BODE) has better predictive power for mortality than FEV1%.27 The differences between this index and ours lie in the main nature of the instrument, given that our index was designed to be used in daily practice, while having discriminative and predictive power. In addition, our patient population included patients from out-patient clinics rather than hospitals. As a result, the overall degree of disease severity is lower in our group (Stage I ATS, 49.7%) than in the BODE study (Stage I ATS, 30%). One of the BODE variables is the BMI. BMI <20 kg/m2 constitutes an independent predictive factor related to COPD mortality,40 but did not include BMI in our score index, as it was not statistically related to death rate in either the univariate or the multivariate analysis. It is possible that this was due to the low number of patients with BMI <20 kg/m2 in our study (26, 2.5%), even though our patient population covered all degrees of COPD severity. Moreover, normal body weight among COPD patients does not necessarily imply preservation of muscle mass.41 In addition, a measurement of mid-thigh muscle cross-sectional area is more closely related to survival than is overall weight.42 Compared with the BODE, our index has some advantages: it is simpler and uses variables that a physician normally collects in an interview with their patients. The BODE index requires a walking test, which is time-consuming and not usually performed in busy out-patient clinics. Whether our finding that BMI was of no use in predicting mortality will hold true for other patient groups, remains to be seen.
Until the BODE score, the FEV1 and patient age were the best predictors of mortality.33,34 In this study, our score predicts mortality slightly better than the FEV1 alone, as measured by the C statistic, although slightly worse than the results obtained by BODE index in their study (not directly comparable). However, the HADO score had a much better correlation with HRQoL parameters (SF-36 PCSS, total SGRQ, and total CRQ) than did FEV1. This aspect of the BODE index has not been studied so far. Therefore, our score has advantages over FEV1 in classifying patients according to the severity of their illness as measured by HRQoL instruments.
The correlation of the HADO with mortality, and with the three HRQoL instruments used in our study, provides information about the validity of this tool. Additionally, the use of the C statistic in the regression models is a form of measuring the accuracy of our score.
Our study has some limitations. The patient population was composed almost exclusively of men, which reflects the distribution of COPD in Spain, where women began to smoke cigarettes only late in the 20th century. Similar gender distributions have been observed in other studies performed in our country.43,44 Therefore, this fact does not imply selection bias. Nonetheless, it limits the generalizability of our results. To assess physical activity, we used a simple scale based on a self-reported answer to a single question. Likewise, overall health status was assessed by means of a single question. Nevertheless, both assessments functioned appropriately, as shown in the analyses presented previously. Finally, we have shown that the HADO score has good discriminative and predictive properties and, therefore, have partially validated this measurement. Nevertheless, a cross-validation or validation in other settings would add value to the results.
In conclusion, the Health-Activity-Dyspnoea-Obstruction index can be easily and quickly obtained during a patient visit, reliably classifies a patient's clinical severity and predicts survival at 3 years of follow up. Future studies are needed to improve the discriminative power of the variables chosen for this index, while keeping the simplicity of the current score index, as well as to study other psychometric properties in more detail and validate this simple tool in other settings.
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Acknowledgments |
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Thanks to J.B. Soriano, PhD, for his interesting comments on the manuscript. This study was supported in part by a grant from the Fondo de Investigación Sanitaria (97/0326).
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References |
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1. Mannino SM, Gagnon RC, Petty TL, Lydick E. Obstructive lung disease and low lung function in adults in the United States: data from the National Health and Nutrition Examination Survey, 1988–1994. Arch Intern Med 2000; 160:1683–9.
2. Pena VS, Miravitlles M, Gabriel R, Jimenez-Ruiz CA, Villasante C, Masa JF, Viejo JL, Fernandez-Fau L. Geographic variations in prevalence and underdiagnosis of COPD: results of the IBERPOC multicentre epidemiological study. Chest 2000; 118:981–9.
3. Hurd S. The impact of COPD on lung health worlwide. Epidemiology and incidence. Chest 2000; 117:1–4S.
4. Pellicer C, Fullana J, Cremades MJ, Rivera ML, Perpiña M. Perfil de una consulta de neumología de ámbito comarcal. Arch Bronconeumol 1996; 32:447–52.[Medline]
5. Decramer M, Goselink R, Troosters T, Verschueren M, Evers G. Muscle weakness is related to utilization of health care resources in COPD patients. Eur Respir J 1997; 10:417–23.[Abstract]
6. Wegner RE, Jörres RA, Kirsten DK, Magnussen H. Factor analysis of exercise capacity, dyspnoea ratings and lung function in patients with severe COPD. Eur Respir J 1994; 7:725–9.[Abstract]
7. Yohannes AM, Baldwin RC, Connolly MS. Depression and anxiety in elderly outpatients with chronic obstructive pulmonary disease: prevalence and validation of the BASDEC screening questionnaire. J Geriatr Psychiatry 2000; 15:1090–6.
8. Andreassen H and Vestbo J. Chronic obstructive pulmonary disease as a systemic disease: an epidemiological perspective. Eur Respir J 2003; 46:2–4s.
9. Fletcher CM, Elmes PC, Wood CH. The significance of respiratory symptoms and the diagnosis of chronic bronchitis in a working population. Br Med J 1959; 2:257–66.[ISI][Medline]
10. Medical Section of the American Lung Association. Standardization of spirometry: 1994 update. Am Rev Respir Dis 1994; 152:1107–36.
11. Sanchis J. Normativa para la espirometría forzada: Grupo de trabajo de la SEPAR para la práctica de la espirometría clínica. Arch Bronconeumol 1989; 25:132–42.
12. Quanjer PH, Tammeling GJ, Cotes JE, Pedersen OF, Peslin R, Yernault JC. Lung volumes and forced ventilatory flows. Report working party standardization of lung function test, European Community for Steel and Coal. Official statement of the European Respiratory Society. Eur Respir J 1993; 16:5–40.
13. American Thoracic Society. Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1995; 152:Suppl., S77–120.
14. Ware JE, Snow KK, Kosinsky M, Gandek B. SF-36 health survey: manual and interpretation guide1993;Boston The Health Institute.
15. Jones PW, Quirk FH, Baveystock CM. The St George's Respiratory Questionnaire. Respir Med 1991; 85:Suppl. B, 25–31.
16. Guyatt GH, Berman LB, Townsend M, Pugsley SO, Chambers LW. A measure of quality of life for clinical trials in chronic lung disease. Thorax 1987; 42:773–8.[Abstract]
17. Alonso J, Prieto L, Ferrer M, Vilagut G, Broquetas JM, Roca J, Batlle JS, Antó JM. Testing the measurement properties of the Spanish version of the SF-36 health survey among male patients with chronic obstructive pulmonary disease. J Clin Epidemiol 1998; 51:1087–94.[CrossRef][ISI][Medline]
18. Ferrer M, Alonso J, Prieto L, Plaza V, Monsó E, Marrades R, Aguar MC, Khalaf A, Antó JM. Validity and reliability of the St George's Respiratory Questionnaire after adaptation to a different language and culture: the Spanish example. Eur Respir J 1996; 9:1160–6.[Abstract]
19. Güell R, Casan P, Sangenís M, Morante F, Belda J, Guyatt GH. Quality of life in patient with chronic respiratory disease: the spanish version of the chronic respiratory questionnaire (CRQ). Eur Respir J 1998; 11:55–60.
20. Rutten-van Mölken M, Roos B, Van Noord JA. An empirical comparison of the St George's Respiratory Questionnaire (SGRQ) and the Chronic Respiratory Disease Questionnaire (CRQ) in a clinical trial setting. Thorax 1999; 54:995–1003.
21. BTS guidelines for the management of chronic obstructive pulmonary disease. The COPD Guidelines Group of the Standards of Care Committee of the BTS. Thorax 1997; 52:Suppl. 5, S1–28.[Medline]
22. Siafakas NM, Vermeire P, Pride NB, Paoletti P, Gibson J, Howard P, Yernault JC, Decramer M, Higenbottam T, Postma DS, Rees J. Optimal assessment and management of chronic obstructive pulmonary disease (COPD). Eur Respir J 1995; 8:1398–20.[CrossRef][ISI][Medline]
23. Pauwels RA, Buist AS, Calverley PM, Jenkins CR, Hurd SS. GOLD Scientific Committee. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: NHLBI Global Initiative for Chronic Obstructive Lung Disease (GOLD) Workshop Summary. Am J Respir Crit Care Med 2001; 163:1256–76.
24. Mahler DA and Harver A. A factor analisys of dyspnea ratings, respiratory muscle strength, and lung function in patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 1992; 145:467–70.[ISI][Medline]
25. Wegner RE, Jörres RA, Kirsten DK, Magnussen H. Factor analysis of exercise capacit, dyspnoea ratings and lung function in patients with severe COPD. Eur Respir J 1994; 7:725–9.[Abstract]
26. Engström CP, Persson LO, Larsson S, Rydén A, Sullivan M. Functional status and well being in chronic obstructive pulmonary disease with regard to clinical parameters and smoking: a descriptive and comparative study. Thorax 1996; 51:825–30.[Abstract]
27. Celli BR, Cote CG, Marín JM, Casanova C, Montes de Oca M, Mendez RA, Pinto Plata V, Cabral HJ. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004; 350:1005–12.
28. Wilson IB and Cleary PD. Linking clinical variables with health related quality of life. A conceptual model of patient outcomes. JAMA 1995; 273:59–65.[Abstract]
29. Hajiro T, Nishimura K, Tsukino M, Ikeda A, Oga T, Izumi T. A comparison of the level of dyspnea vs disease severity in indicating the health-related quality of life of patients with COPD. Chest 1999; 116:1632–7.
30. Mahler DA, Faryniarz K, Tomlinson D, Colice GL, Robins AG, Olmstead EM, O'Connor GT. Impact of dysnea and physiologic function on general health status in patients with chronic obstructive pulmonary disease. Chest 1992; 102:395–401.
31. Bestall JC, Paul EA, Garrod R, Garnham R, Jones PW, Wedzicha JA. Usefulness of the Medical Research Council (MRC) dyspnoea scale as a measure of disability in patients with chronic obstructive pulmonary diseas. Thorax 1999; 54:581–6.
32. Garcia-Aymerich J, Farrero E, Félez MA, Izquierdo J, Marrades RM, Antó JM. Risk factors of readmission to hospital for a COPD exacerbation: a prospective study. Thorax 2003; 58:100–5.
33. Anthonisen NR, Wright EC, Hodgkin JE. Prognosis in chronic obstructive pulmonary disease. Am Rev Respir Dis 1986; 133:14–20.[ISI][Medline]
34. Traver GA, Cline MG, Burrows B. Predictors of mortality in chronic obstructive pulmonary disease. A 15 year follow up study. Am Rev Respir Dis 1979; 119:895–902.[ISI][Medline]
35. Nishimura K, Takateru I, Tsukino M, Oga T. Dyspnea is a better predictor of 5 year survival than airway obstruction in patients with COPD. Chest 2002; 121:1434–40.
36. Oga T, Nishimura K, Tsukino M, Sato S, Hajiro T. Analysis of the factors related to mortality in chronic obstructive pulmonary disease. Role of exercise capacity and health status. Am J Respir Crit Care Med 2003; 167:544–9.
37. Bowen JB, Votto JJ, Thrall RS, Haggerty MC, Stockdale-Woolley R, Bandyopadhyay T, ZuWallack RL. Functional status and survival following pulmonary rehabilitation. Chest 2000; 118:697–703.
38. Gerardi DA, Lovett ML, Benoit-Connors JZ, Reardon RL, ZuWallack RL. Variables related to increased mortality following out patient pulmonary rehabilitation. Eur Respir J 1996; 9:431–5.[Abstract]
39. Domingo-Salvany A, Lamarca R, Ferrer M, Garcia-Aymerich J, Alonso J, Félez M, Khalaf A, Marrades RM, Monsó E, Serra-Batlles J, Antó JM. Health related quality of life and mortality in male patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2002; 166:680–5.
40. Landbo C, Presscott E, Lange P, Vestbo J, Almdal T. Prognostic value of nutritional status in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1999; 160:1856–61.
41. Schols AMWJ, Soeters PB, Dingemans MC, Mostert R, Frantzen PJ, Wouters EFM. Prevalence and characteristics of nutritional deplection in patients with stable COPD elegible for pulmonary rehabilitation. Am Rev Respir Dis 1993; 147:1151–6.[ISI][Medline]
42. Marquis K, Debigare R, Lacasse Y, LeBlanc P, Jobin J, Carrier G, Maltais F. Midthigh muscle cross sectional area is a better predictor of mortality than body mass index in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2002; 166:809–13.
43. Ferrer M, Alonso J, Morera J, Marrades RM, Khalaf A, Aguar MC, Plaza V, Prieto L, Antó JM. Chronic obstructive pulmonary disease stage and health related quality of life. Ann Intern Med 1997; 127:1072–9.
44. Otero I, Blanco M, Montero C, Valino P, Verea H. The epidemiology of COPD and asthma exacerbations in a general hospital. Arch Bronconeumol 2002; 38:256–62.[ISI][Medline]
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