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Original Research: COPD |

Clinical Application of the COPD Assessment TestClinical Application of the COPD Assessment Test: Longitudinal Data From the COPD History Assessment in Spain (CHAIN) Cohort FREE TO VIEW

Juan P. de Torres, MD; Jose M. Marin, MD; Cristina Martinez-Gonzalez, MD; Pilar de Lucas-Ramos, MD; Isabel Mir-Viladrich, MD; Borja Cosio, MD; German Peces-Barba, MD; Miryam Calle-Rubio, MD; Ingrid Solanes-García, MD; Ramón Agüero Balbin, MD; Alfredo de Diego-Damia, MD; Nuria Feu-Collado, MD; Inmaculada Alfageme Michavila, MD; Rosa Irigaray, MD; Eva Balcells, MD; Antònia Llunell Casanovas, MD; Juan Bautista Galdiz Iturri, MD; Margarita Marín Royo, MD; Juan J. Soler-Cataluña, MD; Jose Luis Lopez-Campos, MD; Joan B. Soriano, MD; Ciro Casanova, MD; for the COPD History Assessment in Spain (CHAIN) Cohort*
Author and Funding Information

From the Pulmonary Department (Dr de Torres), Clínica Universidad de Navarra, Pamplona; Pulmonary Department (Dr Marin), Hospital Universitario Miguel Servet, Zaragoza; Pulmonary Department (Dr Martinez-Gonzalez), Hospital Central de Asturias, Oviedo; Pulmonary Department I (Dr de Lucas-Ramos), Hospital Gregorio Marañon, Madrid; Pulmonary Department (Dr Mir-Viladrich), Hospital Son Llátzer, Palma de Mallorca; Pulmonary Department (Dr Cosio), Hospital Son Espases, Palma de Mallorca; Pulmonary Department (Dr Peces-Barba), Fundación Jimenez Diaz, Madrid; Pulmonary Department (Dr Calle-Rubio), Hospital Clinico San Carlos, Madrid; Pulmonary Department (Dr Solanes-García), Hospital San Pablo y la Santa Cruz, Barcelona; Pulmonary Department (Dr Agüero Balbin), Hospital Marques de Valdecilla, Santander; Pulmonary Department (Dr de Diego-Damia), Hospital Universitario y Politécnico La Fe, Valencia; Pulmonary Department (Dr Feu-Collado), Hospital Universitario Reina Sofía, Cordoba; Pulmonary Department (Dr Alfageme Michavila), Hospital Universitario de Valme, Sevilla; Pulmonary Department (Dr Irigaray), Hospital de Manacor, Mallorca; Pulmonary Department (Dr Balcells), Hospital del Mar, Barcelona; Pulmonary Department (Dr Llunell Casanovas), Hospital de Terrassa, Terrassa; Pulmonary Department (Dr Galdiz Iturri), Hospital de Cruces, UPV/EHU, Bilbao; Pulmonary Department (Dr Marín Royo), Hospital General de Castellón, Castellón; Pulmonary Department (Dr Soler-Cataluña), Hospital General de Requena, Valencia; Unidad Médico-Quirúrgica de Enfermedades Respiratorias (Dr Lopez-Campos), Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío, Sevilla; Epidemiology and Clinical Research (Dr Soriano), CIMERA, Bunyola, Mallorca; Pulmonary Department (Dr Casanova), Hospital Universitario Nuestra Señora de Candelaria, Tenerife; and CIBER de Enfermedades Respiratorias (CIBERES) (Drs Marin, Cosio, Peces-Barba, Balcells, Galdiz Iturri, Soler-Cataluña, and Lopez-Campos), Instituto de Salud Carlos III, Madrid, Spain.

CORRESPONDENCE TO: Juan P. de Torres, MD, Pulmonary Department, Clinica Universidad de Navarra, Avenida Pío XII, 36, 31800 Pamplona, Spain; e-mail: jupa65@hotmail.com


*A complete list of CHAIN participants in available in e-Appendix 1.

FUNDING/SUPPORT: The PII SEPAR de EPOC endorsed this study. AstraZeneca provided partial funding for this study.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2014;146(1):111-122. doi:10.1378/chest.13-2246
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OBJECTIVE:  The COPD Assessment Test (CAT) has been proposed for assessing health status in COPD, but little is known about its longitudinal changes. The objective of this study was to evaluate 1-year CAT variability in patients with stable COPD and to relate its variations to changes in other disease markers.

METHODS:  We evaluated the following variables in smokers with and without COPD at baseline and after 1 year: CAT score, age, sex, smoking status, pack-year history, BMI, modified Medical Research Council (mMRC) scale, 6-min walk distance (6MWD), lung function, BODE (BMI, obstruction, dyspnea, exercise capacity) index, hospital admissions, Hospital and Depression Scale, and the Charlson comorbidity index. In patients with COPD, we explored the association of CAT scores and 1-year changes in the studied parameters.

RESULTS:  A total of 824 smokers with COPD and 126 without COPD were evaluated at baseline and 441 smokers with COPD and 66 without COPD 1 year later. At 1 year, CAT scores for patients with COPD were similar (± 4 points) in 56%, higher in 27%, and lower in 17%. Of note, mMRC scale scores were similar (± 1 point) in 46% of patients, worse in 36%, and better in 18% at 1 year. One-year CAT changes were best predicted by changes in mMRC scale scores (β-coefficient, 0.47; P < .001). Similar results were found for CAT and mMRC scale score in smokers without COPD.

CONCLUSIONS:  One-year longitudinal data show variability in CAT scores among patients with stable COPD similar to mMRC scale score, which is the best predictor of 1-year CAT changes. Further longitudinal studies should confirm long-term CAT variability and its clinical applicability.

TRIAL REGISTRY:  ClinicalTrials.gov; No.: NCT01122758; URL: www.clinicaltrials.gov

Figures in this Article

COPD remains a major public health problem and is expected to be the fifth-ranked burden of disease worldwide in 2020.1 COPD is characterized by a persistent airflow limitation that usually is progressive and associated with a chronic enhanced inflammatory response in the airways and lungs to noxious particles or gases, primarily cigarette smoke.2

A recently updated GOLD (Global Initiative for Chronic Obstructive Lung Disease) strategy2 recommends that the assessment of COPD severity include an evaluation of the severity of the airflow limitation, degree of dyspnea, impairment of the patient’s health status, and risk of future events (eg, exacerbations, hospital admissions). The GOLD strategy also recommends the use of the COPD Assessment Test (CAT), a validated eight-item questionnaire that assesses and quantifies the impact of COPD symptoms on patient health status.3 CAT scores correlate well with other specific health-related quality-of-life indicators in patients with COPD,4 capturing the effects of various treatments such as those included in COPD exacerbation or pulmonary rehabilitation (PR).5,6 CAT scores have been associated with important representative parameters of the disease,5 such as lung function, dyspnea, exercise capacity, and exacerbation in the previous year, and perform well across various European countries.4 However, little is known how the CAT performs longitudinally.

Therefore, we explored the database of the COPD History Assessment in Spain (CHAIN) cohort, a large, ongoing, longitudinal Spanish study that aimed to determine the natural history of COPD through a multidimensional evaluation of patients with COPD. The main objective of the present work was to evaluate 1-year follow-up CAT variability in patients with stable COPD and to relate its changes to those in other well-recognized disease markers.

Participants

CHAIN is a Spanish multicenter study carried out in pulmonary clinics and includes active and former smokers with COPD and a control group of patients without COPD. COPD was defined by a history of smoking of at least 10 pack-years and an FEV1/FVC < 0.70 after 400 μg inhaled albuterol. The main goal of this prospective observational study is to perform a multidimensional evaluation of the evolution of patients with COPD to better define the natural history and phenotypes of the disease. The control group included active and former smokers without COPD, which was defined as a history of smoking of at least 10 pack-years and an FEV1/FVC ≥ 0.70 after 400 μg inhaled albuterol. This group includes male and female participants aged 40 to 75 years who are free from significant disease as determined by history, physical examination, and screening investigations. The recruitment period was January15, 2010, to March 31, 2012. The patients are currently in the follow-up period, but the data analyzed in the present study are from the baseline and 1-year appointment data available at the time of analysis (February 2013).

We evaluated anthropometric data (ie, age, sex, height, weight, BMI), comorbidities, smoking status and pack-year history, respiratory symptoms (modified Medical Research Council [mMRC] scale), self-reported exacerbations during the previous year (hospital admissions), health-related quality of life as assessed by CAT, anxiety and depression, treatments, respiratory function (ie, spirometry, lung volume, diffusing capacity of lung for carbon monoxide), exercise capacity (6-min walk distance [6MWD]), arterial blood gas levels, and the BODE (BMI, obstruction, dyspnea, exercise capacity) index in patients with COPD. The methodologic aspects of the study were published previously.7 Patient data were anonymized in a database with hierarchical access control to guarantee secure access to the information. To participate in the study, the patients provided informed consent as approved by each ethics committee of the participating centers (Comité de Etica de la Investigación, Universidad de Navarra Institutional Review Board No.: 043/2006).

Clinical and Physiologic Measurements

In a personal interview, trained personnel obtained the participants’ age, sex, and BMI at the time of recruitment and 1-year appointment. A specific questionnaire was used to determine smoking status (current or former) and smoking history (age at initiation and discontinuation and intensity). From this information, we calculated the total smoking exposure and expressed it as pack-years. The presence of comorbidities was evaluated by the Charlson comorbidity index.8 Pulmonary function tests were performed following American Thoracic Society guidelines.9 The diffusing capacity of lung for carbon monoxide was determined by the single-breath technique following European Respiratory Society/American Thoracic Society guidelines.9 Arterial blood gas levels were measured from an arterial radial puncture while at rest (after 15 min), breathing room air for at least 45 min while in the sitting position. The 6MWD test measured the better of two walks separated by at least 30 min.10 Dyspnea was evaluated by the mMRC scale.11 The %FEV1, BMI, 6MWD, and mMRC scale values were integrated into the BODE index as previously described.12

COPD Assessment Test

To evaluate health-related quality of life, we used the CAT, a validated eight-item questionnaire that assesses and quantifies the impact of COPD symptoms on patient health status. The resulting score out of 40 indicates disease impact, with a higher score associated with a worse health-related quality of life.3 We used the validated Spanish version of CAT,6 which was self-administered by each patient.

Hospital Anxiety and Depression Scale

The Hospital Anxiety and Depression Scale (HADS) is a self-administered test with a 14-item scale that generates ordinal data.13 The score assesses both anxiety and depression symptoms; seven items relate to anxiety and seven to depression. The scores are categorized as normal (0-7), mild (9-11), and moderate or severe (12-15).

Statistical Analysis

Quantitative data with a normal distribution were described using mean ± SD. Quantitative data with nonnormal distribution were described by median and interquartile range (IQR). Categorical data were described using relative frequencies. Associations between baseline CAT scores and the studied parameters were estimated using a univariate linear regression model. Significant associations (P < .05) were included in a multiple regression model to determine those that best predict CAT scores. We arbitrarily defined two cutoff values for changes in the CAT scores at 1 year: +4 and −4 points, a variation previously described to be associated with exacerbations,5,6,13,14 and +2 and −2 points, a variation recently proposed by Jones15 for mapping the four points associated with the minimum clinically important difference (MCID) in the St. George’s Respiratory Questionnaire (SGRQ). For changes in the CAT score, we explored the association of the CAT score with the other studied parameters, using univariate and multivariate linear regression models as described for baseline CAT scores. Calculations were performed with SPSS, version 20.0 (IBM) statistical software.

A total of 824 smokers with COPD and 126 smokers without COPD were evaluated at baseline. Their clinical and physiologic characteristics are provided in Table 1. This mainly middle-aged male population of patients with COPD had a median smoking history of 50 pack-years (one-third still smoked) and represented all degrees of airway obstruction with few comorbidities and hospital admissions. The patients with COPD had mild symptomatic impairment, with a median mMRC scale score of 1; median CAT score of 11; and median anxiety and depression scores of 12 and 8, respectively, implying that these patients had symptoms of anxiety and depression. Figures 1 and 2 show the frequencies of each CAT score for patients with COPD and smokers without COPD. The patients were older than the smokers; had a greater pack-year history (but fewer were actively smoking); and had impaired lung function parameters, less exercise capacity, and higher CAT scores. However, the two groups had similar BMI, Charlson comorbidity index values, and HADS scores.

Table Graphic Jump Location
TABLE 1  ] Baseline Characteristics of All Participants

Data are presented as mean ± SD, median (interquartile range), or %. 6MWD = 6-min walk distance; BODE = BMI, obstruction, dyspnea, exercise capacity; CAT = COPD Assessment Test; Dlco = diffusing capacity of lung for carbon monoxide; GOLD = Global Initiative for Chronic Obstructive Lung Disease; HADS = Hospital Anxiety and Depression Scale; mMRC = modified Medical Research Council; NA = not applicable.

Figure Jump LinkFigure 1  Distribution of CAT scores in patients with COPD. CAT = COPD Assessment Test.Grahic Jump Location
Figure Jump LinkFigure 2  Distribution of CAT scores in smokers without COPD. See Figure 1 legend for expansion of abbreviation.Grahic Jump Location

Table 2 shows the independent association between CAT scores and representative parameters of the disease. CAT scores were directly associated with female sex, pack-year history, mMRC scale score, BODE index, and HADS scores and indirectly associated with %FEV1, Pao2, and 6MWD. The results of a multivariate linear regression model showed that mMRC scale score, HADS anxiety score, and HADS depression score are the best independent predictors of baseline CAT scores (Table 3).

Table Graphic Jump Location
TABLE 2  ] Univariate Analysis With Baseline CAT Scores in Patients With COPD as the Dependent Variable

See Table 1 legend for expansion of abbreviations.

Table Graphic Jump Location
TABLE 3  ] Multivariate Analysis With Baseline CAT Scores in Patients With COPD as the Dependent Variable

Variables included in the model were sex, pack-y history, mMRC, %FEV1, Pao2, 6MWD, HADS anxiety, and HADS depression. r2 = 0.86. See Table 1 legend for expansion of abbreviations.

At the time of this analysis, only 441 patients with COPD (53.5%) and 66 smokers without COPD (52.3%) were able to complete the follow-up at 1 year. At 1 year, 96% of patients with COPD (423 of 441) remained on the same treatment. The baseline characteristics of the patients with COPD lacking 1-year follow-up data are provided in the e-Table 1. No differences were found in 1-year follow-up data between patients with COPD and smokers without COPD (P > .05 for all comparisons).

The intraclass correlation coefficient between baseline and 1-year CAT scores was r = 0.58 (P < .001) for patients with COPD. CAT scores improved in 27% and worsened in 17% of these patients based on the ± 4-point cutoff value (Fig 3A), whereas CAT scores improved in 32% and worsened in 21% of these patients based on the ± 2-point cutoff value (Fig 3B). The intraclass correlation coefficient between baseline and 1-year CAT scores was r = 0.60 (P < .001) in smokers without COPD. Figure 4 shows the changes in CAT score at 1 year in smokers without COPD. Based on the ± 4-point cutoff value, the CAT scores improved in 21% and worsened in 14% of smokers without COPD (Fig 4A), whereas the CAT scores improved in 36% and worsened in 26% of smokers without COPD based on the ± 2-point cutoff value (Fig 4B). Of note, 1-year changes in mMRC scale score (at least ± 1 point) improved (36%), worsened (18%), or remained the same (46%) in a similar percentage of patients with COPD when the ± 4-point cutoff was used for changes in CAT score. The mMRC scale scores improved in 27%, worsened in 11%, and remained the same in 62% of smokers without COPD.

Figure Jump LinkFigure 3  A, Changes in the CAT scores of patients with COPD at 1 y compared with baseline (cutoff values ± 4 points). B, Changes in the CAT scores of patients with COPD at 1 y compared with baseline (cutoff values ± 2 points). See Figure 1 legend for expansion of abbreviation.Grahic Jump Location
Figure Jump LinkFigure 4  A, Changes in the CAT scores of smokers without COPD at 1 y compared with baseline (cutoff values ± 4 points). B, Changes in the CAT scores of smokers without COPD at 1 y compared with baseline (cutoff values ± 2 points). See Figure 1 legend for expansion of abbreviation.Grahic Jump Location

Figures 3 and 4 show the regression to the mean, where high scores tended to decrease when measured again in the following year. Therefore, this phenomenon was considered when the multivariate analysis was performed.

Patients with COPD who exhibited greater variability at 1 year had baseline CAT scores between 10 and 25. As shown in Table 4, changes in the CAT scores at 1 year were independently associated with changes in mMRC scale scores, BODE index, HADS anxiety score, and HADS depression score but not with changes in %FEV1 or hospital admissions during the previous year. Only 40 patients with COPD (9%) experienced at least one hospital admission during the follow-up period. Patients who were admitted to the hospital had higher baseline CAT scores (median, 11; IQR, 11-19) than those who were not (median, 11; IQR, 7-17; P < .05), but the admission did not cause changes in the CAT score during the following year (median, −1 [IQR, −4 to 3] vs 0 [IQR, −4 to 3]; P > .05).

Table Graphic Jump Location
TABLE 4  ] Univariate Analysis With Changes in CAT Scores in Patients With COPD at 1 Y as the Dependent Variable

See Table 1 legend for expansion of abbreviations.

The multivariate linear regression analysis shown in Table 5 indicates that the best predictors of changes in the CAT scores of patients with COPD were those in mMRC scale scores, with borderline prediction by HADS scores. Of note, patients with baseline HADS scores > 7, indicating at least mild anxiety and depression symptoms, had greater variation in their median changes compared with normal baseline HADS scores (median, −1 [IQR, −6 to 2] vs 0 [IQR, −3 to 3]; P = .02).

Table Graphic Jump Location
TABLE 5  ] Multivariate Analysis With Changes in CAT Scores in Patients With COPD at 1 Y as the Dependent Variable

Variables included in the model were mMRC scale, HADS anxiety, and HADS depression. r2 = 0.34 adjusted. See Table 1 legend for expansion of abbreviations.

This 1-year longitudinal observational study of a well-characterized cohort of patients with stable COPD who were maintained on the same treatment demonstrated that changes in CAT scores are associated only with changes in the degree of dyspnea measured by the mMRC scale. The 1-year longitudinal CAT scores of patients with stable COPD exhibited variability similar to that of their mMRC scale scores or the CAT scores in smokers who lacked airway obstruction. The mMRC scale and CAT perform equally well in smokers with and without airway obstruction.

Current guidelines for COPD management recommend a multidimensional evaluation of the disease, including assessment of the patient’s health status. The 2013 update to the GOLD strategy includes the use of the CAT to evaluate symptoms, defining a score of ≥ 10 as health impairment and including them in GOLD grades B and D.2 CAT is an easy-to-use, validated, and reproducible tool that allows disease severity to be categorized,4 and it is sensitive to health status changes during exacerbation and following PR.5 The CAT performs in the same way across various European countries.4 It is also associated with other descriptors of COPD, such as dyspnea by the mMRC scale score, degree of obstruction by % FEV1, exercise capacity by the 6MWD, the presence of comorbidities, and the number of exacerbations reported during the past 12 months.5 The association of CAT scores with other important prognostic parameters, such as the BODE index; Pao2; and potential determinants of patient health status, including anxiety and depression, is unknown. Most importantly, nothing is known about the longitudinal performance of CAT scores at 1 year in patients with stable COPD.

Cross-sectional Data

Jones et al3 first reported that the negative relationship between CAT scores and %FEV1 is weak (r = −0.23, P < .001) when studying a large sample of European patients with COPD (n = 1,817). In a later, smaller study, Jones et al5 investigated changes in CAT scores following exacerbation and PR, exploring the response to PR (n = 61-121). They found an association of CAT scores at baseline with %FEV1 (−0.23, P = .07), mMRC scale score (0.42, P = .007), 6MWD (−0.24, P = .009), and the number of exacerbations during the previous 12 months (−0.12, P = .30). The present work confirms these associations and the lack of association with the number of exacerbations during the previous year, which was unexpected. This finding was confirmed by the longitudinal data and indicates that the number of admissions during the 1-year follow-up in the present study did not affect changes in the CAT score. Similarly, the present data support the presence of comorbidities having little impact on CAT scores.4

The novel information presented in this study indicates that an important physiologic prognostic parameter, the degree of arterial oxygenation (Pao2), is indirectly and significantly associated with CAT scores such that patients with low Pao2 levels have experienced important effects evaluated by the CAT, such as breathless going up hills and stairs, activity limitations at home, amount of sleep, and energy level. Another finding is the direct association with the BODE index, a multidimensional evaluation of disease severity that predicts mortality in patients with COPD.12 This finding implies that the CAT is an easy-to-use tool that can capture the multidimensional aspects of COPD represented in the BODE index (nutritional status, airway obstruction, dyspnea, and exercise capacity). Finally, the present study showed that CAT scores are directly associated with symptoms of anxiety and depression as measured by the HADS. This association has not been previously reported in patients with COPD and highlights the importance of anxiety and depression symptoms and their impact on health status.

Longitudinal Changes at 1 Year

To our knowledge, this study presents the first longitudinal data on CAT scores in a large population of patients with stable COPD. Previous longitudinal data came from two small studies investigating changes in CAT scores after an exacerbation (14 days) or a PR program (42 days)5 and from another Spanish study in which CAT scores were measured at the time of exacerbation and 4 weeks later.6 Here, we present a different type of longitudinal data not related to any intervention and with the aim of investigating the stability of the signal at 1 year. The data at 1 year indicate that the CAT has a strong and significant intraclass association (r = 0.58, P < .001) with baseline scores. Of note, the same association was also found in smokers without COPD (r = 0.60, P < .001). This information demonstrates the consistency of CAT measurements at 1 year. From the previous data published on CAT score variations during a COPD exacerbation,5,6,14 we arbitrary designated ± 4 points as a significant longitudinal variation in the CAT score. We acknowledge the potential limitations of this cutoff value, but because of the limited information available on longitudinal changes in CAT scores, we decided to use the available data to select a score that is known to indicate changes beyond the natural variation and that is associated with an exacerbation of the disease. With the onset of exacerbation, Mackay et al14 showed an increase of 4.7 points, and Agustí et al6 showed a much better and slightly better health status associated with a decrease of 8.9 and 4.6 points, respectively. In the present study, > 50% of the patients had the same CAT score (baseline score ± 4 points), which was similar in smokers without COPD, probably indicating a similar variability in the signal at 1 year in this population with lower baseline CAT scores (median, 6; IQR, 2.5-11.5). When we used the ± 2 points proposed by Jones et al5,16 as the possible MCID for the CAT associated with significant changes after PR, a lower percentage of patients with COPD and smokers without COPD had similar scores at 1 year (47% and 38%, respectively). This finding suggests great variability in the CAT score at 1 year in patients with stable COPD with the same maintenance therapy.

The patients with greater variability were those with baseline CAT scores between 10 and 25 (Fig 3A). In this score range, a greater proportion of smokers with COPD improved at 1 year, indicating the beneficial effect of being included in a longitudinal follow-up study. This information should be considered in the longitudinal clinical follow-up of patients with COPD because these patients have baseline scores compatible with an impaired health status that is susceptible to changes at 1 year and are potential targets of specific therapies.

In patients in whom the CAT scores changed, the changes were significantly associated with only the mMRC dyspnea score. These variations were not associated with exacerbations during the previous year or with physiologic domains of the disease, but they were associated with the most important predictor of health status in COPD: the degree of dyspnea. This association is not surprising, considering that the CAT includes two questions that evaluate breathless and exercise limitation: “When I walk uphill or one flight of stairs, I am very breathless,” and “I am very limited doing activities at home.” This information implies that treatment options that target the degree of dyspnea may be associated with changes in health status captured by the CAT as recommended by the GOLD strategy.

Of note, when we compared the longitudinal behavior of the CAT to another patient-centered outcome, the mMRC dyspnea score,17 both signals had a similar profile of change over 1 year. This finding supports the previous report from Oga et al18 indicating that dyspnea (mMRC scale) and health status (CAT) reflect the longitudinal variability of patient-centered outcomes in a multidimensional disease like COPD.

In a cross-sectional study of 1,817 patients with COPD, including a representation of all grades of disease severity, Jones et al19 showed a clear relationship between mMRC scale and health status scores measured by different tools (CAT, SGRQ, Short Form Health Survey, and Functional Assessment of Chronic Illness Therapy-Fatigue). Of note, an mMRC scale score ≥ 1 and a CAT score ≥ 10 were approximately equivalent in determining patients with few symptoms, and some patients with mMRC scale grade 0 had modestly elevated health status scores (CAT score, 11.7 ± 6.8).

As mentioned by its developers, the CAT is a health status tool for the assessment and quantification of symptoms in patients with COPD. The present study suggests that the CAT captures a symptomatic domain present in some smokers without COPD, which changes over a 1-year period to a similar degree as in patients with COPD. This symptomatic signal captured by the CAT is consistent in smokers with and without COPD and behaves the same as the signal captured by the mMRC scale. This novel finding is based on the inclusion of a control group of smokers without COPD.

Another interesting finding is that CAT scores need to be evaluated based on the baseline psychologic status of the patient because patients with HADS values > 7 (suggesting mild anxiety and depression symptoms) had greater changes in longitudinal CAT scores at 1 year. This important information should be considered in the longitudinal evaluation of patients with COPD by the CAT.

The present study has several limitations. First, it was only a 1-year follow-up assessment that could include measurement noise; therefore, serial measurements for a longer period would likely show a reliable trend of variability. At any rate, this study is the first to show 1-year longitudinal data in patients with COPD. Second, the findings should be restricted to the type of patients studied. Third, the impact of maintenance therapy on health status was not studied because it was not the aim of the study. Most of the patients (96%) received the same maintenance therapy, and the potential impact that the different treatments could have had on disease exacerbation did not seem to affect health status. Finally, we selected an arbitrary cutoff value to determine a longitudinal change in the CAT score. This selection was based on the available evidence5,6,14 and the proposed score suggested by mapping the MCID of the SGRQ.15 As previously mentioned, the MCID for the CAT is unknown, and appropriately designed longitudinal studies will determine this threshold.

The study also has notable strengths. It is the first report, to our knowledge, of longitudinal data for the CAT in patients with COPD that also includes a control population of smokers without COPD.

In conclusion, in this large, well-characterized cohort, CAT and mMRC dyspnea scores exhibited similar variability at 1 year in a high percentage of patients with stable COPD. This performance was also found in smokers without COPD. In patients with COPD, 1-year variations in CAT scores were associated with changes in the degree of dyspnea evaluated by the mMRC scale. The mMRC scale and CAT perform equally well in smokers with and without airway obstruction. The data suggest that either tool could allow a longitudinal evaluation of changes in symptoms in patients with COPD. Further long-term longitudinal studies should confirm these findings and help to elucidate the applicability of these tools in clinical practice, as suggested by the GOLD strategy.

Author contributions: J. P. d. T. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. J. P. d. T., J. M. M., P. d. L.-R., G. P.-B., J. J. S.-C., J. L. L.-C., J. B. S., and C. C. contributed to study concept and design; J. P. d. T., J. M. M., C. M.-G., P. d. L.-R., I. M.-V., B. C., G. P.-B., M. C.-R., I. S.-G., R. A. B., A. d. D.-D, N. F.-C., I. A. M., R. I., E. B., A. L. C., J. B. G. I., M. M. R., J. J. S.-C, J. L. L.-C., J. B. S., and C. C. contributed to data analysis and interpretation; J. P. d. T., J. M. M., P. d. L.-R., G. P.-B., J. J. S.-C., J. L. L.-C., J. B. S., and C. C. contributed to the drafting and review of the manuscript for important intellectual content; and C. M.-G., I. M.-V., B. C., M. C.-R., I. S.-G., R. A. B., A. d. D.-D, N. F.-C., I. A. M., R. I., E. B., A. L. C., J. B. G. I., and M. M. R. contributed to the revision of the manuscript.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr de Torres received fees for speaking activities for GlaxoSmithKline plc, AstraZeneca, Novartis AG, Merck Sharp & Dohme Corp, and Takeda Pharmaceuticals International GmbH and received consultancy fees for participating on advisory boards for Takeda Pharmaceuticals International GmbH and Novartis AG between 2010 and 2013. Dr Martinez-Gonzalez received fees for speaking activities for Almirall, SA; AstraZeneca; Boehringer Ingelheim GmbH; Pfizer Inc; GlaxoSmithKline plc; and Chiesi Farmaceutici SpA between 2010 and 2013. Dr de Lucas-Ramos received fees for speaking activities for Almirall, SA; Boehringer Ingelheim GmbH; Takeda Pharmaceuticals International GmbH; and GlaxoSmithKline plc and received grants from Almirall, SA, and Foundation Vital Aire between 2010 and 2013. Dr Cosio received fees for speaking activities for Almirall, SA; Takeda Pharmaceuticals International GmbH; The Menarini Group; Boehringer Ingelheim GmbH; Pfizer Inc; GlaxoSmithKline plc; and Chiesi Farmaceutici SpA between 2010 and 2013. Dr Peces-Barba received fees for speaking activities for Almirall, SA; Takeda Pharmaceuticals International GmbH; Novartis AG; Boehringer Ingelheim GmbH; AstraZeneca; Esteve; GlaxoSmithKline plc, and Chiesi Farmaceutici SpA; received consultancy fees for participating in advisory boards of Takeda Pharmaceuticals International GmbH, Novartis AG, and Ferrer Internacional; and received grants from GlaxoSmithKline plc between 2010 and 2013. Dr Solanes-García received fees for speaking activities for Esteve; AstraZeneca; The Menarini Group; Boehringer Ingelheim GmbH; Pfizer Inc; GlaxoSmithKline plc, Biodatos Investigación SL, and Chiesi Farmaceutici SpA between 2010 and 2013. Dr Agüero Balbin received fees for speaking activities for Almirall, SA; AstraZeneca; Novartis AG; Boehringer Ingelheim GmbH; Takeda Pharmaceuticals International GmbH; GlaxoSmithKline plc; and Chiesi Farmaceutici SpA between 2010 and 2013. Dr de Diego-Damia received fees for speaking activities for Boehringer Ingelheim GmbH, AstraZeneca, Pfizer Inc, Merck Sharp & Dohme Corp, GlaxoSmithKline plc, and Chiesi Farmaceutici SpA between 2010 and 2013. Dr Alfageme Michavila received fees for speaking activities for Almirall, SA; Boehringer Ingelheim GmbH; and Pfizer Inc between 2010 and 2013. Dr Irigaray received fees for speaking activities for Novartis AG, Takeda Pharmaceuticals International GmbH, GlaxoSmithKline plc, and Chiesi Farmaceutici SpA between 2010 and 2013. Dr Llunell Casanovas received fees for speaking activities for AstraZeneca, Eli Lilly and Co, and Chiesi Farmaceutici SpA between 2010 and 2013. Dr Galdiz Iturri received fees for speaking activities for Almirall, SA; Novartis AG; AstraZeneca; Boehringer Ingelheim GmbH; GlaxoSmithKline plc; and Chiesi Farmaceutici SpA between 2010 and 2013. Dr Soler-Cataluña participated in speaking activities, on an industry advisory committee, or with other related activities sponsored by Almirall, SA; AstraZeneca; Boehringer Ingelheim GmbH; Pfizer Inc; Ferrer Internacional; GlaxoSmithKline plc; Takeda Pharmaceuticals International GmbH; Merck Sharp & Dohme Corp; Novartis AG; and Grupo Uriach between 2010 and 2013. Dr Soriano received grants from GlaxoSmithKline plc in 2011 and Chiesi Farmaceutici SpA in 2012 through his home institution and participated in speaking activities, on an industry advisory committee, or with other related activities sponsored by Almirall, SA; Boehringer Ingelheim GmbH; Pfizer Inc; Chiesi Farmaceutici SpA; GlaxoSmithKline plc; and Novartis AG between 2010 and 2013. Dr Casanova participated in speaking activities for Almirall, SA; Takeda Pharmaceuticals International GmbH; Chiesi Farmaceutici SpA; GlaxoSmithKline plc; and Novartis AG between 2010 and 2013. Drs Marin, Mir-Viladrich, Calle-Rubio, Feu-Collado, Balcells, Marín Royo, and Lopez-Campos have reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Role of sponsors: The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.

Additional information: The e-Appendix and e-Table can be found in the Supplemental Materials section of the online article.

6MWD

6-min walk distance

BODE

BMI, obstruction, dyspnea, exercise capacity

CAT

COPD Assessment Test

GOLD

Global Initiative for Chronic Obstructive Lung Disease

HADS

Hospital Anxiety and Depression Scale

IQR

interquartile range

MCID

minimum clinically important difference

mMRC

modified Medical Research Council

PR

pulmonary rehabilitation

SGRQ

St. George’s Respiratory Questionnaire

Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study. Lancet. 1997;349(9064):1498-1504. [CrossRef] [PubMed]
 
Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. Global Initiative for Chronic Obstructive Pulmonary Disease website. http://www.goldcopd.org/uploads/users/files/GOLD_Report_2013_Feb20.pdf. Accessed April 15, 2013.
 
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):648-654. [CrossRef] [PubMed]
 
Jones PW, Brusselle G, Dal Negro RW, et al. Properties of the COPD assessment test in a cross-sectional European study. Eur Respir J. 2011;38(1):29-35. [CrossRef] [PubMed]
 
Jones PW, Harding G, Wiklund I, et al. Tests of the responsiveness of the COPD Assessment Test following acute exacerbation and pulmonary rehabilitation. Chest. 2012;142(1):134-140. [PubMed]
 
Agustí A, Soler JJ, Molina J, et al. Is the CAT questionnaire sensitive to changes in health status in patients with severe COPD exacerbations? COPD. 2012;9(5):492-498. [CrossRef] [PubMed]
 
López-Campos JL, Peces-Barba G, Soler-Cataluña JJ, et al; en nombre del grupo de estudio CHAIN. Chronic obstructive pulmonary disease history assessment in Spain: a multidimensional chronic obstructive pulmonary disease evaluation. Study methods and organization. Arch Bronconeumol. 2012;48(12):453-459. [CrossRef] [PubMed]
 
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. [CrossRef] [PubMed]
 
American Thoracic Society. Lung function testing: selection of reference values and interpretative strategies. Am Rev Respir Dis. 1991;144(5):1202-1218. [CrossRef] [PubMed]
 
ATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med. 2002;166(1):111-117. [CrossRef] [PubMed]
 
Mahler DA, Wells CK. Evaluation of clinical methods for rating dyspnea. Chest. 1988;93(3):580-586. [CrossRef] [PubMed]
 
Celli BR, Cote CG, Marin JM, 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):1005-1012. [CrossRef] [PubMed]
 
Dowson C, Laing R, Barraclough R, et al. The use of the Hospital Anxiety and Depression Scale (HADS) in patients with chronic obstructive pulmonary disease: a pilot study. N Z Med J. 2001;114(1141):447-449. [PubMed]
 
Mackay AJ, Donaldson GC, Patel AR, Jones PW, Hurst JR, Wedzicha JA. Usefulness of the Chronic Obstructive Pulmonary Disease Assessment Test to evaluate severity of COPD exacerbations. Am J Respir Crit Care Med. 2012;185(11):1218-1224. [CrossRef] [PubMed]
 
Jones PW. COPD assessment test—rationale, development, validation and performance. COPD. 2013;10(2):269-271. [CrossRef] [PubMed]
 
Dodd JW, Marns PL, Clark AL, et al. The COPD Assessment Test (CAT): short- and medium-term response to pulmonary rehabilitation. COPD. 2012;9(4):390-394. [PubMed]
 
Nishimura K, Izumi T, Tsukino M, Oga T. Dyspnea is a better predictor of 5-year survival than airway obstruction in patients with COPD. Chest. 2002;121(5):1434-1440. [CrossRef] [PubMed]
 
Oga T, Tsukino M, Hajiro T, Ikeda A, Nishimura K. Analysis of longitudinal changes in dyspnea of patients with chronic obstructive pulmonary disease: an observational study. Respir Res. 2012;13:85. [CrossRef] [PubMed]
 
Jones PW, Adamek L, Nadeau G, Banik N. Comparisons of health status scores with MRC grades in COPD: implications for the GOLD 2011 classification. Eur Respir J. 2013;42(3):647-654. [CrossRef] [PubMed]
 

Figures

Figure Jump LinkFigure 1  Distribution of CAT scores in patients with COPD. CAT = COPD Assessment Test.Grahic Jump Location
Figure Jump LinkFigure 2  Distribution of CAT scores in smokers without COPD. See Figure 1 legend for expansion of abbreviation.Grahic Jump Location
Figure Jump LinkFigure 3  A, Changes in the CAT scores of patients with COPD at 1 y compared with baseline (cutoff values ± 4 points). B, Changes in the CAT scores of patients with COPD at 1 y compared with baseline (cutoff values ± 2 points). See Figure 1 legend for expansion of abbreviation.Grahic Jump Location
Figure Jump LinkFigure 4  A, Changes in the CAT scores of smokers without COPD at 1 y compared with baseline (cutoff values ± 4 points). B, Changes in the CAT scores of smokers without COPD at 1 y compared with baseline (cutoff values ± 2 points). See Figure 1 legend for expansion of abbreviation.Grahic Jump Location

Tables

Table Graphic Jump Location
TABLE 1  ] Baseline Characteristics of All Participants

Data are presented as mean ± SD, median (interquartile range), or %. 6MWD = 6-min walk distance; BODE = BMI, obstruction, dyspnea, exercise capacity; CAT = COPD Assessment Test; Dlco = diffusing capacity of lung for carbon monoxide; GOLD = Global Initiative for Chronic Obstructive Lung Disease; HADS = Hospital Anxiety and Depression Scale; mMRC = modified Medical Research Council; NA = not applicable.

Table Graphic Jump Location
TABLE 2  ] Univariate Analysis With Baseline CAT Scores in Patients With COPD as the Dependent Variable

See Table 1 legend for expansion of abbreviations.

Table Graphic Jump Location
TABLE 3  ] Multivariate Analysis With Baseline CAT Scores in Patients With COPD as the Dependent Variable

Variables included in the model were sex, pack-y history, mMRC, %FEV1, Pao2, 6MWD, HADS anxiety, and HADS depression. r2 = 0.86. See Table 1 legend for expansion of abbreviations.

Table Graphic Jump Location
TABLE 4  ] Univariate Analysis With Changes in CAT Scores in Patients With COPD at 1 Y as the Dependent Variable

See Table 1 legend for expansion of abbreviations.

Table Graphic Jump Location
TABLE 5  ] Multivariate Analysis With Changes in CAT Scores in Patients With COPD at 1 Y as the Dependent Variable

Variables included in the model were mMRC scale, HADS anxiety, and HADS depression. r2 = 0.34 adjusted. See Table 1 legend for expansion of abbreviations.

References

Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study. Lancet. 1997;349(9064):1498-1504. [CrossRef] [PubMed]
 
Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. Global Initiative for Chronic Obstructive Pulmonary Disease website. http://www.goldcopd.org/uploads/users/files/GOLD_Report_2013_Feb20.pdf. Accessed April 15, 2013.
 
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):648-654. [CrossRef] [PubMed]
 
Jones PW, Brusselle G, Dal Negro RW, et al. Properties of the COPD assessment test in a cross-sectional European study. Eur Respir J. 2011;38(1):29-35. [CrossRef] [PubMed]
 
Jones PW, Harding G, Wiklund I, et al. Tests of the responsiveness of the COPD Assessment Test following acute exacerbation and pulmonary rehabilitation. Chest. 2012;142(1):134-140. [PubMed]
 
Agustí A, Soler JJ, Molina J, et al. Is the CAT questionnaire sensitive to changes in health status in patients with severe COPD exacerbations? COPD. 2012;9(5):492-498. [CrossRef] [PubMed]
 
López-Campos JL, Peces-Barba G, Soler-Cataluña JJ, et al; en nombre del grupo de estudio CHAIN. Chronic obstructive pulmonary disease history assessment in Spain: a multidimensional chronic obstructive pulmonary disease evaluation. Study methods and organization. Arch Bronconeumol. 2012;48(12):453-459. [CrossRef] [PubMed]
 
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. [CrossRef] [PubMed]
 
American Thoracic Society. Lung function testing: selection of reference values and interpretative strategies. Am Rev Respir Dis. 1991;144(5):1202-1218. [CrossRef] [PubMed]
 
ATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med. 2002;166(1):111-117. [CrossRef] [PubMed]
 
Mahler DA, Wells CK. Evaluation of clinical methods for rating dyspnea. Chest. 1988;93(3):580-586. [CrossRef] [PubMed]
 
Celli BR, Cote CG, Marin JM, 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):1005-1012. [CrossRef] [PubMed]
 
Dowson C, Laing R, Barraclough R, et al. The use of the Hospital Anxiety and Depression Scale (HADS) in patients with chronic obstructive pulmonary disease: a pilot study. N Z Med J. 2001;114(1141):447-449. [PubMed]
 
Mackay AJ, Donaldson GC, Patel AR, Jones PW, Hurst JR, Wedzicha JA. Usefulness of the Chronic Obstructive Pulmonary Disease Assessment Test to evaluate severity of COPD exacerbations. Am J Respir Crit Care Med. 2012;185(11):1218-1224. [CrossRef] [PubMed]
 
Jones PW. COPD assessment test—rationale, development, validation and performance. COPD. 2013;10(2):269-271. [CrossRef] [PubMed]
 
Dodd JW, Marns PL, Clark AL, et al. The COPD Assessment Test (CAT): short- and medium-term response to pulmonary rehabilitation. COPD. 2012;9(4):390-394. [PubMed]
 
Nishimura K, Izumi T, Tsukino M, Oga T. Dyspnea is a better predictor of 5-year survival than airway obstruction in patients with COPD. Chest. 2002;121(5):1434-1440. [CrossRef] [PubMed]
 
Oga T, Tsukino M, Hajiro T, Ikeda A, Nishimura K. Analysis of longitudinal changes in dyspnea of patients with chronic obstructive pulmonary disease: an observational study. Respir Res. 2012;13:85. [CrossRef] [PubMed]
 
Jones PW, Adamek L, Nadeau G, Banik N. Comparisons of health status scores with MRC grades in COPD: implications for the GOLD 2011 classification. Eur Respir J. 2013;42(3):647-654. [CrossRef] [PubMed]
 
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