Prognosis and management of chronic obstructive pulmonary disease in a real-world setting: A 5-year follow-up analysis of a multi-institutional registry

Prognosis and management of chronic obstructive pulmonary disease in a real-world setting: A 5-year follow-up analysis of a multi-institutional registry | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognosis and management of chronic obstructive pulmonary disease in a real-world setting: A 5-year follow-up analysis of a multi-institutional registry Tomotsugu Takano, Kazuya Tsubouchi, Naoki Hamada, Katsuyuki Ichiki, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4043733/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2024 Read the published version in BMC Pulmonary Medicine → Version 1 posted 21 You are reading this latest preprint version Abstract Background We conducted a prospective observational study to elucidate the long-term prognosis and management of chronic obstructive pulmonary disease (COPD) in Japan in the mid-2010s in clinical practice. Methods This prospective cohort study included 29 facilities. A total of 427 patients clinically diagnosed with COPD, enrolled between September 2013 and April 2016, were analysed. Interstitial pneumonia was excluded through a central multidisciplinary discussion. Follow-up data were collected for up to 5 years after patient registration. Results In total, 67 patients clinically diagnosed with COPD did not have airflow limitations (AFL) at the time of registration. In the cohort with AFL (n=370), 266 patients completed a 5-year follow-up, while 58 patients died during the 1556.7 person-years of observation. The overall 5-year survival rate was 85.2%. Stratified by % forced expiratory volume in one second (FEV1), survival rates were 93.4% in the 50 ≤ %FEV1 group, 82.5% in the 30 ≤ %FEV1 < 50 group, and 66.1% in the %FEV1 < 30 group. The prognosis of the subpopulation without AFL was poor with 5-year survival of 83.0%. This subpopulation exhibited respiratory symptoms, low vital capacity and total lung capacity, and emphysematous changes. Conclusions Our study presents the 5-year survival and real-world clinical practice scenario of a prospective cohort of patients clinically diagnosed with COPD in Japan in the mid-2010s. Overall, 13% of the patients in this cohort did not show AFL at registration with respiratory symptoms, distinct spirometric patterns, and poor prognosis. COPD 5-year survival rate normal spirometry real-world registry Figures Figure 1 Figure 2 Background Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, and its incidence is expected to increase.[1] However, its prognosis has improved with treatment such as long-term oxygen therapy for patients with resting hypoxemia, non-invasive ventilation, and inhaled corticosteroids for patients with low pulmonary function and frequent exacerbations.[2–6] This suggests that COPD prognosis and treatment can change over time. A comparison of two independent cohorts in the 1990s and the mid-2000s revealed that the latter group had better prognoses in severe cases of COPD.[7,8] However, there are no data on the prognosis of COPD in real-world settings after 2010s in Japan. Currently, Japan is experiencing the most advanced demographic aging worldwide, and other countries are predicted to follow soon.[9] Therefore, the Japanese data on COPD prognosis and management can act as a reference for other countries with aging populations, especially in East Asia. The diagnosis of COPD requires a forced expiratory volume in one second (FEV1) / forced vital capacity (FVC) < 0.7, signifying airflow limitation (AFL). However, several smokers reportedly experience respiratory symptoms without AFL. Individuals who do not meet the definition of an obstructive pattern and are associated with decreased FEV1, termed preserved ratio impaired spirometry (PRISm), reportedly have higher mortality than those with normal lung function and even mild COPD in community-based observational studies.[12–14] These data highlight the importance of focusing on populations that do not meet the COPD criteria. Therefore, in this study, we analysed a multi-centre prospective cohort to elucidate the prognosis and treatment of COPD in a real-world setting in the mid-2010s. We also analysed the prognosis and clinical characteristics of patients diagnosed with COPD, including those without AFL. Methods Study design This was a prospective and multicentre observational study of patients with COPD and idiopathic interstitial pneumonias (IIPs) from 29 centres who were followed up longitudinally for 5 years. Study patients The main inclusion criteria were age > 20 years and diagnosis of COPD or IIPs. The diagnosis of COPD was made by clinicians based on the symptoms and clinical data described in the international guideline.[15] However, no exclusion criteria for pulmonary function tests were established at enrolment. The diagnosis of IIPs was made as previously described through a central multidisciplinary discussion (MDD).[16] Patients (regardless of whether newly or previously diagnosed) were sequentially enrolled after a diagnosis of COPD or IIPs by respiratory specialists at the participating facilities. The total planned enrolment number was set at 1000 without statistical sample size calculations. A total of 1024 patients were recruited between September 1, 2013, and April 30, 2016, eight of whom were subsequently excluded on the basis of patient refusal (n = 4), missing test results (n = 2), failure to meet the inclusion criteria (n = 1), or an unknown reason (n = 1). The remaining 1016 patients were enrolled in the study. A total of 461 patients were diagnosed with COPD and 543 patients were diagnosed with IIPs.[16] Follow-up surveillance was conducted annually for 5 years to evaluate exacerbation and death. Exacerbation was determined by a clinician based on international criteria[15] and worsening of dyspnoea due to other causes, including bacterial pneumonia, was excluded. All examinations and investigations were performed as part of routine care for each patient at the physician’s discretion and no additional visits or investigations were mandated for this study. Pulmonary function tests and CT Predicted FEV1 and VC were calculated using a reference equation reported by the Japanese Respiratory Society (JRS) in 2001.[17] The predicted total lung capacity (TLC) and residual volume (RV) were calculated using the JRS prediction formula as standard values. To determine the volume of emphysema, the volume of low-attenuation areas (LAA) on 5-mm collimation computed tomography (CT) scans was calculated with a threshold of -910 Hounsfield units as previously described.[18] Dedicated software programs (3DSlicer; http://www.slicer.org) were used to quantify LAA. Total lung capacity by CT (TLCct) was calculated using inspiratory CT, and TLCct-VC was obtained as a surrogate value for RV; the ratio of TLCct-VC to predicted RV was calculated as %RV. Informed consent This prospective, multicentre, observational study was approved by the Institutional Review Board of Kyushu University (#25-135, August 23, 2013; #555-00, August 27, 2013) and by the institutional review boards of all participating hospitals. Written informed consent was obtained from all patients. Patient and Public involvement Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research. Statistical analysis The cumulative proportion of survival was estimated using the Kaplan–Meier analysis. Hazard ratios (HR) were estimated using a Cox regression model to evaluate the association between background characteristics and survival. In the analysis of 370 patients with AFL, statistical comparisons between groups were made with the 50 ≤ %FEV1 with AFL group as the reference group. Clinical characteristics of the 57 patients without AFL were evaluated by statistical comparison with 50 ≤ %FEV1 with AFL group. A backward stepwise method was adopted with the significance level for removal set at 0.05 to select a model to predict survival. Statistical significance was set at p < 0.05. All statistical analyses were performed using the SAS version 9.4 (SAS Institute, Cary, NC, USA). Results Study subjects Between September 2013 and April 2016, 461 patients were enrolled based on clinical data and symptoms. Among them, five with inadequate imaging, nine who underwent lobectomy, and 20 with missing follow-up data after initial enrolment were excluded from the analysis; the remaining 427 patients were analysed (Figure 1). The median age of the total population was 72 years, with 87.1% being male, 97.4% having a history of smoking, and 99.5% presenting more than 5% LAA on CT. In total, 370 patients (86.7%) in the COPD cohort showed AFL (defined as FEV1/FVC < 70%), while 57 (13.3%) had no AFL. Upon categorizing the 370 patients with AFL according to %FEV1, 193 patients had 50 ≤ %FEV1, 112 had 30 ≤ %FEV1 < 50, and 65 had %FEV1 < 30 (Table 1). Characteristics of the FEV1/FVC < 70% population Among the 370 patients with AFL, the most common comorbidity was hypertension, which accounted for 40.8% of all patients. Past history of bronchial asthma accounted for 17.8% of patients with AFL and was more frequent in the 30 ≤ %FEV1 < 50 group. Dyspnoea, as assessed using the modified Medical Research Council (mMRC) score, was significantly higher in patients with severe AFL. Sputum and weight loss were more frequent in the %FEV1 < 30 group, and body mass index was also lower in the %FEV1 < 30 group. %VC at registration was preserved in the 50 ≤ %FEV1 and 30 ≤ %FEV1 < 50 groups, while it decreased in the %FEV1 < 30 group. Additionally, RV/TLC, %RV, and LAA increased with increasing severity. The blood test showed no significant differences between the groups except for the white blood cell count (Table 1, 2, 3 and S1). Regarding treatment, 87% of the 50 ≤ %FEV1 group, 96.4% of the 30 < %FEV1 < 50 group, and 98.5% of the %FEV1 < 30 group used inhaled drugs. The most commonly used medication was a long-acting muscarinic antagonist (LAMA). Inhaled corticosteroids (ICS) were used by 37.8% patients in the 50 ≤ %FEV1 group, 66.9% patients in the 30 ≤ %FEV1 < 50 group, and 76.9% patients in the %FEV1< 30 group. Home oxygen therapy was used in 58.5% of the %FEV1 < 30 group (Table 4). 5-year survival and exacerbation among the FEV1/FVC < 70% population Among 370 patients with AFL, 266 completed the 5-year follow-up while 58 died during the 1556.7 person-year observation period (Figure S1). The 5-year survival rates of all patients with AFL were 85.2%, 93.4% in the 50 ≤ %FEV1 group, 82.5% in the 30 ≤ %FEV1 < 50 group, and 66.1% in the %FEV1 < 30 group (Figure 2). The HR for mortality adjusted for age, sex, body mass index (BMI), and smoking (pack-year) was 2.37 (95%CI: 1.07–5.23) in the 30 ≤ %FEV1 < 50 group and 7.56 (95%CI: 3.43–16.65) in the %FEV1 < 30 group considering the 50 ≤ %FEV1 group as reference (Table S2). The most common cause of death was respiratory diseases other than lung cancer, accounting for 46.9% of all deaths, with the frequency increasing in groups with lower %FEV1. Deaths associated with lung and non-lung cancer malignancies each accounted for 8.2% of all deaths. In contrast, cardiovascular death occurred in only two cases (Table 5). The percentages of patients having at least one exacerbation and more than two exacerbations in five years were as follows: 8.8% and 4.7% in the 50 ≤ %FEV1 group, 31.3% and 11.6% in the 30 ≤ %FEV1 < 50 group, and 63.1% and 35.4% in the %FEV1 < 30 group, respectively (Table S3). Prognostic factors for mortality of FEV1/FVC < 70% population The predictors of mortality in patients with FEV1/FVC < 70% were examined using a simple Cox regression analysis of baseline factors (Table S4). In multivariate Cox regression analysis (stepwise variable selection), age, number of comorbidities, performance status, weight loss, C-reactive protein level, and % RV were independently associated with mortality (Table S5). Characteristics of patients without airflow limitation In total, 57 patients (13.3%) were FEV1/FVC ≥ 70% at the time of enrolment. This population was also analysed retrospectively for mortality and clinical characteristics. Their 5-year survival rate was 83.0%, which was significantly lower than that of the 50 ≤ %FEV1 with AFL group (adjusted HR of 2.67 with a 95% CI of 1.06–6.73) and similar to that of the 30 ≤ %FEV1 < 50 group (Figure 2 and Table S2). No differences in age, sex, smoking prevalence, and respiratory symptoms were found between the FEV1/FVC ≥ 70% and 50 ≤ %FEV1 with AFL groups. The FEV1/FVC ≥ 70% group had lower BMI, VC and TLC, and certain area of LAA. Treatment in the FEV1/FVC ≥ 70% group was similar to that of 50 ≤ %FEV1 with AFL group, and 82.5% used inhaler at registration (Table 1-4). Discussion We conducted a 5-year survival analysis of a cohort of patients with clinically diagnosed COPD. We found that 13% of patients in this cohort did not show AFL at registration with respiratory symptoms and low VC and TLC, and had worse prognosis than patients in the 50 ≤ %FEV1 with AFL group. The main analysis included 370 patients with AFL at registration and presented the clinical practice and prognosis of COPD in Japan in the mid-2010s. Our cohort exhibited a high prevalence of males and low BMI, similar to previous Japanese reports.[19–21] In the 1990s, a prospective study in Japan reported 5-year survival rates of approximately 90% for patients with 50 ≤ %FEV1, 80% for patients with 30 ≤ %FEV1<50, and 60% for patients with %FEV1 < 30.[22] Although we were unable to statistically compare our cohort with previous ones, the 5-year survival rates of the 30 ≤ %FEV1 < 50 and %FEV1 < 30 groups in our cohort were numerically better, regardless of the high median age of this cohort, which may be attributed to advancements in treatment and healthcare. All prognostic factors at registration revealed in this cohort study have been previously reported. Life expectancy in Japan increased from 79.6 to 83.9 years in the 20 years between 1995 and 2015, as observed in other countries.[9] Thus, the mean age of our cohort was 71.7 years, and approximately 20% of them were over 80 years of age, representing the real world scenario in Japan, which is a super-aging society. In our study, 91.4% patients used inhaler and 53.5% used ICS in all the cases with AFL. The high prevalence ICS use may be because concomitant ICS use was recommended for patients with COPD with %FEV1 < 50 or repeated exacerbations in 2013.[15] Furthermore, this cohort included patients with a history of bronchial asthma. Nonetheless, the exacerbation rate was low in our cohort. Recent randomized controlled trials have defined exacerbations by the use of antibiotics or oral corticosteroids.[23] However, in our study, isolated lower respiratory tract infections were excluded from exacerbations. This difference in definition may have contributed to the lower frequency observed in our cohort. Moreover, lower exacerbation rates have been reported in the Japanese population, suggesting potential racial differences in exacerbation frequencies.[24] In this cohort, 13% patients did not have AFL and were treated as having COPD. The majority of this population had a history of heavy smoking, 30% had dyspnoea on exertion with mMRC: ≥ 2, and 60% had wet cough. On examination, VC and TLCct were low and FEV1 was preserved with constant emphysema. 84% of this population underwent respiratory treatment at the time of registration. However, pulmonary function test results at the time of diagnosis were not available. Some longitudinal observational studies have reported that approximately 10% of patients with obstructive patterns in pulmonary function tests become free of obstructive patterns after annual follow-up.[12–14] Therefore, it is possible that a part of the FEV1/FVC ≥ 70% group met the definition of COPD at the time of diagnosis. However, considering high %FEV1 and low TLCct and VC in this group, most of FEV1/FVC ≥ 70% population were thought to be clinically diagnosed with COPD even without AFL. In the SPIROMICS and COPDGene studies, two large longitudinal observational studies, 23–50% of current and former smokers with normal spirometry had respiratory symptoms, and 20–42% received treatment interventions.[10, 11] Similarly, a meta-analysis showed that approximately one-fourth of patients treated for COPD in primary healthcare settings did not show obstructive impairment on spirometry.[25] Consistent with these studies, our study indicates the presence of a population with respiratory symptoms that clinicians treat as COPD, even without AFL in real world settings. The FEV1/FVC ≥ 70% group showed poor prognosis. Populations with restrictive spirometric patterns have higher mortality rates,[26–28] partially because of the inclusion of interstitial pneumonia. In our cohort, patients with interstitial pneumonia, including CPFE, were excluded through a MDD.[16, 29] Among 57 cases in FEV1/FVC ≥ 70% group in our cohort, 22 cases met the criteria for PRISm (data not shown), which has been reported to have higher mortality than that with mild COPD in community-based observational studies. Additionally, respiratory symptoms and the extent of emphysema are associated with poor prognosis, even in smokers without AFL.[28, 30–32] Therefore, poor prognosis of the FEV1/FVC ≥ 70% group may be because of the considerable rate of respiratory symptoms, imaging abnormalities, and low VC, all of which are reportedly associated with mortality. Therefore, our study suggests the importance of multidimensional evaluation of patients with respiratory symptoms, including pulmonary function tests and imaging. The limitations of this study were that a part of the enrolled patients were not newly diagnosed, lung function was not confirmed at the time of diagnosis, airway reversibility was not assessed at the time of enrolment, and the time of diagnosis was not recorded. Moreover, only patients who provided informed consent, and not consecutive patients, were enrolled. Finally, despite the execution of annual protocolized assessments, some data were missing. Conclusions we present the 5-year survival and real-world clinical practice data of a prospective cohort of patients clinically diagnosed with COPD in Japan in the mid-2010s. Thirteen percent of the patients in this cohort did not show AFL at registration with respiratory symptoms, distinct spirometric patterns, and poor prognosis. List Of Abbreviations chronic obstructive pulmonary disease (COPD), multidisciplinary discussion (MDD), forced expiratory volume 1 second (FEV1), forced vital capacity (FVC), airflow limitation (AFL), preserved ratio impaired spirometry (PRISm), idiopathic interstitial pneumonias (IIPs), Japanese Respiratory Society (JRS), total lung capacity (TLC), residual volume (RV), low attenuation areas (LAA), computed tomography (CT), Total lung capacity by CT (TLCct), Hazard ratio (HR), modified Medical Research Council (mMRC), long-acting muscarinic antagonist (LAMA), Inhaled corticosteroids (ICS), Home oxygen therapy (HOT), performance status (PS), body mass index (BMI) Declarations Ethics approval and consent to participate This study involves human participants and this prospective, multicentre observational study was approved by the Institutional Review Board of Kyushu University (#25-135, 23 August 2013; #555-00, 27 August 2013) as well as by the institutional review boards of all participating hospitals. Participants provided informed consent before participating in the study. Consent for publication Not applicable since there are no details on individuals reported within the manuscript. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests STakata has received personal fees from AstraZeneca, Nippon Boehringer Ingelheim, Novartis, GSK, and Teijin Pharma. KY has received personal fees from Nippon Boehringer Ingelheim, GSK and Teijin Pharma. MO has received personal fees from Nippon Boehringer Ingelheim. IO has received personal fees from AstraZeneca, Nippon Boehringer Ingelheim and Novartis. Funding This study was supported by a grant from the Ministry of Education, Culture, Sports, Science and Technology: the broad-area, network-based project to drive clinical research at Kyushu University Hospital, a grant from by Boehringer Ingelheim, and a grant to the Diffuse Lung Diseases Research Group from the Ministry of Health, Labor and Welfare, Japan. Authors’ Contributions TT, KTsubouchi and IO contributed to the literature search, figures, the study design, data collection, data analysis, data interpretation, and writing approved the final version of the review. NH and YN contributed to the literature search, study design, data collection, and data interpretation and approved the final version of the review. FK and STokunaga contributed to the literature search, figures, data analysis, and writing and approved the final version of the review. KI, RT, STakata, SK, NN, MY, YK, KTobino, EH, HI, HW, TM, MF, KY and MO contributed to the literature search, data collection, and data interpretation and approved the final version of the review. HY contributed to the literature search, data analysis, and writing, and approved the final version of the review. IO accepts full responsibility for the work and/or conduct of the study, has access to the data and controls the decision to publish. Acknowledgements The authors thank the patients, their families, and all the investigators participating in the Fukuoka Tobacco-Related Lung Disease (FOLD) registry group. The authors would like to thank the Clinical Research Support Center of Kyushu for their official work on the study. References World Health Organization: The top 10 causes of death. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death. Cranston JM, Crockett AJ, Moss JR, Alpers JH. Domiciliary oxygen for chronic obstructive pulmonary disease. 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Tables TABLE 1 Baseline characteristics of participants, according to pulmonary function categories at registration FEV1/FVC < 70% FEV1/FVC ≥ 70% Total 50 ≤ %FEV1 (N = 193) 30 ≤ %FEV1 < 50 (N = 112) %FEV1 < 30 (N = 65) Dunnett test* (N = 57) P-value † Age (year.), median (IQR) 72 (67-76) 73 (67-78) 71 (65-75) 0.362/0.158 71 (66-78) 0.698 72 (66-77) Sex, n (%) Male 170 (88.1) 94 (83.9) 59 (90.8) 0.491/0.811 49 (86.0) 0.670 372 (87.1) Female 23 (11.9) 18 (16.1) 6 (9.2) 8 (14.0) 55 (12.9) BMI(kg/m 2 ), median (IQR) 22.5 (20.2-24.4) 22.1 (19.7-24.0) 19.6 (17.1-22.2) § 0.371/ <0.001 21.2 (17.9-24.2) § 0.037 21.9 (19.2-24.1) Smoking (pack-years), median (IQR) ‡ 48.5 (30.0-80.0) 50.0 (40.0-72.5) 57.5 (40.0-80.0) 0.981/0.101 48.0 (31.0-80.0) 0.556 50 (36.5-70) Never, n (%) 4 (2.1) 2 (1.8) 2 (3.1) 0.476/0.666 3 (5.3) 0.320 11 (2.6) Former, n (%) 136 (70.5) 86 (76.8) 48 (73.8) 41 (71.9) 72.8 (72.8) Current, n (%) 53 (27.5) 24 (21.4) 15 (23.1) 13 (22.8) 24.5 (24.6) Past history of Bronchial asthma 27 (14.0) 28 (25.0) § 11 (16.9) 0.030/0.820 5 (8.8) 0.300 71 (16.6) Comorbidity, n (%) Diabetes 27 (14.0) 12 (10.7) 10 (15.4) 0.650/0.946 7 (12.3) 0.741 56 (13.1) Dyslipidaemia 33 (17.1) 14 (12.5) 5 (7.7) 0.449/0.112 9 (15.8) 0.816 61 (14.3) Hypertension 81 (42.0) 45 (40.2) 25 (38.5) 0.930/0.849 28 (49.1) 0.339 179 (41.9) Heart disease 34 (17.6) 18 (16.1) 6 (9.2) 0.918/0.199 12 (21.1) 0.556 70 (16.4) GERD 13 (6.7) 9 (8.0) 6 (9.2) 0.893/0.753 4 (7.0) 0.941 32 (7.5) FVC, forced vital capacity; FEV1, forced expiratory volume in one second; IQR, interquartile range; BMI, body mass index; GERD, gastroesophageal reflux disease. *: Reference: FEV1/FVC < 70% and 50 ≤ %FEV1. † : Chi-square test for nominal scale variables, Wilcoxon rank sum test for continuous variables. ‡ : Numbers of patients (FEV1/FVC < 70% and 50 ≤ %FEV1, FEV1/FVC < 70% and 30 ≤ %FEV1<50, FEV1/FVC < 70% and %FEV1 < 30, and FEV1/FVC ≥ 70%, respectively) were as follows: Smoking (N=192, 110, 64, 57). § : P<0.05 with Dunnett’s test, Chi-square, or Wilcoxon rank sum test. TABLE 2 Baseline symptom and performance status according to pulmonary function categories at registration FEV1/FVC < 70% FEV1/FVC ≥ 70% 50 ≤ %FEV1 (n = 193) 30 ≤ %FEV1 < 50 (n = 112) %FEV1 < 30 (n = 65) Dunnett test* (n = 57) P-value † mMRC score, n (%) ‡ § § <0.001/ <0.001 0.992 0 21 (14.7) 6 (5.8) 1 ( 1.8) 7 (15.6) 1 69 (48.3) 32 (31.1) 13 (22.8) 23 (51.1) 2 38 (26.6) 36 (35.0) 12 (21.1) 6 (13.3) 3 13 (9.1) 18 (17.5) 20 (35.1) 9 (20.0) 4 2 (1.4) 11 (10.7) 11 (19.3) 0 (0.0) Symptom, n (%) Cough 91 (47.2) 59 (52.7) 39 (60.0) 0.568/ 0.138 35 (61.4) 0.059 Sputum 82 (42.5) 47 (42.0) 39 (60.0) § 0.994/ 0.027 29 (50.9) 0.263 Body weight loss 5 (2.6) 9 (8.0) 18 (27.7) § 0.161/ <0.001 4 (7.0) 0.115 Wheeze 11 (5.7) 8 (7.1) 9 (13.8) 0.868/ 0.061 2 (3.5) 0.513 Clubbed fingers 19 (9.8) 11 (9.8) 4 (6.2) 0.999/ 0.598 4 (7.0) 0.516 PS, n (%) § § <0.001/ <0.001 0.382 0 88 (45.6) 24 (21.4) 13 (20.0) 24 (42.1) 1 94 (48.7) 68 (60.7) 28 (43.1) 26 (45.6) 2 10 (5.2) 15 (13.4) 16 (24.6) 7 (12.3) 3 1 (0.5) 5 (4.5) 7 (10.8) 0 (0.0) 4 0 (0.0) 0 (0.0) 1 (1.5) 0 (0.0) FVC, forced vital capacity; FEV1, forced expiratory volume in one second; IQR, interquartile range; mMRC, modified Medical Research Council; PS, Performance Status. *: Reference: FEV1/FVC < 70% and 50 ≤ %FEV1. † : Chi-square test for nominal scale variables, Wilcoxon rank sum test for ordered categorical variables. ‡ : Numbers of patients (FEV1/FVC < 70% and 50 ≤ %FEV1, FEV1/FVC < 70% and 30 ≤ %FEV1 < 50, FEV1/FVC < 70% and %FEV1 < 30, and FEV1/FVC ≥ 70%, respectively) were as follows: mMRC (N=143, 103, 57, 45). § : P < 0.05 with Dunnett’s test, Chi-square or Wilcoxon rank sum test. TABLE 3 Baseline CT and Pulmonary function test of participants, according to pulmonary function categories at registration FEV1/FVC < 70% FEV1/FVC ≥ 70% 50 ≤ %FEV1 (n = 193) 30 ≤ %FEV1 < 50 (n = 112) %FEV1 < 30 (n = 65) Dunnett test* (n = 57) P-value † LAA (%), median (IQR) 43.8 (34.1-54.4) 49.2 (40.5-57.7) § 62.8 (55.6-69.0) § 0.005/ <0.001 33.9 (28.0-50.8) § 0.008 TLC (L), median (IQR) 5.20 (4.41-5.88) 5.13 (4.30-5.98) 5.66 (5.24-6.56) § 0.786/ <0.001 4.73 (3.98-5.44) § 0.017 %TLC (%), median (IQR) 97.1 (85.3-108.1) 98.9 (88.3-109.9) 109 (99.2-120.3) § 0.900/ <0.001 89.2 (77.6-103.5) § 0.011 VC (L), median (IQR) ‡ 3.40 (2.85-3.88) 2.64 (2.10-3.07) § 2.13 (1.88-2.60) § <0.001/ <0.001 3.02 (2.50-3.41) § <0.001 %VC (%), median (IQR) ‡ 100.0 (88.6-110.8) 80.5 (70.7-89.5) § 65.8 (55.9-75.4) § <0.001/ <0.001 88.1 (72.9-97.7) § <0.001 FVC (L), median (IQR) 3.23 (2.75-3.81) 2.39 (1.98-2.95) § 1.80 (1.43-2.22) § <0.001/ <0.001 2.87 (2.31-3.27) § <0.001 FEV1 (L), median (IQR) 1.79 (1.49-2.13) 1.00 (0.85-1.17) § 0.61 (0.51-0.69) § <0.001/ <0.001 2.16 (1.81-2.45) § <0.001 %FEV1 (%), median (IQR) 68.1 (58.5-80.1) 40.5 (35.3-45.0) § 23.0 (19.9-25.5) § <0.001/ <0.001 84.3 (73.1-93.9) § <0.001 RV/TLC (%), median (IQR) ‡ 35.7 (26.6-42.4) 46.6 (41.7-55.1) § 61.8 (56.9-67.8) § <0.001/ <0.001 36.9 (28.4-46.3) 0.175 %RV (%), median (IQR) ‡ 99.8 (76.5-119.9) 132.7 (116.5-151.5) § 177.9 (163.3-191.6) § <0.001/ <0.001 108.2 (80.0-128.3) 0.132 FVC, forced vital capacity; FEV1, forced expiratory volume in one second; IQR, interquartile range; VC, vital capacity; TLC, total lung capacity; RV, residual volume; LAA, low attenuation area. *: Reference: FEV1/FVC < 70% and 50 ≤ %FEV1. † : Wilcoxon rank sum test. ‡ : Numbers of patients (FEV1/FVC < 70% and 50 ≤ %FEV1, FEV1/FVC < 70% and 30 ≤ %FEV1 < 50, FEV1/FVC < 70% and %FEV1 < 30, and FEV1/FVC ≥ 70%, respectively) were as follows: VC, %VC, RV/TLC and %RV/TLC (N=193, 111, 64, 57). § : P < 0.05 with Dunnett’s test or Wilcoxon rank sum test. TABLE 4 Baseline treatment of participants, according to pulmonary function categories at registration FEV1/FVC < 70% FEV1/FVC ≥ 70% 50 ≤ %FEV1 (n = 193) 30 ≤ %FEV1 < 50 (n = 112) %FEV1 < 30 (n = 65) Dunnett test * (n = 57) P-value † Medication, n (%) No 16 (8.3) 4 (3.6) 1 (1.5) 0.159/0.080 9 (15.8) 0.097 Yes 177 (91.7) 108 (96.4) 64 (98.5) 48 (84.2) Use of inhaler, n (%) § § No 25 (13.0) 4 (3.6) 3 (4.6) 0.009/0.070 10 (17.5) 0.380 Yes 168 (87.0) 108 (96.4) 62 (95.4) 47 (82.5) ICS, n (%) 5 (2.6) 10 (8.9) § 10 (15.4) § 0.060/<0.001 1 (1.8) 0.717 LABA, n (%) 46 (23.8) 13 (11.6) 14 (21.5) 0.019/0.897 17 (29.8) 0.360 ICS/LABA, n (%) 68 (35.2) 65 (58.0) § 40 (61.5) § <0.001/ <0.001 20 (35.1) 0.984 LAMA, n (%) 109 (56.5) 69 (61.6) 50 (76.9) § 0.593/0.006 24 (42.1) 0.056 LABA/LAMA, n (%) 17 (8.8) 16 (14.3) 3 (4.6) 0.218/0.530 4 (7.0) 0.668 Theophylline, n (%) 25 (13.0) 26 (23.2) 31 (47.7) § 0.058/<0.001 5 (8.8) 0.393 Macrolide, n (%) 11 (5.7) 10 (8.9) 8 (12.3) 0.500/0.162 3 (5.3) 0.900 LTRA, n (%) 9 (4.7) 19 (17.0) § 10 (15.4) § 0.001/0.020 7 (12.3) 0.039 Tulobuterol tape, n (%) 1 (0.5) 3 (2.7) 0 (0.0) 0.147/0.921 1 (1.8) 0.357 § HOT, n (%) 7 (3.6) 20 (17.9) § 38 (58.5) § <0.001/ <0.001 3 (5.3) 0.580 FVC, forced vital capacity; FEV1, forced expiratory volume in one second; IQR, interquartile range; ICS, inhaled corticosteroid; LABA, long-acting beta-antagonist; LAMA, long-acting muscarinic antagonist; LTRA, leukotriene receptor antagonist; HOT, home oxygen therapy. *: Reference: FEV1/FVC < 70% and 50 ≤ %FEV1. † : Chi-square test. § : P < 0.05 with Dunnett’s test, or Chi-square test. TABLE 5 Cause of death of participants, according to pulmonary function categories at registration FEV1/FVC < 70% FEV1/FVC ≥ 70% Total Cause of death 50 ≤ %FEV1 (n = 193) 30 ≤ %FEV1 < 50 (n = 112) %FEV1 < 30 (n = 65) (n = 57) (n = 427) All-cause, n (%) 11 17 21 9 58 Respiratory disease, n (%) 4 (36.4) 7 (41.2) 12 (57.1) 2 (22.2) 25 (43.1) Lung cancer, n (%) 2 (18.2) 2 (11.8) 0 (0.0) 2 (22.2) 6 (10.3) Cancer other than lung cancer, n (%) 0 (0.0) 2 (11.8) 2 (9.5) 2 (22.2) 6 (10.3) Non-respiratory infection, n (%) 1 (9.1) 1 (5.9) 0 (0.0) 1 (11.1) 3 (5.2) Cardiovascular disease, n (%) 1 (9.1) 1 (5.9) 0 (0.0) 0 (0.0) 2 (3.4) Others, n (%) 0 (0.0) 2 (11.8) 3 (14.3) 0 (0.0) 5 (8.6) Unknown, n (%) 3 (27.3) 2 (11.8) 4 (19.0) 2 (22.2) 11 (19.0) FVC, forced vital capacity; FEV1, forced expiratory volume in one second. Additional Declarations Competing interest reported. STakata has received personal fees from AstraZeneca, Nippon Boehringer Ingelheim, Novartis, GSK, and Teijin Pharma. KY has received personal fees from Nippon Boehringer Ingelheim, GSK and Teijin Pharma. MO has received personal fees from Nippon Boehringer Ingelheim. IO has received personal fees from AstraZeneca, Nippon Boehringer Ingelheim and Novartis. 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disease.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4043733/v1/3e30ac6cb711d14c9234cc9d.jpg"},{"id":52620009,"identity":"0066703d-12b9-4df7-83de-6ae0e839224c","added_by":"auto","created_at":"2024-03-13 16:42:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":864021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier plots of survival probability according to baseline lung function categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFVC, forced vital capacity; FEV1, forced expiratory volume in one second.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4043733/v1/61d7959336ee3848111aaff9.jpg"},{"id":68750108,"identity":"6e6339ee-0a9a-44ed-bebb-f20a5f668baf","added_by":"auto","created_at":"2024-11-11 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16:34:13","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":30509,"visible":true,"origin":"","legend":"","description":"","filename":"SupportinginformationTable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-4043733/v1/dfc946fedb2f0f7c62a1b5d2.docx"},{"id":52619022,"identity":"a00dd170-730e-4c52-b9f6-ed7db8b846d1","added_by":"auto","created_at":"2024-03-13 16:34:13","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":34800,"visible":true,"origin":"","legend":"","description":"","filename":"SupportinginformationFIGURES1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4043733/v1/ec9ed27fe28431d0ff38433c.pdf"}],"financialInterests":"Competing interest reported. STakata has received personal fees from AstraZeneca, Nippon Boehringer Ingelheim, Novartis, GSK, and Teijin Pharma. KY has received personal fees from Nippon Boehringer Ingelheim, GSK and Teijin Pharma. MO has received personal fees from Nippon Boehringer Ingelheim. IO has received personal fees from AstraZeneca, Nippon Boehringer Ingelheim and Novartis.","formattedTitle":"Prognosis and management of chronic obstructive pulmonary disease in a real-world setting: A 5-year follow-up analysis of a multi-institutional registry","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic obstructive pulmonary\u0026nbsp;disease (COPD) is the third leading cause of death worldwide, and its incidence is expected to increase.[1]\u0026nbsp;However, its prognosis has improved with treatment such as long-term oxygen therapy for patients with resting hypoxemia, non-invasive ventilation, and inhaled corticosteroids for patients with low pulmonary function and frequent exacerbations.[2\u0026ndash;6]\u0026nbsp;This suggests that COPD prognosis and treatment can change over time. A comparison of two independent cohorts in the 1990s and the mid-2000s revealed that the latter group had better prognoses in severe cases of COPD.[7,8]\u0026nbsp;However, there are no data on the prognosis of COPD in real-world settings after 2010s in Japan. Currently, Japan is experiencing the most advanced demographic aging worldwide, and other countries are predicted to follow soon.[9]\u0026nbsp;Therefore, the Japanese data on COPD prognosis and management can act as a reference for other countries with aging populations, especially in East Asia.\u003c/p\u003e\n\u003cp\u003eThe diagnosis of COPD requires a forced expiratory volume\u0026nbsp;in\u0026nbsp;one second (FEV1) / forced vital capacity (FVC) \u0026lt; 0.7, signifying airflow limitation (AFL). However, several smokers reportedly experience respiratory symptoms without AFL.\u0026nbsp;Individuals who do not meet the definition of an obstructive pattern and are associated with decreased FEV1, termed preserved ratio impaired spirometry (PRISm), reportedly have higher mortality than those with normal lung function and even mild COPD in community-based observational studies.[12\u0026ndash;14]\u0026nbsp;These data highlight the importance of focusing on\u0026nbsp;populations that do not meet the COPD criteria.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, in this study, we analysed a multi-centre prospective cohort to elucidate the prognosis and treatment of COPD in a real-world setting in the mid-2010s. We also analysed the prognosis and clinical characteristics of patients diagnosed with COPD, including those without AFL.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a prospective and multicentre observational study of patients with COPD and idiopathic interstitial pneumonias (IIPs) from 29 centres who were followed up longitudinally for 5 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy patients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main inclusion criteria were age \u0026gt; 20 years and diagnosis of COPD or IIPs. The diagnosis of COPD was made by clinicians based on the symptoms and clinical data\u0026nbsp;described in the\u0026nbsp;international guideline.[15]\u0026nbsp;However, no exclusion criteria for pulmonary function tests were established at enrolment. The diagnosis of IIPs was made as previously described through a central multidisciplinary discussion (MDD).[16]\u0026nbsp;Patients (regardless of whether newly or previously diagnosed) were sequentially enrolled after a diagnosis of COPD or IIPs by respiratory specialists at the participating facilities. The total planned enrolment number was set at 1000 without statistical sample size calculations. A total of 1024 patients were recruited between September 1, 2013, and April 30, 2016, eight of whom were subsequently excluded on the basis of patient refusal (n = 4), missing test results (n = 2), failure to meet the inclusion criteria (n = 1), or an unknown reason (n = 1). The remaining 1016 patients were enrolled in the study.\u0026nbsp;A total of 461 patients were diagnosed with COPD and 543 patients were diagnosed with IIPs.[16]\u003c/p\u003e\n\u003cp\u003eFollow-up surveillance was conducted annually for 5 years to evaluate exacerbation and death. Exacerbation was determined by a clinician based on international criteria[15] and worsening of dyspnoea due to other causes, including bacterial pneumonia, was excluded. All examinations and investigations were performed as part of routine care for each patient at the physician\u0026rsquo;s discretion and no additional visits or investigations were mandated for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePulmonary function tests and CT\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePredicted FEV1 and VC were calculated using a reference equation reported by the Japanese Respiratory Society (JRS) in 2001.[17] The predicted total lung capacity (TLC) and residual volume (RV) were calculated using the JRS prediction formula as standard values. To determine the volume of emphysema, the volume of low-attenuation areas (LAA) on 5-mm collimation computed tomography (CT) scans was calculated with a threshold of -910 Hounsfield units as previously described.[18] Dedicated software programs (3DSlicer; http://www.slicer.org) were used to quantify LAA. Total lung capacity by CT (TLCct) was calculated using inspiratory CT, and TLCct-VC was obtained as a surrogate value for RV; the ratio of TLCct-VC to predicted RV was calculated as %RV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformed consent\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective, multicentre, observational study was approved by the Institutional Review Board of Kyushu University (#25-135, August 23, 2013; #555-00, August 27, 2013) and by the institutional review boards of all participating hospitals. Written informed consent was obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatient and Public involvement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cumulative proportion of survival was estimated using the Kaplan\u0026ndash;Meier analysis. Hazard ratios (HR) were estimated using a Cox regression model to evaluate the association between background characteristics and survival. In the analysis of 370 patients with AFL, statistical comparisons between groups were made with the 50 \u0026le; %FEV1 with AFL group as the reference group. Clinical characteristics of the 57 patients without AFL were evaluated by statistical comparison with 50 \u0026le; %FEV1 with AFL group. A backward stepwise method was adopted with the significance level for removal set at 0.05 to select a model to predict survival. Statistical significance was set at p \u0026lt; 0.05. All statistical analyses were performed using the SAS version 9.4 (SAS Institute, Cary, NC, USA).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy subjects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween September 2013 and April 2016, 461 patients were enrolled based on clinical data and symptoms. Among them, five with inadequate imaging, nine who underwent lobectomy, and 20 with missing follow-up data after initial enrolment were excluded from the analysis; the remaining 427 patients were analysed (Figure 1). The median age of the total population was 72 years, with 87.1% being male, 97.4% having a history of smoking, and 99.5% presenting more than 5% LAA on CT. In total, 370 patients (86.7%) in the COPD cohort showed AFL (defined as FEV1/FVC \u0026lt; 70%), while 57 (13.3%) had no AFL. Upon categorizing the 370 patients with AFL according to %FEV1, 193 patients had 50 \u0026le; %FEV1, 112 had 30 \u0026le; %FEV1 \u0026lt; 50, and 65 had %FEV1 \u0026lt; 30 (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCharacteristics of the FEV1/FVC \u0026lt; 70% population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 370 patients with AFL, the most common comorbidity was hypertension, which accounted for 40.8% of all patients. Past history of bronchial asthma accounted for 17.8% of patients with AFL and was more frequent in the 30 \u0026le; %FEV1 \u0026lt; 50 group. Dyspnoea, as assessed using the modified Medical Research Council (mMRC) score, was significantly higher in patients with severe AFL. Sputum and weight loss were more frequent in the %FEV1 \u0026lt; 30 group, and body mass index was also lower in the %FEV1 \u0026lt; 30 group. %VC at registration was preserved in the 50 \u0026le; %FEV1 and 30 \u0026le; %FEV1 \u0026lt; 50 groups, while it decreased in the %FEV1 \u0026lt; 30 group. Additionally, RV/TLC, %RV, and LAA increased with increasing severity. The blood test showed no significant differences between the groups except for the white blood cell count (Table 1, 2, 3 and S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding treatment, 87% of the 50 \u0026le; %FEV1 group, 96.4% of the 30 \u0026lt; %FEV1 \u0026lt; 50 group, and 98.5% of the %FEV1 \u0026lt; 30 group used inhaled drugs. The most commonly used medication was a long-acting muscarinic antagonist (LAMA). Inhaled corticosteroids (ICS) were used by 37.8% patients in the 50 \u0026le; %FEV1 group, 66.9% patients in the 30 \u0026le; %FEV1 \u0026lt; 50 group, and 76.9% patients in the %FEV1\u0026lt; 30 group. Home oxygen therapy was used in 58.5% of the %FEV1 \u0026lt; 30 group (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5-year survival and exacerbation among the FEV1/FVC \u0026lt; 70% population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 370 patients with AFL, 266 completed the 5-year follow-up while 58 died during the 1556.7 person-year observation period (Figure S1). The 5-year survival rates of all patients with AFL were 85.2%, 93.4% in the 50 \u0026le; %FEV1 group, 82.5% in the 30 \u0026le; %FEV1 \u0026lt; 50 group, and 66.1% in the %FEV1 \u0026lt; 30 group (Figure 2). The HR for mortality adjusted for age, sex, body mass index (BMI), and smoking (pack-year) was 2.37 (95%CI: 1.07\u0026ndash;5.23) in the 30 \u0026le; %FEV1 \u0026lt; 50 group and 7.56 (95%CI: 3.43\u0026ndash;16.65) in the %FEV1 \u0026lt; 30 group considering the 50 \u0026le; %FEV1 group as reference (Table S2). The most common cause of death was respiratory diseases other than lung cancer, accounting for 46.9% of all deaths, with the frequency increasing in groups with lower %FEV1. Deaths associated with lung and non-lung cancer malignancies each accounted for 8.2% of all deaths. In contrast, cardiovascular death occurred in only two cases (Table 5). The percentages of patients having at least one exacerbation and more than two exacerbations in five years were as follows: 8.8% and 4.7% in the 50 \u0026le; %FEV1 group, 31.3% and 11.6% in the 30 \u0026le; %FEV1 \u0026lt; 50 group, and 63.1% and 35.4% in the %FEV1 \u0026lt; 30 group, respectively (Table S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrognostic factors for mortality of FEV1/FVC \u0026lt; 70% population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictors of mortality in patients with FEV1/FVC \u0026lt; 70% were examined using a simple Cox regression analysis of baseline factors (Table S4).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIn multivariate Cox regression analysis (stepwise variable selection), age, number of comorbidities, performance status, weight loss, C-reactive protein level, and % RV were independently associated with mortality (Table S5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCharacteristics of patients without airflow limitation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 57 patients (13.3%) were FEV1/FVC \u0026ge; 70% at the time of enrolment.\u0026nbsp;This population was also analysed retrospectively for mortality and clinical characteristics.\u0026nbsp;Their 5-year survival rate was 83.0%, which was\u0026nbsp;significantly lower than that of the 50 \u0026le; %FEV1 with AFL group (adjusted HR of 2.67 with a 95% CI of\u0026nbsp;1.06\u0026ndash;6.73) and similar to that of the 30 \u0026le; %FEV1 \u0026lt; 50 group (Figure 2 and\u0026nbsp;Table S2).\u003c/p\u003e\n\u003cp\u003eNo differences in age, sex, smoking prevalence, and respiratory symptoms were found between the FEV1/FVC \u0026ge; 70% and 50 \u0026le; %FEV1 with AFL groups. The FEV1/FVC \u0026ge; 70% group had lower BMI, VC and TLC, and certain area of LAA. Treatment in the FEV1/FVC \u0026ge; 70% group was similar to that of 50 \u0026le; %FEV1 with AFL group, and 82.5% used inhaler at registration (Table 1-4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a 5-year survival analysis of a cohort of patients with clinically diagnosed COPD. We found that 13% of patients in this cohort did not show AFL at registration with respiratory symptoms and low VC and TLC, and had worse prognosis than patients in the 50 \u0026le; %FEV1 with AFL group.\u003c/p\u003e\n\u003cp\u003eThe main analysis included 370 patients with AFL at registration and presented the clinical practice and prognosis of COPD in Japan in the mid-2010s. Our cohort exhibited a high prevalence of males and low BMI, similar to previous Japanese reports.[19\u0026ndash;21] In the 1990s, a prospective study in Japan reported 5-year survival rates of approximately 90% for patients with 50 \u0026le; %FEV1, 80% for patients with 30 \u0026le; %FEV1\u0026lt;50, and 60% for patients with %FEV1 \u0026lt; 30.[22] Although we were unable to statistically compare our cohort with previous ones, the 5-year survival rates of the 30 \u0026le; %FEV1 \u0026lt; 50 and %FEV1 \u0026lt; 30 groups in our cohort were numerically better, regardless of the high median age of this cohort, which may be attributed to advancements in treatment and healthcare. All prognostic factors at registration revealed in this cohort study have been previously reported. Life expectancy in Japan increased from 79.6 to 83.9 years in the 20 years between 1995 and 2015, as observed in other countries.[9] Thus, the mean age of our cohort was 71.7 years, and approximately 20% of them were over 80 years of age, representing the real world scenario in Japan, which is a super-aging society.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, 91.4% patients used inhaler and 53.5% used ICS in all the cases with AFL. The high prevalence ICS use may be because concomitant ICS use was recommended for patients with COPD with %FEV1 \u0026lt; 50 or repeated exacerbations in 2013.[15]\u0026nbsp;Furthermore, this cohort included patients with a history of bronchial asthma. Nonetheless, the exacerbation rate was low in our cohort. Recent randomized controlled trials have defined exacerbations by the use of antibiotics or oral corticosteroids.[23]\u0026nbsp;However, in our study, isolated lower respiratory tract infections were excluded from exacerbations. This difference in definition may have contributed to the lower frequency observed in our cohort. Moreover, lower exacerbation rates have been reported in the Japanese population, suggesting potential racial differences in exacerbation frequencies.[24]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this cohort, 13% patients did not have AFL and were treated as having COPD. The majority of this population had a history of heavy smoking, 30% had dyspnoea on exertion with mMRC: \u0026ge; 2, and 60% had wet cough. On examination, VC and TLCct were low and FEV1 was preserved with constant emphysema. 84% of this population underwent respiratory treatment at the time of registration. However, pulmonary function test results at the time of diagnosis were not available. Some longitudinal observational studies have reported that approximately 10% of patients with obstructive patterns in pulmonary function tests become free of obstructive patterns after annual follow-up.[12\u0026ndash;14]\u0026nbsp;Therefore, it is possible that a part of the FEV1/FVC \u0026ge; 70% group met the definition of COPD at the time of diagnosis. However, considering high %FEV1 and low TLCct and VC in this group, most of FEV1/FVC \u0026ge; 70% population were thought to be clinically diagnosed with COPD even without AFL. In the SPIROMICS and COPDGene studies, two large longitudinal observational studies, 23\u0026ndash;50% of current and former smokers with normal spirometry had respiratory symptoms, and 20\u0026ndash;42% received treatment interventions.[10, 11]\u0026nbsp;Similarly,\u0026nbsp;a meta-analysis showed that approximately one-fourth of patients treated for COPD in primary healthcare settings did not show obstructive impairment on spirometry.[25]\u0026nbsp;Consistent with these studies, our study indicates the presence of a population with respiratory symptoms that clinicians treat as COPD, even without AFL in real world settings.\u003c/p\u003e\n\u003cp\u003eThe FEV1/FVC \u0026ge; 70% group showed poor prognosis. Populations with restrictive spirometric patterns have higher mortality rates,[26\u0026ndash;28]\u0026nbsp;partially because of the inclusion of interstitial pneumonia. In our cohort, patients with interstitial pneumonia, including CPFE, were excluded through a MDD.[16, 29]\u0026nbsp;Among 57\u0026nbsp;cases in FEV1/FVC \u0026ge; 70% group in our cohort, 22 cases met the criteria for PRISm (data not shown), which has been reported to have higher mortality than that with mild COPD in community-based observational studies.\u0026nbsp;Additionally,\u0026nbsp;respiratory symptoms and the extent of emphysema are associated with poor prognosis, even in smokers without AFL.[28, 30\u0026ndash;32]\u0026nbsp;Therefore, poor prognosis of the FEV1/FVC \u0026ge; 70% group may be because of the considerable rate of respiratory symptoms, imaging abnormalities, and low VC, all of which are reportedly associated with mortality. Therefore, our study suggests the importance of multidimensional evaluation of patients with respiratory symptoms, including pulmonary function tests and imaging.\u003c/p\u003e\n\u003cp\u003eThe limitations of this study were that a part of the enrolled patients were not newly diagnosed, lung function was not confirmed at the time of diagnosis, airway reversibility was not assessed at the time of enrolment, and the time of diagnosis was not recorded. Moreover, only patients who provided informed consent, and not consecutive patients, were enrolled. Finally, despite the execution of annual protocolized assessments, some data were missing.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ewe present the 5-year survival and real-world clinical practice data of a prospective cohort of patients clinically diagnosed with COPD in Japan in the mid-2010s. Thirteen percent of the patients in this cohort did not show AFL at registration with respiratory symptoms, distinct spirometric patterns, and poor prognosis.\u0026nbsp;\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003echronic obstructive pulmonary disease (COPD), multidisciplinary discussion (MDD), forced expiratory volume 1 second (FEV1), forced vital capacity (FVC), airflow limitation (AFL), preserved ratio impaired spirometry (PRISm), idiopathic interstitial pneumonias (IIPs), Japanese Respiratory Society (JRS), total lung capacity (TLC), residual volume (RV), low attenuation areas (LAA), computed tomography (CT), Total lung capacity by CT (TLCct), Hazard ratio (HR), modified Medical Research Council (mMRC), long-acting muscarinic antagonist (LAMA), Inhaled corticosteroids (ICS), Home oxygen therapy (HOT), performance status (PS), body mass index (BMI)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involves human participants and this prospective, multicentre observational study was approved by the Institutional Review Board of Kyushu University (#25-135, 23 August 2013; #555-00, 27 August 2013) as well as by the institutional review boards of all participating hospitals. Participants provided informed consent before participating in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable since there are no details on individuals reported within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTakata has received personal fees from AstraZeneca, Nippon Boehringer Ingelheim, Novartis, GSK, and Teijin Pharma. KY has received personal fees from Nippon Boehringer Ingelheim, GSK and Teijin Pharma. MO has received personal fees from Nippon Boehringer Ingelheim. IO has received personal fees from AstraZeneca, Nippon Boehringer Ingelheim and Novartis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a grant from the Ministry of Education, Culture, Sports, Science and Technology: the broad-area, network-based project to drive clinical research at Kyushu University Hospital, a grant from by Boehringer Ingelheim, and a grant to the Diffuse Lung Diseases Research Group from the Ministry of Health, Labor and Welfare, Japan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTT, KTsubouchi and IO contributed to the literature search, figures, the study design, data collection, data analysis, data interpretation, and writing approved the final version of the review. NH and YN contributed to the literature search, study design, data collection, and data interpretation and approved the final version of the review. FK and STokunaga contributed to the literature search, figures, data analysis, and writing and approved the final version of the review.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKI, RT, STakata, SK, NN, MY, YK, KTobino, EH, HI, HW, TM, MF, KY and MO contributed to the literature search, data collection, and data interpretation and approved the final version of the review. \u0026nbsp;HY contributed to the literature search, data analysis, and writing, and approved the final version of the review. IO accepts full responsibility for the work and/or conduct of the study, has access to the data and controls the decision to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the patients, their families, and all the investigators participating in the Fukuoka Tobacco-Related Lung Disease (FOLD) registry group. The authors would like to thank the Clinical Research Support Center of Kyushu for their official work on the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization: The top 10 causes of death. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death.\u003c/li\u003e\n\u003cli\u003eCranston JM, Crockett AJ, Moss JR, Alpers JH. Domiciliary oxygen for chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2005;2005:CD001744.\u003c/li\u003e\n\u003cli\u003eTashkin DP, Celli B, Senn S, Burkhart D, Kesten S, Menjoge S, et al. A 4-Year Trial of Tiotropium in Chronic Obstructive Pulmonary Disease. N Engl J Med. 2008;359:1543\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eLipson DA, Crim C, Criner GJ, Day NC, Dransfield MT, Halpin DMG, et al. Reduction in All-Cause Mortality with Fluticasone Furoate/Umeclidinium/Vilanterol in Patients with Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2020;201:1508\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eRabe KF, Martinez FJ, Ferguson GT, Wang C, Singh D, Wedzicha JA, et al. Triple Inhaled Therapy at Two Glucocorticoid Doses in Moderate-to-Very-Severe COPD. N Engl J Med. 2020;383:35\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eMurphy PB, Rehal S, Arbane G, Bourke S, Calverley PMA, Crook AM, et al. Effect of Home Noninvasive Ventilation With Oxygen Therapy vs Oxygen Therapy Alone on Hospital Readmission or Death After an Acute COPD Exacerbation: A Randomized Clinical Trial. JAMA. 2017;317:2177\u0026ndash;86.\u003c/li\u003e\n\u003cli\u003eAlmagro P, Salvado M, Garcia-Vidal C, Rodriguez-Carballeira M, Delgado M, Barreiro B, et al. Recent improvement in long-term survival after a COPD hospitalisation. Thorax. 2010;65:298\u0026ndash;302.\u003c/li\u003e\n\u003cli\u003eSato S, Oga T, Muro S, Tanimura K, Tanabe N, Nishimura K, et al. Changes in mortality among patients with chronic obstructive pulmonary disease from the 1990s to the 2000s: a pooled analysis of two prospective cohort studies. BMJ Open. 2023;13:e065896.\u003c/li\u003e\n\u003cli\u003eHealth Status. https://stats.oecd.org/index.aspx?queryid=24879#. Accessed 6 Nov 2023.\u003c/li\u003e\n\u003cli\u003eRegan EA, Lynch DA, Curran-Everett D, Curtis JL, Austin JHM, Grenier PA, et al. Clinical and Radiologic Disease in Smokers With Normal Spirometry. JAMA Intern Med. 2015;175:1539.\u003c/li\u003e\n\u003cli\u003eWoodruff PG, Couper D, Kanner RE, Rennard S. Clinical Significance of Symptoms in Smokers with Preserved Pulmonary Function. n engl j med. 2016.\u003c/li\u003e\n\u003cli\u003eWijnant SRA, De Roos E, Kavousi M, Stricker BH, Terzikhan N, Lahousse L, et al. Trajectory and mortality of preserved ratio impaired spirometry: the Rotterdam Study. Eur Respir J. 2020;55:1901217.\u003c/li\u003e\n\u003cli\u003eWan ES, Hokanson JE, Regan EA, Young KA, Make BJ, DeMeo DL, et al. Significant Spirometric Transitions and Preserved Ratio Impaired Spirometry Among Ever Smokers. Chest. 2022;161:651\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eWashio Y, Sakata S, Fukuyama S, Honda T, Kan-o K, Shibata M, et al. Risks of Mortality and Airflow Limitation in Japanese Individuals with Preserved Ratio Impaired Spirometry. Am J Respir Crit Care Med. 2022;206:563\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eVestbo J, Hurd SS, Agust\u0026iacute; AG, Jones PW, Vogelmeier C, Anzueto A, 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;187:347\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eTsubouchi K, Hamada N, Tokunaga S, Ichiki K, Takata S, Ishii H, et al. Survival and acute exacerbation for patients with idiopathic pulmonary fibrosis (IPF) or non-IPF idiopathic interstitial pneumonias: 5-year follow-up analysis of a prospective multi-institutional patient registry. BMJ Open Respir Res. 2023;10:e001864.\u003c/li\u003e\n\u003cli\u003eSpecial Committee of Pulmonary Physiology, The Japanese Respiratory Society. Standard values of spirogram and arterial blood gas in normal Japanese subjects. J Jpn Respir Soc. 2001;39:S1\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller NL, Staples CA, Miller RR, Abboud RT. \u0026ldquo;Density mask\u0026rdquo;. An objective method to quantitate emphysema using computed tomography. Chest. 1988;94:782\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eIchinose M, Taniguchi H, Takizawa A, Gr\u0026ouml;nke L, Loaiza L, Vo\u0026szlig; F, et al. The efficacy and safety of combined tiotropium and olodaterol via the Respimat(\u0026reg;) inhaler in patients with COPD: results from the Japanese sub-population of the Tonado(\u0026reg;) studies. Int J Chron Obstruct Pulmon Dis. 2016;11:2017\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eIchinose M, Nishimura M, Akimoto M, Kurotori Y, Zhao Y, de la Hoz A, et al. Tiotropium/olodaterol versus tiotropium in Japanese patients with COPD: results from the DYNAGITO study. Int J Chron Obstruct Pulmon Dis. 2018;13:2147\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eFukuchi Y, Fernandez L, Kuo H-P, Mahayiddin A, Celli B, Decramer M, et al. Efficacy of tiotropium in COPD patients from Asia: a subgroup analysis from the UPLIFT trial. Respirology. 2011;16:825\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eOga 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\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eHurst JR, Vestbo J, Anzueto A, Locantore N, M\u0026uuml;llerova H, Tal-Singer R, et al. Susceptibility to Exacerbation in Chronic Obstructive Pulmonary Disease. N Engl J Med. 2010;363:1128\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eIshii T, Nishimura M, Akimoto A, James M, Jones P. Understanding low COPD exacerbation rates in Japan: a review and comparison with other countries. COPD. 2018;Volume 13:3459\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003ePerret J, Yip SWS, Idrose NS, Hancock K, Abramson MJ, Dharmage SC, et al. Undiagnosed and \u0026lsquo;overdiagnosed\u0026rsquo; COPD using postbronchodilator spirometry in primary healthcare settings: a systematic review and meta-analysis. BMJ Open Resp Res. 2023;10:e001478.\u003c/li\u003e\n\u003cli\u003eGuerra S, Sherrill DL, Venker C, Ceccato CM, Halonen M, Martinez FD. Morbidity and mortality associated with the restrictive spirometric pattern: a longitudinal study. Thorax. 2010;65:499\u0026ndash;504.\u003c/li\u003e\n\u003cli\u003eMannino DM. Lung function and mortality in the United States: data from the First National Health and Nutrition Examination Survey follow up study. Thorax. 2003;58:388\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eMannino DM, Doherty DE, Sonia Buist A. Global Initiative on Obstructive Lung Disease (GOLD) classification of lung disease and mortality: findings from the Atherosclerosis Risk in Communities (ARIC) study. Respiratory Medicine. 2006;100:115\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eOgata-Suetsugu S, Hamada N, Tsuda T, Takata S, Kitasato Y, Inoue N, et al. Characteristics of tobacco-related lung diseases in Fukuoka Prefecture, Japan: A prospective, multi-institutional, observational study. Respiratory Investigation. 2020;58:74\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eStavem K, Sandvik L, Erikssen J. Can Global Initiative for Chronic Obstructive Lung Disease Stage 0 Provide Prognostic Information on Long-term Mortality in Men? Chest. 2006;130:318\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eJohannessen A, Skorge TD, Bottai M, Grydeland TB, Nilsen RM, Coxson H, et al. Mortality by level of emphysema and airway wall thickness. Am J Respir Crit Care Med. 2013;187:602\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eOelsner EC, Hoffman EA, Folsom AR, Carr JJ, Enright PL, Kawut SM, et al. Association Between Emphysema-like Lung on Cardiac Computed Tomography and Mortality in Persons Without Airflow Obstruction: A Cohort Study. Ann Intern Med. 2014;161:863.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1 Baseline characteristics of participants, according to pulmonary function categories at registration\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"870\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.3134328358209%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC\u0026nbsp;\u0026lt;\u0026nbsp;70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC\u0026nbsp;\u0026ge;\u0026nbsp;70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N\u0026nbsp;=\u0026nbsp;193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e30\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u0026nbsp;\u0026lt;\u0026nbsp;50\u003c/p\u003e\n \u003cp\u003e(N\u0026nbsp;=\u0026nbsp;112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e%FEV1\u0026nbsp;\u0026lt;\u0026nbsp;30\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N\u0026nbsp;=\u0026nbsp;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003eDunnett test*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N\u0026nbsp;=\u0026nbsp;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eAge (year.), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e72 (67-76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e73 (67-78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e71 (65-75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.362/0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e71 (66-78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e72\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(66-77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e170 (88.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e94 (83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e59 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.491/0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e49 (86.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e372 (87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e23 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e18 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e6 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e8 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e55 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e22.5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(20.2-24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e22.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(19.7-24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e19.6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(17.1-22.2) \u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.371/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e21.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(17.9-24.2)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e21.9\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(19.2-24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(pack-years), median (IQR) \u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e48.5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(30.0-80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e50.0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(40.0-72.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e57.5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(40.0-80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.981/0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e48.0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(31.0-80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e50\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(36.5-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eNever, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e4 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.476/0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e3 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e11 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eFormer, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e136 (70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e86 (76.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e48 (73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e41 (71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e72.8 (72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e53 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e24 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e15 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e13 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e24.5 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003ePast history of\u003c/p\u003e\n \u003cp\u003eBronchial asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e27 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e28 (25.0)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e11 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.030/0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e5 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e71 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eComorbidity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e27 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e12 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e10 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.650/0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e7 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e56 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eDyslipidaemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e33 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e14 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e5 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.449/0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e9 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e61 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e81 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e45 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e25 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.930/0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e28 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e179 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eHeart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e34 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e18 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e6 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.918/0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e12 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e70 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.51779563719862%\" valign=\"top\"\u003e\n \u003cp\u003eGERD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.1699196326062%\" valign=\"top\"\u003e\n \u003cp\u003e13 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.629161882893227%\" valign=\"top\"\u003e\n \u003cp\u003e9 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.51435132032147%\" valign=\"top\"\u003e\n \u003cp\u003e6 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.595866819747418%\" valign=\"top\"\u003e\n \u003cp\u003e0.893/0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.940298507462687%\" valign=\"top\"\u003e\n \u003cp\u003e4 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.544202066590127%\" valign=\"top\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.088404133180253%\" valign=\"top\"\u003e\n \u003cp\u003e32 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFVC, forced vital capacity; FEV1, forced expiratory volume in one second; IQR, interquartile range; BMI, body mass index; GERD, gastroesophageal reflux disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*: Reference: FEV1/FVC \u0026lt; 70% and 50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e: Chi-square test for nominal scale variables, Wilcoxon rank sum test for continuous variables.\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e:\u0026nbsp;Numbers of patients (FEV1/FVC \u0026lt; 70% and 50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1, FEV1/FVC \u0026lt; 70% and 30\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u0026lt;50, FEV1/FVC \u0026lt; 70% and %FEV1 \u0026lt; 30, and FEV1/FVC\u0026nbsp;\u0026ge;\u0026nbsp;70%, respectively) were as follows: Smoking (N=192, 110, 64, 57).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e: P\u0026lt;0.05 with Dunnett\u0026rsquo;s test, Chi-square, or Wilcoxon rank sum test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2 Baseline symptom and performance status according to pulmonary function categories at registration\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"862\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.67981438515081%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC\u0026nbsp;\u0026lt;\u0026nbsp;70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026ge; 70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e30\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u0026nbsp;\u0026lt;\u0026nbsp;50\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e%FEV1\u0026nbsp;\u0026lt;\u0026nbsp;30\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003eDunnett test*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003emMRC score, n (%) \u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e21 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e1 ( 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e7 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e69 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e32 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e13 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e23 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e38 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e36 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e12 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e6 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e13 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e18 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e20 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e9 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e11 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e11 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eSymptom, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e91 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e59 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e39 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e0.568/\u003c/p\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e35 (61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eSputum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e82 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e47 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e39 (60.0)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e0.994/\u003c/p\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e29 (50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eBody weight loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e5 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e9 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e18 (27.7)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e0.161/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e4 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eWheeze\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e11 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e8 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e9 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e0.868/\u003c/p\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eClubbed fingers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e19 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e11 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e4 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e0.999/\u003c/p\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e4 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003ePS, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e88 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e24 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e13 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e24 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e94 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e68 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e28 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e26 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e10 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e15 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e16 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e7 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e5 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e7 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFVC, forced vital capacity; FEV1, forced expiratory volume in one second; IQR, interquartile range; mMRC, modified Medical Research Council; PS, Performance Status.\u003c/p\u003e\n\u003cp\u003e*: Reference: FEV1/FVC \u0026lt; 70% and 50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e: Chi-square test for nominal scale variables, Wilcoxon rank sum test for ordered categorical variables.\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e:\u0026nbsp;Numbers of patients (FEV1/FVC \u0026lt; 70% and 50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1, FEV1/FVC \u0026lt; 70% and 30\u0026nbsp;\u0026le;\u0026nbsp;%FEV1 \u0026lt; 50, FEV1/FVC \u0026lt; 70% and %FEV1 \u0026lt; 30, and FEV1/FVC\u0026nbsp;\u0026ge;\u0026nbsp;70%, respectively) were as follows:\u0026nbsp;mMRC (N=143, 103, 57, 45).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e: P \u0026lt; 0.05 with Dunnett\u0026rsquo;s test, Chi-square or Wilcoxon rank sum test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3 Baseline CT and Pulmonary function test of participants, according to pulmonary function categories at registration\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"862\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.67981438515081%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC\u0026nbsp;\u0026lt;\u0026nbsp;70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026ge; 70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e30\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u0026nbsp;\u0026lt;\u0026nbsp;50\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e%FEV1\u0026nbsp;\u0026lt;\u0026nbsp;30\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003eDunnett test*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eLAA (%), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e43.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(34.1-54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e49.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(40.5-57.7)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e62.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(55.6-69.0)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e0.005/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e33.9\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(28.0-50.8)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eTLC (L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e5.20 (4.41-5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e5.13 (4.30-5.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e5.66 (5.24-6.56)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e0.786/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e4.73\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(3.98-5.44)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e%TLC (%), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e97.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(85.3-108.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e98.9\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(88.3-109.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e109\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(99.2-120.3)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e0.900/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e89.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(77.6-103.5)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eVC (L), median (IQR)\u003csup\u003e\u0026nbsp;\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e3.40\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.85-3.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e2.64\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.10-3.07)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e2.13\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.88-2.60)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e3.02\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.50-3.41)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e%VC (%), median (IQR)\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e100.0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(88.6-110.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e80.5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(70.7-89.5)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e65.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(55.9-75.4)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e88.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(72.9-97.7)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eFVC (L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e3.23\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.75-3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e2.39\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.98-2.95)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e1.80\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.43-2.22)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e2.87\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.31-3.27)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eFEV1 (L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e1.79\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.49-2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.85-1.17)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e0.61\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.51-0.69)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e2.16\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.81-2.45)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e%FEV1 (%),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e68.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(58.5-80.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e40.5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(35.3-45.0)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e23.0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(19.9-25.5)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e84.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(73.1-93.9)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003eRV/TLC (%),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emedian (IQR)\u003csup\u003e\u0026nbsp;\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e35.7\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(26.6-42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e46.6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(41.7-55.1)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e61.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(56.9-67.8)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e36.9\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(28.4-46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.893271461716937%\" valign=\"top\"\u003e\n \u003cp\u003e%RV (%),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emedian (IQR)\u003csup\u003e\u0026nbsp;\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.429234338747099%\" valign=\"top\"\u003e\n \u003cp\u003e99.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(76.5-119.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.241299303944317%\" valign=\"top\"\u003e\n \u003cp\u003e132.7\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(116.5-151.5)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.009280742459396%\" valign=\"top\"\u003e\n \u003cp\u003e177.9\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(163.3-191.6)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.31322505800464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.45707656612529%\" valign=\"top\"\u003e\n \u003cp\u003e108.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(80.0-128.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.65661252900232%\" valign=\"top\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFVC, forced vital capacity; FEV1, forced expiratory volume in one second; IQR, interquartile range; VC, vital capacity; TLC, total lung capacity; RV, residual volume; LAA, low attenuation area.\u003c/p\u003e\n\u003cp\u003e*: Reference: FEV1/FVC \u0026lt; 70% and 50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e: Wilcoxon rank sum test.\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e:\u0026nbsp;Numbers of patients (FEV1/FVC \u0026lt; 70% and 50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1, FEV1/FVC \u0026lt; 70% and 30\u0026nbsp;\u0026le;\u0026nbsp;%FEV1 \u0026lt; 50, FEV1/FVC \u0026lt; 70% and %FEV1 \u0026lt; 30, and FEV1/FVC\u0026nbsp;\u0026ge;\u0026nbsp;70%, respectively) were as follows:\u0026nbsp;VC, %VC, RV/TLC and %RV/TLC (N=193, 111, 64, 57).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e: P \u0026lt; 0.05 with Dunnett\u0026rsquo;s test or Wilcoxon rank sum test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4 Baseline treatment of participants, according to pulmonary function categories at registration\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"866\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.84295612009238%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC\u0026nbsp;\u0026lt;\u0026nbsp;70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC\u0026nbsp;\u0026ge;\u0026nbsp;70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e30\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u0026nbsp;\u0026lt;\u0026nbsp;50\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e%FEV1\u0026nbsp;\u0026lt;\u0026nbsp;30\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003eDunnett test\u0026nbsp;*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n\u0026nbsp;=\u0026nbsp;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003eP-value \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eMedication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e16 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e4 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.159/0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e9 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e177 (91.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e108 (96.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e64 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e48 (84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eUse of inhaler, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e25 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e4 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.009/0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e10 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e168 (87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e108 (96.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e62 (95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e47 (82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eICS, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e5 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e10 (8.9)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e10 (15.4)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.060/\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eLABA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e46 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e13 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e14 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.019/0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e17 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eICS/LABA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e68 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e65 (58.0)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e40 (61.5)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e20 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eLAMA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e109 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e69 (61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e50 (76.9)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.593/0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e24 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eLABA/LAMA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e17 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e16 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e3 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.218/0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e4 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eTheophylline, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e25 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e26 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e31 (47.7)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.058/\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e5 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eMacrolide, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e11 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e10 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e8 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.500/0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e3 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eLTRA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e9 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e19 (17.0)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e10 (15.4)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.001/0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e7 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eTulobuterol tape, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e0.147/0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.357\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.976905311778292%\" valign=\"top\"\u003e\n \u003cp\u003eHOT, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.04849884526559%\" valign=\"top\"\u003e\n \u003cp\u003e7 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.59122401847575%\" valign=\"top\"\u003e\n \u003cp\u003e20 (17.9)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e38 (58.5)\u003csup\u003e\u0026nbsp;\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.203233256351039%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.009237875288683%\" valign=\"top\"\u003e\n \u003cp\u003e3 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.967667436489608%\" valign=\"top\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFVC, forced vital capacity; FEV1, forced expiratory volume in one second; IQR, interquartile range; ICS, inhaled corticosteroid; LABA, long-acting beta-antagonist;\u0026nbsp;LAMA,\u0026nbsp;long-acting muscarinic antagonist;\u0026nbsp;LTRA, leukotriene receptor antagonist; HOT, home oxygen therapy.\u003c/p\u003e\n\u003cp\u003e*: Reference: FEV1/FVC \u0026lt; 70% and 50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e: Chi-square test.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e: P \u0026lt; 0.05 with Dunnett\u0026rsquo;s test, or Chi-square test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 5 Cause of death of participants, according to pulmonary function categories at registration\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"861\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.9953596287703%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC \u0026lt; 70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFEV1/FVC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026ge; 70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003eCause of death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e50\u0026nbsp;\u0026le;\u0026nbsp;%FEV1\u003c/p\u003e\n \u003cp\u003e(n = 193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e30\u0026nbsp;\u0026le;\u0026nbsp;%FEV1 \u0026lt; 50\u003c/p\u003e\n \u003cp\u003e(n = 112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e%FEV1 \u0026lt; 30\u003c/p\u003e\n \u003cp\u003e(n = 65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003eAll-cause, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e4 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e7 (41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e12 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e25 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003eLung cancer, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e2 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e2 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e6 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003eCancer other than lung cancer, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e2 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e2 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e6 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003eNon-respiratory infection, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e1 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e1 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003eCardiovascular disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e1 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e1 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003eOthers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e2 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e3 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e5 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.94663573085847%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" valign=\"top\"\u003e\n \u003cp\u003e3 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.473317865429234%\" valign=\"top\"\u003e\n \u003cp\u003e2 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.296983758700696%\" valign=\"top\"\u003e\n \u003cp\u003e4 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.109048723897912%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.948955916473318%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e11 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.11600928074245939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFVC, forced vital capacity; FEV1, forced expiratory volume in one second.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COPD, 5-year survival rate, normal spirometry, real-world registry","lastPublishedDoi":"10.21203/rs.3.rs-4043733/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4043733/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a prospective observational study to elucidate the long-term prognosis and management of chronic obstructive pulmonary disease (COPD) in Japan in the mid-2010s in clinical practice.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective cohort study included 29 facilities. A total of 427 patients clinically diagnosed with COPD, enrolled between September 2013 and April 2016, were analysed. Interstitial pneumonia was excluded through a central multidisciplinary discussion. Follow-up data were collected for up to 5 years after patient registration.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 67 patients clinically diagnosed with COPD did not have airflow limitations (AFL) at the time of registration. In the cohort with AFL (n=370), 266 patients completed a 5-year follow-up, while 58 patients died during the 1556.7 person-years of observation. The overall 5-year survival rate was 85.2%. Stratified by % forced expiratory volume in one second (FEV1), survival rates were 93.4% in the 50 ≤ %FEV1 group, 82.5% in the 30 ≤ %FEV1 \u0026lt; 50 group, and 66.1% in the %FEV1 \u0026lt; 30 group. The prognosis of the subpopulation without AFL was poor with 5-year survival of 83.0%. This subpopulation exhibited respiratory symptoms, low vital capacity and total lung capacity, and emphysematous changes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur study presents the 5-year survival and real-world clinical practice scenario of a prospective cohort of patients clinically diagnosed with COPD in Japan in the mid-2010s. Overall, 13% of the patients in this cohort did not show AFL at registration with respiratory symptoms, distinct spirometric patterns, and poor prognosis.\u003c/p\u003e","manuscriptTitle":"Prognosis and management of chronic obstructive pulmonary disease in a real-world setting: A 5-year follow-up analysis of a multi-institutional registry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 16:34:08","doi":"10.21203/rs.3.rs-4043733/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-28T06:52:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-16T14:34:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227889686493365091189179783994594793459","date":"2024-08-13T14:07:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71845225234585721968835589676668001177","date":"2024-08-13T06:49:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-11T11:54:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-09T03:19:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-08T19:46:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-08T11:28:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25479342357194877971799669522226685918","date":"2024-08-08T07:34:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59230632678954492551267751677227730203","date":"2024-08-01T15:35:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214990751742924329639426558568041500569","date":"2024-08-01T05:03:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310626030892978880173747352748289547196","date":"2024-07-29T16:23:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280760731695920156944982471808417504460","date":"2024-07-29T14:44:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-28T16:27:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76284089623192022356938547377892089035","date":"2024-07-28T10:15:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26841150177210651244204406995235367076","date":"2024-05-20T09:47:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-18T02:35:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-09T23:25:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-08T18:58:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-08T18:45:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-03-08T12:06:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"71cc3893-6a6e-4223-899f-f3a51629e78b","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T16:04:41+00:00","versionOfRecord":{"articleIdentity":"rs-4043733","link":"https://doi.org/10.1186/s12890-024-03347-5","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2024-11-06 15:57:21","publishedOnDateReadable":"November 6th, 2024"},"versionCreatedAt":"2024-03-13 16:34:08","video":"","vorDoi":"10.1186/s12890-024-03347-5","vorDoiUrl":"https://doi.org/10.1186/s12890-024-03347-5","workflowStages":[]},"version":"v1","identity":"rs-4043733","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4043733","identity":"rs-4043733","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}