Rationale: Loss of skeletal muscle mass and physical inactivity are important manifestations of chronic obstructive pulmonary disease (COPD), and both are closely related to poor prognoses in patients with COPD. Antigravity muscles are involved in maintaining normal posture and are prone to atrophy with inactivity. The erector spinae muscles (ESM) are one of the antigravity muscle groups, and they can be assessed by chest computed tomography (CT).
Objectives: We hypothesized that the cross-sectional area of ESM (ESMCSA) visualized on chest CT images may serve as a predictor of mortality in patients with COPD.
Methods: This study was part of the prospective observational study undertaken at Kyoto University Hospital. ESMCSA was measured on a single-slice axial CT image at the level of the 12th thoracic vertebra in patients with COPD. The cross-sectional area of the pectoralis muscles (PMCSA) was also measured. We evaluated the relationship between ESMCSA and clinical parameters, including mortality, in patients with COPD. Age- and height-matched smoking control subjects were also evaluated.
Measurements and Main Results: In total, 130 male patients and 20 smoking control males were enrolled in this study. ESMCSA was significantly lower in patients with COPD than in the smoking control subjects and was significantly correlated with disease severity. There was a significant but only moderate correlation between ESMCSA and PMCSA. ESMCSA was significantly correlated with previously reported prognostic factors, such as body mass index, dyspnea (modified Medical Research Council dyspnea scale score), FEV1 percent predicted value, inspiratory capacity to total lung capacity ratio, and emphysema severity (percentage of the lung field occupied by low attenuation area). Compared with PMCSA, ESMCSA was more strongly associated with mortality in patients with COPD. Stepwise multivariate Cox proportional hazards analysis revealed that, among these known prognostic factors, ESMCSA was the strongest risk factor for mortality (hazard ratio, 0.85; 95% confidence interval, 0.79–0.92; P < 0.001) and mMRC dyspnea scale score was an additional factor (hazard ratio, 2.35; 95% confidence interval, 1.51–3.65; P < 0.001).
Conclusions: ESMCSA assessed by chest CT may be a valuable clinical parameter, as ESACSA correlates significantly with physiological parameters, symptoms, and disease prognosis.
Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and is characterized by progressive airflow limitations and systemic inflammation (1). Weight loss is a common systemic manifestation of the disease and is considered a strong predictor of mortality in patients with COPD (2). However, skeletal muscle mass has attracted increasing attention because it reflects COPD severity more accurately than body mass index (BMI) does (3) and because the loss of skeletal muscles is an excellent predictor of mortality (4). Loss of skeletal muscle mass may not be accompanied by a similar degree of adipose tissue loss (5). Therefore, the assessment of skeletal muscle mass is an important clinical assessment in patients with COPD.
Bioelectrical impedance analysis, dual-energy X-ray absorptiometry, magnetic resonance imaging, and B-mode ultrasound are widely used to quantify both total and local skeletal muscle mass (5–8). Measurement of the cross-sectional area (CSA) of skeletal muscles on single-slice axial computed tomography (CT) scans is an alternative method for assessing local skeletal muscle mass (9–11). In previous studies, several local muscles, such as the pectoralis muscles (PMs), intercostal muscles, abdominal muscles, or midthigh leg muscles, were evaluated using CT images with or without additional scans. However, it is possible that the loss of skeletal muscle mass occurs heterogeneously because of these specific roles and physiological activity in patients with disease.
Antigravity muscles, or the muscles involved in maintaining normal posture via the opposition of gravity, reflect physical activity more than other muscle groups do (8). In this study, we focused on the erector spinae muscles (ESMs), one of the major antigravity muscle groups that may be assessed using chest CT. Because chest CT is usually performed to diagnose and evaluate emphysema and to evaluate and/or exclude other critical pulmonary diseases, such as lung cancer and pulmonary fibrosis, these images may simultaneously be used to evaluate local skeletal muscle mass without requiring additional radiation exposure. We hypothesized that the CSA of the ESMs (ESMCSA) may serve as a predictor of mortality in patients with COPD.
This study was a part of our prospective observational study and was performed at Kyoto University (12, 13). Male outpatients with stable COPD were enrolled in the study between March 2006 and April 2009. The entry criteria were as follows: (1) a smoking history of 20 pack-years or more, (2) a diagnosis of COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria (14), (3) a previous chest CT scan, and (4) no COPD exacerbations within the past 4 weeks. The exclusion criteria were as follows: (1) alpha-1 antitrypsin deficiency; (2) a combination of other respiratory diseases, such as bronchial asthma, interstitial pneumonia, or bronchiectasis; (3) a history of malignant neoplasms other than prostate cancer or endoscopically resected early cancer within the past 5 years; (4) abnormal shadows on chest CT scans, such as fibrosis; (5) a prior history of lobectomy; and (6) degenerative disease in the thoracic or upper lumber vertebrae or a history of surgery on the vertebrae.
The patients underwent both pulmonary function testing and chest CT (12, 13). Information regarding dyspnea severity (measured using the modified Medical Research Council [mMRC] dyspnea scale), health-related quality of life (evaluated using the St. George’s Respiratory Questionnaire), and comorbidities was obtained by conducting patient interviews in the outpatient clinic. Exacerbations (date and severity) were evaluated using patient diary entries. Comorbidity indices (Charlson comorbidity index and COPD specific comorbidity test [COTE index]) were calculated as reported previously (15, 16). Hospital records were examined to determine both the dates and causes of any deaths. When contact with a particular patient was lost, we attempted to contact, by telephone or by letter, the patient, the patient’s family, or the hospital to which the patient was transferred. All of the data at the entry point were reviewed and fixed retrospectively at January 2015, when the patients’ survival status was fixed.
Age- and height-matched smoking male subjects were also enrolled as controls and evaluated using anthropometry, spirometry, and chest CT. The ethics committee of Kyoto University approved this study (approval E182), and all subjects provided written informed consent before participating.
Following bronchodilator inhalation (400 μg of salbutamol and 80 μg of ipratropium), pulmonary function tests and chest CT were performed. Spirometry, lung volume subdivisions, and diffusing capacity of carbon monoxide (DlCO) were measured using a Chestac-65V instrument (Chest M.I., Inc., Tokyo, Japan). The predicted pulmonary function values were calculated on the basis of the Japanese Respiratory Society guidelines (17).
Chest CT scans (Aquilion 64 scanner; Toshiba, Tokyo, Japan) were obtained with the use of 0.5-mm collimation, a scan time of 500 milliseconds, 120 kilovolts peak, and automatic exposure control. Routine calibration of the CT scanner was performed using air and water phantoms.
For quantitative analysis of pulmonary emphysema, whole-lung CT images with 0.5-mm thickness were obtained using reconstruction kernel FC56. Using in-house software, the percentage of low attenuation area (LAA%) was calculated using a cutoff value of −960 Hounsfield units, as described previously (12, 13). For quantitative analysis of the ESMs, chest CT images reconstructed using the mediastinal setting (reconstruction kernel FC13) were used. Using a modified method described in a previous article (18), we performed the analysis on a single-slice axial chest CT image at the level of the lower margin of the 12th thoracic vertebra using a SYNAPSE VINCENT volume analyzer (FUJIFILM Medical Co., Ltd., Tokyo, Japan). The details regarding ESMCSA measurements are described in the online supplement. Briefly, after imaging, the left and right ESMs were identified and manually shaded (Figure 1A), the CSAs of both ESMs were calculated, and the ESMCSA was presented as the sum of the right and left muscles. The CSA of the pectoralis muscles (PMCSA) was also determined as described previously (9) (Figure 1B).
The data pertaining to the continuous variables are expressed as the mean ± SD unless otherwise specified. All statistical analyses were performed using IBM SPSS version 18 software (IBM Japan, Tokyo, Japan). A bivariate correlation analysis was performed between ESMCSA and clinical parameters such as anthropometry, pulmonary function, and emphysema severity. Analysis of variance and post hoc Tukey–Kramer tests were performed to compare ESMCSA values between groups. The relationship of ESMCSA categories and all-cause mortality was analyzed using Kaplan–Meier survival curves and log-rank tests. In these analyses, the determined cutoff values were based on the distribution of measurements obtained from smoking controls for categorization, such as the mean, mean −1SD, and mean −2SD.
Both univariate and multivariate Cox proportional hazards analyses were performed to investigate the relationship between the clinical parameters and mortality. Stepwise multivariate Cox proportional hazards analysis was performed to identify the parameter that correlated most significantly with mortality. The results of the Cox proportional hazards analysis are depicted as hazard ratios (HRs) with 95% confidence intervals (CIs). P values less than 0.05 were considered statistically significant.
The characteristics of the study subjects are provided in Table 1. In total, 154 patients with COPD were enrolled in this study. We excluded 24 patients, comprising 4 patients who had interstitial pneumonia, 2 who had asthma, 1 who had bronchiectasis, 11 who had abnormal shadows on chest CT scans, 5 who had a history of malignancy within the past 5 years, and 1 who had undergone lobectomy for treatment of lung cancer. Ultimately, 130 male patients were studied. The number of study subjects within each GOLD stage were as follows: stage I, 23 patients (17.7%); stage II, 61 patients (46.9%); stage III, 34 patients (26.2%); and stage IV, 12 patients (9.2%). Comorbidities are provided in Table E1 in the online supplement. The characteristics of the 20 smoking control subjects are provided in Table E2. The weight of the patients with COPD was significantly less than that in the smoking control subjects (P < 0.0001).
|Total number of patients||130|
|Age, yr||71.6 ± 8.4 (46–89)|
|Height, cm||164.4 ± 6.1 (150.0–180.0)|
|Weight, kg||57.8 ± 8.7 (41.0–94.0)|
|BMI, kg/m2||21.4 ± 2.9 (14.7–29.0)|
|Smoking status, former/current||106/24|
|Smoking history, pack-years||70.0 ± 38.5 (20–220)|
|mMRC dyspnea scale score, 0/1/2/3/4||34/55/28/13/0|
|GOLD stage, I/II/III/IV||23/61/34/12|
|LTOT, n (%)||6 (4.6)|
|Prior exacerbations*, yes/no||29/101|
|Charlson comorbidity index||1.6 ± 0.9 (1–5)|
|COTE index||0.3 ± 0.6 (0–3)|
|SGRQ total score, units||28.9 ± 15.5 (1.6–67.8)|
|SGRQ activity score, units||40.2 ± 22.3 (0–92.5)|
|FEV1, L||1.60 ± 0.67 (0.5–3.42)|
|FEV1, % predicted||57.6 ± 20.3 (18.7–100.1)|
|FVC, L||3.31 ± 0.82 (1.68–5.56)|
|FEV1/FVC, %||47.2 ± 12.7 (22.4–68.8)|
|VC, L||3.27 ± 0.75 (1.37–5.23)|
|IC/TLC, %||37.4 ± 7.4 (18.0–54.9)|
|RV/TLC, %||42.4 ± 8.3 (25.5–70.4)|
|DlCO, ml/min/mm Hg||12.4 ± 4.77 (2.66–23.0)|
|LAA% less than −960 HU, %||33.2 ± 8.9 (10.2–53.9)|
|PMCSA, cm2||25.91 ± 7.41 (13.50–44.52)|
|ESMCSA, cm2||29.77 ± 6.97 (12.88–46.29)|
The ESMCSA of the patients with COPD was significantly less than that of the smoking control subjects (39.20 ± 6.98 cm2 vs. 29.77 ± 6.97 cm2 for smoking control subjects vs. patients with COPD, respectively; P < 0.0001). Among the patients with COPD, ESMCSA was significantly decreased and correlated with disease severity (Figure 2). Even among the patients with GOLD stage I COPD, ESMCSA was significantly less than that of the control subjects (31.3 ± 7.2 cm2 vs. 39.2 ± 7.0 cm2; P < 0.01). The distribution of PMCSA was similar to the distribution of ESMCSA (Figure E1).
The ESMCSA of the patients with COPD correlated significantly with age (r = −0.36; P < 0.0001), BMI (r = 0.44; P < 0.0001), mMRC dyspnea scale score (r = −0.36; P < 0.0001), Charlson comorbidity index (r = −0.21; P = 0.02), and pulmonary function indices such as FEV1 percent predicted value (r = 0.31; P = 0.0004), inspiratory capacity to total lung capacity (IC/TLC) ratio (r = 029; P = 0.0009), DlCO (r = 0.42; P = 0.0002), and LAA% (r = −0.32; P = 0.0002) (Table 2). ESMCSA also correlated significantly but not strongly with PMCSA (r = 0.49; P < 0.0001) (also see Figure E2). In addition, PMCSA correlated significantly with similar clinical parameters to ESMCSA (Table E3).
|Variable||r Value||P Value|
|Smoking history, pack-years||0.06||0.5|
|mMRC dyspnea scale score, units||−0.36||<0.0001|
|Prior exacerbations*, n||−0.10||0.2|
|Charlson comorbidity index, units||−0.21||0.02|
|COTE index, units||−0.15||0.08|
|SGRQ total score, units||−0.35||<0.0001|
|SGRQ activity score, units||−0.32||0.0003|
|FEV1, % predicted||0.31||0.0004|
|DlCO, ml/min/mm Hg||0.42||<0.0001|
|PaO2, mm Hg||0.09||0.3|
|LAA% less than −960 HU, %||−0.32||0.0002|
The median follow-up time as of January 2015 was 2,541.5 days (range, 391–3,136 d). Of the 130 patients with COPD, 24 died during the follow-up period. The causes of death were as follows: respiratory failure (n = 7 [29.2%]), lung cancer (n = 3 [12.5%]), malignancy other than lung cancer (n = 3 [12.5%]), sudden death (n = 5 [20.8%]), cardiovascular disease (n = 3 [12.5%]), and unknown cause (n = 3 [12.5%]).
Kaplan–Meier survival curves based on the ESMCSA subgroups with cutoff values based on the distribution of the smoking control subjects (with mean, mean −1SD, and mean −2SD values of 39, 35, and 32 cm2, respectively) are shown in Figure 3. The patients with COPD with lower ESMCSA values exhibited significantly worse survival (P < 0.0001 by log-rank test). The patients’ characteristics based on the cutoff value of ESMCSA are provided in Table E4.
The relationship between all-cause mortality and clinical parameters, including ESMCSA, was evaluated using univariate Cox proportional hazards analysis. Older age (P = 0.03), lower BMI (P = 0.003), higher mMRC dyspnea scale score (P < 0.0001), lower FEV1 percent predicted value (P = 0.007), lower IC/TLC ratio (P < 0.0001), lower DlCO (P < 0.0001), higher LAA% (P = 0.0004), lower PMCSA (P = 0.0004), and lower ESMCSA (P < 0.0001) significantly correlated with all-cause mortality (Table 3). The comorbidity indices (Charlson comorbidity index and COTE index) did not correlate with all-cause mortality.
|Variable||HR||95% CI||P Value|
|Smoking history, pack-years||1.00||0.98–1.01||0.4|
|mMRC dyspnea scale score, units||2.89||1.88–4.53||<0.0001|
|Charlson comorbidity index||1.36||0.84–2.03||0.2|
|SGRQ total score, units||1.03||1.01–1.06||0.01|
|SGRQ activity score, units||1.03||1.01–1.05||0.01|
|FEV1, % predicted||0.97||0.95–0.99||0.007|
|FVC % predicted||0.97||0.95–0.99||0.005|
|VC, % predicted||0.97||0.95–0.99||0.001|
|DlCO, ml/min/mm Hg||0.82||0.74–0.90||<0.0001|
|PaO2, mm Hg||0.98||0.94–1.03||0.5|
|LAA% less than −960 HU, %||1.09||1.04–1.15||0.0004|
Multivariate Cox proportional hazards analyses were performed to compare the contributions of these indices (Table 4). Four different models were analyzed. In model 1, we included established parameters, such as dyspnea (mMRC dyspnea scale score), pulmonary function (FEV1 percent predicted value, IC/TLC, DlCO), and ESMCSA as explanatory variables. We included PMCSA instead of ESMCSA in model 2. We included BMI instead of ESMCSA or PMCSA in model 3 because BMI is a classically reported surrogate marker of COPD mortality. In addition, we included LAA% instead of ESMCSA, PMCSA, or BMI in model 4 because we previously reported that LAA% is a better predictor of mortality than BMI (19). In model 1, both ESMCSA (HR, 0.86; 95% CI, 0.78–0.93; P = 0.0002) and mMRC dyspnea scale score (HR, 2.28; 95% CI, 1.29–4.19; P = 0.005) correlated significantly with all-cause mortality. In models 2 and 3, only mMRC dyspnea scale score correlated significantly with all-cause mortality. In model 4, mMRC dyspnea scale score (HR, 2.10; 95% CI, 1.25–3.66; P = 0.005) and IC/TLC ratio (HR, 0.93; 95% CI, 0.86–1.00; P = 0.04) correlated significantly with all-cause mortality. Stepwise Cox proportional hazards analysis demonstrated that ESMCSA (HR, 0.85; 95% CI, 0.79–0.92; P < 0.001) was the most significant risk factor for all-cause mortality, and mMRC dyspnea scale score (HR, 2.35; 95% CI, 1.51–3.65; P < 0.001) was also a significant factor (Table 5).
|Variable||Model 1||Model 2||Model 3||Model 4|
|HR||95% CI||P Value||HR||95% CI||P Value||HR||95% CI||P Value||HR||95% CI||P Value|
|mMRC dyspnea scale score, units||2.28||1.29–4.19||0.005||2.03||1.20–3.54||0.008||2.20||1.29–3.90||0.004||2.10||1.25–3.66||0.005|
|FEV1, % predicted||1.02||0.99–1.05||0.2||1.02||0.99–1.05||0.3||1.02||0.98–1.05||0.3||1.01||0.98–1.05||0.4|
|DlCO, ml/min/mm Hg||0.99||0.86–1.13||0.8||0.93||0.82–1.06||0.3||0.92||0.81–1.04||0.2||0.91||0.79–1.04||0.2|
|LAA% less than −960 HU, %||—*||—*||—*||1.00||0.93–1.08||0.96|
|Variable||HR||95% CI||P Value|
|mMRC dyspnea scale score, units||2.35||1.51–3.65||<0.001|
|LAA% less than −960 HU, %||0.8|
|DlCO, ml/min/mm Hg||0.7|
|FEV1, % predicted||0.6|
Finally, we conducted a preliminary analysis measuring the daily step counts of 32 patients with COPD. ESMCSA correlated significantly with daily step count (Figure E3).
To our knowledge, this study is the first to demonstrate the clinical utility of quantitatively analyzing ESMs using chest CT in patients with COPD. ESMCSA is associated with severe airflow limitations, respiratory symptoms, and emphysema severity. We have also uncovered evidence that ESMCSA is the greatest risk factor for all-cause mortality among both established and reported prognostic predictors, such as FEV1 percent predicted value, BMI, LAA%, IC/TLC ratio, COTE index, and mMRC dyspnea scale score.
Loss of skeletal muscle mass is induced by various mechanisms, such as smoking, systemic inflammation, inactivity, malnutrition, and enhanced energy expenditure (20–22). Recent studies have revealed that skeletal muscle mass can be considered a useful prognostic index in the context of COPD (4), and that even a local assessment of lower limb muscle mass is sufficiently useful for predicting mortality or frailty of patients (10, 23, 24). In the present study, we focused on the ESMs as antigravity muscles and also evaluated the PMs as respiratory muscles (9). There was a significant but only moderate correlation between these muscle groups (r = 0.61 among all study subjects; r = 0.48 among patients with COPD) (Figure E2). Both ESMCSA and PMCSA correlated significantly with BMI, mMRC dyspnea scale score, pulmonary function, and emphysema severity; however, in our preliminary analysis of 32 patients with COPD, ESMCSA correlated more strongly than PMCSA with physical activity as measured via pedometry (Figure E3). Although the physical activity levels of all subjects were not directly assessed in the present study, this observation implies that ESMCSA is a comprehensive parameter because ESMCSA reflected both physical activity and physiological severity indexes of COPD. Because physical activity is reportedly the best predictor of the prognosis for patients with COPD (25–27), ESMCSA is a stronger risk factor for mortality in patients with COPD than PMCSA.
The measurement of ESMCSA has several advantages over parameters described in other reports pertaining to the skeletal muscles, such as the PMs (9) and midthigh muscles (23). PMCSA may be affected by the position of the upper extremities during scanning. Other skeletal muscles, such as the midthigh muscles and psoas muscles, require additional scans and X-ray exposure for analysis.
The most important finding in the present study is that, among other established prognostic predictors, such as lung function, BMI, LAA%, and mMRC dyspnea scale score, ESMCSA was observed to be the strongest risk factor for all-cause mortality (Tables 3–5). For BMI in particular, because an index of skeletal muscle mass is reportedly a better prognostic factor than BMI (4), our finding that ESMCSA is a better predictor than BMI is quite consistent with previous studies. A previous study by our colleagues indicated that dyspnea is a strong predictor for all-cause mortality (28). However, the characteristics of patients enrolled in this study were different, as they were younger. In addition, some of the participants were female and exhibited lower FEV1 values. Despite these differences, the present study also demonstrated and confirmed that mMRC dyspnea scale score is a significant risk factor for all-cause mortality (Tables 3–5). Although we did not have patients with mMRC dyspnea scale scores of 4 (Figure E4), this does not appear to have been a disadvantage of our investigation. Patients with the most severe dyspnea (mMRC dyspnea scale score of 4) may also exhibit more severe ESM loss due to nearly bed-bound conditions. In another one of our previous studies, LAA%, age, and BMI were independent predictors of all-cause mortality (19). The present cohort included older patients, excluded females, and was characterized by higher FEV1, higher LAA%, and lower DlCO/Va values. Although the previous investigation may have included more patients without emphysema, in the present study we found and confirmed that LAA% is a risk factor for all-cause mortality (Table 3).
Compared with other previous studies in which researchers have evaluated prognostic predictors, such as IC/TLC ratio, residual volume/TLC ratio, and comorbidities in patients with COPD, our results are mostly consistent, except for comorbidities (29, 30). The comorbidity indices (Charlson comorbidity index and COTE index) did not correlate with mortality in the present study. The subjects enrolled in the present study exhibited lower comorbidity indices, perhaps because only patients with fewer comorbidities were recruited and because differences existed in the prevalence of comorbidities (such as cardiovascular disease) between patients in East Asia (including Japan) and those in Europe (1). Despite these differences, the validity of the prognostic factors described in previous reports was confirmed in the present study (Tables 3–5). We also observed a consistent trend involving specific causes of death (Figure E5) with recent findings by Sin and coworkers (31). On the basis of these observations, we conclude that ESMCSA may be universally applicable as a risk measure of all-cause mortality in patients with COPD.
There are several limitations of this study. First, the sample size was small, female patients were excluded, and there was no replication cohort. However, the validity of the reported prognostic factors was also confirmed in this study population, which supports the consistency of the present findings. Second, a standard method for measuring the ESMCSA has not yet been established. ESMCSA was analyzed via manual shading of a specific muscle area after the application of a density mask between −50 and +90 Hounsfield units. Automated programs to measure ESMCSA are necessary to allow a more objective and precise analysis. We chose a single slice at the level of the 12th thoracic vertebra to analyze ESMCSA according to a previous report (18). We conducted additional analyses of ESMCSA using three consecutive slices and found that the CSA of the single slice correlated strongly with the average of the three slices (r = 0.98; P < 0.0001). Because our intent was to develop a measure that may be applied to existing clinically acquired imaging data, we are confident in the sufficient utility and validity of the single-slice image analysis. Third, to qualify the mechanisms of ESM loss, the physical activity levels of all study subjects were not directly evaluated. However, on the basis of our preliminary analysis and other reports, we thought that ESMCSA was a comprehensive parameter reflecting both physical activity and physiological parameters. An objective evaluation of physical activity levels may allow detailed discussions regarding the relationship between ESMCSA and physical activity levels.
The quantitative analysis of ESMs has good potential for application in clinical settings. Because pulmonary comorbidities, such as interstitial pneumonia (32) or lung cancer (33), are important in patients with COPD, chest CT is of growing importance and is often performed even in large-scale investigations, such as the COPDGene (34) and ECLIPSE (35) studies. In this situation, developing and validating an additional utility of chest CT is crucial and beneficial. Moreover, there is no need for the use of additional instruments, such as a pedometer or a triaxial accelerometer, just to assess physical activity. Furthermore, it is possible to evaluate longitudinal changes of ESACSA in these patients and observe precursory phenomena of disease worsening. Therefore, the quantitative analysis of ESMs on chest CT images would enable clinicians and researchers to explore a significant objective index of the disease severity or condition in individual patients.
In conclusion, ESMCSA obtained from a single-slice axial chest CT image is correlated with the clinical parameters of COPD and may be the strongest risk factor for all-cause mortality in patients with COPD. Without extra radiation exposure, ESM analysis via existing chest CT scans provides an additional and important objective index reflecting disease severity and future prognosis in patients with COPD.
The authors thank Yohei Oshima, a physical therapist at Kyoto University Hospital, for his advice on ESMCSA measurement. The authors also thank Tomoki Aoyama, M.D., Ph.D., and Kazuya Yoshimura at Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan, for help in acquiring preliminary data. The authors also thank Motonari Fukui, M.D., Ph.D. (Respiratory Disease Center), Hajime Nakamura, M.D., Ph.D. (Preventive Medicine), and Hisao Ito (Radiology), at Tazuke Kofukai Medical Research Institute, Kitano Hospital, Osaka, Japan, for help in acquiring CT data of smoking control subjects.
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Supported by Grant-in-aid for scientific research 25461156 from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
Author Contributions: K.T.: designed the study, collected and analyzed the data, and wrote the manuscript; S.S.: designed the study, analyzed and interpreted the data, assisted with the editing of the manuscript, and takes responsibility for the integrity of the project as a whole, from its inception to the manuscript’s publication; Y.F., K.H., K.U., A.S., T.O., and T.H.: contributed to the study design as well as data collection and analysis; M.M.: contributed to the acquisition of funding and the data interpretation; and S.M.: contributed to the study design, data interpretation, and acquisition of funding. All authors approved the final version of the manuscript.
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