CHEST

Original Research
COPD

Physical Activity Is the Strongest
Predictor of All-Cause Mortality
in Patients With COPD
A Prospective Cohort Study
Benjamin Waschki, MD; Anne Kirsten, MD; Olaf Holz, PhD; Kai-Christian Müller, PhD;
Thorsten Meyer, PhD; Henrik Watz, MD; and Helgo Magnussen, MD

Background: Systemic effects of COPD are incompletely reﬂected by established prognostic
assessments. We determined the prognostic value of objectively measured physical activity incomparison with established predictors of mortality and evaluated the prognostic value of noninvasive assessments of cardiovascular status, biomarkers of systemic inﬂammation, and adipokines.
Methods: In a prospective cohort study of 170 outpatients with stable COPD (mean FEV1,
56% predicted), we assessed lung function by spirometry and body plethysmography; physicalactivity level (PAL) by a multisensory armband; exercise capacity by 6-min walk distance test;
cardiovascular status by echocardiography, vascular Doppler sonography (ankle-brachial index[ABI]), and N-terminal pro-B-type natriuretic peptide level; nutritional and muscular status by
BMI and fat-free mass index; biomarkers by levels of high-sensitivity C-reactive protein, IL-6,
ﬁbrinogen, adiponectin, and leptin; and health status, dyspnea, and depressive symptoms byquestionnaire. Established prognostic indices were calculated. The median follow-up was 48 months(range, 10-53 months).
Results: All-cause mortality was 15.4%. After adjustments, each 0.14 increase in PAL was associated with a lower risk of death (hazard ratio [HR], 0.46; 95% CI, 0.33-0.64; P , .001). Comparedwith established predictors, PAL showed the best discriminative properties for 4-year survival(C statistic, 0.81) and was associated with the highest relative risk of death per standardized decrease.
Novel predictors of mortality were adiponectin level (HR, 1.34; 95% CI, 1.06-1.71; P 5 .017),
leptin level (HR, 0.81; 95% CI, 0.65-0.99; P 5 .042), right ventricular function (Tei-index) (HR,1.26; 95% CI, 1.04-1.54; P 5 .020), and ABI , 1.00 (HR, 3.87; 95% CI, 1.44-10.40; P 5 .007). A stepwise
Cox regression revealed that the best model of independent predictors was PAL, adiponectin level,
and ABI. The composite of these factors further improved the discriminative properties (C statistic, 0.85).
Conclusions: We found that objectively measured physical activity is the strongest predictor of
all-cause mortality in patients with COPD. In addition, adiponectin level and vascular status provide independent prognostic information in our cohort.
CHEST 2011; 140(2):331–342
Abbreviations: ABI 5 ankle-brachial index; ADO 5 age, dyspnea, and airﬂow obstruction; AUROC 5 area under thereceiver operating characteristic; BODE 5 BMI, airﬂow obstruction, dyspnea, and exercise capacity; GOLD 5 Global
Initiative for Chronic Obstructive Lung Disease; HR 5 hazard ratio; hs-CRP 5 high-sensitivity C-reactive protein;
IC/TLC 5 inspiratory to total lung capacity ratio; MMRC 5 modiﬁed Medical Research Council; NT-proBNP 5 N-terminal
pro-B-type natriuretic peptide; PAL 5 physical activity level; SGRQ 5 St. George Respiratory Questionnaire

is an important and growing cause of morCOPD
bidity and mortality worldwide. It has been char1

acterized as a preventable and treatable disease withsigniﬁcant extrapulmonary effects that may contributeto its severity in individual patients.2 Cardiovascular,
musculoskeletal, metabolic, and mental comorbidities are considered to be part of the frequently
prevalent nonpulmonary sequelae of the disease.3,4

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Increasing evidence suggests that extrapulmonary
effects of COPD and airﬂow limitation are only poorly
correlated.5 Prognostic research in COPD has estabFor editorial comment see page 279
lished several assessments beyond airﬂow limitation: Hyperinﬂation,6 reduced exercise capacity,7,8
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© 2011 American College of Chest Physicians

331malnutrition,9 muscle wasting,10,11 degree of dyspnea,12
impaired health status,8 degree of depressive symptoms,13 and presence of low-grade systemic inﬂammation14,15 were shown to predict mortality in COPD.
Furthermore, two multidimensional scores were developed that predict mortality in patients with COPD
better than their single components: the BODE
(BMI, airway obstruction, dyspnea, and exercise
capacity) index16 and the ADO (age, dyspnea, and airﬂow obstruction) index.17
Physical activity is an important predictor of mortality in healthy subjects18 and those with several
chronic diseases.19-21 In patients with COPD, physicalactivity is related to pulmonary limitations22-24 andextrapulmonary effects of the disease.25 One epidemiologic study demonstrated that some level of physicalactivity is associated with a lower risk of mortality in
COPD.26 In that study, physical activity was assessedby a questionnaire, which is subject to misclassiﬁcation biases.27 The predictive value of objectively measured physical activity still needs to be determined.
Cardiovascular disease plays an important role inmortality of patients with mild and severe COPD.28,29
However, no noninvasive assessments of cardiovascular status have been established as predictors of mortality in stable COPD. Furthermore, several systemic
biomarkers are considered to be related to disease
pathology and inﬂammatory processes in COPD.30
Among them, serum levels of high-sensitivity C-reactive
protein (hs-CRP) have been demonstrated to berelated to mortality,14,15 but ambiguity remains.31
Recently, elevated adiponectin levels have beenfound in patients with COPD.32 Elevated adiponectin levels predict mortality in several chronic diseases.33-36 To our knowledge, the prognostic value ofelevated adiponectin levels in COPD has not beenstudied so far.
The present study aimed to determine the prognostic value of objectively measured physical activity
Manuscript received October 6, 2010; revision accepted January 3,
2011.
Afﬁliations: From the Pulmonary Research Institute at Hospital
Grosshansdorf (Drs Waschki, Kirsten, Watz, and Magnussen), andHospital Grosshansdorf Center for Pneumology and Thoracic Surgery (Drs Holz and Müller), Grosshansdorf, and Institute of Social
Medicine (Dr Meyer), University of Luebeck, Luebeck, Germany.
Drs Watz and Magnussen contributed equally to this work.
Funding/Support: This study was supported by Deutsche
Rentenversicherung Nord. Some baseline measurements for crosssectional analyses were supported by an unrestricted research
grant from AstraZeneca Germany.
Correspondence to: Henrik Watz, MD, Pulmonary ResearchInstitute at Hospital Grosshansdorf, Center for Pneumology andThoracic Surgery, Woehrendamm 80, D-22927 Grosshansdorf,
Germany; e-mail: h.watz@pulmoresearch.de© 2011 American College of Chest Physicians. Reproductionof this article is prohibited without written permission from theAmerican College of Chest Physicians (http://www.chestpubs.org/
site/misc/reprints.xhtml).
DOI: 10.1378/chest.10-2521

compared with established predictors of mortality
and to evaluate the prognostic value of noninvasive
assessments of cardiovascular status, biomarkers ofsystemic inﬂammation, and adipokines. A secondary
aim was to identify the best predictive model of independent risk factors for all-cause mortality.
Materials and Methods
Study Population
In 2006, 170 stable outpatients with COPD (128 men,
42 women) with GOLD (Global Initiative for Chronic Obstructive Lung Disease) stages I (n 5 34), II (n 5 57), III (n 5 43), andIV (n 5 36) were investigated at the Pulmonary Research Instituteat Hospital Grosshansdorf, Grosshansdorf, Germany. The patientswere enrolled in a prospective observational study to investigate
the role of extrapulmonary effects of COPD for disease severity
and disease progression. The objective measurement of physicalactivity had a central role. Patients were recruited from the institute’s database that is used for clinical trials in COPD; it consistedof 691 ambulatory patients with COPD (mean FEV1, 54% predicted; mean age, 62 years). Patients were contacted by a study nurse,
and those who expressed their availability and interest in the study
were enrolled. Exclusion criteria were an exacerbation of COPD
within the past 2 months, clinical signs of acute heart failure, andsevere pain syndromes that could interfere with physical activity.
Further details on methods and cross-sectional analyses havebeen described previously.23,25,37,38 The study was approved by
the Ethics Committee of Medical Association Schleswig-Holstein,
Bad Segeberg, Germany [III/EK 116/05(I)], and participants gave
written informed consent.
Physical Activity
Physical activity was measured by a multisensory armband(SenseWear; BodyMedia; Pittsburgh, Pennsylvania). We assessed
the physical activity level (PAL) and step counts per day over a
period of 5 to 6 consecutive days for at least 22.5 h a day as previously described.23,25 Reliability of this assessment period has beenshown.23 PAL was calculated by dividing the total daily energy
expenditure by whole-night sleeping energy expenditure.25 According to the World Health Organization, a PAL of ⱖ 1.70 deﬁnes anactive person, 1.40 to 1.69 deﬁnes a sedentary person, and , 1.40
deﬁnes a very inactive person.18,23 Energy expenditure estimates ofthe multisensory armband have been validated against the doubly
labeled water technique in healthy subjects39 and against indirectcalorimetry in patients with COPD.40,41 Step counts of the armband
showed a high correlation (r 5 0.86) with vector magnitude units
derived by another validated accelerometer in 120 patients whowore both devices simultaneously over a period of 5 to 6 days.42
Lung Function and Functional Exercise Capacity
Postbronchodilator spirometry (performed 15 min after administration of 400 mg salbutamol) and body plethysmography wereused to measure lung function according to current guidelines
and established reference values.43,44 The 6-min walk test was
conducted according to current guidelines.45
Cardiovascular Status
Cardiac function was assessed by transthoracic echocardiography (Vivid 3; GE Healthcare; Chalfont St Giles, England),and serum levels of N-terminal pro-B-type natriuretic peptide

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© 2011 American College of Chest Physicians(NT-proBNP) (Elecsys proBNP Immunoassay; Roche Diagnostics GmbH; Mannheim, Germany) were determined as previously
described.25,38 We measured systolic left-sided heart function(left ventricular ejection fraction), diastolic left-sided heart function(ratio of the peak velocity of the early E-wave to atrial A-wave),46
and global right ventricular function (Tei index, which is the sumof isovolumetric contraction time and isovolumetric relaxation
time divided by the ejection time).47
To assess peripheral artery disease, we performed vascular
Doppler sonography (SonoMate100; DEGO GmbH; Nagold,
Germany) and calculated the ankle-brachial index (ABI) as previously described.48 ABI was analyzed as a categorized variable. An
ABI , 1.00 is associated with an increased risk of death in several
populations.49
Nutritional and Muscular Status
BMI was calculated as weight in kilograms divided by height inmeters squared. Fat-free mass was determined by bioelectrical
impedance analysis (Nutriguard-M; Data Input GmbH; Darmstadt,
Germany) using a recently developed formula.50 The fat-freemass index was calculated as fat-free mass in kilograms divided
by height in meters squared. Muscle depletion was deﬁned as a
fat-free mass index , 17.05 kg/m2 in men and , 14.62 kg/m2 inwomen.11,50
Adipokines and Systemic Inﬂammation
Total serum adiponectin and leptin levels were measured
by enzyme-linked immunosorbent assay (R&D Systems; Oxford,
England). Hs-CRP (latex assay) (Roche Diagnostics), ﬁbrinogenlevel (modiﬁed method of Clauss),25 and IL-6 (enzyme-linked
immunosorbent assay) (R&D Systems; Minneapolis, Minnesota)
were measured according to the manufacturers’ instructions.
Questionnaires and Multidimensional Mortality Scores
Dyspnea was assessed by the modified Medical ResearchCouncil (MMRC) dyspnea scale. Health-related quality of life wasassessed by the St. George Respiratory Questionnaire (SGRQ).
The activity subdomain was analyzed separately. Depressive
symptoms were measured by the Beck Depression Inventory.
Patients were classiﬁed according to the criteria of the BODE
index,16 updated BODE index,17 and ADO index.17

The relation of physical activity categories and all-cause mortality was analyzed by Kaplan-Meier survival plots and log-rank
tests using established cut points for categorization. To analyze
the relative risk of mortality for physical activity as a continuous
variable, we used multivariate Cox proportional hazard analyses.
To further test for a linear association between physical activity
and all-cause mortality, we added quadratic terms for the PAL
and steps per day to each model.18
To compare the discriminative properties of the prognostic
assessments for 4-year survival, we calculated the C statistics for
all predictors, which is analogous to the area under the receiver
operating characteristic (AUROC) curve. Furthermore, we used
multivariate Cox regression analyses to estimate the relative riskof all-cause mortality associated with the prognostic assessments.
First, we calculated the crude hazard ratios (HRs) for each variable. Second, to compare the relative risks among the predictors,
we divided all continuous variables by a one-half SD to reveal standardized units and adjusted for age and sex (e-Appendix 1).
To identify the best predictive model of mortality, we performed a stepwise multivariate Cox regression with a backwardelimination (entry threshold, P , .05; removal threshold, P , .10)ﬁtted with all candidate variables that were signiﬁcant predictors at the univariate level. To compare the model ﬁt of therevealed predictors with the model fit of established models(ie, the linear forms of the continuous BODE and ADO predictors), we calculated the –2 log-likelihoods. A lower level of the–2 log-likelihood indicates a better model ﬁt. To compare thediscriminative properties of these predictors for 4-year survivalwith the discriminative properties of the BODE and ADO indexes,
we used AUROC curves (e-Appendix 1). To analyze the intermediary impact of established predictors on the relative risk that
was associated with the independent predictors of our model, wequantiﬁed the attenuation of each HR by adding the potential
intermediate predictors in separate Cox regression models(e-Appendix 1). Data analysis was performed with the statisticalsoftware SPSS, version 16.0 (SPSS Inc; Chicago, Illinois).

Results
Baseline Characteristics and Survival

The outcome of this study was all-cause mortality. Vital status
was ascertained by follow-up visits and telephone contacts.
The censor date was July 26, 2010. One patient in GOLD stage I
was lost to follow-up; therefore, 169 patients were eligible forsurvival analyses. Date of death was veriﬁed by contacting relatives and general practitioners. Survival time was deﬁned as thetime of the baseline visit to the date of death or date of the lastcontact.

Patient characteristics at baseline are shown in
Table 1. Twenty-six (15.4%) patients died over a
median follow-up of 48 months (range, 10-53 months).
There were signiﬁcant differences between survivors
and nonsurvivors in FEV1, inspiratory to total lungcapacity ratio (IC/TLC), PAL, step counts per day,6-min walk distance, BMI, fat-free mass index,
Tei index, NT-pro-BNP, ABI, adiponectin level, degreeof dyspnea, SGRQ total score and activity subdomain, and mortality risk scores. Patient medicationand comorbidities according to their medical history
are shown in e-Table 1.

Statistical Analyses

Association Between Physical Activity and Mortality

Characteristics of survivors and nonsurvivors are presented
as mean and SD (normally distributed variables), median andinterquartile range (skewed or categorical variables), or number
and percent (dichotomous variables). Differences were compared using two-tailed t test for normally distributed variables,
Mann-Whitney U test for categorical variables or in the case ofheteroscedasticity, and x2 tests for dichotomous variables. Skewed
variables were log-transformed to normalize the distribution.

The probability of survival differed signiﬁcantly
among the World Health Organization categories
of PAL (Fig 1). The absolute risk of 4-year mortality
for very inactive, sedentary, and active patients was
31%, 9%, and 0%, respectively. Next, we evaluated theprognostic value of PAL and steps per day as continuous variables. After standardization and adjustment

Mortality

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© 2011 American College of Chest Physicians333Table 1—Patient Characteristics by Survival Status
Characteristic

Survivors

Description
Patients
Age, yMen
Current smokers
Pack-y smoked
Lung function
FEV1, % predicted
IC/TLC
Physical activity
Physical activity level
Steps per day
Exercise capacity
6-min walk distance, mNutritional status
BMI, kg/m2
Muscular status
Fat-free mass index, kg/m2
Muscle depletiona
Cardiovascular status
LVEF ⱕ 50%
E-wave/A-wave
Tei index
NT-proBNP, pg/mL
ABI
ABI , 1.00
Adipokines
Adiponectin, ng/mL
Leptin, ng/mL
Systemic inﬂammation
hs-CRP, mg/L
IL-6, pg/mL
Fibrinogen, mg/dL
Questionnaires
MMRC grade
SGRQ (total score)
SGRQ (activity score)
Beck Depression Inventory
Mortality scores
BODE index
Updated BODE index
ADO index

Nonsurvivors

P Value

143 (84.6)
63.6 (6.6)
107 (74.8)
62 (43.4)
52 (26)26 (15.4)
66.1 (6.4)
20 (76.9)9 (34.6)
52 (20)

.083
.82
.41
.98

58.8 (21.1)
34.5 (9.6)

41.4 (21.9)
25.2 (11.0), .001, .0011.55 (0.27)
6424 (3679)

1.27 (0.18)3006 (2081), .001, .001450 (107)317 (144), .00126.7 (5.1)

23.5 (4.3).004

18.9 (2.6)
13 (9.5)

17.6 (2.4)
7 (26.9)

.023
.013

4 (3)
0.93 (0.21)
0.41 (0.11)
64 (38-106)
1.02 (0.92-1.11)
66 (46)1 (4)0.88 (0.20)
0.47 (0.15)
98 (49-209)
0.93 (0.79-1.01)
21 (81)

.79
.32
.033
.038
.012
.001

5649 (3,998-8,689)
7348 (3,325-14,898)

9042 (3,402-19,453)
5548 (1,902-10,555)

.039
.057

2.8 (1.2-6.3)
2.8 (1.7-4.9)
431 (96)

2.9 (1.1-5.6)
3.0 (1.5-6.2)
462 (105)

.96
.68
.13

2 (1-3)43 (20)56 (24)7 (3-13)

3 (2-4)54 (20)70 (19)7 (3-12)

.002
.014
.004
.74

2 (0-3)
1 (0-3)3 (2-4)4 (2-7)
6 (2-10)5 (4-6), .001, .001, .001

Variables that were normally distributed are presented as mean (SD) and were tested by two-tailed t test or, in case of heteroscedasticity (steps perday), by Mann-Whitney U test. Skewed variables are presented as median (interquartile range) and were tested log-transformed. Categorizedvariables are presented as median (interquartile range) and were tested by Mann-Whitney U test. Dichotomous variables are presented as No. (%)
and were tested by x2 test. Data were missing for fat-free mass index and muscle depletion (n 5 5), LVEF (n 5 3), E-wave/A-wave (n 5 8), Tei index(n 5 24), and leptin level (n 5 7). ABI 5 ankle-brachial index; ADO 5 age, dyspnea, and airﬂow obstruction; BODE 5 BMI, airﬂow obstruction,
dyspnea, and exercise capacity; hs-CRP 5 high-sensitivity C-reactive protein; IC/TLC 5 inspiratory to total lung capacity ratio; LVEF 5 left
ventricular ejection fraction; MMRC 5 modiﬁed Medical Research Council; NT-proBNP 5 N-terminal pro-B-type natriuretic peptide; SGRQ 5
St George Respiratory Questionnaire.
aMuscle depletion was deﬁned as fat-free mass index , 17.05 kg/m2 in men and , 14.62 kg/m2 in women.

for age and sex, each one-half SD increase in PAL(0.14) and steps per day (1,845) was associated witha lower risk of death (HR, 0.46; 95% CI, 0.33-0.64;
P , .001, and HR, 0.49; 95% CI, 0.35-0.69; P , .001,
respectively) (Table 2). There was no evidence of acurvilinear association when quadratic terms wereadded (PAL squared, P 5 .75; steps per day squared,
P 5 .71).

Comparison of the Prognostic Value of Physical
Activity With Established Predictors
Table 2 presents the results of the C statistic analyses (AUROC curve) and the Cox regression analyses(crude and adjusted HRs). The C statistic revealed
the PAL to have the best ability to distinguish between
survivors and nonsurvivors (AUROC curve, 0.81),
followed by steps per day (AUROC curve, 0.80).

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© 2011 American College of Chest PhysiciansTable 2—Prognostic Value of Physical Activity and Established Predictors of Mortality in Patients With COPD
Crude Cox Regression of the
Raw Predictors

C Statistic

Lung function
FEV1, %
IC/TLC
Physical activity
Physical activity level
Steps per day
Exercise capacity
6-min walk distance, mNutritional and muscular status
BMI, kg/m2
Fat-free mass index, kg/m2
Systemic inﬂammation
Log hs-CRP, mg/L
Questionnaires
MMRC grade
SGRQ (total score)
SGRQ (activity score)
Log Beck Depression Inventory
Mortality scores
BODE index
Updated BODE index
ADO index

Adjusted Cox Regression of
the Standardized Predictorsa

AUROC

HR

95% CIP ValueHR

95% CIP Value

0.75
0.75

0.96
0.91

0.94-0.98
0.87-0.95, .001, .0010.56
0.59

0.41-0.75
0.46-0.75, .001, .001

0.81
0.80

0.005
0.99960.001-0.050
0.9994-0.9998, .001, .0010.46
0.49

0.33-0.64
0.35-0.69, .001, .001

0.77

0.993

0.990-0.996, .001

0.63

0.53-0.75, .001

0.68
0.64

0.87
0.82

0.79-0.96
0.70-0.97

.003
.022

0.67
0.61

0.52-0.86
0.45-0.83

.002
.001

0.51

0.98

0.62-1.56

.94

1.00

0.82-1.21

.98

0.68
0.64
0.67
0.50

1.63
1.02
1.03
1.08

1.20-2.22
1.00-1.04
1.01-1.05
0.70-1.66

.002
.018
.006
.73

1.28
1.38
1.45
1.07

1.10-1.49
1.11-1.72
1.15-1.83
0.87-1.32

.002
.004
.002
.51

0.76
0.75
0.77

1.44
1.24
1.76

1.24-1.66
1.14-1.35
1.37-2.25, .001, .001, .001

1.47
1.26
1.76

1.26-1.71
1.15-1.38
1.37-2.25, .001, .001, .001

Values were missing for fat-free mass index (n 5 5). AUROC 5 area under the receiver operating characteristic; HR 5 hazard ratio. See Table 1
legend for expansion of other abbreviations.
aHRs were adjusted for age and sex, except the HR of the ADO index. The ADO index was adjusted for sex only. For standardization, all continuous
variables were divided by one-half SD. The MMRC grade was standardized by multiplying by 2.

After adjustment for age and sex, highly signiﬁcant(P , .001) HRs were observed for PAL, steps perday, FEV1, IC/TLC, 6-min walk distance test, BODE
index, updated BODE index, and ADO index. Signiﬁcant (P , .01) HRs were observed for BMI, fatfree mass index, MMRC dyspnea grade, and SGRQ
(total score and activity subdomain). Categorizing predictors with established cut points (ie, BMI , 21 kg/m2,
fat-free mass index , 14.62 kg/m2 in womenand , 17.05 kg/m2 in men, hs-CRP . 3.0 mg/L, andBeck Depression Inventory score ⱖ 15) did not
improve the associations (data not shown). The highest
relative risk of death was observed for a standardized
decrease in PAL (117%), followed by steps per day(104%) (Fig 2). The comparison of the independent
prognostic value of physical activity with those of
FEV1, IC/TLC, 6-min walk distance, BMI, fat-freemass index, MMRC, and SGRQ total score when
included simultaneously in different multivariate Cox
regression analyses showed the superiority of physical activity (Table 3).
Evaluation of Further Potential Predictorsof Mortality
The predictive values of cardiac function, peripheral vascular status, adipokines, IL-6 level, and ﬁbrinogen level for all-cause mortality are shown in Table 4.
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After adjustment for age and sex, Cox regression
analyses revealed a higher Tei index, higher serum
adiponectin levels, lower serum leptin levels, and an
ABI , 1.00 to be signiﬁcantly associated with a higher
risk of all-cause mortality (Table 4).
Best Model of Independent Predictors of Mortality
In a stepwise multivariate Cox regression model,
PAL, adiponectin level, and ABI , 1.00 were the bestindependent predictors of mortality (Table 5). Thismodel revealed a better model ﬁt than did those withthe linear forms of the continuous BODE and ADO
index predictors (–2 log-likelihood, 210, 224, and235, respectively). C statistic analyses revealed PAL,
adiponectin level, and ABI , 1.00 to have better discriminative properties than the BODE and ADO
indexes (AUROC curve, 0.85, 0.76, and 0.75, respectively) (Fig 3).
Next, we analyzed the attenuation of the adjusted
HRs that were associated with each independent
predictor by adding potential intermediate factors(e-Table 2), one at a time, in separate Cox regression
models (e-Table 3). The HR associated with PAL wasonly partly attenuated by FEV1, IC/TLC, MMRC,
or 6-min walk distance (e-Table 3). The largest, but
still small, attenuation (28.6%) was observed when
FEV1, MMRC, and 6-min walk distance were added
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© 2011 American College of Chest Physicians335

Table 3—Different Multivariate Cox Regression Models Comparing the Independent Prognostic Value of Physical
Activity With Those of Established Predictors of Mortality
Multivariate Cox Regressionsa

Multivariate Cox Regressionsa

Paired Variables

HR

95% CIP Value

Paired Variables

HR

95% CIP Value

FEV1, per 11% predicted
Physical activity level, per 0.14b

0.74
0.55

0.54-1.03
0.39-0.79

.071
.001

FEV1, per 11% predicted
Steps per day, per 1,845

0.72
0.60

0.52-1.01
0.42-0.88

.058
.008

IC/TLC, per 5.2%
Physical activity level, per 0.14

0.73
0.55

0.57-0.95
0.39-0.78

.017
.001

IC/TLC, per 5.2%
Steps per day, per 1,845

0.72
0.59

0.56-0.92
0.41-0.84

.010
.003

6-min walk distance, per 61 m
Physical activity level, per 0.14

0.76
0.58

0.62-0.94
0.41-0.83

.010
.003

6-min walk distance, per 61 m
Steps per day, per 1,845

0.76
0.66

0.59-0.96
0.45-0.98

.023
.041

BMI, per 2.6 kg/m2
Physical activity level, per 0.14

0.75
0.50

0.58-0.98
0.36-0.69.033, .001

Body-mass index, per 2.6 kg/m2
Steps per day, per 1,845

0.70
0.51

0.55-0.90
0.37-0.70.006, .001

Fat-free mass index,
per 1.3 kg/m2
Physical activity level, per 0.14

0.71

0.52-0.99

.043

0.63

0.46-0.87

.005

0.50

0.36-0.69, .001

Fat-free mass index,
per 1.3 kg/m2
Steps per day, per 1,845

0.49

0.35-0.68, .001MMRC grade, per 0.5 grade
Physical activity level, per 0.14

1.07
0.49

0.90-1.27
0.34-0.69.44, .001MMRC grade, per 0.5 grade
Steps per day, per 1,845

1.03
0.51

0.86-1.23
0.34-0.74

.78
.001

SGRQ total score, per 10 points
Physical activity level, per 0.14

1.05
0.48

0.81-1.36
0.33-0.69.70, .001

SGRQ total score, per 10 points
Steps per day, per 1,845

1.02
0.50

0.78-1.34
0.34-0.74

.88
.001

Values were missing for fat-free mass index (n 5 5). See Table 1 and 2 legends for expansion of abbreviations.
aFourteen different multivariate Cox regression analyses are presented, each adjusted for age and sex. For standardization, all predictors weredivided by one-half SD.
bReplacing the physical activity level by SGRQ activity score revealed an HR of 1.12 (95% CI, 0.85-1.48; P 5 .43) for the SGRQ activity score.

simultaneously to the basic model. The HR associated with adiponectin level was partly attenuated(35.5%) after IC/TLC and fat-mass index were added
to a basic model, which was adjusted for age, sex,
history of diabetes, homeostatic model assessment
index, and levels of triglycerides, high-density lipoproteins, leptin, and hs-CRP. The HR associated with
ABI , 1.00 was attenuated by 43.6% after FEV1 and6-min walk distance was added. In a subgroup analysis that included patients in GOLD stages I to III
only, the mortality risk associated with ABI was
higher than the risk in the total cohort and was only
attenuated by 24.2% when FEV1 and 6-min walk distance were added (e-Table 3).
Discussion
The main ﬁnding of our study is that objectively
measured physical activity is the best predictor of allcause mortality in patients with COPD. In addition,higher levels of adiponectin and an impaired vascular
status provide independent prognostic information in
our cohort.
Physical Activity
Physical activity is deﬁned as any bodily movement
produced by skeletal muscles that results in energy

expenditure beyond resting energy.51 Physical activity
has been shown to be an important predictor ofmortality in healthy subjects18 and in those with several
chronic diseases.19-21 The physiologic processes underlying the relationship between physical activity andsurvival are complex and only incompletely understood. It has been speculated that physical inactivity
leads to dysregulated cellular and molecular circuitry,
which directly contributes to multiple chronic healthdisorders.52 For COPD, there is only one epidemiologic study that addressed the relationship between
physical activity and mortality during a mean follow-up
of 12 years.26 Subjects with airﬂow obstruction who
reported a very low level of physical activity on aquestionnaire had a higher risk of death than those
who reported higher levels of physical activity.26 However, such observations must be interpreted withcaution because questionnaire-based assessments ofphysical activity are subject to misclassiﬁcation biases,27
typically overestimate absolute amounts of physicalactivity,53,54 and do not provide accurate estimates ofabsolute amounts of activity per day.18,54 In the present study, we show that objectively measured physical activity is a strong predictor of mortality in patientswith COPD. Furthermore, we found a linear association between physical activity and mortality. For
every 0.14 decrease in PAL, the relative risk ofdeath more than doubled. A 0.14 decrease in PAL

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© 2011 American College of Chest Physicians

Figure 1. Kaplan-Meier survival curves according to World
Health Organization categories of physical activity level (PAL). A
higher 4-year risk of mortality was observed for sedentary patients(PAL, 1.40-1.69 [dashed line]; log-rank P 5 .002) and very inactive
patients (PAL , 1.40 [solid line]; log-rank P , .001) than for active
patients (PAL ⱖ 1.70 [dotted line]). Number of deaths and corresponding total number of patients per category were 20 and 65,
respectively, for very inactive patients; 6 and 67, respectively, forsedentary patients; and 0 and 37, respectively for active patients.
df 5 degrees of freedom.

corresponded to a decrease of 200 to 250 kcal in active
daily energy expenditure. Compared with other prognostic assessments in our cohort, PAL was the best
predictor of all-cause mortality: It revealed the best
discriminative properties, had the highest relative riskof mortality per standardized decrease, and remained
the strongest independent predictor in a stepwise
Cox regression model. We speculate that increasing
physical activity in daily life in patients with COPD
by interventional programs as shown before55 might
have an impact on mortality risk, which, however, has
to be demonstrated by future studies.
Adipokines, Cardiovascular Status, and Mortality
Adipokines, such as adiponectin and leptin, aresecreted by the white adipose tissue.56 They are
involved in inﬂammatory processes, immune response,
lipid metabolism, insulin sensitivity, vascular homeostasis, angiogenesis, and regulation of energy balance.56
Decreased circulating concentrations of adiponectin
are associated with obesity, the metabolic syndrome,insulin resistance, and cardiovascular disease.57 By
contrast, serum levels of adiponectin are elevated in
the presence of autoimmune diseases and chronic
inﬂammatory conditions, such as rheumatoid arthritis,
systemic lupus erythematosus, inﬂammatory bowel
disease, type 1 diabetes, and cystic ﬁbrosis.58 Furthermore, higher levels of adiponectin have been reported
to predict mortality in healthy elderly persons,59 inpatients with chronic heart failure,33 and in patients
www.chestpubs.org

with chronic renal failure.34 Little is known about therole of adipokines in patients with COPD. Expressionof adiponectin and leptin by bronchial epithelial cells
in COPD have been described.60,61 A smaller crosssectional study has demonstrated that serum concentrations of adiponectin are elevated in patients withstable COPD when compared with healthy control subjects.32 Furthermore, both leptin and adiponectin areassociated with the systemic inﬂammatory processduring exacerbations of COPD.62 In the present
study, we show that a higher level of adiponectin anda lower level of leptin are associated with an increased
risk of mortality in patients with COPD. However,the prognostic value of adiponectin level was superior and independent of leptin level, sex, lipid metabolism, insulin resistance, systemic inﬂammation, andfat mass.
The spectrum of the cardiovascular sequelae inpatients with COPD includes generalized atherosclerosis, right ventricular dysfunction, and left ventricular diastolic dysfunction.25,63,64 The ABI is a reliablemeasure of peripheral arterial disease,48 is an indicator of generalized atherosclerosis,65 and improves cardiovascular risk prediction in the general population
beyond traditional risk factors.65 An ABI , 1.00 is
associated with an increased risk of death in healthyelderly persons and in several patient populations.49
In the present study, we found that an impaired ABI
is a signiﬁcant predictor of mortality in patients with
COPD. However, the ABI appears to provide better
prognostic information for patients in earlier stages ofthe disease. Furthermore, we found that the Tei index
predicts mortality in our study population. The Tei index
provides information about global right ventricularfunction independent of mean pulmonary artery pressure and correlates with stroke volume on right heart
catheterization.47,66 Reduced right ventricular strokevolume has been posited to be related to the degreeof emphysema, hyperinﬂation, and left ventricularﬁlling impairment in patients with COPD.67,68
Best Model of Independent Predictors
PAL, adiponectin level, and ABI are independent
predictors of the best model of mortality in COPD.
No lung function variable remained an independent
predictor in the ﬁnal model. The model ﬁt of the ﬁnal
predictors was better than the model ﬁt of BMI, airﬂow obstruction, dyspnea, and exercise capacity andof age, dyspnea, and airﬂow obstruction. Furthermore,
PAL, adiponectin level, and ABI revealed a better
ability to distinguish between survivors and nonsurvivors than did the BODE and ADO indexes. One possible explanation for the superiority of this model isthat it covers several important systemic aspects ofthe disease: the cardiovascular sequelae, metabolic
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© 2011 American College of Chest Physicians337

Figure 2. Comparison of the relative risk of death associated with physical activity and establishedpredictors of mortality. After adjustment for age and sex, the relative risk was calculated for each onehalf SD increase (1) or decrease (–) in continuous predictors and for each 1-point increase in categorized scores. The ADO index already includes the effect of age and was adjusted for sex only. MMRC
dyspnea grade was multiplied by 2. All predictors were distributed within a range of 9 to 11 integral
numbers or score points. The relative risk of mortality was calculated from hazard ratios (HRs) shown in
Table 2 as follows: for HRs , 1.00, [(1/HR) – 1] 3 100%; for HRs . 1.00, (HR – 1) 3 100%. Values were
missing for fat-free mass index (n 5 5). ADO 5 age, dyspnea, and airﬂow obstruction; BODE 5 BMI,
airway obstruction, dyspnea, and exercise capacity; IC/TLC 5 inspiratory to total lung capacity ratio;
MMRC 5 modiﬁed Medical Research Council; SGRQ 5 St. George Respiratory Questionnaire.

alterations, and physical inactivity as the integral ofseveral organ dysfunctions. Furthermore, these markers partly capture the effect of other established predictors of mortality, such as lung function impairment,
exercise intolerance, and body composition.

Established Predictors of Mortality
Highly signiﬁcant predictors with good discriminative properties in our study were the 6-min walkdistance, FEV1, and IC/TLC, all of which have been

Table 4—Prognostic Value of Potential Predictors of Mortality in Patients With COPD
Crude Cox Regression of the
Raw Predictors

C Statistic

Cardiovascular status
E-wave/A-wave
Tei-indexb
Log NT-proBNP, pg/mL
ABI
ABI , 1.00
Adipokines
Log adiponectin, ng/mL
Log leptin, ng/mL
Systemic inﬂammation
Log IL-6, pg/mL
Fibrinogen, mg/dL

Adjusted Cox Regression of theStandardized Predictorsa

AUROC

HR

95% CIP ValueHR

95% CIP Value

0.56
0.61
0.62
0.66

0.35
1.48
1.47
0.16
4.34

0.04-3.43
1.06-2.06
1.05-2.06
0.04-0.70
1.64-11.52

.36
.021
.027
.015
.003

0.93
1.26
1.16
0.85
3.87

0.74-1.17
1.04-1.54
0.97-1.39
0.72-0.99
1.44-10.40

.53
.020
.12
.041
.007

0.63
0.59

1.99
0.71

1.07-3.69
0.51-0.99

.029
.044

1.34
0.81

1.06-1.71
0.65-0.99

.017
.042

0.51
0.60

1.12
1.003

0.70-1.80
0.999-1.006

.64
.14

1.05
1.13

0.86-1.27
0.96-1.33.66.15

Values were missing for E-wave/A-wave (n 5 8), Tei index (n 5 24), and leptin level (n 5 7). See Table 1 and 2 legends for expansion ofabbreviations.
aHRs were adjusted for age and sex. For standardization, all continuous variables were divided by one-half SD.
bThe raw Tei index was multiplied by 10 to get a useful HR.
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© 2011 American College of Chest PhysiciansTable 5—Best Model of Independent Predictors of AllCause Mortality Revealed by a Stepwise Multivariate
Cox Regression Analyses
Regression
Coefﬁcient
Physical activity
level
Log adiponectin,
ng/mL
ABI , 1.00

24.718SE

HR

95% CI

1.148 0.009 0.001-0.085

P Value, .0010.825

0.333 2.28

1.19-4.38

.013

1.182

0.504 3.26

1.22-8.75.019

Model ﬁt: –2 log-likelihood 5 210; x2 5 39.4; degrees of freedom 5 3;
P , .001; N 5 169. There were some missing values in the initial stepwise
Cox regression; therefore, the remaining signiﬁcant predictors of the
stepwise model (physical activity level, adiponectin level, and ABI)
were analyzed again in a separate multivariate Cox regression without
missing values, which is given in this table. See Table 1 and 2 legends
for expansion of abbreviations.

shown to be relevant predictors of mortality inpatients with COPD.6-8 Furthermore, airways obstruction, hyperinﬂation, and functional exercise capacity
have been shown to be important physiologic variables
related to physical activity in patients with COPD.22,23
Accordingly, we found that these measurements partly
contributed to the increased risk of mortality that is
associated with decreased physical activity.
The BODE index was developed in patients withsevere to very severe COPD as a prognostic index
that includes information about BMI, airflow
obstruction, dyspnea, and exercise capacity.16 The
BODE index has been demonstrated to be a better

Figure 3. Receiver operating characteristic curves for the BODE
index, the ADO index, and the composite of PAL, adiponectinlevel, and ABI as predictors of mortality in patients with COPD.
The corresponding areas under the curves for the composite
of PAL, adiponectin level, and ABI; the BODE index; and theADO index were 0.85, 0.76, and 0.75, respectively. ABI 5 anklebrachial index. See Figure 1 and 2 legends for expansion of otherabbreviations.
www.chestpubs.org

predictor of mortality than airﬂow obstruction16 andhyperinﬂation.6 The present study conﬁrms theseobservations.
Because of poor calibration of the BODE index intwo external cohorts, Puhan and coworkers17 updated
the BODE index. However, the updated BODE
index did not improve the discriminative ability intheir cohort, which suggests that important predictors are still missing. In line with the findings ofPuhan et al,17 we were unable to demonstrate an
improvement in the discriminative properties of the
updated BODE index in our cohort. Furthermore,Puhan and coworkers developed the ADO index asa simpliﬁed risk score for primary care. In the present
study, the ADO index showed good discriminative
properties and a large increase in the mortality risk
per 1-point increase. The strength of the ADO index
is that it is driven by age, which is the most important determinant of survival independent of diseasediagnosis.69
In the present study, we evaluated the predictive
power of several other established prognostic factors
in patients with COPD, including malnutrition,9,70-72
muscle wasting,10,11 systemic inﬂammation,14,15 healthstatus,8 the degree of depressive symptoms,13 anddyspnea.12 We conﬁrmed that muscle wasting andmalnutrition are signiﬁcant determinants of mortality.
However, their predictive properties were inferior
to measures of physical activity, exercise capacity,and lung function. We did not ﬁnd an association
between mortality and levels of hs-CRP or othermarkers of systemic inﬂammation. Hs-CRP level was
shown to predict mortality in two epidemiologic
studies of COPD14,15 but showed no association withmortality in another COPD cohort.31 Health status8
and the degree of dyspnea12 have been found to be
independent predictors of mortality in patients with
COPD. In the present study, we conﬁrm the prognostic properties of health status and the MMRC
dyspnea scale, but these predictors had a prognostic
value inferior to that of the physiologic measures inour cohort. Of interest, in our study population, theactivity subdomain of the SGRQ had a better prognostic value than the total score but was inferior to
objectively measured physical activity. Furthermore,when replacing objectively measured physical activity by SGRQ activity score in a model adjusted for
FEV1, the SGRQ activity score no longer remained
an independent predictor of mortality. The degree ofdepressive symptoms has been found to predict mortality in patients with severe to very severe COPD.13
The present study population covered the whole
spectrum of disease severity and had, on average, alower score on the Beck Depression Inventory; we
did not observe an impact of depression on mortality
in our cohort.
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© 2011 American College of Chest Physicians339

Study Limitations and Conclusions
Our study has some limitations. First, the total
number of deaths in our study was low, which is apotential source of bias. However, resulting HRs andC statistics for established predictors showed similarities with the results in larger COPD cohort studies, for example: (1) in Celli et al,16 the BODE index
HR was 1.34 (95% CI, 1.26-1.42) and C statistic, 0.74,
vs the present cohort HR of 1.44 (95% CI, 1.24-1.66)
and C statistic of 0.76; (2) in Casanova et al,6 the
IC/TLC C statistic was 0.74, vs the present cohort
C statistic of 0.75; and (3) in Oga et al,8 SGRQ activity
score HR was 1.04 (95% CI, 1.02-1.06) vs the present
cohort HR of 1.03 (95% CI, 1.01-1.05). Therefore, it
is unlikely that our results are biased by the low number of deaths. Second, a selection bias has to be considered because our patients were recruited from theinstitute’s database, which consists of patients who
also participate in clinical studies. Therefore, one
might assume that the frequency of comorbid conditions in the cohort might be less than the frequency
of comorbid conditions in other COPD populations.
However, patients of our cohort had similar frequencies of comorbidities according to their medical history when compared with recently reported data ofother cohorts.73,74 Therefore, it is not likely that aselection bias explains the observed results. Third,
losses to follow-up are a main cause of selection bias
in longitudinal studies. In our study, only one patient
was lost to follow-up.
In conclusion, we found that objectively measured
physical activity is the best predictor of mortality
compared with a broad range of other prognostic
assessments in patients with COPD. Furthermore,
we identiﬁed adiponectin level, leptin level, vascular
status, and echocardiographically assessed right ventricular dysfunction to be novel predictors of mortality. PAL, adiponectin level, and vascular statusprovided the best independent prognostic information for patients with COPD. The current ﬁndings
need to be conﬁrmed in larger COPD cohorts.
Acknowledgments
Author contributions: Dr Waschki had full access to all of thedata in the study and takes responsibility for the integrity of thedata and the accuracy of the data analysis.
Dr Waschki: contributed to the data collection, statistical analysis,
and data interpretation and wrote the ﬁrst draft of the manuscript.
Dr Kirsten: contributed to the data collection and interpretation
and revision of the manuscript.
Dr Holz: contributed to the data collection and interpretation andrevision of the manuscript.
Dr Müller: contributed to the data collection and interpretation
and revision of the manuscript.
Dr Meyer: contributed to the statistical analysis, data interpretation, and revision of the manuscript.
Dr Watz: contributed to the study design, the data collection andinterpretation, and writing of the manuscript.

Dr Magnussen: contributed to the study design, data interpretation, and writing of the manuscript.
Financial/nonﬁnancial disclosures: The authors have reported
to CHEST that no potential conﬂicts of interest exist with any
companies/organizations whose products or services may be discussed in this article.
Role of sponsors: The sponsors had no role in the design of thestudy, the collection and analysis of the data, or in the preparationof the manuscript.
Other contributions: We thank Martin Claussen, MD;
Beke Feindt, PhD; Miriam Schaper, MD; Anna-Lena Sack, MD;
Kirsten Paasch; Ines Zimmermann; and Jeanette Kotzur for their
help in collecting patient data and Mary McKenney for a criticalreview of the manuscript.
Additional information: The e-Appendix and e-Tables can befound in the Online Supplement at http://chestjournal.chestpubs.
org/content/140/2/331/suppl/DC1.

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