Skip Navigation

European Journal of Heart Failure 2006 8(2):198-202; doi:10.1016/j.ejheart.2005.07.008
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (6)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Ingle, L.
Right arrow Articles by Clark, A.L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ingle, L.
Right arrow Articles by Clark, A.L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2006 European Society of Cardiology

Predicting peak oxygen uptake from 6-min walk test performance in male patients with left ventricular systolic dysfunction

L. Ingle*, K. Goode, A.S.R. Rigby, J.G.F. Cleland and A.L. Clark

Department of Academic Cardiology, Castle Hill Hospital Castle Road, Cottingham, Hull, HU16 5JQ, UK

* Corresponding author. Tel.: +44 148 262 3732; fax: +44 148 262 4071. E-mail address: L.Ingle{at}hull.ac.uk


   Abstract
Top
 Abstract
1. Introduction
 2. Methods
 3. Results
 4. Discussion
 5. Conclusion
 References
 
Background: In patients with left ventricular systolic dysfunction (LVSD), peak oxygen uptake (pVO2) has strong predictive power for mortality, and can be used to guide management. However, many patients cannot tolerate standard test protocols. The 6-min walk test (6-MWT) is often used to estimate functional capacity due to its simplicity, cost effectiveness and familiarity to patients with LVSD. The relationship between 6-MWT performance and pVO2 is not certain, but if closely related could allow substitution of an expensive and cumbersome test for a cheaper and more familiar one.

Methods and results: 120 male patients with LVSD (LVEF <40%; (mean±S.D.) age 68±13 years; BMI 28±5) performed, in random order, a maximal incremental treadmill exercise test with metabolic gas exchange measurements to derive peak oxygen consumption (pVO2=19.8±5.8 mL·kg·1·min·1), and a standardised 6-MWT (308±142 m; r=0.44; P=0.00001). In multivariate models including demographic data, resting blood pressure and heart rate, spirometry, routine blood samples, and walk distance, five variables were independently predictive of peak oxygen consumption. pVO2 = 11.92+(1.48xFEV1(L))+(1.12xhaemoglobin (g dl–1))+(0.016xdistance walked (m))–(0.33xBMI)–(0.11xage (years)). This equation accounted for 48% of the variation in pVO2.

Conclusions: Using these five simple variables, peak oxygen consumption can be estimated with moderate accuracy. In clinical practice, however, when an estimate of peak oxygen consumption is required, incremental exercise testing with metabolic gas exchange measurements cannot be avoided in male patients with LVSD. Further work is needed to assess the relation between estimated pVO2 and outcome.

Key Words: Peak oxygen uptake • 6-MWT • LVSD

Received April 12, 2005; Revised June 20, 2005; Accepted July 19, 2005


   1. Introduction
Top
 Abstract
 1. Introduction
2. Methods
 3. Results
 4. Discussion
 5. Conclusion
 References
 
Left ventricular systolic dysfunction (LVSD) causes a decline in peak oxygen uptake (pV2), resulting in worsening quality-of-life [1-3]. pV2 provides important information on risk stratification [4], can be used to guide management [5], and establish exercise training [6]. However, tests involving cycling- or walking-based protocols of increasing speed, gradient or resistance, are not well tolerated in patients with LVSD. Equipment for measuring metabolic gas exchange is expensive and cumbersome, and availability of trained staff is limited. The 6-min walk test (6-MWT) is an alternative method for assessing functional capacity, and previous reports suggest it is a reliable test in patients with heart failure [7-9]. We wanted to explore the relation between pV2 and 6-MWT performance, and to see if any combination of routinely collected variables could predict peak oxygen uptake with sufficient accuracy to be clinically useful in patients with LVSD.


   2. Methods
Top
 Abstract
 1. Introduction
 2. Methods
3. Results
 4. Discussion
 5. Conclusion
 References
 
The Hull and East Riding ethics committee approved the study, and all patients provided informed consent for participation. Patients were recruited from a chronic disease management programme. Inclusion criteria were: evidence of left ventricular systolic dysfunction (LVSD); and symptoms of heart failure (NYHA classes I-IV). 69% of patients had heart failure of ischaemic aetiology, and all had suffered from the condition for at least 6 months before the study. Non-cardiac comorbidities including hypertension, osteoarthritis, COPD, diabetes mellitus, cerebrovascular disease, and peripheral vascular disease of moderate severity or less were included. Patients were excluded if they were unable to walk without assistance from another person. Heart failure was defined in accordance with National Institute for Clinical Excellence Guidelines [10] and the European Society of Cardiology [11]. Left ventricular function was determined from 2D-echocardiography or magnetic resonance imaging. Echocardiography was carried out by one of three trained operators. Left ventricular function was assessed by estimation on a scale of normal, mild, moderate, and severe impairment, and was re-estimated by a second operator blind to the assessment of the first; where there was disagreement on the severity of LV dysfunction, the echocardiogram was reviewed jointly with the third operator and a consensus reached. Left ventricular ejection fraction (LVEF) was calculated using the Simpson's formula from measurements of end-diastolic and end-systolic volumes on apical 2D views, following the guidelines of Schiller et al. [12] and LVSD was diagnosed if LVEF was ≤40%. When the echocardiogram was of low quality, patients underwent a cardiac magnetic resonance (CMR) scan to determine left ventricular volume and function. The cause of heart failure, current medications and time since diagnosis were noted.

2.1. Participants
Patients were studied when they were clinically stable, without any changes in medication during the previous 3 weeks. Digoxin was prescribed to 34 (28%) patients; 91 (76%) were taking diuretics; 98 (82%) were on ACE-inhibitors; and 85 (71%) were on beta-blockers. There was a history of smoking in 82 patients (68%), but none was a current smoker at the time of the study. In random order, participants performed a 6-MWT and cardiopulmonary exercise test.

2.2. 6-MWT protocol
The 6-MWT was conducted following a standardised protocol, between 10 am and 4 pm after usual medication [7]. A 15 m flat, obstacle-free corridor, with chairs placed at either end was used. Patients were instructed to walk as far as possible, turning 180° every 15 m in the allotted time of 6 min. Patients were able to rest, if needed, and time remaining was called every second minute [13]. Patients walked unaccompanied so as not to influence walking speed. After 6 min, patients were instructed to stop and total distance covered was calculated to the nearest metre. Standardised verbal encouragement was given to patients after 2 min and 4 min, respectively.

2.3. Cardiopulmonary exercise test
Patients underwent a symptom-limited, treadmill-based maximal test using the Bruce protocol modified by the addition of a Stage 0 with 1.0 miles.h–1 and 5% gradient. Expired air was sampled using an Oxycon Delta metabolic cart (VIASYS Healthcare, Inc., PA, USA). Participants wore a tightly fitting facemask and breathed into a TripleV non-re-breathing turbine via a low resistance mouthpiece (VIASYS Healthcare Inc., PA, USA). Oxygen and carbon dioxide concentrations were determined breath-by-breath. The instrument was re-calibrated before each test. Patients were encouraged to exercise to exhaustion and a respiratory exchange ratio (RER) of ≥1.1 was taken to suggest a maximal effort. 12-lead ECG was monitored throughout (Cambridge Heart Inc., MA, USA). Peak oxygen uptake (pV2) was defined as the mean pV2 attained during the final 30 s of the exercise test. VE/VCO2 slope was calculated as the slope of the VE/VCO2 relationship by linear regression using data points from the entire test. Anaerobic threshold (AT) was calculated by the V-slope method [14]. Spirometry was measured via the same metabolic cart. Forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) were measured, and FEV1/FVC ratio was derived as the best of three efforts. Other routinely collected measures including resting blood pressure and heart rate, and blood samples were undertaken.

2.4. Statistical analysis
All data were analysed using SPSS statistical software for Windows version 11.5 (SPSS Inc, Chicago, Illinois, USA). Mean, standard deviation (S.D.) and range were calculated. A P value of <5% was taken to be statistically significant. To explore the relation between pV2 and potential predictor variables, candidate variables were assessed using univariate and multivariable regression. For the final statistical model the goodness-of-fit was assessed by calculating both the explained variance (R2), and by plotting the residuals. The residuals followed an approximate normal distribution. A forwards stepwise model building process was used to identify the ‘best set’ of predictor variables using routinely collected data including demographics, resting blood pressure and heart rate, spirometry, blood samples, and walk distance. Multicollinearity was tested using Spearman's correlation and collinearity diagnostics (i.e. eigenvalues of the scaled, uncentred cross-product matrix, condition indices, variance-decomposition proportions, variance inflation factors ((VIF)) and tolerances).


   3. Results
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
4. Discussion
 5. Conclusion
 References
 
120 male patients were included in the study, and are presented in Table 1. Mean age (mean±S.D.) was 68 years (34.7% aged ≥75 years); BMI was 28 and 26% were classified as obese (BMI ≥30). Mean 6-MWT distance was 308±142 m. Distance walked was compared with generalised equations for healthy controls [15]. Males with LVSD walked 69.2% of their age/sex-specific predicted walking distance, respectively. pV2 (mean±S.D.) was 19.8±5.8 mL.kg–1.min–1. A moderate association was evident between peak oxygen uptake and 6-MWT performance (r=0.44; P=0.00001; Fig. 1).


View this table:
[in this window]
[in a new window]

  		Table 1 Demographic data for patients with left ventricular systolic dysfunction

 


Figure 1
View larger version (7K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 1 Scatterplot for pV2 (mL.kg–1.min–1) and 6-MWT distance (m).

 
3.1. Univariate analysis
The frequency distributions of all the continuous variables were approximately normal and therefore no normalisation transform was performed prior to univariate regression. FEV1, FVC, age, 6-MWT, and haemoglobin (Hb) were significant predictors of peak oxygen uptake in univariate analysis. FEV1 was the best univariate predictor of pV2, followed by distance walked and FVC, explaining 23.2%, 19.6% and 19.6% of the variance, respectively (Table 2; Fig. 2). The relationship between the dependent (pV2) and each of the independent variables was assessed using curve estimation. All variables exhibited a linear or uncorrelated relationship.


View this table:
[in this window]
[in a new window]

  		Table 2 Comparison of candidate univariate predictors of pV2: ranked in order of magnitude of explained variance (R2)

 


Figure 2
View larger version (8K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 2 (a-c) Scatterplots for pV2 (mL.kg–1.min–1) (a) and FEV1 (L); (b) haemoglobin (g dl–1); and (c) age (years).

 
3.2. Multivariable analysis
Spearman's correlation coefficients were calculated between each of the candidate predictors. They were thus entered separately into a multivariable model to avoid collinearity. Similarly, BMI and weight, and FEV1 and FVC were highly correlated (r=0.89 and 0.84) and were also considered separately. FEV1, distance walked, haemoglobin, age, and BMI were entered into a multivariable model using a stepwise elimination algorithm (using a significance of P<0.05 to include, P>0.10 to exclude in the t-test).

The derived regression model: pV2=11.92+(1.48xFEV1 (L))+(1.12xhaemoglobin (g dl–1))+(0.016xdistance walked (m))–(0.33xBMI)–(0.11xage (years)) explained 48% of the variance in pV2 (Table 3).


View this table:
[in this window]
[in a new window]

  		Table 3 Regression model explaining the variance in peak oxygen uptake

 
Each variable passed tests of collinearity. The substitution of FVC for FEV1 and weight for BMI gave no improvement in explained variance. The standardised residuals of the best model were approximately normal in distribution. The model upholds the assumption of no multicollinearity by having individual VIF scores of much less than 10 [16] and tolerances not less than 0.2 [17]. The regression avoids being biased by having an average VIF of greater than 1 [16]. Analysis of the collinearity diagnostics shows the difference between the eigenvalues to be relatively small and there are no overlapping proportions between variables in the higher order dimensions (i.e. smaller eigenvalues).


   4. Discussion
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
5. Conclusion
 References
 
To our knowledge, this is the first study to predict peak oxygen uptake from 6-MWT performance in a representative sample of male patients with LVSD. Our study shows that from a multivariable model, five routinely collected variables were independently predictive of peak oxygen consumption. While the candidate variables account for only 48% of the variance in pV2, diagnostic analysis confirmed that they were unbiased, and did not suffer from problems of multicollinearity. In clinical practice, when an estimate of peak oxygen consumption is required, incremental exercise testing with metabolic gas exchange measurements cannot be avoided.

In a similar study in patients with COPD, Carter et al. [5] identified six predictive variables including an index of work done during the 6-MWT, DLCO (diffusion capacity of carbon monoxide), mean inspiratory pressure (MIP), FVC, weight, and age which accounted for 79% of the variance in pV2. Spirometry, weight (BMI) and age were common predictors in both studies. However, Carter et al. [5] reported a much higher explained variance than in our study. Previous studies identified leg strength and muscle cross-sectional area as variables accounting for between 57% and 82% of the variance in pV2 in patients with heart failure [18-20]. These findings indicate that abnormalities in the periphery may largely determine exercise capacity, which may explain the limited accuracy of our model. We had wanted to see if easy to acquire variables could predict peak oxygen consumption with sufficient accuracy to be clinically useful, not requiring specialist equipment or training.

FEV1 and FVC were the most significant predictors of pV2 (R2=19.6% (FVC) vs. 19.6% (6-MWT distance)) in our patients, highlighting the important association between spirometric performance and exercise capacity. Our model identified FEV1 as being a slightly better predictor variable than FVC (R2=23.2% vs. 19.6%), which is contrary to other models in patients with COPD [5,21,22], this is likely to be due to increased airways obstruction in patients with COPD. Intuitively, one would expect patients with a high BMI to have a shorter 6-MWT distance. Excess body weight can increase the work of breathing and reduce exercise capacity. When these changes accompany a loss in lung function, exercise capacity is further compromised. Thus, it is not surprising that BMI was included in our multivariate model, although it was non-significant during univariate analysis.

Identification of multivariable predictors are less susceptible to inflation errors caused by significantly different standard errors, and should be used in preference to non-standardised predictors for model identification. However, for the purposes of interpretation, these predictors should then be entered into the regression model in their unstandardised form. It is unclear whether other studies have followed these ‘good practice’ guidelines.


   5. Conclusion
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 5. Conclusion
References
 
In male patients with LVSD, the 6-MWT can be used to predict pV2 with moderate accuracy using these five simple variables: pV2=11.92+(1.48xFEV1 (L))+(1.12xhaemoglobin (g dl–1))+(0.016xdistance walked (m))–(0.33xBMI)–(0.11xage (years)). The utility of the 6-MWT is increased as an alternative to metabolic gas analysis when these tests are unavailable or impractical. However, in clinical practice, when an estimate of peak oxygen consumption is required, incremental exercise testing with metabolic gas exchange measurements cannot be avoided. Further work is needed to assess the relation between estimated peak oxygen consumption and outcome.


   References
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 5. Conclusion
 References
 

  1. Rector T.S., Kubo S.H., Cohn J.N. Patients' self-assessment of their congestive heart failure. Content, reliability and validity of a new measure—the Minnesota-living with heart failure questionnaire. Heart Fail (1987) 3:198–207.
  2. Green C., Porter C.B., Bresnahan D.R., Spertus J.A. Developing and evaluation of the Kansas City Cardiomyopathy Questionnaire: a new health status measure for heart failure. J Am Coll Cardiol (2000) 35:1245–1255.[Abstract/Free Full Text]
  3. Juenger J., Schellberg D., Kraemer S., et al. Health related quality of life in patients with congestive heart failure: comparison with other chronic diseases and relation to functional variables. Heart (2002) 87:235–241.[Abstract/Free Full Text]
  4. Schalcher C., Rickli H., Brehm M., et al. Prolonged oxygen uptake kinetics during low-intensity exercise are related to poor prognosis in patients with mild-to-moderate congestive heart failure. Chest (2003) 124:580–586.[Abstract/Free Full Text]
  5. Carter R., Holiday D.B., Stocks J., Grothues C., Tiep B. Predicting oxygen uptake for men and women with moderate to severe chronic obstructive pulmonary disease. Arch Phys Med Rehabil (2003) 84:1158–1164.[CrossRef][Web of Science][Medline]
  6. Vanhees L., Stevens A., Schepers D., Defoor J., Rademakers F., Fagard R. Determinants of the effects of physical training and of the complications requiring resuscitation during exercise in patients with cardiovascular disease. Eur J Cardiovasc Prev Rehabil (2004) 11:304–312.[CrossRef][Web of Science][Medline]
  7. Guyatt G.H., Sullivan M.J., Thompson P.L., et al. The six-minute walk: a new measure of exercise capacity in patients with chronic heart failure. Can Med Assoc J (1985) 132:919–923.[Abstract]
  8. Demers C., McKelvie R.S., Negassa A., Yusuf S. Reliability, validity, and responsiveness of the six-minute walk test in patients with heart failure. Am Heart J (2001) 142:698–703.[CrossRef][Web of Science][Medline]
  9. Ingle L, Shelton RJ, Rigby AS, Nabb S, Clark AL, Cleland JGF. The reproducibility and sensitivity of the 6-min walk test in elderly patients with chronic heart failure. Eur Heart J in press.
  10. National Institute for Clinical Excellence (NICE). Chronic heart failure. Management of chronic heart failure in adults in primary and secondary care. Clin. Guidel. (2003) 5. London, NICE.
  11. Remme W.J., Swedberg K. Comprehensive guidelines for the diagnosis and treatment of chronic heart failure. Task force for the diagnosis and treatment of chronic heart failure of the European Society of Cardiology. Eur J Heart Fail (2002) 4:11–22.[Free Full Text]
  12. Schiller N.B., Shah P.M., Crawford M., et al. Recommendations for quantification of the left ventricle by two-dimensional echocardiography. J Am Soc Echocardiogr (1989) 2:358–367.[Medline]
  13. Bittner V., Weiner D.H., Yusuf S., et al. Prediction of mortality and morbidity with a 6-minute walk test in patients with left ventricular dysfunction. JAMA (1993) 270:1702–1707.[Abstract/Free Full Text]
  14. Beaver W.L., Wasserman K., Whipp B.J. A new method for detecting anaerobic threshold by gas exchange. J Appl Physiol (1986) 60:2020–2027.[Abstract/Free Full Text]
  15. Enright P.L., Sherrill D.L. Reference equations for the six-minute walk in healthy adults. Am J Respir Crit Care Med (1998) 158:1384–1387.[Abstract/Free Full Text]
  16. Bowerman B.L., O'Connell R.T. Linear statistical models: an applied approach. (1990) 2nd Edition. Belmont, CA: Duxbury.
  17. Menard S. Applied logistic regression analysis. Sage University paper series on quantitative applications in the social sciences, 07-106. (1995) Thousands Oaks, CA: Sage.
  18. Astrand P.O., Rodahl K. Physiological basis of exercise. In: 1970. New York: McGraw-Hill. 141–262.
  19. Senden P.J., Sabelis L.W., Zonderland M.L., et al. Determinants of maximal exercise performance in chronic heart failure. Eur J Cardiovasc Prev Rehabil (2004) 11:41–47.[CrossRef][Web of Science][Medline]
  20. Anker S.D., Swan J.W., Volterrani M., et al. The influence of muscle mass, strength, fatigability and blood flow on exercise capacity in cachectic and non-cachectic patients with chronic heart failure. Eur Heart J (1997) 18:187–189.[Free Full Text]
  21. Carlson D.G., Ries A.L., Kaplan R.M. Prediction of maximum exercise tolerance in patients with COPD. Chest (1991) 100:307–311.[Abstract/Free Full Text]
  22. Ramirez-Venegas A., Ward J.L., Olmstead E.M., et al. Effect of exercise training on dyspnea measures in patients with chronic obstructive pulmonary disease. J Cardiopulm Rehabil (1997) 17:103–109.[CrossRef][Medline]

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?



This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (6)
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Ingle, L.
Right arrow Articles by Clark, A.L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ingle, L.
Right arrow Articles by Clark, A.L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?