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European Heart Journal Advance Access originally published online on February 21, 2007
European Heart Journal 2007 28(5):560-568; doi:10.1093/eurheartj/ehl527
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© The European Society of Cardiology 2007. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Prognostic value of the 6 min walk test and self-perceived symptom severity in older patients with chronic heart failure

Lee Ingle1, Alan S. Rigby2, Sean Carroll1, Ron Butterly1, Rod F. King1, Carlton B. Cooke1, John G.J.F. Cleland2 and Andrew L. Clark2,*

1 Carnegie Research Institute, Leeds Metropolitan University, Beckett's Park Campus, Leeds LS6 3QS, UK
2 Department of Academic Cardiology, University of Hull, Castle Hill Hospital, Cottingham Kingston-upon-Hull HU16 5JQ, UK

Received 3 October 2006; revised 7 December 2006; accepted 17 January 2007; online publish-ahead-of-print 21 February 2007.

* Corresponding author. Tel: 01482 624012; fax: 01482 624071. E-mail address: A.L.Clark{at}hull.ac.uk


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Appendix 1
 Acknowledgements
 References
 
Background The 6 min walk test (6-MWT) is a simple and popular test for evaluating functional status in patients with chronic heart failure (CHF). However, the prognostic value of the 6-MWT in a large, representative sample of CHF patients, and in patients with different degrees of left ventricular systolic dysfunction (LVSD) remains unclear.

Methods and results Of an initial population of 1592 patients, 212 died representing a crude death rate of 13.3%. In surviving patients, the median time to follow-up period was 36.6 months [inter-quartile range (IQR) 28–45 months]. Five variables remained independent predictors of all-cause mortality; decreasing 6-MWT distance, self-perceived signs of breathlessness at night (SOBAN), beta-blocker usage, elevated log NT-proBNP, and reduced haemoglobin concentration. We also dichotomized our analysis by LVSD status (≤mild LVSD or >mild LVSD). For patients with >mild LVSD, 6-MWT remained an important prognostic indicator but not in patients with ≤mild LVSD.

Conclusion The 6-MWT is an important independent predictor of mortality in CHF patients, and this was especially evident in patients with >mild LVSD. The 6-MWT provides little prognostic utility in patients with ≤mild LVSD. While log NT-proBNP was the most potent independent predictor, an additive prognostic effect was evident with the additional selection of 6-MWT. Patients' self-perceived symptoms, especially SOBAN was an independent predictor of mortality in our patients.

Key Words: Chronic heart failure • Exercise • Prognosis • Survival


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Appendix 1
 Acknowledgements
 References
 
Despite recent improvements in pharmacological1 and device-based therapy,2 the prognosis in patients with severe chronic heart failure (CHF) remains poor. The evaluation of functional capacity is an important method for stratifying risk in these patients.3 The ‘gold standard’ method for the assessment of functional capacity is the cardiopulmonary exercise test (CPET). Derived variables including peak oxygen uptake,4 anaerobic threshold,5 and the abnormal relation between minute ventilation (VE) and carbon dioxide production (VCO2)6,7 provide important prognostic information. However, CPET equipment is expensive and cumbersome, and availability of trained staff is limited.

A simple, self-paced, and submaximal8 alternative is the 6 min walk test (6-MWT) which is widely used to assess functional status in patients with CHF.9,10 The 6-MWT is reproducible, sensitive to changes in quality of life,11–13 and mimics activities of daily living.14 The prognostic value of the 6-MWT has been reported previously in patients with CHF.15–19 With the exception of the SOLVD study15 (n = 898 patients), other studies16–19 have included only modest sample sizes (n = 45–307 patients). Indeed, in the only study for patients with advanced heart failure, report of Cahalin et al.16 was based on 45 middle-aged (mean age 49 ± 8 years) patients being evaluated for cardiac transplantation. These patients could not be categorized as typical CHF patients who would generally be older and suffer from a range of other age-related co-morbidities.

We have previously shown the relationship between physical symptoms and 6-MWT performance in patients with CHF.11 The aim of the current study was to compare the prognostic value of the 6-MWT and physical symptoms in a large, representative sample of patients, and secondly, to perform a sub-group analysis according to severity of LV systolic dysfunction (LVSD).


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Appendix 1
 Acknowledgements
 References
 
Patients were referred to our local community clinic with signs of breathlessness and were in New York Heart Association (NYHA) functional class I–IV. Clinical information obtained included past medical history and drug and smoking history. Clinical examination included assessment of body mass index (BMI), heart rate, rhythm, and blood pressure (BP). Diabetes mellitus was defined as a previous diagnosis of this condition. Hypertension was defined as a prior history of receiving drug treatment for these conditions. Patients were excluded if they were unable to walk without assistance from another person (not including mobility aids) (n = 8), or if they were unable to exercise because of non-cardiac limitations e.g. osteoarthritis (n = 23), or chronic obstructive pulmonary disease (n = 34). The Hull and East Riding Ethics Committee approved the study, and all patients provided informed consent for participation. Symptoms of heart failure were determined by methodology used in the EuroHeart Failure Survey.11 Patients were asked a series of six questions graded from 1 to 6, where 1 was unimpaired and 6 was very much impaired (Appendix 1). These questions related to perceived heart failure symptoms during physical function.

Heart failure was defined as current symptoms of heart failure, or a history of symptoms controlled by ongoing therapy, due to cardiac dysfunction and in the absence of any more likely cause.20,21 Left ventricular (LV) function was determined from 2D-echocardiography and was carried out by one of three trained operators. LV function was assessed by estimation on a scale of normal, mild, mild-to-moderate, moderate, moderate-to-severe, and severe impairment and was assessed 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. LV 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.22 and LVSD was diagnosed if LVEF was ≤45%. Tables 1 and 2 show baseline clinical characteristics and physical symptom data in surviving and deceased patients.


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  		Table 1 Baseline clinical characteristics in surviving and decreased patients

 

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  		Table 2 Patient perceived physical symptom severity in surviving and deceased patients

 
The 6-MWT was conducted following a standardized protocol.9,10 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. During the 6-MWT, patients were able to rest, if needed, and time remaining was called every second minute.15 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. Standardized verbal encouragement was given to patients after 2 min and 4 min.

Statistical analysis
Continuous variables are presented as median with inter-quartile range (IQR); categorical data as percentages. Differences between continuous variables were determined by the independent samples t-test; differences between categorical data by the Chi-squared ({chi}2) test. Continuous variables were assessed for normality by the Kolmogorov–Smirnov test. The linearity assumption was assessed by including squared terms. For all but one variable (NT-proBNP) the linearity assumption was satisfied. As a result, NT-proBNP was log-transformed for analytical purposes (log NT-proBNP). Kaplan–Meier curves are presented for mortality data using the guidance of Pocock et al.23 For presentation purposes, 6-MWT data were divided into quartiles (≤240 m, 241–345 m, 346–420, >240 m). Cox regression models were used to develop predictor models for all-cause mortality using all baseline variables (Tables 1 and 2). The assumption of proportionality was tested for each variable using the method of Grambsch and Therneau.24 We checked for collinearity by calculating Person correlation coefficients, and for physical symptoms data Spearman's correlation coefficients. We used a cut-off value of 0.3 to identify collinearity. Model selection and validation is discussed elsewhere.

Model selection and validation
Sequential procedures, such as backwards elimination and forwards selection, are established methods of model building and are available in many statistical packages. A common feature is that starting from the full model (backwards elimination) or null model (forwards selection) a new variable is deleted or added until a pre-specified selection level is reached. The ease of application is a reason for their popularity. Despite their widespread use, some statistical objections have been raised. Results can lead to standard errors that may be too low.25–27 If important variables are omitted from the final model, the regression coefficients are known to be biased.28 The selection method affects the properties of the tests of the final model itself.29

With increasing computational power re-sampling based on the bootstrap has become a popular way to validate models.30 Applications with Cox model are less well developed. What makes bootstrapping difficult for the Cox model is censoring, the very thing that makes survival data unique.31,32 Bootstrap software is widely available for the logistic model but less so for Cox regression (e.g. none of SPSS, BMDP, GLIM, or MINITAB are equipped to do bootstrapping). We combined bootstrap re-sampling with backwards elimination.33 Backwards elimination is preferred to forwards selection.34 We generated 100 bootstrap samples, and for each one generated a Cox model. At least 100 bootstrap samples are recommended.33 We listed the frequency with which each variable appears in a model. This ‘inclusion’ frequency determines our choice of models. The goodness-of-fit was assessed by calculating an r2 equivalent (proportion of explained variation) based on the difference between the likelihood ratio (LR) of the model and LR of the null model with no covariates.35,36

SPSS (version 13.0) was used to analyse the data. An arbitrary level of 5% statistical significance was used throughout (two-tailed), with the exception of model validation as previously discussed. One of the statistical issues to address is the problem of multiple testing when many variables are present and the possible inflation of Type I error. However, there is no consensus on what procedure to adopt to allow for multiple comparisons.37 Hence, in order to account for the inflation of experimentwise Type I error due to multiple testing, we have followed the recommendations of Perneger38 and not adjusted for this. Our outcome measure was all-cause mortality.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Appendix 1
 Acknowledgements
 References
 
Of an initial population of 1592 patients, 212 died representing a crude death rate of 13.3%. In surviving patients, the median time to follow-up period was 36.6 months (IQR 28.2–45.0 months). Table 1 shows that deceased patients had a significantly poorer 6-MWT than surviving patients (P = 0.0001). Table 2 shows that deceased patients experienced ‘a lot’ and ‘very much’ worse physical symptoms than surviving patients (P ≤ 0.05). A Kaplan–Meier survival curve showing cumulative survival in relation to 6-MWT distance in all patients is shown in Figure 1. Patients with a lower 6-MWT distance had a poorer prognosis (P < 0.001).


Figure 1
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  		Figure 1 Kaplan-Meier curve showing survival based on 6-MWT distance (quartiles).

 
Nineteen variables were significantly associated with mortality on univariate Cox analysis (Table 3). All these variables were included in the final multivariable Cox model of which five remained independent predictors of all-cause mortality; decreasing 6-MWT distance, signs of breathlessness at night (SOBAN), elevated log NT-proBNP, reduced haemoglobin concentration, and use of beta-blockers (Table 4). We also examined the loss in {chi}2-score if a variable was removed from the final model. The largest loss in {chi}2-statistic occurred when log NT-proBNP was removed (22.63) from the model, followed by the 6-MWT (16.15). To determine if an additive effect was achieved by adding the 6-MWT to log NT-proBNP (enter method), we re-ran the Cox analysis. The addition of 6-MWT to log NT-proBNP increased the {chi}2 statistic by 42.3 showing an additive effect. There was an inverse relation between log NT-proBNP and 6-MWT distance (r = –0.38, P < 0.05; Figure 2).


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  		Table 3 Univariate Cox regression analysis—age-adjusted predictors

 

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  		Table 4 Hazard ratios for the full multivariable Cox model

 

Figure 2
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  		Figure 2 Relation between 6-MWT distance (m) and log-transformed NT-proBNP (pg·mL–1

 
Table 5 presents the inclusion frequencies from 100 bootstrapped samples using backwards elimination (P for entry = 0.05, P for exit = 0.01). No one variable appeared in all the models. Four variables had an inclusion frequency ≥65% (SOBAN, decreasing 6-MWT performance, elevated log NT-proBNP, and reduced haemoglobin concentration). Four variables had an inclusion frequency <20% (diagnosis of diabetes mellitus, signs of breathlessness during normal activity, diagnosis of LVSD, and elevated resting systolic BP). Taking an inclusion frequency ≥65%, our four most potent prognostic indicators are SOBAN, decreasing 6-MWT performance, elevated log NT-proBNP, and reduced haemoglobin concentration. Simulation has shown that strong predictors will nearly always be included in a model in most replications.39 The exception to this rule is when two variables are highly correlated. Table 5 allows the reader to choose a lower threshold for inclusion should they wish. Table 5 also shows a replication matrix based on the first 10 bootstrapped samples.


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  		Table 5 Replication matrix for the first 10 bootstrapped samples (ranked order) and inclusion frequencies (P = 0.05 for entry, P = 0.1 for exit) on 100 bootstrapped samples

 
We adopted a similar process after stratifying by the severity of LVSD (>mild LVSD and ≤mild LVSD) using an inclusion frequency of ≥65%. Hazard ratios are presented in Table 6 along with an estimate of the goodness-of-fit. For patients with ≤mild LVSD, five variables were significantly related to survival (sex, haemoglobin, SOBAN, log-NT proBNP, and beta-blocker usage). Patients with a higher haemoglobin concentration had a better outcome. Conversely, patients with SOBAN had a poor outcome. For patients with >mild LVSD, a different set of variables were related to survival (6-MWT, sodium, and QRS duration). Patients with a better 6-MWT performance have a better outcome. Given the few number of variables in each of these models, we do not think either one is overfitted. Selection bias is not usually a problem for models that contain strong factors only.33


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  		Table 6 Cox regression models in patients with >mild LVSD and ≤mild LVSD

 
SOBAN was the only independent predictor of mortality for the physical symptoms data.

We compared the relation between SOBAN and the 6-MWT (Figure 3), and box plots showed a linear inverse relation. Patients with SOBAN described as ‘some’, ‘a lot’, ‘very much’, performed the 6-MWT significantly poorer than patients with no SOBAN (P < 0.05). Figures 4 and 5 show risk of death based on 6-MWT distance at 12 and 24 months. Figure 4 shows that in patients with >mild LVSD who walk ≤120 m, risk of death is ~11% after 12 months. Conversely, in patients who walk ≥420 m, risk of death is negligible after 12 months. After 24 months, risk of death increases to ~30% in patients who walk ≤120 m, and is negligible in patients who walk ≥420 m (Figure 5).


Figure 3
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  		Figure 3 Relation between 6-MWT distance and SOBAN (1) no; (2) very little; (3) a litle; (4) some; (5) a lot; (6) very much. *Significantly different from (1) no SOBAN, P < 0.05.

 

Figure 4
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  		Figure 4 Risk of death at 12 months based on 6-MWT performance in patients with >mild LVSD.

 

Figure 5
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  		Figure 5 Risk of death at 24 months based on 6-MWT performance in patients with > mild LVSD.

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Appendix 1
 Acknowledgements
 References
 
The aim of the current study was to compare the prognostic value of the 6-MWT and physical symptoms in a large, representative sample of CHF patients. We have shown that the 6-MWT is an independent predictor of mortality in our patients. While log NT-proBNP was the most potent independent predictor, an additive prognostic effect was evident with the additional selection of 6-MWT. We also noted that 6-MWT remained a prognostic marker in patients with >mild LVSD; however, this was not the case in patients with ≤mild LVSD. The 6-MWT should not be used to stratify risk in patients with mild LV dysfunction. We have previously shown that predictors of poor 6-MWT performance were increased age, increased BMI, lowered haemoglobin concentration, increased resting heart rate, female sex, and elevated serum creatinine and NT-proBNP in patients with LVSD.40 It is noteworthy that our independent predictors of mortality were similar to predictors of poor 6-MWT performance in patients with LVSD.

Previous studies which have reported the prognostic significance of the 6-MWT have employed sample sizes <310 patients, with the exception of Bittner et al.15 Eight hundred and ninety eight patients with LVSD (LVEF ≤45%) were followed up for 242 days. The authors reported a death rate of ~10% in patients with a 6-MWT performance <350 m. This is a better survival rate than in our patients with >mild LVSD (30%). However, our patients had more severe LVSD and also had a poorer 6-MWT (the most symptomatic quartile walked ≤240 m). Bittner et al.15 reported that LVEF and 6-MWT distance were equally strong and independent predictors of mortality or hospitalization rates in their patients. However, we found that LVEF did not remain in the final multivariable model.

Roul et al.17 followed up 121 LVSD patients in NYHA class II–III for 18 months. Patients were separated into an event free group and those who reached the combined end point of death or hospitalization for heart failure. Although a mean difference of 38 m existed, it did not reach statistical significance (P = 0.08). However, only 47 patients experienced an event in 18 months, and low numbers may be a reason for the non-significant P-value. Roul et al.17 also reported that patients who walked <300 m had a higher rate of combined events. A more recent study conducted by Rostagno et al.19 evaluated the prognostic significance of the 6-MWT in patients with mild-to-moderate CHF. Two hundred and forty patients (mean age = 64 years) were followed for 34 months to assess event-free survival. Prior to follow up, 66 patients died. For patients who walked <300 m, survival rate was 62% compared to 82% in patients who walked >300 m. A Cox regression model showed that LV fractional shortening (P = 0.009) and 6-MWT distance (P = 0.0005) were the strongest prognostic indicators in patients with mild-to-moderate CHF. It is difficult to compare these findings with our data because we included all patients in our analysis, and then performed a separate sub-group analysis according to severity of LVSD.

There are few studies that have shown the prognostic significace of the 6-MWT in patients with severe heart failure. Lucas et al.18 compared 6-MWT distance with peak oxygen uptake in 307 patients with advanced heart failure. In contrast to peak oxygen uptake, the 6-MWT did not predict survival. The authors concluded that the 6-MWT was not a surrogate for peak oxygen uptake in assessing prognosis for individuals with advanced heart failure. We did not include other important prognostic markers such as peak oxygen uptake,4 anaerobic threshold,5 or the VE/VCO2 slope6,7 as covariates in our model; however, we have shown that in patients with >mild LVSD, the 6-MWT is an important prognostic marker.

Mechanisms for worsening functional capacity in >mild LVSD
Functional capacity is markedly reduced in patients with >mild LVSD, due to an increased ventilatory response. Mechanisms for this abnormal exercise response have been discussed in depth elsewhere. The interested reader is directed to Clark et al.41 and Clark42 for a comprehensive review of the topic. Briefly, in CHF, skeletal muscle activity is abnormal from an early stage in the disease progression. Cardiac output is reduced during exercise43 and muscle bulk, strength, and endurance are lost.44,45 Ventilation-perfusion mismatches arise from haemodynamic dysfunction and from an altered control of ventilation, as indicated by the augmentation of the chemoreflex. Abnormal haemodynamic function is associated with a poor prognosis in these patients.46,47 Mechanisms of chemoreflex overactivity are associated with sympathetic activation and neurohormonal imbalance, both of which also affect survival in CHF.48,49 The chemoreflex may be directly affected by a reduced blood flow to the chemoreceptors reflecting haemodynamic dysfunction.

Self-perceived symptom severity as a predictor of survival in patients with and without LVSD
SOBAN was an independent predictor of survival in all patients. We examined the relation between SOBAN and 6-MWT performance, and found an inverse linear relation. The relation between self-perceived physical symptoms and functional capacity has not been well documented in patients with CHF. We have previously shown that the 6-MWT is sensitive to changes in self-perceived physical symptoms.11 In practical terms, this indicates that the 6-MWT can relate how a patient judges a change (either improvement or deterioration) in their physical symptoms. Further research is required to extend the relation between self-perception and functional status.

Relation between 6-MWT and NT-proBNP in patients with and without LVSD
Our findings showed a moderate correlation between the 6-MWT and log NT-proBNP. This is similar to the findings of Wieczorek et al.50 who compared BNP concentration to 6-MWT in 44 patients with CHF. A significant inverse correlation was observed between BNP and 6-MWT (r = –0.47, P < 0.001). The authors suggested that BNP concentration correlates inversely with the degree of physical incapacity in CHF patients. However, no relation between BNP and 6-MWT has been reported by others. Hogenhuis et al.51 found a very weak correlation between BNP and 6-MWT (r = –0.01, P = 0.87) in 229 CHF patients. The authors suggested that BNP and 6-MWT represent different aspects of the clinical syndrome of CHF. BNP was more related to cardiac function, whereas the 6-MWT reflected functional capacity and quality of life. However, these patients had recently been hospitalized for cardiac-related causes and it is possible that 6-MWT performance had not normalized to pre-event levels (mean 6-MWT distance = 259 m).

Statistical limitations
Box52 wrote that all models are wrong but some are useful. Our choice of models were determined by three criteria; (i) an arbitrary ≥65% inclusion frequency; (ii) a re-sampling frequency of 100. Although precision improves with re-sampling >100, model selection is not altered much, at least for a re-sampling frequency <1000.33 (iii) P-values for entry/exit—we selected 0.05 and 0.1, respectively.28 The ‘variable selection’ problem has been widely discussed with many different stopping rules proposed53–57 with no apparent agreement. All subsets regression58 is too difficult when lots of variables are involved. We have 30 variables giving 230–1 possible subsets. Limiting the subsets to a particular size (for example, 5)59 would give us fewer models (30C5) but with no guarantee of not missing an important one.28 Finally, the bootstrap itself is not without criticism. Young60 said ‘the bootstrap is no surrogate for careful thought ... The bootstrap must be applied consciously, not blindly’. The theoretical basis for re-sampling is not well developed.39 Between 1989 and 2000, over 1000 publications addressed theoretical issues surrounding re-sampling methods.61 The bottom line is that models must be validated but the actual method is open to debate.

In conclusion, the 6-MWT is an important independent predictor of mortality in CHF patients, and this was especially evident in patients with >mild LVSD. The 6-MWT provides little prognostic benefit in patients with ≤mild LVSD. While log NT-proBNP was the most potent independent predictor, an additive prognostic effect was evident with the additional selection of 6-MWT. Patients' self-perceived symptoms, specifically, SOBAN was an independent predictor of mortality in our patients. In clinical practice, the addition of a questionnaire of daily living activities is worthwhile for risk stratification in these patients.


   Appendix 1
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix 1
Acknowledgements
 References
 
For each question, patients responded by providing one of six responses based on the options follow: (1) no; (2) very little; (3) a little; (4) some; (5) a lot; (6) very much.

The six questions relating to symptoms are listed below.

In the last month, how much did the following affect you?

  1. shortness of breath limiting daily activities;
  2. fatigue limiting daily activities;
  3. fatigue at rest;
  4. swelling of ankles;
  5. shortness of breath at rest;
  6. shortness of breath at night.


   Acknowledgements
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix 1
 Acknowledgements
References
 
This study did not receive any funding.

Conflict of interest: none declared.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Appendix 1
 Acknowledgements
 References
 

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  2. Cleland JG, Daubert JC, Erdmann E, Freemantle N, Gras D, Kappenberger L, Tavazzi L. Cardiac Resynchronization-Heart Failure (CARE-HF) Study Investigators. (2005) The effect of cardiac resynchronization on morbidity and mortality in heart failure. N Engl J Med 15:1539–1549.
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