Abstract
Introduction. The cardiopulmonary exercise test (CPET) and the 6-minute walk test (6MWT) are used to prescribe the appropriate training load for cycling and walking exercise in patients with chronic obstructive pulmonary disease (COPD). The primary aims were: (i) to compare estimated peak work rate (Wpeakestimated) derived from six existing Wpeak regression equations with actual peak work rate (Wpeakactual); and (ii) to derive a new Wpeak regression equation using six-minute walk distance (6MWD) and conventional outcome measures in COPD patients. Methods. In 2906 patients with COPD, existing Wpeak regression equations were used to estimate Wpeak using 6MWD and a new equation was derived after a stepwise multiple regression analysis. Results. The 6 existing Wpeak regression equations were inaccurate to predict Wpeakactual in 82% of the COPD patients. The new Wpeak regression equation differed less between Wpeakestimated and Wpeakactual compared to existing models. Still, in 74% of COPD patients Wpeakestimated and Wpeakactual differed more than (±) 5 watts. Conclusion. In conclusion, estimating peak work load from 6MWD in COPD is inaccurate. We recommend assessment of Wpeak using CPET during pre-rehabilitation assessment in addition to 6MWT.
Introduction
It is well-recognized that pulmonary rehabilitation is an important part of the integrated care of patients with chronic obstructive pulmonary disease (COPD) (Citation1, 2). Pulmonary rehabilitation programs typically include components such as patient assessment, supervised exercise training, health education, nutritional modulation, and behavioural support (Citation1, Citation3). Indeed, supervised exercise training is the cornerstone of a comprehensive, multidisciplinary pulmonary rehabilitation in COPD, which improves health status and exercise performance (Citation4).
To date, about half of the exercise-based pulmonary rehabilitation programs for patients with COPD use leg cycling ergometry as the main exercise training modality (Citation1, Citation5, Citation6). International guidelines suggest that cycling training of at least 8 weeks at an intensity of at least 60% of actual peak work rate (Wpeakactual) is required to improve health status and exercise performance in patients with COPD (Citation1). Therefore, it is important to assess patients’ Wpeakactual during a pre-rehabilitation assessment in order to prescribe a patient-tailored training load (Citation1).
The cardiopulmonary exercise test (CPET) on a stationary cycle ergometer is recommended in daily clinical practice to determine baseline Wpeakactual (Citation7). Based on the characterisation of pulmonary rehabilitation programmes in Canada (Citation8) and the United kingdom (Citation9) it is estimated that over 50% of the pulmonary rehabilitation programs (outpatient and inpatient) do not perform pre-rehabilitation CPET. Therefore, exercise testing is mostly limited to field walking tests, like an incremental shuttle walk test or a 6-minute walk test (6MWT)(Citation10).
Several authors (Citation11–14) have developed regression equations to estimate peak work rate (Wpeakestimated) by using the 6-min walk distance (6MWD) of 22 to 53 patients with COPD (). Validation of these Wpeak regression equations in a large sample of patients with COPD seems necessary before clinical implementation can be considered. Indeed, Holland et al. (Citation15) showed substantial variation between Wpeakestimated and Wpeakactual in 64 patients with COPD using the Wpeak regression equations of Hill et al. (Citation12) and Luxton et al. (Citation14). Moreover, generalizability of existing Wpeak regression equations is limited due to strict exclusion criteria, like use of supplemental oxygen (Citation12, Citation14), ambulation aid during 6MWT (Citation12), very mild symptoms (Medical Research Council, MRC, dyspnea grade 1) (Citation13), and/or a body mass index (BMI, weight in kilograms divided by the square of height in meters) greater than 35 kg/m2 (Citation12). Therefore, it seems reasonable to derive a new Wpeak regression equation in a large sample of patients with COPD entering pulmonary rehabilitation using 6MWD and a number of conventional characteristics of COPD, like forced expiratory volume in the first second (FEV1), gender, age, height and weight (Citation16, 17).
Table 1. Regression equations predicting peak work rate including 6-min walk distance in patients with COPD
The aim of the present study was two-fold: (i) to compare the accuracy of current Wpeak regression equations in a large sample of patients with COPD entering pulmonary rehabilitation; and (ii) to derive a new Wpeak regression equation based on a large cohort of COPD patients using 6MWD and conventional outcome measures.
Methods
Data were extracted of 2906 patients with COPD who were referred to four specialized pulmonary rehabilitation centres in the Netherlands (n = 1522 in CIRO+ in Horn (2); n = 251 in UCCZ Dekkerswald Nijmegen; n = 764 in Revant Rehabilitation Centre Breda; and n = 369 in Asthma Centre Heideheuvel Hilversum) from January 2005 to June 2010. Indeed, since January 2005 the participating four specialized pulmonary rehabilitation centres standardized test procedures according to international guidelines (Citation7, Citation18) and in agreement with request from Dutch healthcare insurers.
All patients were referred by chest physicians from multiple hospitals for a comprehensive pulmonary rehabilitation program (Citation2). Patients met the following inclusion criteria: (i) primary diagnosis of COPD (Citation19); (ii) no acute COPD exacerbations in the past 4 weeks; and (iii) absence of co-morbidities precluding exercise testing, e.g., neuromuscular disorders or joint disorders in hip, leg and/or knee. These retrospective analyses were institutional review board exempt due to the use of de-identified, preexisting data.
As part of a standardized routine baseline assessment (Citation2) patients underwent, amongst other tests, a CPET in accordance with the latest international guidelines where Wpeakactual was determined (Citation7). All exercise tests were performed on electromagnetically braked bicycle ergometers (UCCZ Dekkerswald Nijmegen and Asthma Centre Heideheuvel Hilversum: Lode, Groningen, the Netherlands; Revant Rehabilitation Centre Breda and CIRO+ Horn, Ergoline Ergoselect 200 P, Carefusion, Houten, The Netherlands).
Post-bronchodilator spirometry was performed according to international recommendations (Citation20–22). All centres used lung function equipment of Carefusion (Houten, The Netherlands). Patients underwent physical examination by a chest physician (including assessment of body weight and height) and medical history as described before (Citation23). Fat-free mass index (FFMI, fat free mass in kilograms divided by the square of height in meters) was determined using bio-electrical impedance assessment (Citation24). A 6MWT was performed according to ATS guidelines (Citation18), including a practice walk (Citation25). The best of two tests was used for further analysis.
All statistical analyses were performed using SPSS for Windows, Version 17.0.1 (SPSS, Inc., Chicago, Il, USA) and GraphPad Prism Version 4.03 (GraphPad Software, Inc., La Jolla, CA, USA). Continuous data were tested for normality and presented as mean and standard deviation unless otherwise stated. All data were normally distributed. Agreement between the Wpeakestimated (using existing Wpeak regression equations) and Wpeakactual was assessed using Bland and Altman plots and intraclass correlation coefficients (Citation26). The Bland and Altman plot shows the mean of Wpeakestimated and Wpeakactual against the difference of Wpeakestimated and Wpeakactual. A priori, the authors defined a difference of -5 to +5 watts between Wpeakestimated and Wpeakactual as acceptable. Pearson single correlation coefficients were calculated and a stepwise multiple regression analysis was used to develop a new Wpeak regression equation. Differences were considered significant at p < 0.05 (two-tailed).
Results
Characteristics
On average, patients had mild-to-very severe COPD (GOLD I: n = 121; GOLD II: n = 832; GOLD III: n = 1075; and GOLD IV: n = 878); a slight overweight; a normal fat free mass; and a poor peak and functional exercise capacity (). Of the included patients, 24% used long-term oxygen therapy and 28% used a walking aid during the 6MWT. Oxygen supplement was in all centres provided through a nasal cannula.
Table 2. Characteristics
Accuracy of existing Wpeak regression equations
shows Bland and Altman plots and the distribution of the COPD patients according to existing Wpeak regression equations. Clear differences were found between Wpeakestimated and Wpeakactual for the regression equations of Hill et al. (regression #1) (mean: -6 watts (range: -177 to +70); ), Hill et al. (regression #2) (-41 watts (-163 to +68); ), Luxton et al. (14) (+16 watts (-136 to +139); ), Cavalheri et al. (11) (-16 watts (-158 to +93); ), Kozu et al. (regression #1) (Citation13)(+3 watts (-183 to +73); ), and Kozu et al. (regression #2) (Citation13)(+16 watts (-128 to +84); ).
Figure 1. Bland and Altman plots and distribution of COPD patients of the existing Wpeak regression equations.
Fair to moderate intraclass correlation coefficients (95% confidence interval) were found for the regression equations of Hill et al. (regression #1) 0.58 (0.56-0.61), Hill et al. (regression #2) (12) 0.59 (0.57-0.62), Luxton et al. (14) 0.68 (0.66-0.70), Cavalheri et al. (Citation11) 0.74 (0.72-0.76), Kozu et al. (regression #1) 0.59 (0.57-0.62), and Kozu et al. (regression #2) 0.73 (0.71-0.74). The mean of Wpeakestimated and Wpeakactual resulted in a negative value in 19 patients in the regression equation of Cavalheri et al. (Citation11).
Proportion of COPD patients with a difference of only -5 to +5 watts between Wpeakestimated and Wpeakactual varied amongst existing Wpeak regression equations: 22% for Hill et al. (#1) (); 20% for Hill et al. (#2) (Citation12) (); 14% for Luxton et al. (Citation14) (); 16% for Cavalheri et al. (Citation11) (); 18% for Kozu et al. (#1) (); and 13% for Kozu et al. (#2) (Citation13) (). The differences expressed as the quotient of Wpeakactual between Wpeakestimated and Wpeakactual were very broad in all regression equations ().
Table 3. Percentage differences between Wpeakestimated and Wpeakactual
Derivation of a new Wpeak regression equation
Using the data of 2906 patients with COPD, single correlation coefficients of gender, age, height, body weight, FFM, 6MWD, and FEV1 with Wpeakactual were 0.23, 0.19, 0.37, 0.34, 0.40, 0.67, and 0.68, respectively (all p < 0.001). Abovementioned variables were retained in a stepwise multiple regression analysis. The new Wpeak regression equation is:
Wpeakestimated = -51.994 - (0.505*gender (1 for men, 0 for women)) - (0.234*age (in years)) + (0.091*height (in centimeters )) + (0.200*body weight (in kilograms)) + (0.353*FFM (in kilograms)) + (0.132*6MWD (in meters)) + (23.361*FEV1 (in litres)).
Accuracy of new Wpeak regression equation
The new Wpeak regression equation explains 67% of the variance in Wpeakactual. The mean difference between Wpeakestimated and Wpeakactual of the new regression equations is 1 Watt (range: -128 to +84 watts; ). The percentage of COPD patients with differences of -5 to +5 watts between Wpeakestimated and Wpeakactual was 26% (). shows the percentage differences between Wpeakestimated and Wpeakactual. The intraclass correlation coefficient (95% confidence interval) for Wpeakestimated and Wpeakactual for the new Wpeak regression equation was 0.81 (0.79-0.82).
Figure 2. Bland and Altman plots and distribution of COPD patients of the new Wpeak regression equations.
Differences between Wpeakactual and Wpeakestimated
shows the characteristics of the COPD patients after stratification for a difference between Wpeakestimated and Wpeakactual of <-6 watts, -5 to +5 watts or >+6 watts of the new Wpeak regression equation. Patients in the middle group (-5 to +5 watts) contained a higher proportion of women, were shorter, had a lower body weight and fat-free mass, and a worse pulmonary function compared to groups where the new Wpeak regression equation was underestimating (<-6 watts) or overestimating (>+6 watts) Wpeak (). Similar findings were found after using the existing Wpeak regression equations (see online supplement for details).
Table 4. Characteristics of the study subjects after stratification for Wpeakestimated-Wpeakactual
Discussion
The main finding of the present study is the lack of accuracy to predict Wpeakactual by using existing Wpeak regression equations. Also a new regression equation, which includes more conventional clinical variables than previous Wpeak regression equations, shows a poor accuracy for individual patients with COPD. Indeed, in 74% of COPD patients Wpeakestimated and Wpeakactual differed more than (±) 5 watts in the new Wpeak regression equation. Moreover, in the group of COPD patients where Wpeakestimated-Wpeakactual was between -5 and +5 watts, the difference expressed as percentage of Wpeakactual ranged still between -34% and 43%. So, Wpeakactual needs to be assessed in order to prescribe a patient-tailored training load during cycle ergometry training.
Cycling at 60% of Wpeak or higher is considered sufficient to elicit physiologic training effects (Citation1). Due to the low absolute Wpeakactual in patients with COPD (Citation23), we decided that a difference of -5 to +5 watts between Wpeakestimated and Wpeakactual was acceptable to target cycling load during exercise training. However, all Wpeak regression equations (), including the new regression equation, show a broad range of differences between Wpeakestimated and Wpeakactual ( and ). Therefore, 82% of the patients in the existing Wpeak regression equations and 74% of the patients in the newly derived Wpeak regression equation had a difference between Wpeakestimated and Wpeakactual <-5 watts and >+5 watts. Moreover, the mean of the estimated and actual Wpeak resulted in a negative value in 19 COPD patients with the regression equation of Cavalheri et al. (Citation11) () and in 3 COPD patients with the new Wpeak regression equation (). So, using Wpeak regression equations will result in an over- or underestimation of the Wpeak. In turn, most COPD patients would start the exercise training program not at an optimal cycling training load. This is in line with the findings of Holland and colleagues (Citation15).
Wpeak is dependent of multiple clinical confounding variables, like gender, age and FFM (Citation27-29). Nevertheless, only the Wpeak regression equation of Luxton et al. (Citation14) included gender and age; while the Wpeak regression equation of Cavalheri et al. (Citation11) included FFM. This may partially explain the moderate accuracy of the existing Wpeak regression equations. Then again, the new Wpeak regression equation did include gender, age, height, body weight, fat-free mass, 6MWD and FEV1. Obviously, the new Wpeak regression equation needs validation in a new cohort of patients with COPD, but it still appears not highly accurate to predict Wpeakactual ().
A possible explanation of the moderate accuracy to estimate Wpeakactual is that the CPET and the 6MWT measure different aspects of exercise tolerance. For example, in patients with COPD peak aerobic capacity was similar for CPET and 6MWT, while ventilation, carbon dioxide production, respiratory exchange ratio and arterial lactate concentration were lower during the 6MWT compared with CPET (Citation30). Thereby, during 6MWT oxygen uptake showed a steady-state profile from min 3 to 6 of the test which did not occur during CPET (Citation30). In a recent study ventilatory responses were compared during an incremental treadmill test versus CPET (Citation31). At peak exercise, partial pressure of alveolar oxygen and peak lactate levels were higher during cycling and the anaerobic threshold occurred at a lower oxygen uptake during cycling versus walking (Citation31).
Oxyhemoglobin desaturation was greater during walking compared with cycling (Citation31). Man and colleagues showed a significant reduction in maximum voluntary contraction force of the quadriceps after cycling, while after walking no change was found in maximum voluntary contraction force (Citation32). Quadriceps fatigability is also reflected by the onset of leg discomfort occurred at lower oxygen uptake values for cycling compared with treadmill walking.
In addition, the performance of whole body exercise (e.g., walking, cycling) requires complex interactions involving many similar physiological functions.
In human exercise a comparatively high level of specificity exists (Citation33). Although there is some carryover of training effects, the specificity principle of training states that to make adequate progression in one type of activity, training must closely match that activity (Citation33). Moreover, activity specific outcome tools are essential to obtain pertinent results (consistency between training and testing). Because walking exercise outputs (on treadmill or field tests) do not relate exactly to peak work in cycle ergometry it was perhaps unreasonable to expect a regression equation to work in any case.
Some methodological considerations need to be made. Although the exercise tests were performed in the four centres at the same standardized manner, the equipment among the centres differ from each other. Also, it can be argued that the existing Wpeak regression equations are region-specific, and, in turn, not to be validated in a large cohort of Dutch COPD patients which in particular consisted of Caucasians. Other ethnic groups and patients in other demographic regions may require other regression equations. Then again, Holland et al. showed a poor accuracy of the existing Australian-derived Wpeak regression equations to predict Wpeakactual in a group 64 Australian COPD patients (Citation15).
In about 50% of the pulmonary rehabilitation programs pre-rehabilitation CPET is not assessed. This is often due to limited resources, staff and expertise necessary to conduct a CPET (Citation8, 9). Nevertheless, the current results do justify pre-rehabilitation CPET, especially if the focus is high-intensity cycle-ergometry training (Citation1). Other methods to anchor the training intensity such as dyspnoea rating, or pulse rate are less well defined or not reliable without the results of CPET.
In fact, CPET is generally important in the clinical assessment and evaluation of patients with COPD. CPET is recommended to determine exercise capacity, to determine the magnitude of hypoxemia, to establish exercise limitation(s) and to assess other potential contributing factors, especially occult ischemic heart disease (Citation7). Regarding the specificity of exercise training it can be argued to choose an exercise test which suits best to the exercise modality which will be trained.
It is recommended that CPET should be applied to assess peak work rate only in those programmes that use cycle ergometry for training (Citation7), which may not necessarily constitute the majority of programmes. Indeed, programmes which use walk training as main exercise modality are also beneficial for patients with COPD (Citation34). Moreover, these (brisk) walking programmes may want to consider the use of an incremental shuttle walk test or a 6MWT as exercise test.
In conclusion, estimating Wpeak from 6MWD is inaccurate in patients with COPD, also when conventional clinical variables are used in a Wpeak regression equation. Therefore, we recommend assessment of Wpeak using CPET during pre-rehabilitation assessment in addition to 6MWT in patients with COPD.
Declaration of interest
The authors do not have any conflict of interest with the contents of the present manuscript. The authors are responsible for the content and the writing of this paper.
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Online supplement
The online supplement shows the characteristics of the COPD patients after stratification for a difference between Wpeakestimated and Wpeakactual of <-6 watts, -5 to +5 watts or >+6 watts of the regression equations of Hill et al. (regression #1), Hill et al. (regression #2), Luxton et al., Cavalheri et al., Kozu et al. (regression #1), and Kozu et al. (regression #2)