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Exp Clin Cardiol. 2012 Winter; 17(4): 179–182.
PMCID: PMC3627270
Clinical Cardiology: Original Article

Influence of body mass on risk prediction during cardiopulmonary exercise testing in patients with chronic heart failure

Abstract

INTRODUCTION:

Peak oxygen uptake (VO2) during a maximal exercise test is used to stratify patients with chronic heart failure (CHF) and is usually corrected for body mass.

OBJECTIVE:

To explore the influence of body mass on risk prediction during treadmill cardiopulmonary exercise testing (CPET) in patients with CHF.

METHODS:

A total of 411 patients with suspected CHF (mean [± SD] age 64±12 years; 81% male; mean left ventricular ejection fraction 39±6%) underwent symptom-limited, maximal CPET on a treadmill. Patients were categorized as normal weight, overweight or obese based on body mass index.

RESULTS:

One hundred fifteen patients died during a median follow-up period of 8.7±2.3 years in survivors. In the univariable analysis, peak VO2 adjusted for body mass (χ2=41.4) and unadjusted (χ2=40.2) were similar for predicting all-cause mortality. Peak VO2 adjusted for body mass showed marginally higher χ2 values in normal weight, overweight and obese categories than unadjusted values. Anaerobic threshold had similar prognostic power regardless of whether it was corrected for body mass (χ2=22.4 and χ2=24.4), with no difference between the two in any of the subgroups separately. In all patients, unadjusted ventilation (VE)/carbon dioxide production (VCO2) slope (χ2=40.6) was a stronger predictor of all-cause mortality than body mass adjusted values (χ2=32.8), and unadjusted values remained stronger in normal weight, overweight and obese subgroups.

CONCLUSION:

Correcting peak VO2 for body mass slightly improves risk prediction, especially in obese patients with CHF. The adjustment of other CPET-derived variables including anaerobic threshold and VE/VCO2 slope for body mass appears to provide less prognostic value.

Keywords: CPET, Obesity, OUES, Peak oxygen uptake, Prognosis, VEqCO2 nadir

Cardiopulmonary exercise testing (CPET) is used to stratify risk and to evaluate exercise capacity in patients with chronic heart failure (CHF) (13). Patients with CHF exhibit a reduced exercise capacity, as shown by a lower peak oxygen uptake (VO2) (4) and lower anaerobic threshold (AT) (5). CHF patients also exhibit an increase in the slope relating ventilation (VE) to carbon dioxide production (VCO2). Indexes of exercise capacity and of ventilatory response to exercise are both related to adverse outcomes (6,7).

The choice of exercise modality during exercise testing is important. Tests are usually conducted on either a treadmill or cycle ergometer. Walking on a treadmill is a weight-bearing activity and, thus, at a given grade of exercise, heavier patients perform more work than lighter patients. During cycle exercise, because body weight is largely supported, the difference between light and heavy patients is less marked (8). Peak VO2 can be reported in absolute terms (L/min), but to account for the effects of mass, it is usually reported corrected for body mass (mL/kg/min).

To be most helpful in patients with CHF, a peak VO2 measurement should be recorded at peak exercise, when exercise is limited by cardiovascular performance. Peak VO2 is dependent on patient effort and may be influenced by cardiovascular and orthopedic problems such as chest pain, fatigue, shortness of breath and knee or hip discomfort (9). Overweight and obese patients may be limited by factors other than cardiovascular performance during maximal CPET (1012). We hypothesized that submaximal CPET-derived predictors, such as ventilatory equivalent of carbon dioxide production (VEqCO2) nadir or the oxygen uptake efficiency slope (OUES), may be more sensitive prognostic risk factors in ambulatory obese patients with CHF.

METHODS

The Hull and East Riding Ethics Committee (United Kingdom) approved the present study, and all patients provided informed consent. Consecutive patients referred to a community heart failure clinic with symptoms of breathlessness (New York Heart Association functional class II and III) who were found to have left ventricular (LV) systolic dysfunction were eligible for inclusion in the present study. Clinical information obtained included medical history, and drug and smoking history. Clinical examination included assessment of mass (kg), body mass index (BMI), heart rate, heart rhythm and blood pressure. Patients were excluded if they had significant respiratory disease (defined as a predicted forced expiratory volume in 1 s/forced vital capacity ratio <70%).

Heart failure was defined as the presence of current symptoms, or a history of symptoms controlled by ongoing therapy, and impaired LV systolic function. LV function was determined from two-dimensional echocardiography. 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 regarding the severity of LV dysfunction, the echocardiogram was reviewed jointly with a third operator and a consensus was reached. Where possible, LV ejection fraction (LVEF) was calculated using the Simpson formula from measurements on apical, two-dimensional views.

Patients underwent a symptom-limited, maximal CPET on a treadmill using the Bruce protocol, modified by the addition of a stage 0 (2.74 km/h and 0% gradient) at the onset of exercise. Metabolic gas exchange was measured using an Oxycon Delta metabolic cart (VIASYS Healthcare Inc, USA). Peak VO2 was calculated as the mean VO2 in the final 30 s of exercise and was reported in both absolute (L/min) and weight-corrected terms (mL/kg/min). The ventilatory AT was calculated using the V-slope method (13) and was also reported in absolute (L/min) and weight-corrected terms (mL/kg/min). The gradient of the relationship between minute ventilation (VE) and carbon dioxide production (VE/VCO2 slope) was calculated by linear regression analysis using data acquired from the entire test and was reported as a ratio, and ratio adjusted according to body mass. The instantaneous relationship between VE and VCO2 (ie, VEqCO2) and VE and VO2 was plotted from the start to finish of exercise. The means of each consecutive 30 s reading were calculated and the lowest point was defined as the VEqCO2 nadir (14). The OUES was calculated in accordance with the method proposed by Baba et al (15). The OUES was calculated using the following formula:

equation mm1

in which ‘a’ corresponds to the constant regression slope that represents the rate of increase in VO2 in response to VE, logVE denotes log10 transformation of VE and ‘b’ represents the intercept on the y axis. The OUES is not corrected for body mass. The peak respiratory exchange ratio (pRER) was calculated as the mean VCO2/VO2 ratio for the final 30 s of exercise.

Statistical analysis

SPSS version 17.0 (IBM Corporation, USA) was used for statistical analysis. Continuous variables are presented as mean ± SD, and categorical data are presented as percentages. Continuous variables were assessed for normality by the Kolmogorov-Smirnov test. Patients were stratified using the WHO’s classification system for obesity (16) based on BMI (normal 18.5 kg/m2 to 24.99 kg/m2; overweight 25 kg/m2 to 29.99 kg/m2; obese ≥30 kg/m2). A one-way ANOVA was used to explore differences between normal weight, overweight and obese patients. All survivors were followed for a minimum of 12 months.

All baseline variables were entered as potential age-adjusted univariable predictors of mortality using Cox analysis for normal weight, overweight and obese patients. Predictive models often overestimate the real performance of variables, especially where data sets are small or there are relatively few outcomes compared with candidate predictors (17). If the number of events per variable decreases, then the regression coefficient increases to produce an overestimate of the true effect (18). To reduce the effect of ‘overfitting’, an events-per-variable ratio of 10:1 (17) was used in the analysis. A multivariable Cox proportional hazards model using the backward likelihood ratio method was used to identify independent predictors of all-cause mortality from all candidate predictor variables in Tables 1 and and2.2. The outcome measure was all-cause mortality. An arbitrary level of 5% statistical significance (two-tailed) was used throughout.

TABLE 1
Baseline clinical characteristics among normal weight, overweight and obese patients
TABLE 2
Cardiopulmonary exercise testing responses in normal weight, overweight and obese patients

RESULTS

A total of 411 patients with CHF (mean [± SD] age 64±12 years; 81% male; LVEF 39±6%; peak VO2 22.3±8.1 mL/kg/min;VE/VCO2 slope 33.9±7.7) were included in the present study. Of these, 72% were taking angiotensin-converting enzyme inhibitors, 72% beta-blockers and 63% loop diuretics. Patients were stratified according to body mass into normal weight (n=121), overweight (n=178) and obese (n=112) groups (Table 1). All groups achieved the same respiratory exchange ratio at peak exercise, suggesting that they had all made approximately the same effort during exercise. The obese group were younger and had a lower one-year mortality rate than the other two groups.

Compared with normal weight patients, the obese patients had a significantly higher LVEF (41±7% versus 36±6%), higher absolute peak VO2 (usually associated with a better outcome), but lower peak VO2 per kg body mass (usually associated with a worse outcome) (Table 2). They had a higher absolute AT (usually associated with a better outcome), but a lower AT per kg body mass (usually associated with a worse outcome). Obese patients had a lower VE/VCO2 slope (absolute and per kg body mass) (usually associated with a worse outcome), but a lower VEqCO2 nadir and higher OUES (usually associated with a better outcome) (all P<0.05).

One hundred fifteen patients died during follow-up, corresponding to a crude death rate of 28%. In surviving patients, the median follow-up period was 8.7±2.3 years. Neither body mass (HR 0.99 [95% CI 0.98 to 1.00]; χ2=0.6; P=0.43) or BMI (HR 0.98 [95% CI 0.95 to 1.00]; χ2=0.7; P=0.40) were univariable predictors of all-cause mortality either in all patients taken together or in any of the BMI subgroups; eg, body weight (HR 1.00 [95% CI 0.99 to 1.01]; χ2=1.7; P=0.20) was not a univariable predictor in obese patients.

The leading univariable predictors of all-cause mortality are reported in Table 3 for all patients and normal weight, overweight and obese subgroups. In all patients, peak VO2 adjusted for body mass (χ2= 41.4) and unadjusted peak VO22=40.2) were similar for predicting all-cause mortality. Peak VO2 adjusted for body mass showed marginally higher χ2 values in normal weight, overweight and obese categories compared with unadjusted values. AT had similar prognostic power whether corrected for body mass (χ2=22.4) or not (χ2=24.4), with no difference between the two in any of the subgroups separately. In all patients, unadjusted VE/VCO2 slope (χ2=40.6) was a stronger predictor of all-cause mortality than body mass-adjusted values (χ2=32.8), and unadjusted values remained stronger in normal weight, overweight and obese subgroups.

TABLE 3
Body mass-corrected and absolute univariable predictors of all-cause mortality in all patients, and normal weight, overweight and obese subgroups

In an age-adjusted Cox multivariable proportional hazards model, OUES and VEqCO2 nadir were the strongest predictors of all-cause mortality in all patients (Table 4). In normal weight patients, the strongest predictor was exercise duration, and in overweight patients it was VEqCO2 nadir and OUES. In obese patients, peak VO2 per kg body mass was the most significant predictor of all-cause mortality.

TABLE 4
Age-adjusted multivariable predictors of all-cause mortality in all patients, and normal weight, overweight and obese subgroups

DISCUSSION

We found that correcting peak VO2 for body mass slightly improved risk prediction, especially in obese patients with CHF. The adjustment of other CPET-derived variables, including AT and VE/VCO2 slope, for body mass appears to provide less prognostic value. The convention of adjusting peak VO2 for body mass, known as ratio standard adjustment, is used to remove the potentially influential differences in body mass or body size among individuals. However, some authors have argued that traditional ratio standards methods fail to render oxygen uptake independent of body mass. Therefore, other methods of adjustment, such as power function relationships, may be more appropriate (19).

We hypothesized that submaximal CPET-derived predictors, such as VEqCO2 nadir or the OUES, may be more sensitive prognostic risk factors in obese patients with CHF. We previously reported that more than 40% of CHF patients were unable to complete maximal CPET (defined as achieving a pRER >1.0) using the modified Bruce protocol (20), and speculated that the proportion would be greater in obese patients. However, this was not the case. In a subgroup of 112 obese patients, 21% completed CPET with a pRER <1.0, and 34% completed the test with a pRER >1.10. Our study indicates that body mass does not influence whether a patient with CHF can undergo maximal CPET.

While we are not aware of any other studies involving CHF patients that have established the impact of body mass on risk prediction during CPET per se, Chase et al (21) specifically evaluated the prognostic power of the VE/VCO2 slope according to BMI subgroups in patients with CHF. A total of 704 patients were divided into three BMI subgroups and were tracked for major cardiac events for two years after testing. The authors reported that the VE/VCO2 slope was the strongest prognostic indicator in each BMI subgroup. In our study, while VE/VCO2 slope was an important univariable predictor of all-cause mortality, it was not retained in any multivariable model in any of our BMI subgroups (normal weight, overweight and obese patients).

The seminal work of Mancini et al (22), which recommended a peak VO2 threshold of 14 mL/kg/min for the selection of transplant candidates, used treadmill walking in its protocol. However, it was unfortunate that only weight-adjusted peak VO2 was presented and the debate regarding whether to correct for weight when presenting peak VO2 data was not discussed.

Limitations

Body fat is metabolically inert and consumes virtually no oxygen; however, fat does represent a significant amount of total body weight, especially in obese individuals. Consequently, some authors have advocated that peak VO2 should be adjusted for lean body mass to improve prognostic sensitivity (23,24). Unfortunately, we were unable to adjust for lean body mass. An additional limitation was the relatively low number of events in the obese cohort of patients (77% event-free survival in obese compared with 68% in normal weight patients [Table 1]). Also, all of our patients underwent CPET on a treadmill; therefore, we cannot comment on the value of weight correction for nonweight-bearing exercise modalities such as cycling.

CONCLUSION

Correcting peak VO2 for body mass slightly improves risk prediction, especially in obese patients with CHF. The adjustment of other CPET-derived variables, including AT and VE/VCO2 slope, for body mass appears to provide less prognostic value. Clinicians should continue to adjust peak VO2 for body mass.

Footnotes

DISCLOSURES: The authors have no financial disclosures or conflicts of interest to declare.

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