Rationale: Patients with chronic heart failure have limited exercise capacity, which cannot be completely explained by markers of cardiac dysfunction. Reduced pulmonary diffusing capacity at rest and excessively high ventilation during exercise are common in heart failure. We hypothesized that the reduced pulmonary diffusing capacity in patients with heart failure would predict greater dead space ventilation during exercise and that this would lead to impairment in exercise capacity.
Objectives: To determine the relationship between pulmonary diffusing capacity at rest and dead space ventilation during exercise, and to examine the influence of dead space ventilation on exercise in heart failure.
Methods: We analyzed detailed cardiac and pulmonary data at rest and during maximal incremental cardiopulmonary exercise testing from 87 consecutive heart transplant assessment patients and 18 healthy control subjects. Dead space ventilation was calculated using the Bohr equation.
Measurements and Main Results: Pulmonary diffusing capacity at rest was a significant predictor of dead space ventilation at maximal exercise (r = −0.524, P < 0.001) in heart failure but not in control subjects. Dead space at maximal exercise also correlated inversely with peak oxygen consumption (r = −0.598, P < 0.001), peak oxygen consumption per kilogram (r = −0.474, P < 0.001), and 6-minute-walk distance (r = −0.317, P = 0.021) in the heart failure group but not in control subjects.
Conclusions: Low resting pulmonary diffusing capacity in heart failure is indicative of high dead space ventilation during exercise, leading to excessive and inefficient ventilation. These findings would support the concept of pulmonary vasculopathy leading to altered ventilation perfusion matching (increased dead space) and resultant dyspnea, independent of markers of cardiac function.
Heart failure is associated with dyspnea and reduced exercise capacity; however, traditional markers of cardiac dysfunction, such as left ventricular ejection fraction or cardiac index, do not predict exercise tolerance. Pulmonary diffusing capacity has been shown to predict exercise capacity in heart failure; however, the mechanism for this has not been established.
This study demonstrates that impairment in pulmonary diffusing capacity predicts the development of excessive dead space ventilation during exercise in heart failure with reduced ejection fraction, leading to inefficient ventilation and reduced exercise capacity. This increased dead space ventilation is the result of increasing ventilation–perfusion mismatch.
Dyspnea is one of the cardinal symptoms of chronic heart failure; however, the underlying pathophysiology is poorly understood. Studies have shown that resting measures of cardiac function, including cardiac output, stroke volume, and left ventricular ejection fraction, are poor predictors of exercise capacity in heart failure (1–3). Resting lung function (4, 5) and ventilatory response to CO2 (6, 7) have been shown to predict exercise capacity in heart failure, although the exact mechanisms responsible have not been well defined.
Lung biopsy studies have demonstrated significant peripheral pulmonary vasculopathy in patients with heart failure due to mitral stenosis (8, 9). These changes have been demonstrated to correlate very strongly with impairment in diffusing capacity of the lung for carbon monoxide (DlCO) (10). Although similar histological studies have not been performed in patients with heart failure from other causes, patients with chronic heart failure do demonstrate similar reductions in DlCO (11, 12). Moreover, the reduction in DlCO has been shown to be due to abnormality of the alveolar–capillary membrane and to not correct after heart transplantation (13), suggesting permanent injury to this interface. Animal studies of heart failure have demonstrated changes to the pulmonary circulation similar to that seen in mitral stenosis in humans (14) with associated reduction in pulmonary capillary function (15). In addition, it has been shown in patients with heart failure that DlCO is reduced and correlates strongly with peak exercise oxygen consumption (po2), heart failure severity class, and prognosis (4, 5). However, the mechanisms relating DlCO to exercise capacity have not been well explained, particularly as patients with heart failure rarely have significant oxygen hemoglobin desaturation or falls in arterial oxygen tension with exercise (16, 17).
Ventilatory efficiency, as measured by the e/co2 slope, has also been shown to be a good predictor of peak exercise in heart failure (6, 7). Minute ventilation (e) in patients with heart failure is higher than in normal control subjects for any given amount of co2 (18). This leads to a raised e/co2 slope, which has been shown to correspond to a reduced po2 (17, 19) and reduced prognosis (20). Patients with heart failure do not however have a mechanical limitation to ventilation, reaching the same proportion of maximal voluntary ventilation as healthy control subjects (6). As patients with heart failure rarely have significant arterial oxygen desaturation during exercise and do not have a mechanical limitation to breathing, the mechanisms for DlCO and e/co2 slope being such good predictors of exercise capacity require further investigation.
We propose that pulmonary vasculopathy contributing to impaired pulmonary perfusion and increased dead space may be the unifying mechanism behind the relationship between DlCO, e/co2, and po2 in patients with heart failure. Impaired pulmonary perfusion due to pulmonary vasculopathy and reduced cardiac output would result in increased dead space ventilation, leading to inefficient ventilation and increased work of breathing in heart failure (21). We sought to test our hypothesis in patients with chronic heart failure who were being assessed for heart transplantation. We hypothesized that resting DlCO would predict the magnitude of dead space ventilation at maximal exercise and that dead space ventilation at maximal exercise would inversely correlate with exercise capacity (po2).
Following approval from our institution’s research and ethics committee (project number 203/11), retrospective data were collected from consecutive patients undergoing heart transplant assessment from June 2006 to October 2011. Control group data were collected from another study of healthy subjects that had been performed during the same period (project number 461/12). Patients with a left ventricular assist device or on intravenous inotropes were excluded, as were patients with a previous heart transplant or who were being considered for heart–lung transplantation. Clinical information (demographics, medical history, imaging, medication, and 6-minute-walk distance) was collected.
Respiratory data collected at rest included spirometry, single-breath DlCO, and static lung volumes. Tests were performed according to American Thoracic Society/European Respiratory Society criteria (24). Percentage predicted values were calculated using previously published data (25–27).
Cardiac data collected at rest included right heart catheterization, transthoracic echocardiography, and cardiac gated blood pool scan. New York Heart Association class was recorded (28).
A maximal stage 1 incremental cardiopulmonary exercise test was performed on a cycloergometer (SensorMedics Corp., Yorba Linda, CA) and respiratory and metabolic data were collected on a breath-by-breath basis and then averaged over 30-second intervals. Prediction equations were based on previously reported data (29).
Dead space ventilation at maximum exercise (DSmax) was defined as the proportion of tidal ventilation made up of dead space calculated at peak exercise using the Bohr equation (30) (Equation 1) with adjustment for equipment dead space. This measure of dead space includes both the relatively fixed anatomical dead space and the variable alveolar dead space caused by ventilation–perfusion mismatching.
All measurements of gas partial pressures were in millimeters of mercury and were corrected to body temperature and pressure, saturated. PeCO2 was derived from the fraction of expired CO2,as measured from the metabolic cart and multiplied by barometric pressure.
Anaerobic threshold was determined from the recorded data using the V-slope method originally described by Beaver and colleagues (32), and peak workloads and gas exchange values were taken as the average of the last 30 seconds of maximal work.
A group of healthy control subjects with normal cardiopulmonary exercise tests was used as a comparator group.
The relationships between DSmax and DlCO, po2, peak work, 6-minute-walk distance, anaerobic threshold, and e/co2 were assessed with the Pearson coefficient of correlation. The patients with heart failure were divided into high- and low-DSmax groups to allow comparison of dead space at differing levels of work, as well as to allow more detailed analysis of exercise and cardiac data within the patient group. Patients were defined as having a high DSmax if their DSmax was above the median value of 0.19. Patients with low and high DSmax were compared using independent samples t tests or Fisher’s exact test. Statistical analyses were performed with the use of PASW Statistics 18 (SPSS Inc., Chicago, IL). Unless otherwise stated, results are given as mean (±SD).
Data were collected on 87 patients and 18 control subjects. Demographics are shown in Table 1. The heart failure group had a left ventricular ejection fraction of 25.8 (±11.7)% and had New York Heart Association class 2.9 (±0.5) symptoms. Heart failure etiology was ischemic (33%), idiopathic dilated cardiomyopathy (47%), hypertrophic obstructive cardiomyopathy (9%), congenital (5%), valvular (4%), and other (2%). Mean duration of heart failure was 12 (±10) years. The heart failure group had significantly lower FEV1, FVC, DlCO, and all markers of exercise capacity, plus greater dead space compared with the healthy control group (Table 1). Pulmonary radiology reports of all patients, including chest computed tomography in 87%, were reviewed, and no patients had reported evidence of emphysema or gas trapping. One patient had evidence of pulmonary emboli, while a second demonstrated mild pulmonary fibrosis. Reanalysis with these two patients excluded did not alter our findings. Minor ground-glass opacity was seen in five patients, suggesting pulmonary edema.
|Characteristics||Control (n = 18)||Heart Failure (n = 87)||Significance (P Value)|
|Male sex, %||100||80.5||0.038|
|Age, yr||35 ± 9||51 ± 12||<0.001|
|BMI, kg/m2||24 ± 2||26 ± 5||<0.001|
|FEV1, % predicted||103 ± 8||80 ± 16||<0.001|
|FVC, % predicted||111 ± 9||84 ± 17||<0.001|
|Total lung capacity, % predicted||108 ± 9||92 ± 13||<0.001|
|Residual volume, % predicted||89 ± 16||98 ± 18||0.060|
|DlCO, % predicted||113 ± 10||63 ± 14||<0.001|
|Peak work, W||369 ± 47||74 ± 31||<0.001|
|Peak work, % predicted||161 ± 16||44 ± 14||<0.001|
|po2, % predicted||106 ± 8||34 ± 9||<0.001|
|po2 ⋅ kg−1, ml/min/kg||49.4 ± 6.4||12.2 ± 3.7||<0.001|
|Dead space at maximal exercise||0.08 ± 0.03||0.19 ± 0.05||<0.001|
In heart failure, DlCO correlated with po2 (r = 0.596, P > 0.001). In the heart failure group, both resting DlCO (r = −0.524, P < 0.001) and percentage predicted DlCO (r = −0.399, P < 0.001) correlated inversely with DSmax (see Figure 1), whereas, in the control group, it did not (r = −0.083, P = 0.760). Of note, DlCO did not correlate with dead space ventilation at rest in the heart failure group (r = −0.182, P = 0.095).
In the heart failure group, DSmax correlated inversely with po2 (absolute: r = −0.598, P < 0.001, see Figure 2; and percentage predicted: r = −0.391, P < 0.001), po2 ⋅ kg−1 (r = −0.474, P < 0.001, Figure 3), peak work (absolute: r = −0.638, P < 0.001, Figure 4; and percentage predicted: r = −0.521, P < 0.001), and the 6-minute-walk distance (r = −0.317, P = 0.021, Figure 5). DSmax inversely correlated with anaerobic threshold, both as an absolute value (r = −0.297, P = 0.001) and as a percentage of predicted (r = −0.280, P = 0.016). DSmax correlated positively with e/co2 (r = 0.348, P = 0.001) and e/co2 at anaerobic threshold (r = 0.474, P = 0.001). DSmax inversely correlated with alveolar volume (Va; r = −0.524, P < 0.001) but not with DlCO/Va (r = −0.144, P = 0.189). DSmax in the control group did not significantly correlated with any of the above.
As patients must be nonsmokers before transplant assessment at our institute, there were no current smokers in the cohort. The mean pack-year history of the smokers in the cohort was 18 (±14). Within the heart failure group, there was no statistical difference between ex-smokers and never-smokers in DSmax (0.20 ± 0.06 vs. 0.19 ± 0.05, P = 0.644), DlCO (63.7 ± 13.4 vs. 62.8 ± 15.0% predicted, P = 0.791), peak work (73.0 ± 32.4 W vs. 73.4 ± 28.5 W, P = 0.951), po2 (0.98 ± 0.32 vs. 0.97 ± 0.39, P = 0.956), or po2 ⋅ kg−1 (12.2 ± 3.8 vs. 12.1 ± 3.8, P = 0.888). DSmax correlated more strongly with po2 absolute (r = −0.618, P < 0.001) and percentage predicted (r = − 0.439, P = 0.003), po2 ⋅ kg−1 (r = −0.574, P < 0.001), and peak work absolute (r = −0.648, P < 0.001) and percentage predicted (r = −0.547, P < 0.001) when ex-smokers were excluded in the heart failure cohort. Analysis defining ex-smokers as those with pack-years over 10 did not change these results: specifically, DlCO % predicted in pack-years over 10 (60.7%) versus pack-years under 10 (64.2%; P = 0.325).
A comparison of patients with heart failure dichotomized into high- and low-DSmax groups is shown in Tables 2–4. Of note, there is a significant difference in exercise capacity, but little difference in terms of cardiac or demographic parameters.
|Parameters||Low Dead Space||High Dead Space||Significance (P Value)|
|Dead space at rest||0.29 ± 0.10||0.33 ± 0.08||0.052|
|Dead space at maximal exercise||0.16 ± 0.03||0.24 ± 0.03||<0.001|
|DlCO, % predicted||67.0 ± 12.7||59.0 ± 14.8||0.008|
|Hemoglobin, g/dl||13.5 ± 1.6||13.0 ± 2.0||0.152|
|Ventilation at po2, % predicted maximum reserve||60.5 ± 12.2||62.8 ± 17.5||0.484|
|po2, L/min||1.1 ± 0.4||0.8 ± 0.2||<0.001|
|po2, % predicted||36.1 ± 10.5||32.0 ± 7.6||0.042|
|po2 ⋅ kg−1, ml/min/kg||13.4 ± 4.3||10.8 ± 2.5||0.001|
|Peak work, W||88.6 ± 32.1||57.7 ± 21.3||<0.001|
|Peak work, % predicted||48.0 ± 15.0||38.5 ± 9.9||0.001|
|o2 at anaerobic threshold, ml/min/kg||8.3 ± 2.2||7.4 ± 1.5||0.042|
|o2 at anaerobic threshold, % predicted||28.1 ± 9.5||24.5 ± 6.4||0.011|
|e/co2||38.8 ± 7.9||44.7 ± 12.5||0.011|
|e/co2 at anaerobic threshold||37.3 ± 6.0||43.4 ± 7.6||<0.001|
|Six-minute-walk distance, m||437.0 ± 78.7||379.5 ± 110.9||0.036|
|Medication||Low Dead Space (%)||High Dead Space (%)||Significance (P Value)|
|Angiotensin-converting enzyme inhibitors||59||68||0.48|
|Angiotensin-2 receptor blockers||30||13||0.08|
|Characteristics||Low Dead Space||High Dead Space||Significance (P Value)|
|Male sex, %||93||66||0.002|
|Age, yr||49 ± 13||53 ± 10||0.170|
|Body mass index, kg/m2||27 ± 5||26 ± 4||0.259|
|Length of disease, yr||13 ± 10||11 ± 9||0.215|
|New York Heart Association class||2.93 ± 0.5||2.95 ± 0.5||0.889|
|Left ventricular ejection fraction, %||26.2 ± 10.4||25.3 ± 13.2||0.723|
|Pulmonary arterial wedge pressure, mm Hg||21.3 ± 6.2||19.8 ± 8.0||0.363|
|Cardiac index, L/min/m2||2.3 ± 0.8||2.1 ± 0.6||0.179|
|Pulmonary vascular resistance, dyn · s/cm5||198 ± 166||203 ± 113||0.901|
|Hemoglobin, g/dl||13.5 ± 1.6||13.0 ± 2.0||0.144|
The high-DSmax group had a higher dead space as a proportion of tidal ventilation at all levels of work compared with those who had a low DSmax (see Figure 6). This difference was significant (P < 0.015) at all levels displayed except for at rest (P = 0.052).
Dead space per breath (Vd in Equation 1) was similar in all three groups at rest and increased with increasing work. The increase in dead space per breath appeared to be greatest in the high-DSmax group and least in the control subjects (see Figure 7).
This study demonstrates that, in patients with systolic heart failure, impaired DlCO at rest is associated with the development of higher dead space ventilation throughout exercise, resulting in inefficient ventilation and reduced exercise capacity. In addition, the 6-minute-walk distance was significantly reduced in the high–dead space group to a magnitude that has been shown to have significant clinical ramifications (33). This suggests that excessive dead space ventilation results in important functional impairment. Both these results highlight the importance of lung function in understanding the mechanisms of dyspnea in heart failure.
The relationship between DlCO and po2 has been previously described (3, 12), as has the association between DSmax and po2 (4). Our study is the first to demonstrate a relationship between DlCO and DSmax, and to show that dead space ventilation increases during exercise (Figure 6). In addition, by demonstrating that absolute dead space (Figure 7) increases with increasing work, we provide new evidence that this increased dead space is due to ventilation–perfusion mismatch.
There was a relationship between Va and DlCO/Va and po2, as well as Va and DSmax; however, there was no significant relationship between DlCO/Va and DSmax. A previous study has reported a relationship between Va and po2, but failed to report a relationship between DlCO/Va and po2 (12). Although the lack of relationship between DlCO/Va and DSmax raises the possibility that the cause is not at the alveolar–capillary interface, studies have shown that DlCO/Va is a poor predictor of alveolar–capillary function in heart failure. Puri and colleagues (11) demonstrated that membrane conductance, the best measure of the alveolar–capillary function, was significantly reduced in heart failure despite relatively normal DlCO/Va. In addition, Mettauer and colleagues (13) demonstrated that, although membrane conductance/Va in patients with heart failure was reduced, their DlCO/Va remained normal, due to an increase in functional capillary volume. It would be of interest in future studies to test the relationship between membrane conductance and DSmax.
The mechanisms underlying the increased dead space ventilation during exercise in heart failure are unknown. It is recognized that dead space, as calculated by the Bohr equation, is made up of the “anatomical” and “alveolar” dead space (30). Although anatomical dead space is relatively fixed, it may increase as a percentage of total ventilation when either Vt is low and/or respiratory frequency is high. This was seen in our study, where dead space ventilation was highest at rest, but diminished with increasing exercise and tidal ventilation (see Figure 6). Figure 7 demonstrates that the volume of dead space per breath increased with increasing work, and that this was most pronounced in the high-DSmax group. Given that the volume of anatomical dead space per breath is unlikely to increase significantly during exercise, this would suggest that it is alveolar dead space that is increasing and contributing to the higher dead space in the DSmax group. Increased alveolar dead space can have multiple causes: ventilation–perfusion abnormalities at the alveolar level, such as airways disease, right-to-left shunt, pulmonary embolism, and reduced pulmonary perfusion (30). Airways disease and emphysema were excluded based upon clinical assessment, radiology, and spirometry. Physiological shunt has not been observed when serial arterial blood gases have been taken during exercise in patients with heart failure (16). Pulmonary embolism was also excluded on clinical grounds. For these reasons, we propose that the underlying mechanism for increasing DSmax is most likely impaired pulmonary perfusion, resulting in an inability to match the increased ventilation of exercise. In other words, in heart failure, when ventilation increases with exercise, pulmonary perfusion lags behind, resulting in a widening ventilation–perfusion mismatch, and thus increasing dead space ventilation. Our data (Figure 6) demonstrate that patients with a high DSmax have a higher amount of dead space ventilation than those with low DSmax at all levels of work, and that this difference widens with increasing workload. This suggests that the reduced ability to match pulmonary perfusion with ventilation is present and worsens throughout exercise.
Pulmonary vascular remodeling has been well documented in chronic heart failure (34). This remodeling, in the form of pulmonary capillary basement membrane thickening, capillary dilatation, reduced capillary density, and intimal thickening and muscularization of the pulmonary arterioles and venules, is associated with reduced DlCO in patients with mitral stenosis (9, 10). Similar changes have been demonstrated in animal models of nonvalvular heart failure (14, 15).
Moreover, multiple studies (11–13) have shown reductions in the membrane conductance component of DlCO in patients with systolic left heart failure, suggesting abnormalities at the alveolar–capillary level. Of note, this abnormality of membrane conductance is not improved by heart transplantation, suggesting a permanent injury to this interface (13). Vascular remodeling is associated with reduced nitric oxide production in animal models of heart failure (14), and nitric oxide production in exercising patients with heart failure is reduced compared with normal control subjects (35). Thus, vascular remodeling in chronic heart failure would impair the normal physiological exercise-induced vasodilatation, and thereby predispose to raised pulmonary vascular resistance and pulmonary hypertension during exercise, all of which would lead to impaired pulmonary perfusion and elevated dead space. Furthermore, those patients with a high DSmax demonstrated a significantly higher e/co2 at anaerobic threshold, which is consistent with studies that have shown this to be a marker of pulmonary vascular disease (36). Pulmonary vascular resistance at rest did not correlate with DSmax in our patients; however, we would expect it to rise with exercise in those patients who had a high DSmax.
Increased dead space ventilation may be associated with reduced exercise tolerance, either as a marker of underlying cardiopulmonary dysfunction or, more directly, through increasing work of breathing. First, increased dead space may simply reflect the aforementioned pulmonary vasculopathy, which, in its own right, could reduce exercise capacity by predisposing to exercise-induced pulmonary hypertension. It may also be a marker of a coinciding right heart failure resulting in impaired pulmonary perfusion. More directly, inefficient ventilation due to increased dead space ventilation would increase work of breathing. In heart failure, this will be exacerbated, as work of breathing is higher for any given ventilation during exercise (37). This would lead to increased oxygen consumption by the respiratory muscles for any given amount of work performed by the lower limbs. At a high level of respiratory work, total blood flow to the respiratory muscles reaches 10.6% of cardiac output (38). In heart failure, there is limited ability to compensate for this. Blood supply must be distributed to both the respiratory and limb muscles, and once maximal cardiac output has been reached, work of breathing cannot be further increased without affecting the exercising limb muscles.
This diversion of blood flow from the skeletal muscles will result in an earlier onset of anaerobic metabolism. Consistent with this, increased DSmax in our patients resulted in earlier anaerobic thresholds. Given that patients with heart failure report both dyspnea and fatigue as being equally severe during exercise (39), it is likely that this will result in reduced peak exercise capacity. This cardiac output “steal” phenomenon would be most evident in those with severe cardiac disease. In summary, the increased work of breathing caused by excessive dead space ventilation limits the effective maximal cardiac output to the exercising limb muscles. Thus, patients are cardiac limited, but this is significantly influenced by ventilatory efficiency.
We estimated dead space without direct PaCO2 measurement via arterial blood gas analysis. However, there was an excellent correlation between direct PaCO2 measurement and PaCO2 calculated using Equation 2 (r = 0.962, P < 0.001) (31). Another study suggested an underestimation of dead space with our technique in those with higher dead space compared with that using direct PaCO2 (40), which would reduce the possibility of significant correlations being found. Wasserman and colleagues (4) used direct arterial blood gas assessment and produced results similar to our own. Finally, using a noninvasive method of calculating PaCO2 allowed for the estimation of dead space ventilation at multiple workloads.
As the patients in this study had severe systolic heart failure (heart failure with reduced ejection fraction), and were being assessed for transplantation, the findings described here may not apply to those patients with diastolic dysfunction (heart failure with preserved ejection fraction). Moreover, our findings may not be applicable to patients with systolic heart failure with less severe disease.
There was a high proportion of ex-smokers in the heart failure cohort, but there were none in the control group, and this may potentially confound our results. There was not, however, any difference in smoking rates between the high- and low-DSmax groups, nor were there significant differences between the ex-smokers and never-smokers within the heart failure cohort in terms of DlCO, DSmax, or exercise capacity. Finally, analysis of the relationship between DSmax and exercise tolerance in heart failure with the ex-smokers excluded resulted in an increase in the r values and no change in P values, despite a reduction in sample size.
Our findings demonstrate that, in heart failure, a low DlCO is a marker of increased dead space ventilation during exercise, and that this is due to dynamic changes in ventilation–perfusion mismatch. This increased dead space ventilation results in impaired ventilatory efficiency and leads to reduced exercise capacity.
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Supported by the Alfred Research Trust and the Australian Post-Graduate Foundation.
Author Contributions: Conception and design—K.K. and M.T.N.; analysis of data—K.K., C.S.-A., M.J.E., J.P.W., K.N., M.S., B.R.T., and M.T.N.; drafting of manuscript—K.K., C.S.-A., M.S., B.R.T., and M.T.N.; final approval of manuscript—K.K., C.S.-A., M.J.E., J.P.W., K.N., M.S., B.R.T., and M.T.N.
Originally Published in Press as DOI: 10.1164/rccm.201508-1555OC on January 6, 2016