Examination of the Relationship and Dissociation Between Minimum Minute Ventilation/Carbon Dioxide Production and Minute Ventilation vs. Carbon Dioxide Production Slope

Abstract

Background: Minute ventilation/carbon dioxide production (V̇E/V̇CO₂) is a variable of cardiopulmonary exercise testing (CPET), which is evaluated by arterial CO₂ pressure and ventilation-perfusion mismatch via invasive methods. This study evaluated substitute non-invasively obtained variables for minimum V̇E/V̇CO₂ (Min) and V̇E vs. V̇CO₂ slope (Slope) and the relationship between Min and Slope.

Methods and Results: This study enrolled 1,052 patients with heart disease who underwent CPET and impedance cardiography simultaneously. At first, the correlations between the end-tidal CO₂ pressure (PETCO₂), tidal volume/respiratory rate (TV/RR) ratio, V̇E and V̇CO₂ Y-intercept (Y-int), and cardiac index (CI) and the Min and Slope were investigated. Second, the correlation between Min and Slope was investigated. PETCO₂ showed the largest correlation value among the 4 variables. These 4 variables could reveal 84.2% and 81.9% of Min and Slope, respectively. Although Slope correlated with Min (R=0.868) and predicted 78.9% of Min, considering these 4 variables, Slope+Y-int was more strongly correlated with Min (R=0.940); the Slope+Y-int revealed 90.6% of the Min relationship in the multiple regression analysis.

Conclusions: Over 80% of the Min and Slope values were revealed with the above-mentioned 4 variables collected non-invasively. The formula, Min∝Slope+Y-int, can reveal >90% of the Min/Slope relationships, and the Y-int may be a crucial factor to clarify the relationship between Min and Slope.

The number of people with heart failure (HF) and HF with comorbidities is increasing worldwide.^1,2 Although in-hospital mortality is decreasing, 1-year mortality and the proportion of re-hospitalized patients remain high.^2,3 Thus, new treatments and evaluation methods are required.

Cardiopulmonary exercise testing (CPET) is used to assess prognosis in patients with HF. During CPET, the minimum minute ventilation/carbon dioxide production (V̇E/V̇CO₂) and V̇E vs. V̇CO₂ slope are ventilatory efficiency variables (VEVs).^4,5 These VEVs predict mortality and hospitalization in patients with HF and are as important in CPET as the peak rate of oxygen uptake (V̇O₂).^6–10 Although minimum V̇E/V̇CO₂ and V̇E vs. V̇CO₂ slope are evaluated using different calculation methods, these VEVs show similar values.^5,6 However, different VEVs values are occasionally clinically observed during CPET, and the reasons for the dissociation remain unknown.

V̇E/V̇CO₂ can be determined using the arterial pressure of carbon dioxide (PaCO₂) and the physiological dead space (Vd/Vt) of the lungs by using the formula: 863/PaCO₂ [1−Vd/Vt].¹¹ The measurement of PaCO₂ is invasive; however, the measurement of the best estimate of PaCO₂, the end-tidal carbon dioxide pressure (PETCO₂), is non-invasive.^12–14 Vd/Vt indicates low cardiac output (CO) or lung dysfunction.^11,15 CO can be determined from the cardiac index (CI), which is measured non-invasively by impedance cardiography.¹⁶ The impedance cardiography method approximates stroke volume, allowing for CO assessment. The tidal volume to respiratory rate ratio (TV/RR ratio) evaluates ventilatory stability during exercise.^17–19 During incremental exercise testing, the Y-intercept (Y-int) of the linear V̇E and V̇CO₂ relationship, under the respiratory compensation (RC) point, evaluates mechanical lung dead space changes.^5,20–23 Together, these non-invasive methods may reveal VEVs with high accuracy.

In this study, we hypothesized that the VEVs could be evaluated using 4 non-invasive variables, including PETCO₂, TV/RR ratio, CI, and the Y-int, during CPET. Subsequently, we investigated the correlation values between these 4 non-invasive variables and VEVs, the relationship between the VEVs, and the VEVs dissociation.

Methods

CPET was performed on 4,700 consecutive patients between January 2016 and December 2018 at the Gunma Prefectural Cardiovascular Center. We extracted the data of 1,412 patients with heart disease who similarly underwent impedance cardiography. We excluded patients in whom the anaerobic threshold (AT) or RC points could not be identified due to low leg muscle power, knee joint problems, and anemia (hemoglobin level <10 mg/dL), or patients with suspected heart disease but with negative diagnostic tests, such as physical examinations, chest radiography, electrocardiography, transthoracic echocardiography, brain natriuretic peptide <80 pg/mL, and CPET. Finally, data from 1,052 patients with heart disease were retrospectively included in our study (Figure 1). There were no patients with clinically severe lung disease or an unstable condition. We investigated the correlation values between the 4 non-invasive variables and VEVs, the relationship between the VEVs, and the VEVs dissociation.

Figure 1.

Flowchart of the study design.

CPET

The ventilatory pattern at the AT and RC points and the peak V̇O₂ were evaluated using symptom-limited CPET during incremental exercise testing on an upright cycle ergometer (StrengthErgo 8; Mitsubishi Electric Engineering, Tokyo, Japan), as previously reported.²⁴ The test began with a 3-min rest period, followed by a 3-min warm-up performed at 0 W intensity using the mask method. Following this warm-up, the exercise intensity was increased continuously at a rate of 1 W every 6 s until exhaustion was achieved. To ensure CPET was performed at a sufficiently high intensity to reach exhaustion, patients were instructed to maintain a work rate (WR) sufficient to achieve a gas exchange ratio (carbon dioxide production/oxygen uptake [V̇CO₂/V̇O₂]) >1.10. The V̇O₂, V̇CO₂, V̇E, and PETCO₂ were measured on a breath-by-breath basis using a gas analyzer (MINATO 300S; Minato Science Co., Ltd., Osaka, Japan). The peak V̇O₂ was determined at the highest exercise WR, and the AT was measured using the V-slope method.²⁵ The RC point was defined as the point at which there was an increase in the V̇E/V̇CO₂ and a decrease in the PETCO₂.^26,27 Before the RC point, the relationship between V̇E and V̇CO₂ is linear (V̇E=aV̇CO₂ +b), where “a” is the value of the V̇E vs. V̇CO₂ slope, and “b” is the intercept on the V̇E axis (Y-int).^5,27 Minimum V̇E/V̇CO₂ was determined as the nadir of the V̇E/V̇CO₂ ratio during incremental exercise testing and 30 s average data.⁵ Similarly, we recorded the values of TV, RR, and PETCO₂ at rest, warm-up, AT and RC points, and peak WR of the averaged 20 s. The TV/RR ratio was determined at each exercise point.

Impedance Cardiography Protocol

The CO during the CPET was evaluated using impedance cardiography (PhysioFlow Lab-1; Manatec Biomedical, Paris, France).^28,29 The CO was recorded for 20 s at each exercise point. CI was calculated as the CO divided by the body surface area.

Statistical Analysis

All data are expressed as mean±standard deviation or number (%) as appropriate for the data type and distribution. Pearson’s coefficient correlation was used as appropriate. Multiple linear regression analysis was performed to evaluate the VEVs during exercise. Statistical significance was set at P<0.05 for all analyses, and all statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 20 (IBM Corp., Armonk, NY, USA).

Ethical Considerations

This study was conducted in accordance with the Japanese ethical rules and was approved by the Ethics Committee of Gunma Prefectural Cardiovascular Center (no. 31003). The requirement for written informed consent was waived due to the retrospective nature of the study.

Results

Baseline Clinical Characteristics of Patients

The mean age of patients in the study was 66±13 years, and 73% of the patients were men.

Fifty-one percent of the patients had ischemic heart disease, and 19% had congestive HF. The mean left ventricular ejection fraction was 56.9±13.5%, peak V̇O₂ value was 17.3±4.5 mL/min/kg, and minimum V̇E/V̇CO₂ and V̇E vs. V̇CO₂ slope were 36.4±6.0 and 33.1±6.2, respectively (Table 1).

Table 1. Patient Characteristics

	Overall (n=1,052)
Age, years	66±13
Male sex	771 (73)
Body weight, kg	63.1±12.4
Height, cm	162.6±8.3
Smoking history	602 (57)
Heart diseases
Ischemic heart disease	536 (51)
Congestive heart failure	202 (19)
Valve disease	128 (12)
Other heart diseases	315 (18)
Trance echocardiography data
LV ejection fraction, %	56.9±13.5
Intraventricular septum thickness, mm	10.6±2.2
Posterior LV wall thickness, mm	10.2±1.6
LV end-diastolic diameter, mm	47.4±8.4
LV end-systolic diameter, mm	32.9±10.3
E/E’	9.9±4.7
Laboratory data
Hemoglobin, g/dL	13.5±1.8
Cardiopulmonary exercise test
Peak heart rate, beats/min	123±21
AT, mL/min/kg	11.9±2.6
Peak V̇O2, mL/min/kg	17.3±4.5
Peak V̇O2, %	72.1±17.0
Minimum V̇E/V̇CO2	36.4±6.0
V̇E/V̇CO2 slope	33.1±6.2
Peak gas exchange ratio	1.15±0.05
Medications
β-blockers	422 (40)
ACE-Is/ARBs	480 (46)
Calcium channel blockers	241 (23)
Mineralocorticoid antagonists	177 (17)
Diuretics	246 (23)
Statins	497 (47)

Data are presented as mean±standard deviation or number (%). ACE-I, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; AT, anaerobic threshold; LV, left ventricular; V̇CO₂, carbon dioxide production; V̇E, minute ventilation; V̇O₂, oxygen uptake.

Correlation Between PETCO₂, TV/RR Ratio, CI, and Y-int and VEVs During Exercise

Figures 2 and 3 demonstrate the correlations between PETCO₂, TV/RR ratio, CI, and Y-int and VEVs during rest and exercise, respectively. All points were significant and negative.

Figure 2.

Correlation coefficient values between the VEVs and the PETCO₂, TV/RR ratio, and CI at the different exercise points. The correlation values between the VEVs and their proposed substitute measures are all significant from rest to peak WR. The largest value between PETCO₂ and the minimum V̇E/V̇CO₂ was −0.877 at the AT, and that between PETCO₂ and V̇E vs. V̇CO₂ slope is −0.813 at the RCP (A). The largest value of the correlation coefficient between the TV/RR ratio and the minimum V̇E/V̇CO₂ is −0.499 at the RCP, and that between the TV/RR ratio and the V̇E vs. V̇CO₂ slope is −0.422 at peak WR (B). The largest value between CI and minimum V̇E/V̇CO₂ is −0.408 and that between CI and V̇E vs. V̇CO₂ slope is −0.344 at the RCP (C). P values <0.05 are significant. AT, anaerobic threshold; CI, cardiac index; PETCO₂, pressure end-tidal carbon dioxide; RCP, respiratory compensation point; TV, tidal volume; RR, respiratory rate; V̇CO₂, carbon dioxide production; V̇E, minute ventilation; VEVs, ventilatory efficiency variables; WR, work rate.

Figure 3.

Correlation coefficient values between PETCO₂, TV/RR ratio, CI, and Y-int and VEVs at each exercise point. The correlation coefficient value at the AT between PETCO₂ and the minimum V̇E/V̇CO₂ is R=−0.877 (P<0.01), and that between PETCO₂ and V̇E vs. V̇CO₂ slope is R=−0.797 (P<0.01) (A). The correlation coefficient value at the peak WR between TV/RR ratio and the minimum V̇E/V̇CO₂ is R=−0.477 (P<0.01), and that between the TV/RR ratio and the V̇E vs. V̇CO₂ slope is R=−0.422 (P<0.01) (B). The correlation coefficient value at the RCP between CI and the minimum V̇E/V̇CO₂ is R=−0.408 (P<0.01) and V̇E vs. V̇CO₂ slope is R=−0.344 (P<0.01) (C). The correlation coefficient value between Y-int and V̇E vs. V̇CO₂ slope (R=−0.472, P<0.01) is higher than that between Y-int and minimum V̇E/V̇CO₂ (R=−0.098, P<0.01) (D). Y-int, Y intercept. Other abbreviations as in Figure 2. P values <0.05 are significant.

The PETCO₂ showed the largest correlation coefficient value with the minimum V̇E/V̇CO₂ at the AT (−0.877) and with V̇E vs. V̇CO₂ slope at the RC point (−0.813) and at the same exercise point of AT (−0.797).

The TV/RR ratio showed the largest correlation coefficient value with the minimum V̇E/V̇CO₂ at the RC point and with the V̇E vs. V̇CO₂ slope at the peak WR. At the same exercise point of peak WR, the minimum V̇E/V̇CO₂ value was −0.477, and the minimum V̇E vs. V̇CO₂ slope value was −0.422.

Similarly, the CI showed the strongest correlation coefficient value with the minimum V̇E/V̇CO₂ (−0.408) and V̇E vs. V̇CO₂ slope (−0.344) at the RC point.

The correlation coefficient value between the Y-int and V̇E vs. V̇CO₂ slope (R=−0.472, P<0.01) was higher than that between Y-int and Minimum V̇E/V̇CO₂ (R=−0.098, P<0.01).

Figure 3 shows seemingly similar parallel dynamics for PETCO₂, TV/RR, and CI for VEVs. Contrastingly, only the dynamics of Y-int were intersected for VEVs.

Contribution of PETCO₂, TV/RR Ratio, CI and Y-int to the VEVs

Using the largest correlation values between each parameter and the VEVs, a multiple linear regression analysis, adjusted for PETCO₂, TV/RR ratio, CI, Y-int, age, sex, height and body weight calculation formulas of the VEVs, was conducted as follows (Table 2):

Table 2. Results of Multiple Regression Analysis for the Ventilatory Efficacy Variables During Exercise

	Partial regression coefficient (B)	P values	β coefficient	95% confidence interval of B
Model 1
Minimum V̇E/V̇CO2
Age	0.030	<0.01	0.067	0.014~0.046
Sex	0.753	<0.01	0.057	0.293~1.213
Height	0.027	0.06	0.037	−0.001~0.055
Body weight	−0.056	<0.01	−0.117	−0.072~−0.041
PETCO2 at AT	−1.064	<0.01	−0.736	−1.104~−1.025
TV/RR at RCP	−0.050	<0.01	−0.226	−0.057~−0.044
CI at RCP	−0.083	<0.01	−0.078	−0.118~−0.047
Y-int	−0.070	0.04	−0.026	−0.138~−0.003
Constant value 78.894, R=0.919, adjusted R2=0.842, P<0.01
V̇E vs. V̇CO2 slope
Age	0.020	0.02	0.045	0.003~0.038
Sex	1.077	<0.01	0.079	0.565~1.588
Height	0.012	0.43	0.017	−0.018~0.043
Body weight	−0.012	0.15	−0.025	−0.029~0.004
PETCO2 at RCP	−0.974	<0.01	−0.681	−1.016~−0.932
TV/RR at peak	−0.056	<0.01	−0.176	−0.067~−0.046
CI at RCP	−0.053	<0.01	−0.049	−0.092~−0.014
Y-int	−1.001	<0.01	−0.361	−1.075~−0.927
Constant values 75.228, R=0.906, adjusted R2=0.819, P<0.01
Model 2
Minimum V̇E/V̇CO2
Age	0.033	<0.01	0.074	0.017~0.049
Sex	0.857	<0.01	0.064	0.388~1.327
Height	0.028	0.04	0.039	0.001~0.056
Body weight	−0.060	<0.01	−0.125	−0.075~−0.044
PETCO2 at AT	−1.068	<0.01	−0.739	−1.108~−1.028
TV/RR at peak	−0.068	<0.01	−0.218	−0.077~−0.059
CI at RCP	−0.082	<0.01	−0.078	−0.118~−0.046
Y-int	−0.030	0.38	−0.011	−0.099~0.038
Constant values 79.055, R=0.917, adjusted R2=0.839, P<0.01
V̇E vs. V̇CO2 slope
Age	0.024	<0.01	0.054	0.008~0.041
Sex	1.506	<0.01	0.110	1.013~1.998
Height	0.021	0.16	0.029	−0.008~0.051
Body weight	−0.013	0.12	−0.026	−0.029~0.003
PETCO2 at AT	−1.030	<0.01	−0.690	−1.072~−0.988
TV/RR at peak	−0.061	<0.01	−0.191	−0.071~−0.051
CI at RCP	−0.025	0.20	−0.023	−0.062~0.013
Y-int	−1.124	<0.01	−0.406	−1.195~1.054
Constant values 75.939, R=0.913, adjusted R2=0.832, P<0.01

Abbreviations as in Figure 2.

Minimum V̇E/V̇CO₂=−1.064 [PETCO₂ at AT (mmHg)] −0.050 [TV/RR ratio at RC point (mL·min/beats)] −0.083 [CI at RC point (L/min/m²)] −0.070 [Y-int]+0.030 [Age (years)]+0.753 [Sex: Male as 1, Female as 0]+0.027 [Height (cm)] −0.056 [body weight (kg)]+78.894 (R=0.919, adjusted R²=0.842, P<0.01); V̇E vs. V̇CO₂ slope=−0.974 [PETCO₂ at RC point (mmHg)] −0.056 [TV/RR ratio at peak WR (mL·min/beats)] −0.053 [CI at RC point (L/min/m²)] −1.001 [Y-int] −0.020 [Age (years)]+1.077 [Sex: Male as 1, Female as 0]+0.012 [Height (cm)] −0.012 [body weight (kg)]+75.228 (R=0.906, adjusted R²=0.819, P<0.01).

In model 2, when we set the same exercise points for each parameter, similar correlation coefficient values were obtained with the above-mentioned formulas. From model 2, the effect on the VEVs was greater for PETCO₂ than for the TV/RR ratio. CI was only associated with minimum V̇E/V̇CO₂, and Y-int was only associated with V̇E vs. V̇CO₂ slope (Table 2).

Contribution of Y-int to the Correlation Between the VEVs

Figure 4 shows the effect of the V̇E vs. V̇CO₂ slope on the minimum V̇E/V̇CO₂ (R=0.868, P<0.01). However, the combined value of V̇E vs. V̇CO₂ slope and Y-int was more strongly correlated with the minimum V̇E/V̇CO₂ (R=0.940, P<0.01). When the multiple linear regression analysis was adjusted for age, sex, height, and body weight, a moderate correlation coefficient value was observed between the V̇E vs. V̇CO₂ slope and minimum V̇E/V̇CO₂ (R=0.889, adjusted R²=0.789, P<0.01); however, V̇E vs. V̇CO₂ slope+Y-int showed a greater correlation value with minimum V̇E/V̇CO₂ (R=0.952, adjusted R²=0.906, P<0.01).

Figure 4.

Correlation coefficient values between VEVs and Y-int. The correlation value between the minimum V̇E/V̇CO₂ and V̇E vs. V̇CO₂ slope is 0.868 (P<0.01). The correlation value between the minimum V̇E/V̇CO₂ and V̇E vs. V̇CO₂ slope+Y-int is 0.940 (P<0.01). Abbreviations as in Figure 2.

Regardless of heart disease or its medications, correlation coefficient values between VEVs, minimum V̇E/V̇CO₂, and V̇E vs. V̇CO₂ slope+Y-int were similar to individual correlation coefficients of these variables (Supplementary Table).

Discussion

The main findings of this study are as follows. First, we showed that PETCO₂, TV/RR ratio, CI, and the Y-int obtained during CPET were strongly correlated with the VEVs. These 4 variables can predict 84.2% of the minimum V̇E/V̇CO₂ value and 81.9% of the V̇E vs. V̇CO₂ slope value. Second, the VEVs were affected by the CPET parameters in the following order: PETCO₂ >TV/RR ratio; CI was only associated with the minimum V̇E/V̇CO₂; and Y-int was only associated with the V̇E vs. V̇CO₂ slope.

A moderate correlation coefficient value was observed between the V̇E vs. V̇CO₂ slope and minimum V̇E/V̇CO₂ (adjusted R²=0.789). However, considering these 4 variables, adding the Y-int, the V̇E vs. V̇CO₂ slope and Y-int can account for 90.6% of the minimum V̇E/V̇CO₂ value.

We verified that these 4 non-invasive variables could predict the VEVs, as well as their order of contribution to the VEVs. Moreover, we identified a new VEV relationship formula as follows: Minimum V̇E/V̇CO₂ ∝V̇E vs. V̇CO₂ slope+Y-int.

VEVs are important parameters of CPET, as is the peak V̇O₂,³⁰ because the V̇E vs. V̇CO₂ slope and minimum V̇E/V̇CO₂ predict mortality and HF-related hospitalization in patients with HF.^6,8 In particular, V̇E vs. V̇CO₂ slope shows the same accuracy as peak V̇O₂.^31,32 VEV values >34 or 33 predict mortality in patients with HF.^6,8

In previous studies, V̇E was calculated using the following formula:

V̇E = 863V̇CO₂ / PaCO₂ [1 − Vd / Vt].^11,15

Therefore, the V̇E/V̇CO₂ was calculated as 863/PaCO₂ [1−Vd/Vt] and consisted of the PaCO₂ setpoint and physiological dead space. PaCO₂ and Vd/Vt require invasive evaluation. However, the PaCO₂ correlates with PETCO₂, and clinically, Vd/Vt is indicative of cardiac or lung dysfunction.^11,15 Therefore, we presumed that PETCO₂, CI, TV/RR ratio, and Y-int, which can be evaluated by non-invasive methods, can evaluate VEVs. The results revealed that 82–84% of the VEVs were predicted with these 4 variables (Table 2).

PETCO₂ is correlated with Vd/Vt and PaCO₂^12–14 and CI.³³ CI is also a cardiac parameter of Vd/Vt. Belardinelli et al reported that CO evaluated with impedance cardiography was similar to that measured using a thermodilution Swan-Ganz catheter.¹⁶ The TV/RR ratio is one of the variables of breathing stability^17–19 and may correlate with Vd/Vt. Generally, one breath requires 150 mL of mechanical dead space.³⁴ At constant V̇E, respiration patterns with high TV and low RR can deliver sufficient oxygen to the alveoli. In heart disease, there are several pathophysiologies with low TV/RR ratios. The decrease in TV due to lung congestion,³⁵ the stimulation of pulmonary juxtacapillary receptors,³⁶ and the decreased skeletal muscle power that stimulates the ergoreflex^37,38 correlate with higher RR. Therefore, a high TV/RR ratio may reveal superior ventilatory efficiency. During incremental exercise testing, the V̇E and V̇CO₂ have a linear relationship before the RC point, and the value of the Y-int can be mathematically evaluated. Gargiulo et al²³ examined the intentionally increased dead space during exercise via a mouthpiece method and found that the Y-int value increased in both healthy individuals and patients with HF, which theoretically equates to mechanical dead space during exercise.^21,23 We suggest that the TV/RR ratio is a close indicator of pulmonary restrictive disorder or hyperventilation and that Y-int is an indicator of obstructive pulmonary disorder during exercise with the non-invasive method. This explains why PETCO₂, TV/RR ratio, CI, and Y-int were correlated with the VEVs.

Interestingly, the VEVs affected the CPET variables in the following order: PETCO₂ >TV/RR ratio; CI was only weakly correlated with minimum VE/VCO₂; and Y-int was only correlated with V̇E vs. V̇CO₂ slope. Moreover, at the same exercise point of AT, PETCO₂ affects the minimum V̇E/V̇CO₂ more than the V̇E vs. V̇CO₂ slope (Table 2).

PETCO₂ is the most powerful indicator because it has a greater number of correlated factors, as described above (Supplementary Figure). PETCO₂ correlated more with the TV/RR ratio (R=0.334) than with CI (R=0.313) in our study (data are not shown). In addition, the minimum V̇E/V̇CO₂ correlated with the TV/RR ratio more than with the V̇E vs. V̇CO₂ slope (Figure 2). Multicollinearity was not considered in the determination of these values; however, these results suggest that PETCO₂ is more correlated with the minimum V̇E/V̇CO₂ than with the V̇E vs. V̇CO₂ slope because the correlation of the TV/RR ratio with PETCO₂ and minimum V̇E/V̇CO₂ is stronger than its correlation with CI.

Similarly, Neder et al have reported that Y-int is better correlated with V̇E vs. V̇CO₂ slope than with minimum V̇E/V̇CO₂ in patients with chronic obstructive pulmonary disease (COPD).²² In patients with COPD, Y-int is greater than that in patients with HF.^20,39,40 In patients with pulmonary hypertension, the Y-int may even be negative.²⁰ Furthermore, Sullivan et al reported that V̇E/V̇CO₂ correlates with CO.⁴ Although VEVs use similar variables to show ventilatory efficiency, we could observe these new features.

The V̇E vs. V̇CO₂ slope has a significantly strong correlation with minimum V̇E/V̇CO₂ in previous reports. The correlation coefficient values were R=0.85–0.92.^5,6 Our study showed a value that corresponds with those of previous reports (R=0.868) (Figure 4). Furthermore, Figure 3 shows that the Y-int was the only significantly different dynamic for VEVs among the 4 variables. Therefore, by adding Y-int to V̇E vs. V̇CO₂ slope, minimum V̇E/V̇CO₂ was more correlated with V̇E vs. V̇CO₂ slope and Y-int (R=0.940) (Figure 4). Approximately 91% of the minimum V̇E/V̇CO₂ can reveal V̇E vs. V̇CO₂ slope and Y-int. This percentage is higher than that between minimum V̇E/V̇CO₂ and V̇E vs. V̇CO₂ slope (79.8%), and not different with or without heart disease or its medications (R=0.922–0.944) (Supplementary Table).

A high and negative Y-int value may indicate COPD and pulmonary artery hypertension (PAH), respectively.²⁰ In patients without COPD or PAH, the Y-int values were almost 0, and the V̇E vs. V̇CO₂ slope and minimum V̇E/V̇CO₂ were almost identical. When different VEV values were observed, the comorbidities were concealed due to the high or negative Y-int. This formula may indicate heart disease patients with comorbidities.

A Japanese epidemiological report found that 24% of people aged >70 years and 16% of males have COPD.⁴¹ Heart disease patients often have COPD; therefore, the formula, minimum V̇E/V̇CO₂ ∝V̇E vs. V̇CO₂ slope+Y-int, is useful for evaluating comorbidities in patients with heart disease. The values of PETCO₂, TV/RR ratio, CI, and Y-int with non-invasive evaluation methods revealed >80% of the VEV values. Compared with the 2 VEVs, CI correlated closely with minimum V̇E/V̇CO₂, whereas Y-int correlated closely with V̇E vs. V̇CO₂ slope. PETCO₂ was the most influential variable for VEVs among the 4 variables.

Minimum V̇E/V̇CO₂ is the nadir of the V̇E/V̇CO₂ value during incremental exercise, and the V̇E vs. V̇CO₂ slope is the angle of the exercise relationship between V̇E and V̇CO₂. We could clarify similar and different aspects in the new formula in this study. Although VEVs have different evaluation methods, the minimum V̇E/V̇CO₂ and V̇E vs. V̇CO₂ slope generally show similar values,^5,6 as observed in this study at 79%. However, V̇E vs. V̇CO₂ slope+Y-int value can reveal a more accurate value of minimum V̇E/V̇CO₂. The formula: minimum V̇E/V̇CO₂ ∝V̇E vs. V̇CO₂ slope+Y-int, fitted >90% of the VEV values. When different values of VEVs are observed, the evaluation of comorbidities in patients with heart disease should be considered. In this study, the relationship among these 4 variables and the VEVs was shown, and the new relationship and formula between VEVs could be shown.

Our study has some limitations that should be acknowledged. CPET is affected by the intentional breathing pattern of the patient. Accordingly, in our study, the patients were provided sufficient practice (and education) to adopt a natural breathing pattern when using the CPET system. A few patients who used an oscillatory pattern of ventilation were excluded because this pattern of breathing does not allow us to identify the AT or RC points. Therefore, intentional breathing and oscillatory ventilation were rarely noted in our study groups.

Conclusions

Minimum V̇E/V̇CO₂ and V̇E vs. V̇CO₂ slope are important as VEVs and are predictors of mortality from heart disease. The PETCO₂, TV/RR ratio, CI, and Y-int evaluated using non-invasive methods during exercise can predict VEVs. We could establish the contribution order for the VEVs. The formula, minimum V̇E/V̇CO₂ ∝V̇E vs. V̇CO₂ slope+Y-int, fitted >90% of the VEVs values. Identifying high and negative Y-int or different values of the VEVs are useful for diagnosing comorbidities in patients with heart disease. When performing CPET, we propose checking the values and the relationship between minimum V̇E/V̇CO₂ and V̇E vs. V̇CO₂ slope.

Acknowledgments

The authors would like to thank their colleagues who work in the physiological examination department at the Gunma Prefectural Cardiovascular Center.

Sources of Funding

This research received no grant from any funding agency in the public, commercial, or not-for-profit sectors.

Disclosures

The authors declare that they have no conflicts of interest.

Supplementary Files

Please find supplementary file(s);

http://dx.doi.org/10.1253/circj.CJ-21-0261

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