The impact of LV filling pressure indexed to cardiac output on exercise capacity in HF subjects

The impact of LV filling pressure indexed to cardiac output on exercise capacity in HF subjects | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The impact of LV filling pressure indexed to cardiac output on exercise capacity in HF subjects Wei-Ming Huang, Chiao-Nan Chen, Hao-Chih Chang, Yen-Tung Liu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4369398/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Exercise intolerance is the most common symptom of patients with heart failure (HF), regardless of the phenotypes. We aim to investigate the determinants of exercise capacity in chronic stable HF with reduced, mildly reduced, preserved, and recovered ejection fraction (EF). Methods Ambulatory HF subjects were recruited for a combined cardiopulmonary exercise test and exercise stress echocardiography. Impaired exercise capacity was referred to a peak oxygen consumption (peak VO 2 ) of 34 was defined as ventilatory inefficiency. Results Among 66 participants, there were 16 HF with reduced EF, 18 HF with mildly reduced EF, 12 HF preserved EF, and 20 HF recovered EF. Diastolic dysfunction indices were independently predictive of impaired exercise capacity (odds ratio and 95% confidence intervals: 3.847, 1.369–10.810). GLS at rest was independently correlated with ventilatory inefficiency (1.404, 1.050–1.877). Among the exercise indices, the peak medial E/e' to cardiac output ratio was independently associated with impaired exercise capacity (3.478, 1.313–9.214) and peak GLS was best related to ventilatory inefficiency (1.403, 1.076–1.828). Conclusions Among resting and exertional echocardiographic variables, the peak medial E/e' to cardiac output ratio, a non-invasive assessment of exertional left ventricular filling pressure indexed to cardiac output, was the major determinant of exercise capacity in patients with different HF phenotypes. cardiopulmonary exercise testing exercise echocardiographic exam heart failure Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Exercise intolerance is the most common clinical manifestation of heart failure (HF), regardless of the etiologies and pathogenesis, and left ventricular functions[ 1 ]. The exercise capacity could be assessed subjectively by NYHA functional classes or quantitatively by cardiopulmonary exercise testing (CPET), and both methodologies have been associated with adverse cardiovascular events[ 2 ] or guided therapeutic strategies in subjects with HF[ 3 – 5 ]. The peak oxygen consumption (peak VO 2 ), obtained at maximal exercise, and the submaximal exercise indices, including the slope of minute ventilation to CO 2 production (VE/VCO 2 slope), and the slope of the relationship between peak VO 2 and log minute ventilation (OUES) may not only correlated with clinical outcomes in various populations[ 3 ], but also unmask the different mechanisms of exercise intolerance[ 4 , 6 ]. Nowadays, the guidelines have classified HF into difference phenotypes according to the left ventricular ejection fraction (LVEF) and its dynamic change, including HF with reduced LVEF (HFrEF), mildly reduced LVEF (HFmrEF), preserved LVEF (HFpEF) and recovered LVEF (HFrecEF)[ 1 , 7 ]. Although Gardin et al. did not suggest LVEF a major determinant of exercise capacity[ 8 ], Pugliese et al. have disclosed LV systolic function, indexed by global longitudinal strain (GLS) correlated with peak VO 2 during CPET[ 9 ]. In addition, Shimiaie et al. illustrated LV diastolic function, indexed by left atrial volume or early mitral inflow velocity to mitral annular early diastolic velocity ratio (E/e’ ratio) was related to peak VO 2 [ 9 ]. How the left ventricular systolic and diastolic function impacted on the exercise capacity remained elucidated, and the determinants of submaximal parameters in patients with HF were rarely discussed. In addition, the exercise performance of patients with HFrecEF has yet to be compared with the other phenotypes of HF. In the study, we therefore combined CPET and exercise stress echocardiography (ESE) to investigate the correlations between LV systolic and diastolic function and exercise capacity among patients with various HF phenotypes. MATERIALS AND METHODS Study population Between April 2020 and September 2021, ambulatory patients with chronic HF, who have been treated for ≥ 6 months with steady doses for ≥ 4 weeks were eligible for this study. Patients presented with NYHA functional class IV symptoms, or acute coronary syndrome within a month, received cardiovascular intervention or surgery within 3 months, and had chronic pulmonary disease, exercise-induced asthma, severe cognitive impairment, and infective or orthopedic diseases were excluded. According to the guidelines, the diagnosis of HFrEF, HFmrEF and HFpEF were based on LVEF of ≤ 40%, 40%~50% and ≥ 50%, respectively[ 1 ]. Subjects with prior LVEF of < 40%, had ≥ 10% absolute improvement in LVEF to achieve a LVEF of ≥ 50% were defined to have HFrecEF. The demographic and anthropometric data were obtained. Blood samples were harvested to measure hemogram, renal function and N-terminal pro-brain natriuretic peptide (NT-proBNP). The study was approved by the institutional review board of Taipei Veterans General Hospital. Combined cardiopulmonary exercise testing and exercise stress echocardiography The detail of CPET-ESE protocol was mentioned in the previous study.[ 10 ] In short, a symptom-limited stepwise protocol on cycle ergometer in the semi-supine position was performed with an initial workload of 30W for 3 minutes followed 15W-increase every 3 minutes until the cessation criteria, including: 1) when the participant requested to stop, 2) if there was no further increase in heart rate or oxygen consumption despite increasing exercise intensity, 3) if systolic blood pressure exceeded 250 mmHg or diastolic blood pressure surpassed 115 mmHg, 4) a drop in systolic blood pressure of more than 10 mmHg despite an increase in workload, or 5) the onset of uncomfortable symptoms was achieved[ 11 ]. The peak VO 2 [ 12 ], predicted value of peak VO 2 [ 13 ], OUES[ 14 ], and VE/VCO 2 slope[ 15 ] were calculated accordingly. The V-slope methods was applied to determine aerobic threshold (AT)[ 16 ]. Impaired exercise capacity was defined by a peak VO 2 of 34[ 18 ] was referred to ventilatory inefficiency. The GE E95 system and EchoPAC software (GE Healthcare) were used for echocardiographic parameters, trans-mitral early (E) and late (A) diastolic flow velocity, medial mitral annulus tissue velocity at early diastole (e’), peak tricuspid regurgitation flow velocity (TRV) and right ventricular myocardial systolic velocity (RV S’) were measured[ 19 ]. LV global longitudinal strain (GLS) by speckle tracking analyses was reported. Left ventricular internal diameter in end diastole and end systole (LVIDd, and LVIDs), left ventricular ejection fraction (LVEF), left atrial (LA) volume index, stroke volume (SV) and cardiac output (CO) were acquired by m-mode, two- and three-dimensional echocardiography, respectively[ 20 , 21 ] The arterial-venous oxygen content difference (AVO 2 diff) was obtained by using the Fick equation as VO 2 /CO[ 9 ]. The indices of diastolic dysfunction (DD) were the counts of the indicators, included medial e’ velocity 15, TRV max > 2.8 m/s, and LA volume index > 34 ml/m 2 . Normal diastolic function was defined by the presence of none or one of these indices. Subjects with three or four indices were considered to have DD, and the remaining were indeterminate DD[ 22 ]. To address the accuracy and reproducibility of exercise echocardiographic parameters, we have published validation studies focused on our current protocol and imaging analytics, where we reported satisfactory consistency in parameter analysis.[ 10 ] Statistical analysis Continuous variables of descriptive results were presented as means and standard deviations. Categorical variables were expressed as absolute numbers and relative frequencies. ANOVA and Chi-square tests were used to examine the differences between HF subgroups. Linear and logistic regression models were used to evaluate the association between echocardiographic and respiratory variables (continue or binary, respectively). Only those parameters achieved statistical significance in uni-variate models would undergo multi-variate adjustment separately. Receiver operating characteristic curves were used to evaluate the different variables to reduced exercise capacity or ventilatory efficiency by comparing the strengths of the areas under the curve (AUCs). All the statistical analyses were performed SPSS v.20.0 software (SPSS, Inc., Chicago, IL, USA) and the performed tests were two-sided. A P value < 0.05 was considered statistically significant. RESULTS A total of 66 patients (age 60.3 ± 13.6 years, 74% men) were enrolled in this study, including 16 HFrEF, 18 HFmrEF, 12 HFpEF and 20 HFrecEF. The diagram of the study cohort was demonstrated in Fig. 1 . Baseline characteristics, stratified by the HF phenotypes were shown in Table 1. In addition to LVEF, LVIDd, LVIDs, LV-GLS, and medical e’ were significantly different between the phenotypes. In contrast, mitral E/A ratio, RV S’, peak TRV, LAV index, and cardiac output were similar between groups. CPET demonstrated the study participants would achieve similar peak VO 2 , VO 2 at AT, VE/VCO 2 slope, OUES, and AVO 2 diff, regardless of HF phenotypes. However, both peak LV-GLS and peak medial E/e’ were diverse between groups. The associations between echocardiographic parameters and maximal/submaximal respiratory variables Although the exercise achievements were not significantly different between HF phenotypes, the worst LV-GLS at rest among the tertiles would have a higher VE/VCO 2 slope, but similar OUES and peak VO 2 , compared with the others. (Fig. 2 ) In contrast, subjects with normal diastolic function at rest would have better peak VO 2 and OUES than the others. The peak VO 2 and VE/VCO 2 slope were significantly correlated with LVIDd, LVIDs, LVEF, LV-GLS, medial E/e’ ratio and indices of diastolic dysfunction. MV E/A ratio and peak LV-GLS to CO ratio were not associated with peak VO 2 and VE/VCO 2 slope. Few resting echocardiographic parameters were linked to OUES. Considering exercise parameters, only peak LV-GLS was not correlated significantly. Resting medial E/e’ ratio, peak medial E/e’ ratio and peak medial E/e’ to CO ratio were also correlated with peak VO 2 , OUSE and VE/VCO 2 slope. (Table 2 .) Table 2. The association between ventilatory variables and echocardiographic parameters Peak VO2 (ml/kg/min) OUES VE/VCO2 slope coefficients (SE) P value Coefficients (SE) P value Coefficients (SE) P value Parameters at rest LVEF (%) 0.092 (0.04) 0.026 3.768 (4.243) 0.378 -0.103 (0.059) 0.087 LVIDd (mm) -0.131 (0.057) 0.024 -0.431 (6.015) 0.943 0.204 (0.085) 0.019 LVIDs (mm) -0.127 (0.051) 0.015 -5.712 (5.368) 0.291 -0.163 (0.076) 0.036 Rest LV-GLS (%) -0.308 (0.139) 0.031 -1.625 (14.277) 0.910 0.5 (0.206) 0.019 Rest medial E/e’ ratio -0.220 (0.057) < 0.001 -12.754 (6.132) 0.042 0.346 (0.091) < 0.001 MV E/A ratio 1.069 (1.232) 0.389 152.954 (129.905) 0.244 -0.552 (1.855) 0.767 LA volume index (ml/m 2 ) -0.100 (0.033) 0.004 -1.751 (3.566) 0.625 0.077 (0.053) 0.150 Indices of DD -2.476 (0.424) < 0.001 -178.59 (48.703) 0.001 1.536 (0.763) 0.051 Parameters during exercise Peak medial E/e’ ratio -0.220 (0.053) < 0.001 -12.13 (5.772) 0.04 0.617 (0.165) 0.002 Peak LV-GLS (%) -0.398 (0.113) 0.001 -14.336 (12.231) 0.246 0.617 (0.165) < 0.001 Peak cardiac output (ml/min) 0.200 (0.567) < 0.001 103.711 (20.496) < 0.001 -0.516 (0.403) 0.206 Peak medial E/e’ ratio/CO -1.279 (0.213) < 0.001 -87.822 (22.754) < 0.001 1.297 (0.397) 0.002 Peak LV-GLS/CO 0.553 (0.485) 0.259 104.324 (43.211) 0.019 0.898 (0.755) 0.240 CO: cardiac output, DD: diastolic dysfunction, E/A ratio: ratio of the early (E) to late (A) ventricular filling velocities, E/e': ratio of early ventricular filling velocity (E) to early diastolic tissue velocity mitral annulus, LV-GLS: left ventricular global longitudinal strain, LA: left atrium, LV: left ventricular, LVEF: left ventricular ejection fraction, LVIDd: left ventricular internal diameter in end diastole, LVIDs: left ventricular internal diameter in end systole, NT-proBNP: N-terminal pro-brain natriuretic peptide, OUES: oxygen uptake efficiency slope, SE: standard error, VE/VCO2 slope : the relationship between minute ventilation and carbon dioxide production, VO2: oxygen consumption Table 3. Determinants of peak VO2 < 14 mL/min/kg identified by uni- and multivariate logistic regression analysis Univariate Multivariate* OR (95% CI) P value OR (95% CI) P value Parameters at rest LVEF (%) 0.959 (0.916-1.005) 0.077 - - LVIDd (mm) 1.060 (0.993-1.131) 0.080 - - LVIDs (mm) 1.057 (0.997-1.120) 0.062 - - Rest LV-GLS (%) 1.134 (0.971-1.324) 0.113 - - MV E/A ratio 0.299 (0.058-1.544) 0.150 - - Rest medial E/e’ 1.319 (1.099-1.583) 0.003 1.132 (0.986-1.298) 0.078 LA volume index (ml/m 2 ) 1.052 (1.011-1.095) 0.014 1.040 (0.981-1.104) 0.187 Indices of DD 7.641 (2.308-24.114) 0.001 3.847 (1.369-10.810) 0.011 Parameters during exercise Peak medial E/e’ 1.197 (1.061-1.350) 0.003 1.132 (0.986-1.298) 0.078 Peak LV-GLS (%) 1.187 (1.027-1.373) 0.021 1.022 (0.869-1.201) 0.796 Peak cardiac output (ml/min) 0.557 (0.381-0.813) 0.002 0.594 (0.333-1.082) 0.078 Peak medial E/e’ to CO ratio 2.280 (1.400-3.712) 0.001 3.478 (1.313-9.214) 0.012 Peak LV-GLS/CO 0.841 (0.513-1.378) 0.492 - - * After accounting for age, gender, body mass index, levels of NTproBNP and hemoglobin, previous myocardial infarction CI: confidence interval, CO: cardiac output, DD: diastolic dysfunction, E/A ratio: ratio of the early (E) to late (A) ventricular filling velocities, E/e': ratio of early ventricular filling velocity (E) to early diastolic tissue velocity mitral annulus, LV-GLS: left ventricular global longitudinal strain, LA: left atrium, LV: left ventricular, LVEF: left ventricular ejection fraction, LVIDd: left ventricular internal diameter in end diastole, LVIDs: left ventricular internal diameter in end systole, NT-proBNP: N-terminal pro-brain natriuretic peptide, OR : odd ratio, VO2: oxygen consumption Table 4. Determinants of VE/VCO2 slope > 34 identified by uni- and multivariate logistic regression analysis Univariate Multivariate * OR (95% CI) P value OR (95% CI) P value Parameters at rest LVEF (%) 0.958 (0.915-1.003) 0.066 - - LVIDd (mm) 1.058 (0.990-1.131) 0.095 - - LVIDs (mm) 1.064 (1.003-1.129) 0.040 1.062 (0.986-1.144) 0.113 Rest LV-GLS (%) 1.254 (1.046-1.504) 0.014 1.404 (1.050-1.877) 0.022 MV E/A ratio 0.308 (0.062-1.520) 0.148 - - Rest medial E/e’ 1.100 (0.986-1.227) 0.087 - - LA volume index (ml/m 2 ) 1.016 (0.979-1.053) 0.409 - - Indices of DD 2.156 (1.094-2.249) 0.026 2.521 (1.126-5.645) 0.025 Parameters during exercise Peak medial E/e’ 1.084 (0.999-1.176) 0.054 - - Peak LV-GLS (%) 1.284 (1.084-1.520) 0.004 1.403 (1.076-1.828) 0.012 Peak cardiac output (ml/min) 0.731 (0.529-1.010) 0.147 Peak medial E/e’ to CO ratio 1.573 (1.099-2.253) 0.013 1.758 (1.023-3.022) 0.041 Peak LV-GLS to CO ratio 1.347 (0.751-2.415) 0.318 - - * After accounting for age, gender, body mass index, levels of NTproBNP and hemoglobin, previous myocardial infarction CI: confidence interval, CO: cardiac output, DD: diastolic dysfunction, E/A ratio: ratio of the early (E) to late (A) ventricular filling velocities, E/e': ratio of early ventricular filling velocity (E) to early diastolic tissue velocity mitral annulus, LV-GLS: left ventricular global longitudinal strain, LA: left atrium, LV: left ventricular, LVEF: left ventricular ejection fraction, LVIDd: left ventricular internal diameter in end diastole, LVIDs: left ventricular internal diameter in end systole, NT-proBNP: N-terminal pro-brain natriuretic peptide, OR : odd ratio, VE/VCO2 slope : the relationship between minute ventilation and carbon dioxide production Predictors of impaired exercise capacity and ventilatory inefficiency Medial E/e’, LA volume index, and indices of DD were predictive of impaired exercise capacity (Odd ratio and 95% confidence intervals: 1.319, 1.099–1.583; 1.052, 1.011–1.095; 7.641, 2.308–24.114, respectively). The exercise-derived parameters, including peak medial E/e’, peak LV-GLS, peak CO, and peak medial E/e’ to CO ratio were also predictors of impaired exercise capacity. With adjustments for age, gender, body mass index, NT-proBNP and hemoglobin levels, and prior myocardial infarction, the indices of DD (5.557, 1.118–27.624) and peak medial E/e’ to CO ratio (3.478, 1.313–9.214) were independently associated with impaired exercise capacity. (Table 3 ) The receiver-operating characteristic (ROC) curve analysis suggested peak medial E/e’ to CO ratio (AUC: 0.87; 95% CI: 0.73–0.95) outperformed the others to predict impaired exercise capacity. (Fig. 3 A) Among the parameters obtained at baseline, the indices of DD were the best predictors of reduced exercise capacity (AUC: 0.86; 95% CI: 0.72–0.94). LVIDs, rest LV-GLS, indices of DD, peak medial E/e’, peak LV-GLS, peak CO and peak medial E/e’ to CO ratio were crudely correlated with ventilatory inefficiency. (Table 4 ) In multivariate logistic regression analyses, rest LV-GLS (OR: 1.404; 95% CI: 1.050–1.877), peak LV-GLS (OR: 1.403; 95% CI: 1.076–1.828) and peak medial E/e’ to CO ratio (OR: 1.758; 95% CI: 1.023 to 3.022) remained associated with ventilatory inefficiency, independent of age, gender, body mass index, NT-proBNP and hemoglobin levels, and prior myocardial infarction. (Table 4 ) The ROC curve analysis for the prediction of ventilatory inefficiency indicated peak LV-GLS (AUC: 0.82; 95% CI: 0.–7–0.92) the best correlate, followed by rest LV-GLS (AUC: 0.77; 95% CI: 0.–1–0.88). (Fig. 3 B) DISCUSSION Neither LVEF nor LV-GLS was independent associated with impaired exercise capacity. In contrast, while all the phenotypes of HF have similar pattern of DD and elevated LV filling pressure, DD majorly determined the peak VO 2 . The increase in peak VO 2 also depended on the blunt increase in medial E/e’ along with the increased cardiac output during exercise. The study results may suggest that the medial E/e’ normalized by cardiac output as a surrogate of dynamic left-sided filling pressures during incremental exercise was closely related to impaired exercise capacity. (Graphical abstract) In addition, LV systolic function, indexed by GLS but not LVEF was associated with VE/VCO 2 slope. And the increase in peak LV-GLS during exercise may also contribute to the ventilatory efficiency. LV-GLS could reflect the degree of the extent of myocardial fibrosis and attenuate the LV relaxation and contractility[ 23 ]. However, the definite mechanism of how the LV-GLS affect ventilatory efficiency was worth more exploration. The exercise capacity and ventilatory efficiency in heart failure with different phenotypes Although HF could be categorized by LVEF into different phenotypes, patients with HF may all experience equivalent risks of hospitalization for HF and the same loss of lifespan[ 24 ]. Even though they have been characterized with distinct LV systolic function, their exercise capacity might not be different. Pugliese et al. have reported 169 subjects with either HFrEF, HFmrEF or HFpEF had similar peak VO 2[9] . In this study, we also demonstrated ambulatory patients with HF may have similar exercise performance, in terms of peak VO 2 , OUES, and VE/VCO 2 , regardless of the phenotypes. Given the study only recruited subjects with limited symptoms and eligible for exercise tests, the results may not be generalized to patients with poor daily activity. In addition, the study further extended our understandings that patients with HFrecEF also have comparable exercise capacity and ventilatory efficacy with the other phenotypes of HF. Associations of Left ventricular systolic and diastolic functions with exercise capacity Although the exercise capacity was similar between HF phenotypes[ 8 ] [ 9 ], neither LV-GLS nor LVEF was independently predictive of impaired exercise capacity. While the common feature across all the phenotypes of HF is elevated LV end-diastolic pressure, which could be indirectly assessed by echocardiographic parameters, including LV volume, TRV and E/e’ ratio, the indices of DD. In addition, the indices of DD independently correlated with peak VO 2 . The results may support the major determinant of exercise capacity was LV diastolic but not systolic function. In fact, none of the patients with DD could achieve a peak VO 2 of > 14 ml/kg/min in this study. In contrast, only 17.2% of patients with normal diastolic function had a peak VO2 < 14 ml/kg/min. (Table S1 ) The performance of exercise medial E/e’ to CO ratio and LV-GLS with adjustment of cardiac output During exercise, a normal LV would increase the trans-mitral flow by facilitating LV relaxation to augment cardiac output, which may limit the elevation in LA pressure[ 25 ]. In contrast, a failing heart is characterized by elevated LV filling pressure, which would be further enhanced during exercise[ 26 ]. The elevated of LV end-diastolic pressure would be transmitted to pulmonary system and activate the receptor of dyspnea[ 27 ], resulting in cessation of exercise. Exercise-related PCWP change in relation to cardiac output has been an indicator to evaluate hemodynamic changes in the face of the stress load[ 28 ]. In this study, we used the medial E/e' to CO ratio to correct the augmentation in LV filling pressures in response to the increase in cardiac output. Therefore, the peak medial E/e' to CO ratio would reflect the speed at which medial E/e' raised during the exercise. In contrast, the peak LV-GLS but not Peak LV-GLS to CO ratio better correlated with VE/VCO2 slope. This result may support diastolic filling pressure rather than systolic function was better associated with LV volume changes, while LV-GLS but not medial E/e’ a volume-independent indicator. The AVO 2 diff was a measure of oxygen was extracted from blood circulates into the body, which was a derivative of cardiac output and VO 2 . AVO 2 diff was considered as the major determinant of exercise capacity, especially for HFmrEF and HFpEF subtypes but not in HFrEF[ 9 , 29 ]. In this study, though peak AVO 2 diff was crudely associated with reduced exercise capacity. (OR [95%CI] : 0.741[0.550–0.997]) and lower AUCs (0.711)(not shown in Fig. 3 ), the discriminatory ability of AVO 2 diff was lower than peak medial E/e’ ratio or its adjusted value with CO. This difference from previous studies may be due to the different heart failure phenotypes, and to investigate peripheral oxygen utilization one should consider methods other than the indirect calculation of peak AV O 2 difference, such as near-infrared spectroscopy (NIRS). LVEF, as an important parameter for heart failure phenotyping, did not show an independent predictive ability for Peak VO2 or VE/VCO2 slope in our study. This is similar to observations made in the past HF-ACTION trial.[ 8 ] The different impacts of medial E/e’ to CO ratio and GLS on peak VO 2 and VE/VCO 2 slope As mentioned above, the change of LV filling pressure would transmit to LA pressure and then to pulmonary system, which cause dyspnea and affect exercise performances[ 27 ]. This mechanism was compatible with our finding and diastolic dysfunction, at rest or during exercise were the major determinants of reduced exercise capacity. LV-GLS was a surrogate of LV contractility, less load-dependent and associated with clinical adverse events, such hospitalized HF or death in all HF phenotypes[ 30 , 31 ]. VE/VCO 2 slope as an indicator of ventilatory inefficiency had comparable and even superior prognostic value in comparison with peak VO 2 [ 2 ]. Traditionally, the elevation of VE/VCO 2 slope was resulted from decreased pulmonary perfusion caused by high LV filling pressure[ 4 ]. However, the association between LV-GLS, either resting or peak, and VE/VCO 2 slope was more predominant than diastolic dysfunction or medial E/e’ ratio. The mechanism of the association between GLS and VE/VCO2 slope was worth more exploration, and whether peak LV-GLS has a better prognostic value than resting LV-GLS needed to be further investigated. Study limitations Given the sample of the study was comparatively small, there were some limitations. First, the study participants were relatively young and stable. Therefore, the results may not be generated to the sicker and older population, when the muscle strength could be a significant confounder. Second, even exercise intolerance was an important surrogate of the clinical outcomes, a longitudinal cohort study was needed to support the superior prognostic role of CPET in patients with HF. Third, the study was limited by the relatively small sample and single-center, retrospective observational nature of the study. Therefore, there was inevitable selection bias and type II error in this observation study and may impact the generalizability of our results. For example, the difference in peak VO2 between HFrEF and HFrecEF may be statistically significant if the sample size was larger. A case-matched study could provide substantial improvement in the generalizability of the study results. Conclusions The resting echocardiographic variables which associated with diastolic dysfunction were linked to peak VO 2 , the medial E/e’ to CO ratio was the most robust predictor of impaired exercise capacity. On the other hand, the resting and peak LV-GLS, a surrogate of LV contractility, was associated with ventilatory efficiency. It suggested that LV diastolic and systolic dysfunction might jointly lead to exercise intolerance among HF patients via different mechanisms. Further studies were worthwhile to investigate the underlying pathophysiological process and predictive roles of these parameters in clinical events. Declarations Funding Ministry of Health and Welfare, Taiwan, Grant number: MOHW107-TDU-B-211-123001 (to SHS); Taipei Veterans General Hospital, Grant number: V109E-008-01 and V111C-172 (to SHS) and Grant number: V110B-032 and V111B-038 (to WMH). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions W.M.H., C.N.C., and S.H.S. conceived of the study design. Y.T.L., Y.T.W., and T.Y.T. collected the data. W.M.H. analyzed the data. H.M.C. and W.C.Y. interpreted the results. W.M.H. drafted and H.C.C. prepared the manuscript. S.H.S., C.E.C., and C.H.C. revised the manuscript critically for important intellectual content. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by The ethics committee: Institutional Review Board of Taipei Veterans General Hospital; Reference Number: IRB-TPEVGH No.: 2020-04-007ACF Consent to participate Informed consent was obtained from all individual participants included in the study. References McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Bohm M, et al (2021) 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure Eur Heart J (2021);42:3599-3726. 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Nagueh SF, Smiseth OA, Appleton CP, Byrd BF, 3rd, Dokainish H, Edvardsen T, et al (2016) Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging J Am Soc Echocardiogr (2016);29:277-314. Lancellotti P, Pellikka PA, Budts W, Chaudhry FA, Donal E, Dulgheru R, et al (2017) The Clinical Use of Stress Echocardiography in Non-Ischaemic Heart Disease: Recommendations from the European Association of Cardiovascular Imaging and the American Society of Echocardiography J Am Soc Echocardiogr (2017);30:101-138. Lang RM, Bierig M, Devereux RB, Flachskampf FA, Foster E, Pellikka PA, et al (2005) Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology J Am Soc Echocardiogr (2005);18:1440-63. Nagueh SF, Smiseth OA, Appleton CP, Byrd BF, 3rd, Dokainish H, Edvardsen T, et al (2016) Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography (2016);29:277-314. Cameli M, Mondillo S, Righini FM, Lisi M, Dokollari A, Lindqvist P, et al (2016) Left Ventricular Deformation and Myocardial Fibrosis in Patients With Advanced Heart Failure Requiring Transplantation Journal of cardiac failure (2016);22:901-907. Shah KS, Xu H, Matsouaka RA, Bhatt DL, Heidenreich PA, Hernandez AF, et al (2017) Heart Failure With Preserved, Borderline, and Reduced Ejection Fraction: 5-Year Outcomes Journal of the American College of Cardiology (2017);70:2476-2486. Ha JW, Andersen OS, Smiseth OA (2020) Diastolic Stress Test: Invasive and Noninvasive Testing JACC Cardiovasc Imaging (2020);13:272-282. Nagueh SF (2020) Left Ventricular Diastolic Function: Understanding Pathophysiology, Diagnosis, and Prognosis With Echocardiography JACC Cardiovasc Imaging (2020);13:228-244. Obokata M, Olson TP, Reddy YNV, Melenovsky V, Kane GC, Borlaug BA (2018) Haemodynamics, dyspnoea, and pulmonary reserve in heart failure with preserved ejection fraction Eur Heart J (2018);39:2810-2821. Eisman AS, Shah RV, Dhakal BP, Pappagianopoulos PP, Wooster L, Bailey C, et al (2018) Pulmonary Capillary Wedge Pressure Patterns During Exercise Predict Exercise Capacity and Incident Heart Failure Circ Heart Fail (2018);11:e004750. Houstis NE, Eisman AS, Pappagianopoulos PP, Wooster L, Bailey CS, Wagner PD, et al (2018) Exercise Intolerance in Heart Failure With Preserved Ejection Fraction: Diagnosing and Ranking Its Causes Using Personalized O(2) Pathway Analysis Circulation (2018);137:148-161. Park JJ, Park JB, Park JH, Cho GY (2018) Global Longitudinal Strain to Predict Mortality in Patients With Acute Heart Failure J Am Coll Cardiol (2018);71:1947-1957. Janwanishstaporn S, Cho JY, Feng S, Brann A, Seo JS, Narezkina A, et al (2022) Prognostic Value of Global Longitudinal Strain in Patients With Heart Failure With Improved Ejection Fraction JACC Heart Fail (2022);10:27-37. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterial.docx Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4369398","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299573087,"identity":"9899db02-e9e1-497d-b084-905e96f11ead","order_by":0,"name":"Wei-Ming Huang","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei-Ming","middleName":"","lastName":"Huang","suffix":""},{"id":299573090,"identity":"9c5e259f-7803-4b73-ac73-95a92dd1a039","order_by":1,"name":"Chiao-Nan Chen","email":"","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":false,"prefix":"","firstName":"Chiao-Nan","middleName":"","lastName":"Chen","suffix":""},{"id":299573093,"identity":"18743c8a-bd1f-4960-97f6-93fc28384e43","order_by":2,"name":"Hao-Chih Chang","email":"","orcid":"","institution":"Taipei Veterans General Hospital Taoyuan Branch","correspondingAuthor":false,"prefix":"","firstName":"Hao-Chih","middleName":"","lastName":"Chang","suffix":""},{"id":299573096,"identity":"447fa24e-078b-4e96-a519-63a2d43ca122","order_by":3,"name":"Yen-Tung Liu","email":"","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":false,"prefix":"","firstName":"Yen-Tung","middleName":"","lastName":"Liu","suffix":""},{"id":299573099,"identity":"8bc9e51d-9b66-4a9c-86a9-2ec34263c6c3","order_by":4,"name":"Yen-Tze Wu","email":"","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":false,"prefix":"","firstName":"Yen-Tze","middleName":"","lastName":"Wu","suffix":""},{"id":299573102,"identity":"3901f7d0-a95f-490a-94c9-1b39c3ec7589","order_by":5,"name":"Tzu-Ying Tseng","email":"","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":false,"prefix":"","firstName":"Tzu-Ying","middleName":"","lastName":"Tseng","suffix":""},{"id":299573104,"identity":"9df7f2ce-cab8-4f2c-9117-419dcde843ff","order_by":6,"name":"Hao-Min Cheng","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hao-Min","middleName":"","lastName":"Cheng","suffix":""},{"id":299573106,"identity":"2c3db915-ac10-4eb5-b0aa-5bc18fd306cf","order_by":7,"name":"Wen-Chung Yu","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wen-Chung","middleName":"","lastName":"Yu","suffix":""},{"id":299573107,"identity":"62c3ad5b-e201-45de-9c08-9d2b60e327a5","order_by":8,"name":"Chern-En Chiang","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chern-En","middleName":"","lastName":"Chiang","suffix":""},{"id":299573108,"identity":"5cf30d0d-f7df-4fe3-bcce-d6cc9649146e","order_by":9,"name":"Chen-Huan Chen","email":"","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":false,"prefix":"","firstName":"Chen-Huan","middleName":"","lastName":"Chen","suffix":""},{"id":299573109,"identity":"f0a762b8-007a-4290-a035-95b0042862b4","order_by":10,"name":"Shih-Hsien Sung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDADA/YGEGlBihaeAyBSghQtEgkgiggturMPMH/m3VFrby75/OqGHwUSDPzt3Ql4tZidS2CT5j1zPHHn7Jyymz1Ah0mcObsBv5YzDGzMvG3HEgxu56Td4AFqMZDIJagF6LC2Y/YGN8+k3fxDpBYGad62GsYNN9iP3SbSFsY2ybltBxI3nMlhuy1jIMFDhF+YD39421Znb3D8+LObb/7YyPG39+LXwsDA2AAkDgMxjwGIy0NAORzUATH7A2JVj4JRMApGwQgDADMORyrOtngJAAAAAElFTkSuQmCC","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shih-Hsien","middleName":"","lastName":"Sung","suffix":""}],"badges":[],"createdAt":"2024-05-04 17:54:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4369398/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4369398/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56476500,"identity":"6a343b5d-8840-4c2a-bb42-52d8d6346788","added_by":"auto","created_at":"2024-05-14 17:44:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50946,"visible":true,"origin":"","legend":"\u003cp\u003eThe initial and final phenotypes of heart failure, according to the dynamic change of left ventricular ejection fraction.\u003c/p\u003e\n\u003cp\u003eHFmrEF: heart failure with mid-range ejection fraction, HFpEF: heart failure with preserved ejection fraction, HFrEF: heart failure with reduced ejection fraction, HFrecEF: heart failure with recovered ejection fraction.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4369398/v1/07a2e15033bcee21c469db24.png"},{"id":56476502,"identity":"f93fe0c0-b729-456c-84ae-d906765bee05","added_by":"auto","created_at":"2024-05-14 17:44:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70799,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution (mean and standard deviation) of peak oxygen consumption (peak VO\u003csub\u003e2\u003c/sub\u003e), oxygen uptake efficiency slope (OUES), and the relationship between minute ventilation and carbon dioxide production (VE/VCO\u003csub\u003e2\u003c/sub\u003e slope), along with the phenotypes of heart failure, the classification of diastolic function (DF) and the tertiles of left ventricular global longitudinal strain (LV-GLS).\u003c/p\u003e\n\u003cp\u003eDD: diastolic dysfunction, HFmrEF: heart failure with mid-range ejection fraction, HFpEF: heart failure with preserved ejection fraction, HFrEF: heart failure with reduced ejection fraction, HFrecEF: heart failure with recovered ejection fraction.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4369398/v1/e6c831157a6629c200243986.png"},{"id":56476504,"identity":"be0bf538-eba5-4979-9b6e-8bee56d2faa6","added_by":"auto","created_at":"2024-05-14 17:44:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":315001,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver-Operating Characteristic curves of indices of diastolic dysfunction (DD), left ventricular global longitudinal strain at rest and peak (resting and peak LV-GLS), ratio of early ventricular filling velocity (E) to early diastolic tissue velocity mitral annulus at rest and peak (resting and peak medial E/e’), and peak medial E/e’ to cardiac output (CO) ratio in predicting (A) reduced exercise capacity (peak oxygen consumption below 14 ml/min/kg) and (B) ventilatory inefficiency (the relationship between minute ventilation and carbon dioxide production, VE/VCO\u003csub\u003e2\u003c/sub\u003e slope above 34).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4369398/v1/54e316f1a114066e45900492.png"},{"id":57250113,"identity":"f0605416-c69d-4533-a12c-690af70bd768","added_by":"auto","created_at":"2024-05-28 07:01:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1234364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4369398/v1/e30479de-3f59-4d7c-bea5-6deee27f67fa.pdf"},{"id":56476501,"identity":"7828ab1d-7ebc-4184-a3e3-c7b9beb14821","added_by":"auto","created_at":"2024-05-14 17:44:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33186,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4369398/v1/c84807764705b8d465505e84.docx"},{"id":56476503,"identity":"48eb537c-4c82-435d-9c63-3e44f7dbfd61","added_by":"auto","created_at":"2024-05-14 17:44:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38410,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4369398/v1/96ac5150ec5803e43a2aa093.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of LV filling pressure indexed to cardiac output on exercise capacity in HF subjects","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eExercise intolerance is the most common clinical manifestation of heart failure (HF), regardless of the etiologies and pathogenesis, and left ventricular functions[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The exercise capacity could be assessed subjectively by NYHA functional classes or quantitatively by cardiopulmonary exercise testing (CPET), and both methodologies have been associated with adverse cardiovascular events[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] or guided therapeutic strategies in subjects with HF[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The peak oxygen consumption (peak VO\u003csub\u003e2\u003c/sub\u003e), obtained at maximal exercise, and the submaximal exercise indices, including the slope of minute ventilation to CO\u003csub\u003e2\u003c/sub\u003e production (VE/VCO\u003csub\u003e2\u003c/sub\u003e slope), and the slope of the relationship between peak VO\u003csub\u003e2\u003c/sub\u003e and log minute ventilation (OUES) may not only correlated with clinical outcomes in various populations[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], but also unmask the different mechanisms of exercise intolerance[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNowadays, the guidelines have classified HF into difference phenotypes according to the left ventricular ejection fraction (LVEF) and its dynamic change, including HF with reduced LVEF (HFrEF), mildly reduced LVEF (HFmrEF), preserved LVEF (HFpEF) and recovered LVEF (HFrecEF)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although Gardin et al. did not suggest LVEF a major determinant of exercise capacity[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Pugliese et al. have disclosed LV systolic function, indexed by global longitudinal strain (GLS) correlated with peak VO\u003csub\u003e2\u003c/sub\u003e during CPET[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, Shimiaie et al. illustrated LV diastolic function, indexed by left atrial volume or early mitral inflow velocity to mitral annular early diastolic velocity ratio (E/e\u0026rsquo; ratio) was related to peak VO\u003csub\u003e2\u003c/sub\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHow the left ventricular systolic and diastolic function impacted on the exercise capacity remained elucidated, and the determinants of submaximal parameters in patients with HF were rarely discussed. In addition, the exercise performance of patients with HFrecEF has yet to be compared with the other phenotypes of HF. In the study, we therefore combined CPET and exercise stress echocardiography (ESE) to investigate the correlations between LV systolic and diastolic function and exercise capacity among patients with various HF phenotypes.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eBetween April 2020 and September 2021, ambulatory patients with chronic HF, who have been treated for \u0026ge;\u0026thinsp;6 months with steady doses for \u0026ge;\u0026thinsp;4 weeks were eligible for this study. Patients presented with NYHA functional class IV symptoms, or acute coronary syndrome within a month, received cardiovascular intervention or surgery within 3 months, and had chronic pulmonary disease, exercise-induced asthma, severe cognitive impairment, and infective or orthopedic diseases were excluded. According to the guidelines, the diagnosis of HFrEF, HFmrEF and HFpEF were based on LVEF of \u0026le;\u0026thinsp;40%, 40%~50% and \u0026ge;\u0026thinsp;50%, respectively[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Subjects with prior LVEF of \u0026lt;\u0026thinsp;40%, had\u0026thinsp;\u0026ge;\u0026thinsp;10% absolute improvement in LVEF to achieve a LVEF of \u0026ge;\u0026thinsp;50% were defined to have HFrecEF. The demographic and anthropometric data were obtained. Blood samples were harvested to measure hemogram, renal function and N-terminal pro-brain natriuretic peptide (NT-proBNP). The study was approved by the institutional review board of Taipei Veterans General Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCombined cardiopulmonary exercise testing and exercise stress echocardiography\u003c/h2\u003e \u003cp\u003eThe detail of CPET-ESE protocol was mentioned in the previous study.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] In short, a symptom-limited stepwise protocol on cycle ergometer in the semi-supine position was performed with an initial workload of 30W for 3 minutes followed 15W-increase every 3 minutes until the cessation criteria, including: 1) when the participant requested to stop, 2) if there was no further increase in heart rate or oxygen consumption despite increasing exercise intensity, 3) if systolic blood pressure exceeded 250 mmHg or diastolic blood pressure surpassed 115 mmHg, 4) a drop in systolic blood pressure of more than 10 mmHg despite an increase in workload, or 5) the onset of uncomfortable symptoms was achieved[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The peak VO\u003csub\u003e2\u003c/sub\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], predicted value of peak VO\u003csub\u003e2\u003c/sub\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], OUES[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and VE/VCO\u003csub\u003e2\u003c/sub\u003e slope[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] were calculated accordingly. The V-slope methods was applied to determine aerobic threshold (AT)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Impaired exercise capacity was defined by a peak VO\u003csub\u003e2\u003c/sub\u003e of \u0026lt;\u0026thinsp;14 ml/kg/min[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and a VE/VCO\u003csub\u003e2\u003c/sub\u003e slope of \u0026gt;\u0026thinsp;34[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] was referred to ventilatory inefficiency.\u003c/p\u003e \u003cp\u003eThe GE E95 system and EchoPAC software (GE Healthcare) were used for echocardiographic parameters, trans-mitral early (E) and late (A) diastolic flow velocity, medial mitral annulus tissue velocity at early diastole (e\u0026rsquo;), peak tricuspid regurgitation flow velocity (TRV) and right ventricular myocardial systolic velocity (RV S\u0026rsquo;) were measured[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. LV global longitudinal strain (GLS) by speckle tracking analyses was reported. Left ventricular internal diameter in end diastole and end systole (LVIDd, and LVIDs), left ventricular ejection fraction (LVEF), left atrial (LA) volume index, stroke volume (SV) and cardiac output (CO) were acquired by m-mode, two- and three-dimensional echocardiography, respectively[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The arterial-venous oxygen content difference (AVO\u003csub\u003e2\u003c/sub\u003ediff) was obtained by using the Fick equation as VO\u003csub\u003e2\u003c/sub\u003e/CO[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The indices of diastolic dysfunction (DD) were the counts of the indicators, included medial e\u0026rsquo; velocity\u0026thinsp;\u0026lt;\u0026thinsp;7 cm/s, medial E/e\u0026rsquo; ratio\u0026thinsp;\u0026gt;\u0026thinsp;15, TRV max\u0026thinsp;\u0026gt;\u0026thinsp;2.8 m/s, and LA volume index\u0026thinsp;\u0026gt;\u0026thinsp;34 ml/m\u003csup\u003e2\u003c/sup\u003e. Normal diastolic function was defined by the presence of none or one of these indices. Subjects with three or four indices were considered to have DD, and the remaining were indeterminate DD[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To address the accuracy and reproducibility of exercise echocardiographic parameters, we have published validation studies focused on our current protocol and imaging analytics, where we reported satisfactory consistency in parameter analysis.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables of descriptive results were presented as means and standard deviations. Categorical variables were expressed as absolute numbers and relative frequencies. ANOVA and Chi-square tests were used to examine the differences between HF subgroups. Linear and logistic regression models were used to evaluate the association between echocardiographic and respiratory variables (continue or binary, respectively). Only those parameters achieved statistical significance in uni-variate models would undergo multi-variate adjustment separately. Receiver operating characteristic curves were used to evaluate the different variables to reduced exercise capacity or ventilatory efficiency by comparing the strengths of the areas under the curve (AUCs). All the statistical analyses were performed SPSS v.20.0 software (SPSS, Inc., Chicago, IL, USA) and the performed tests were two-sided. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 66 patients (age 60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6 years, 74% men) were enrolled in this study, including 16 HFrEF, 18 HFmrEF, 12 HFpEF and 20 HFrecEF. The diagram of the study cohort was demonstrated in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. Baseline characteristics, stratified by the HF phenotypes were shown in Table\u0026nbsp;1. In addition to LVEF, LVIDd, LVIDs, LV-GLS, and medical e\u0026rsquo; were significantly different between the phenotypes. In contrast, mitral E/A ratio, RV S\u0026rsquo;, peak TRV, LAV index, and cardiac output were similar between groups.\u003c/p\u003e\n\u003cp\u003eCPET demonstrated the study participants would achieve similar peak VO\u003csub\u003e2\u003c/sub\u003e, VO\u003csub\u003e2\u003c/sub\u003e at AT, VE/VCO\u003csub\u003e2\u003c/sub\u003e slope, OUES, and AVO\u003csub\u003e2\u003c/sub\u003ediff, regardless of HF phenotypes. However, both peak LV-GLS and peak medial E/e\u0026rsquo; were diverse between groups.\u003c/p\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eThe associations between echocardiographic parameters and maximal/submaximal respiratory variables\u003c/h2\u003e\n \u003cp\u003eAlthough the exercise achievements were not significantly different between HF phenotypes, the worst LV-GLS at rest among the tertiles would have a higher VE/VCO\u003csub\u003e2\u003c/sub\u003e slope, but similar OUES and peak VO\u003csub\u003e2\u003c/sub\u003e, compared with the others. (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e) In contrast, subjects with normal diastolic function at rest would have better peak VO\u003csub\u003e2\u003c/sub\u003e and OUES than the others.\u003c/p\u003e\n \u003cp\u003eThe peak VO\u003csub\u003e2\u003c/sub\u003e and VE/VCO\u003csub\u003e2\u003c/sub\u003e slope were significantly correlated with LVIDd, LVIDs, LVEF, LV-GLS, medial E/e\u0026rsquo; ratio and indices of diastolic dysfunction. MV E/A ratio and peak LV-GLS to CO ratio were not associated with peak VO\u003csub\u003e2\u003c/sub\u003e and VE/VCO\u003csub\u003e2\u003c/sub\u003e slope. Few resting echocardiographic parameters were linked to OUES. Considering exercise parameters, only peak LV-GLS was not correlated significantly. Resting medial E/e\u0026rsquo; ratio, peak medial E/e\u0026rsquo; ratio and peak medial E/e\u0026rsquo; to CO ratio were also correlated with peak VO\u003csub\u003e2\u003c/sub\u003e, OUSE and VE/VCO\u003csub\u003e2\u003c/sub\u003e slope. (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e.)\u003c/p\u003e\n \u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"931\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. The association between ventilatory variables and echocardiographic parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.870967741935484%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak VO2 (ml/kg/min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOUES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.086021505376344%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVE/VCO2 slope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecoefficients (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficients (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters at rest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;LVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e0.092 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e3.768 (4.243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e-0.103 (0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;LVIDd (mm)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e-0.131 (0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e-0.431 (6.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e0.204 (0.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;LVIDs (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e-0.127 (0.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e-5.712 (5.368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e-0.163 (0.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Rest LV-GLS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e-0.308 (0.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e-1.625 (14.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e0.5 (0.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\"\u003e\n \u003cp\u003e\u0026nbsp;Rest medial E/e\u0026rsquo; ratio\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e-0.220 (0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e-12.754 (6.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e0.346 (0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\"\u003e\n \u003cp\u003e\u0026nbsp;MV E/A ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e1.069 (1.232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e152.954 (129.905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e-0.552 (1.855)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\"\u003e\n \u003cp\u003e\u0026nbsp;LA volume index (ml/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e-0.100 (0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e-1.751 (3.566)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e0.077 (0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\"\u003e\n \u003cp\u003eIndices of DD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e-2.476 (0.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e-178.59 (48.703)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e1.536 (0.763)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters during exercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\"\u003e\n \u003cp\u003e\u0026nbsp;Peak medial E/e\u0026rsquo; ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e-0.220 (0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e-12.13 (5.772)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e0.617 (0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Peak LV-GLS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e-0.398 (0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e-14.336 (12.231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e0.617 (0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Peak cardiac output (ml/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e0.200 (0.567)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e103.711 (20.496)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e-0.516 (0.403)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Peak medial E/e\u0026rsquo; ratio/CO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e-1.279 (0.213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e-87.822 (22.754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e1.297 (0.397)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.387096774193548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Peak LV-GLS/CO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.268817204301076%\"\u003e\n \u003cp\u003e0.553 (0.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.602150537634408%\" valign=\"top\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806451612903226%\"\u003e\n \u003cp\u003e104.324 (43.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.849462365591398%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.451612903225808%\"\u003e\n \u003cp\u003e0.898 (0.755)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.634408602150538%\" valign=\"top\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eCO: cardiac output, DD: diastolic dysfunction, E/A ratio: ratio of the early (E) to late (A) ventricular filling velocities, E/e\u0026apos;: ratio of early ventricular filling velocity (E) to early diastolic tissue velocity mitral annulus, LV-GLS: left ventricular global longitudinal strain, LA: left atrium, LV: left ventricular, LVEF: left ventricular ejection fraction, LVIDd: left ventricular internal diameter in end diastole, LVIDs: left ventricular internal diameter in end systole,\u0026nbsp;NT-proBNP: N-terminal pro-brain natriuretic peptide, OUES: oxygen uptake efficiency slope,\u0026nbsp;SE:\u0026nbsp;standard error, VE/VCO2 slope : the relationship between minute ventilation and carbon dioxide production, VO2: oxygen consumption\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cbr\u003e\n \u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"854\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Determinants of peak VO2 \u0026lt; 14 mL/min/kg identified by uni- and multivariate logistic regression analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.54332552693209%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.54332552693209%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters at rest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e0.959 (0.916-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003eLVIDd (mm)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.060 (0.993-1.131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003eLVIDs (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.057 (0.997-1.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003eRest LV-GLS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.134 (0.971-1.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003eMV E/A ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e0.299 (0.058-1.544)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003eRest medial E/e\u0026rsquo;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.319 (1.099-1.583)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e1.132 (0.986-1.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003eLA volume index (ml/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.052 (1.011-1.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e1.040 (0.981-1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003eIndices of DD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e7.641 (2.308-24.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e3.847 (1.369-10.810)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eParameters during exercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003ePeak medial E/e\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.197 (1.061-1.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e1.132 (0.986-1.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003ePeak LV-GLS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.187 (1.027-1.373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e1.022 (0.869-1.201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003ePeak cardiac output (ml/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e0.557 (0.381-0.813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e0.594 (0.333-1.082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003ePeak medial E/e\u0026rsquo; to CO ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e2.280 (1.400-3.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e3.478 (1.313-9.214)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003ePeak LV-GLS/CO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e0.841 (0.513-1.378)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e* After accounting for age, gender, body mass index, levels of NTproBNP and hemoglobin, previous myocardial infarction\u003c/p\u003e\n \u003cp\u003eCI: confidence interval,\u0026nbsp;CO: cardiac output, DD: diastolic dysfunction, E/A ratio: ratio of the early (E) to late (A) ventricular filling velocities, E/e\u0026apos;: ratio of early ventricular filling velocity (E) to early diastolic tissue velocity mitral annulus, LV-GLS: left ventricular global longitudinal strain, LA: left atrium, LV: left ventricular, LVEF: left ventricular ejection fraction, LVIDd: left ventricular internal diameter in end diastole, LVIDs: left ventricular internal diameter in end systole,\u0026nbsp;NT-proBNP: N-terminal pro-brain natriuretic peptide,\u0026nbsp;OR : odd ratio,\u0026nbsp;VO2: oxygen consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"854\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4. Determinants of VE/VCO2 slope \u0026gt; 34 identified by uni- and multivariate logistic regression analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.91334894613583%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"34.54332552693209%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.54332552693209%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters at rest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e0.958 (0.915-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003eLVIDd (mm)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.058 (0.990-1.131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003eLVIDs (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.064 (1.003-1.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e1.062 (0.986-1.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003eRest LV-GLS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.254 (1.046-1.504)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e1.404 (1.050-1.877)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003eMV E/A ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e0.308 (0.062-1.520)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003eRest medial E/e\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.100 (0.986-1.227)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003eLA volume index (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.016 (0.979-1.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003eIndices of DD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e2.156 (1.094-2.249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e2.521 (1.126-5.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eParameters during exercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003ePeak medial E/e\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.084 (0.999-1.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003ePeak LV-GLS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.284 (1.084-1.520)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e1.403 (1.076-1.828)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003ePeak cardiac output (ml/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e0.731 (0.529-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\"\u003e\n \u003cp\u003ePeak medial E/e\u0026rsquo; to CO ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.573 (1.099-2.253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e1.758 (1.023-3.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.949589683470105%\" valign=\"top\"\u003e\n \u003cp\u003ePeak LV-GLS to CO ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.212192262602578%\"\u003e\n \u003cp\u003e1.347 (0.751-2.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.254396248534583%\" valign=\"top\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.09495896834701%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48886283704572%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e* After accounting for age, gender, body mass index, levels of NTproBNP and hemoglobin, previous myocardial infarction\u003c/p\u003e\n \u003cp\u003eCI: confidence interval,\u0026nbsp;CO: cardiac output, DD: diastolic dysfunction, E/A ratio: ratio of the early (E) to late (A) ventricular filling velocities, E/e\u0026apos;: ratio of early ventricular filling velocity (E) to early diastolic tissue velocity mitral annulus, LV-GLS: left ventricular global longitudinal strain, LA: left atrium, LV: left ventricular, LVEF: left ventricular ejection fraction, LVIDd: left ventricular internal diameter in end diastole, LVIDs: left ventricular internal diameter in end systole,\u0026nbsp;NT-proBNP: N-terminal pro-brain natriuretic peptide,\u0026nbsp;OR : odd ratio, VE/VCO2 slope : the relationship between minute ventilation and carbon dioxide production\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003ePredictors of impaired exercise capacity and ventilatory inefficiency\u003c/h2\u003e\n \u003cp\u003eMedial E/e\u0026rsquo;, LA volume index, and indices of DD were predictive of impaired exercise capacity (Odd ratio and 95% confidence intervals: 1.319, 1.099\u0026ndash;1.583; 1.052, 1.011\u0026ndash;1.095; 7.641, 2.308\u0026ndash;24.114, respectively). The exercise-derived parameters, including peak medial E/e\u0026rsquo;, peak LV-GLS, peak CO, and peak medial E/e\u0026rsquo; to CO ratio were also predictors of impaired exercise capacity. With adjustments for age, gender, body mass index, NT-proBNP and hemoglobin levels, and prior myocardial infarction, the indices of DD (5.557, 1.118\u0026ndash;27.624) and peak medial E/e\u0026rsquo; to CO ratio (3.478, 1.313\u0026ndash;9.214) were independently associated with impaired exercise capacity. (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e) The receiver-operating characteristic (ROC) curve analysis suggested peak medial E/e\u0026rsquo; to CO ratio (AUC: 0.87; 95% CI: 0.73\u0026ndash;0.95) outperformed the others to predict impaired exercise capacity. (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eA) Among the parameters obtained at baseline, the indices of DD were the best predictors of reduced exercise capacity (AUC: 0.86; 95% CI: 0.72\u0026ndash;0.94).\u003c/p\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003eLVIDs, rest LV-GLS, indices of DD, peak medial E/e\u0026rsquo;, peak LV-GLS, peak CO and peak medial E/e\u0026rsquo; to CO ratio were crudely correlated with ventilatory inefficiency. (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e) In multivariate logistic regression analyses, rest LV-GLS (OR: 1.404; 95% CI: 1.050\u0026ndash;1.877), peak LV-GLS (OR: 1.403; 95% CI: 1.076\u0026ndash;1.828) and peak medial E/e\u0026rsquo; to CO ratio (OR: 1.758; 95% CI: 1.023 to 3.022) remained associated with ventilatory inefficiency, independent of age, gender, body mass index, NT-proBNP and hemoglobin levels, and prior myocardial infarction. (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e) The ROC curve analysis for the prediction of ventilatory inefficiency indicated peak LV-GLS (AUC: 0.82; 95% CI: 0.\u0026ndash;7\u0026ndash;0.92) the best correlate, followed by rest LV-GLS (AUC: 0.77; 95% CI: 0.\u0026ndash;1\u0026ndash;0.88). (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eB)\u003c/p\u003e\n\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eNeither LVEF nor LV-GLS was independent associated with impaired exercise capacity. In contrast, while all the phenotypes of HF have similar pattern of DD and elevated LV filling pressure, DD majorly determined the peak VO\u003csub\u003e2\u003c/sub\u003e. The increase in peak VO\u003csub\u003e2\u003c/sub\u003e also depended on the blunt increase in medial E/e\u0026rsquo; along with the increased cardiac output during exercise. The study results may suggest that the medial E/e\u0026rsquo; normalized by cardiac output as a surrogate of dynamic left-sided filling pressures during incremental exercise was closely related to impaired exercise capacity. (Graphical abstract) In addition, LV systolic function, indexed by GLS but not LVEF was associated with VE/VCO\u003csub\u003e2\u003c/sub\u003e slope. And the increase in peak LV-GLS during exercise may also contribute to the ventilatory efficiency. LV-GLS could reflect the degree of the extent of myocardial fibrosis and attenuate the LV relaxation and contractility[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the definite mechanism of how the LV-GLS affect ventilatory efficiency was worth more exploration.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe exercise capacity and ventilatory efficiency in heart failure with different phenotypes\u003c/h2\u003e \u003cp\u003eAlthough HF could be categorized by LVEF into different phenotypes, patients with HF may all experience equivalent risks of hospitalization for HF and the same loss of lifespan[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Even though they have been characterized with distinct LV systolic function, their exercise capacity might not be different. Pugliese et al. have reported 169 subjects with either HFrEF, HFmrEF or HFpEF had similar peak VO\u003csub\u003e2[9]\u003c/sub\u003e. In this study, we also demonstrated ambulatory patients with HF may have similar exercise performance, in terms of peak VO\u003csub\u003e2\u003c/sub\u003e, OUES, and VE/VCO\u003csub\u003e2\u003c/sub\u003e, regardless of the phenotypes. Given the study only recruited subjects with limited symptoms and eligible for exercise tests, the results may not be generalized to patients with poor daily activity. In addition, the study further extended our understandings that patients with HFrecEF also have comparable exercise capacity and ventilatory efficacy with the other phenotypes of HF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of Left ventricular systolic and diastolic functions with exercise capacity\u003c/h2\u003e \u003cp\u003eAlthough the exercise capacity was similar between HF phenotypes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], neither LV-GLS nor LVEF was independently predictive of impaired exercise capacity. While the common feature across all the phenotypes of HF is elevated LV end-diastolic pressure, which could be indirectly assessed by echocardiographic parameters, including LV volume, TRV and E/e\u0026rsquo; ratio, the indices of DD. In addition, the indices of DD independently correlated with peak VO\u003csub\u003e2\u003c/sub\u003e. The results may support the major determinant of exercise capacity was LV diastolic but not systolic function. In fact, none of the patients with DD could achieve a peak VO\u003csub\u003e2\u003c/sub\u003e of \u0026gt;\u0026thinsp;14 ml/kg/min in this study. In contrast, only 17.2% of patients with normal diastolic function had a peak VO2\u0026thinsp;\u0026lt;\u0026thinsp;14 ml/kg/min. (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe performance of exercise medial E/e\u0026rsquo; to CO ratio and LV-GLS with adjustment of cardiac output\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDuring exercise, a normal LV would increase the trans-mitral flow by facilitating LV relaxation to augment cardiac output, which may limit the elevation in LA pressure[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, a failing heart is characterized by elevated LV filling pressure, which would be further enhanced during exercise[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The elevated of LV end-diastolic pressure would be transmitted to pulmonary system and activate the receptor of dyspnea[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], resulting in cessation of exercise. Exercise-related PCWP change in relation to cardiac output has been an indicator to evaluate hemodynamic changes in the face of the stress load[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this study, we used the medial E/e' to CO ratio to correct the augmentation in LV filling pressures in response to the increase in cardiac output. Therefore, the peak medial E/e' to CO ratio would reflect the speed at which medial E/e' raised during the exercise. In contrast, the peak LV-GLS but not Peak LV-GLS to CO ratio better correlated with VE/VCO2 slope. This result may support diastolic filling pressure rather than systolic function was better associated with LV volume changes, while LV-GLS but not medial E/e\u0026rsquo; a volume-independent indicator.\u003c/p\u003e \u003cp\u003eThe AVO\u003csub\u003e2\u003c/sub\u003ediff was a measure of oxygen was extracted from blood circulates into the body, which was a derivative of cardiac output and VO\u003csub\u003e2\u003c/sub\u003e. AVO\u003csub\u003e2\u003c/sub\u003ediff was considered as the major determinant of exercise capacity, especially for HFmrEF and HFpEF subtypes but not in HFrEF[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, though peak AVO\u003csub\u003e2\u003c/sub\u003ediff was crudely associated with reduced exercise capacity. (OR [95%CI] : 0.741[0.550\u0026ndash;0.997]) and lower AUCs (0.711)(not shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the discriminatory ability of AVO\u003csub\u003e2\u003c/sub\u003ediff was lower than peak medial E/e\u0026rsquo; ratio or its adjusted value with CO. This difference from previous studies may be due to the different heart failure phenotypes, and to investigate peripheral oxygen utilization one should consider methods other than the indirect calculation of peak AV O\u003csub\u003e2\u003c/sub\u003e difference, such as near-infrared spectroscopy (NIRS). LVEF, as an important parameter for heart failure phenotyping, did not show an independent predictive ability for Peak VO2 or VE/VCO2 slope in our study. This is similar to observations made in the past HF-ACTION trial.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe different impacts of medial E/e\u0026rsquo; to CO ratio and GLS on peak VO\u003c/em\u003e \u003csub\u003e \u003cem\u003e2\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eand VE/VCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eslope\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAs mentioned above, the change of LV filling pressure would transmit to LA pressure and then to pulmonary system, which cause dyspnea and affect exercise performances[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This mechanism was compatible with our finding and diastolic dysfunction, at rest or during exercise were the major determinants of reduced exercise capacity. LV-GLS was a surrogate of LV contractility, less load-dependent and associated with clinical adverse events, such hospitalized HF or death in all HF phenotypes[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. VE/VCO\u003csub\u003e2\u003c/sub\u003e slope as an indicator of ventilatory inefficiency had comparable and even superior prognostic value in comparison with peak VO\u003csub\u003e2\u003c/sub\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Traditionally, the elevation of VE/VCO\u003csub\u003e2\u003c/sub\u003e slope was resulted from decreased pulmonary perfusion caused by high LV filling pressure[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the association between LV-GLS, either resting or peak, and VE/VCO\u003csub\u003e2\u003c/sub\u003e slope was more predominant than diastolic dysfunction or medial E/e\u0026rsquo; ratio. The mechanism of the association between GLS and VE/VCO2 slope was worth more exploration, and whether peak LV-GLS has a better prognostic value than resting LV-GLS needed to be further investigated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eGiven the sample of the study was comparatively small, there were some limitations. First, the study participants were relatively young and stable. Therefore, the results may not be generated to the sicker and older population, when the muscle strength could be a significant confounder. Second, even exercise intolerance was an important surrogate of the clinical outcomes, a longitudinal cohort study was needed to support the superior prognostic role of CPET in patients with HF. Third, the study was limited by the relatively small sample and single-center, retrospective observational nature of the study. Therefore, there was inevitable selection bias and type II error in this observation study and may impact the generalizability of our results. For example, the difference in peak VO2 between HFrEF and HFrecEF may be statistically significant if the sample size was larger. A case-matched study could provide substantial improvement in the generalizability of the study results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe resting echocardiographic variables which associated with diastolic dysfunction were linked to peak VO\u003csub\u003e2\u003c/sub\u003e, the medial E/e\u0026rsquo; to CO ratio was the most robust predictor of impaired exercise capacity. On the other hand, the resting and peak LV-GLS, a surrogate of LV contractility, was associated with ventilatory efficiency. It suggested that LV diastolic and systolic dysfunction might jointly lead to exercise intolerance among HF patients via different mechanisms. Further studies were worthwhile to investigate the underlying pathophysiological process and predictive roles of these parameters in clinical events.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMinistry of Health and Welfare, Taiwan, Grant number: MOHW107-TDU-B-211-123001 (to SHS); Taipei Veterans General Hospital, Grant number: V109E-008-01 and V111C-172 (to SHS) and Grant number: V110B-032 and V111B-038 (to WMH).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.M.H., C.N.C., and S.H.S. conceived of the study design. Y.T.L., Y.T.W., and T.Y.T. collected the data. W.M.H. analyzed the data. H.M.C. and\u0026nbsp;W.C.Y. interpreted the results. W.M.H. drafted and H.C.C. prepared the manuscript. S.H.S., C.E.C., and C.H.C. revised the manuscript critically for important intellectual content.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by The ethics committee: Institutional Review Board of Taipei Veterans General Hospital; Reference Number: IRB-TPEVGH No.: 2020-04-007ACF\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Bohm M, et al (2021) 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure \u003cem\u003eEur Heart J\u003c/em\u003e (2021);42:3599-3726.\u003c/li\u003e\n\u003cli\u003eLala A, Shah KB, Lanfear DE, Thibodeau JT, Palardy M, Ambardekar AV, et al (2021) Predictive Value of Cardiopulmonary Exercise Testing Parameters in Ambulatory Advanced Heart Failure \u003cem\u003eJACC Heart Fail\u003c/em\u003e (2021);9:226-236.\u003c/li\u003e\n\u003cli\u003eGuazzi M, Bandera F, Ozemek C, Systrom D, Arena R (2017) Cardiopulmonary Exercise Testing: What Is its Value? \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e (2017);70:1618-1636.\u003c/li\u003e\n\u003cli\u003eMalhotra R, Bakken K, D\u0026apos;Elia E, Lewis GD (2016) Cardiopulmonary Exercise Testing in Heart Failure \u003cem\u003eJACC Heart Fail\u003c/em\u003e (2016);4:607-16.\u003c/li\u003e\n\u003cli\u003eLund LH, Aaronson KD, Mancini DM (2005) Validation of peak exercise oxygen consumption and the Heart Failure Survival Score for serial risk stratification in advanced heart failure \u003cem\u003eAm J Cardiol\u003c/em\u003e (2005);95:734-41.\u003c/li\u003e\n\u003cli\u003eArena R, Myers J, Abella J, Pinkstaff S, Brubaker P, Moore B, et al (2009) Determining the preferred percent-predicted equation for peak oxygen consumption in patients with heart failure \u003cem\u003eCirc Heart Fail\u003c/em\u003e (2009);2:113-20.\u003c/li\u003e\n\u003cli\u003eGulati G, Udelson JE (2018) Heart Failure With Improved Ejection Fraction: Is it Possible to Escape One\u0026apos;s Past? \u003cem\u003eJACC Heart Fail\u003c/em\u003e (2018);6:725-733.\u003c/li\u003e\n\u003cli\u003eGardin JM, Leifer ES, Fleg JL, Whellan D, Kokkinos P, Leblanc MH, et al (2009) Relationship of Doppler-Echocardiographic left ventricular diastolic function to exercise performance in systolic heart failure: the HF-ACTION study \u003cem\u003eAm Heart J\u003c/em\u003e (2009);158:S45-52.\u003c/li\u003e\n\u003cli\u003ePugliese NR, Fabiani I, Santini C, Rovai I, Pedrinelli R, Natali A, et al (2019) Value of combined cardiopulmonary and echocardiography stress test to characterize the haemodynamic and metabolic responses of patients with heart failure and mid-range ejection fraction \u003cem\u003eEur Heart J Cardiovasc Imaging\u003c/em\u003e (2019);20:828-836.\u003c/li\u003e\n\u003cli\u003eHuang WM, Chen CN, Chen YH, Yen JH, Tseng TY, Cheng HM, et al (2022) The feasibility and safety of stepwise protocol in cardiopulmonary exercise testing-exercise stress echocardiography for subjects with heart failure \u003cem\u003eJ Chin Med Assoc\u003c/em\u003e (2022);85:815-820.\u003c/li\u003e\n\u003cli\u003eThompson PD, Arena R, Riebe D, Pescatello LS (2013) ACSM\u0026apos;s new preparticipation health screening recommendations from ACSM\u0026apos;s guidelines for exercise testing and prescription, ninth edition \u003cem\u003eCurr Sports Med Rep\u003c/em\u003e (2013);12:215-7.\u003c/li\u003e\n\u003cli\u003eGuazzi M, Adams V, Conraads V, Halle M, Mezzani A, Vanhees L, et al (2012) EACPR/AHA Scientific Statement. Clinical recommendations for cardiopulmonary exercise testing data assessment in specific patient populations \u003cem\u003eCirculation\u003c/em\u003e (2012);126:2261-74.\u003c/li\u003e\n\u003cli\u003eJang WY, Kim W, Kang DO, Park Y, Lee J, Choi JY, et al (2019) Reference Values for Cardiorespiratory Fitness in Healthy Koreans \u003cem\u003eJ Clin Med\u003c/em\u003e (2019);8\u003c/li\u003e\n\u003cli\u003eBaba R, Nagashima M, Goto M, Nagano Y, Yokota M, Tauchi N, et al (1996) Oxygen uptake efficiency slope: a new index of cardiorespiratory functional reserve derived from the relation between oxygen uptake and minute ventilation during incremental exercise \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e (1996);28:1567-72.\u003c/li\u003e\n\u003cli\u003eMyers J, Arena R, Oliveira RB, Bensimhon D, Hsu L, Chase P, et al (2009) The lowest VE/VCO2 ratio during exercise as a predictor of outcomes in patients with heart failure \u003cem\u003eJ Card Fail\u003c/em\u003e (2009);15:756-62.\u003c/li\u003e\n\u003cli\u003eMezzani A (2017) Cardiopulmonary Exercise Testing: Basics of Methodology and Measurements \u003cem\u003eAnn Am Thorac Soc\u003c/em\u003e (2017);14:S3-s11.\u003c/li\u003e\n\u003cli\u003eMancini DM, Eisen H, Kussmaul W, Mull R, Edmunds LH, Jr., Wilson JR (1991) Value of peak exercise oxygen consumption for optimal timing of cardiac transplantation in ambulatory patients with heart failure \u003cem\u003eCirculation\u003c/em\u003e (1991);83:778-86.\u003c/li\u003e\n\u003cli\u003eGitt AK, Wasserman K, Kilkowski C, Kleemann T, Kilkowski A, Bangert M, et al (2002) Exercise anaerobic threshold and ventilatory efficiency identify heart failure patients for high risk of early death \u003cem\u003eCirculation\u003c/em\u003e (2002);106:3079-84.\u003c/li\u003e\n\u003cli\u003eNagueh SF, Smiseth OA, Appleton CP, Byrd BF, 3rd, Dokainish H, Edvardsen T, et al (2016) Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging \u003cem\u003eJ Am Soc Echocardiogr\u003c/em\u003e (2016);29:277-314.\u003c/li\u003e\n\u003cli\u003eLancellotti P, Pellikka PA, Budts W, Chaudhry FA, Donal E, Dulgheru R, et al (2017) The Clinical Use of Stress Echocardiography in Non-Ischaemic Heart Disease: Recommendations from the European Association of Cardiovascular Imaging and the American Society of Echocardiography \u003cem\u003eJ Am Soc Echocardiogr\u003c/em\u003e (2017);30:101-138.\u003c/li\u003e\n\u003cli\u003eLang RM, Bierig M, Devereux RB, Flachskampf FA, Foster E, Pellikka PA, et al (2005) Recommendations for chamber quantification: a report from the American Society of Echocardiography\u0026apos;s Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology \u003cem\u003eJ Am Soc Echocardiogr\u003c/em\u003e (2005);18:1440-63.\u003c/li\u003e\n\u003cli\u003eNagueh SF, Smiseth OA, Appleton CP, Byrd BF, 3rd, Dokainish H, Edvardsen T, et al (2016) Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging \u003cem\u003eJournal of the American Society of Echocardiography : official publication of the American Society of Echocardiography\u003c/em\u003e (2016);29:277-314.\u003c/li\u003e\n\u003cli\u003eCameli M, Mondillo S, Righini FM, Lisi M, Dokollari A, Lindqvist P, et al (2016) Left Ventricular Deformation and Myocardial Fibrosis in Patients With Advanced Heart Failure Requiring Transplantation \u003cem\u003eJournal of cardiac failure\u003c/em\u003e (2016);22:901-907.\u003c/li\u003e\n\u003cli\u003eShah KS, Xu H, Matsouaka RA, Bhatt DL, Heidenreich PA, Hernandez AF, et al (2017) Heart 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Capillary Wedge Pressure Patterns During Exercise Predict Exercise Capacity and Incident Heart Failure \u003cem\u003eCirc Heart Fail\u003c/em\u003e (2018);11:e004750.\u003c/li\u003e\n\u003cli\u003eHoustis NE, Eisman AS, Pappagianopoulos PP, Wooster L, Bailey CS, Wagner PD, et al (2018) Exercise Intolerance in Heart Failure With Preserved Ejection Fraction: Diagnosing and Ranking Its Causes Using Personalized O(2) Pathway Analysis \u003cem\u003eCirculation\u003c/em\u003e (2018);137:148-161.\u003c/li\u003e\n\u003cli\u003ePark JJ, Park JB, Park JH, Cho GY (2018) Global Longitudinal Strain to Predict Mortality in Patients With Acute Heart Failure \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e (2018);71:1947-1957.\u003c/li\u003e\n\u003cli\u003eJanwanishstaporn S, Cho JY, Feng S, Brann A, Seo JS, Narezkina A, et al (2022) Prognostic Value of Global Longitudinal Strain in Patients With Heart Failure With Improved Ejection Fraction \u003cem\u003eJACC Heart Fail\u003c/em\u003e (2022);10:27-37.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cardiopulmonary exercise testing, exercise echocardiographic exam, heart failure","lastPublishedDoi":"10.21203/rs.3.rs-4369398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4369398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eExercise intolerance is the most common symptom of patients with heart failure (HF), regardless of the phenotypes. We aim to investigate the determinants of exercise capacity in chronic stable HF with reduced, mildly reduced, preserved, and recovered ejection fraction (EF).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAmbulatory HF subjects were recruited for a combined cardiopulmonary exercise test and exercise stress echocardiography. Impaired exercise capacity was referred to a peak oxygen consumption (peak VO\u003csub\u003e2\u003c/sub\u003e) of \u0026lt;\u0026thinsp;14 ml/kg/min, and a minute ventilation-carbon dioxide production relationship (VE/VCO2 slope) of \u0026gt;\u0026thinsp;34 was defined as ventilatory inefficiency.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 66 participants, there were 16 HF with reduced EF, 18 HF with mildly reduced EF, 12 HF preserved EF, and 20 HF recovered EF. Diastolic dysfunction indices were independently predictive of impaired exercise capacity (odds ratio and 95% confidence intervals: 3.847, 1.369\u0026ndash;10.810). GLS at rest was independently correlated with ventilatory inefficiency (1.404, 1.050\u0026ndash;1.877). Among the exercise indices, the peak medial E/e' to cardiac output ratio was independently associated with impaired exercise capacity (3.478, 1.313\u0026ndash;9.214) and peak GLS was best related to ventilatory inefficiency (1.403, 1.076\u0026ndash;1.828).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAmong resting and exertional echocardiographic variables, the peak medial E/e' to cardiac output ratio, a non-invasive assessment of exertional left ventricular filling pressure indexed to cardiac output, was the major determinant of exercise capacity in patients with different HF phenotypes.\u003c/p\u003e","manuscriptTitle":"The impact of LV filling pressure indexed to cardiac output on exercise capacity in HF subjects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-14 17:44:11","doi":"10.21203/rs.3.rs-4369398/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4edcb075-ac8c-4433-b5c2-b0710223a908","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-07T11:29:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-14 17:44:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4369398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4369398","identity":"rs-4369398","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}