Multimodal Model for Predicting Exercise-induced pulmonary hypertension Validated by Invasive Exercise Hemodynamics: A Prospective Study

Multimodal Model for Predicting Exercise-induced pulmonary hypertension Validated by Invasive Exercise Hemodynamics: A Prospective Study | 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 Multimodal Model for Predicting Exercise-induced pulmonary hypertension Validated by Invasive Exercise Hemodynamics: A Prospective Study Xinpeng Dai, Rui Fan, Junwei Zhang, Dichen Guo, Qiumeng Xi, Jiayi He, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9243345/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Exercise-induced pulmonary hypertension (EiPH) represents an early stage of pulmonary vascular disease that remains challenging to identify noninvasively, particularly in patients with borderline resting haemodynamics. We aimed to develop and validate a multimodal non-invasive model integrating clinical characteristics, exercise echocardiography, and cardiopulmonary exercise testing (CPET) to accurately predict invasively confirmed EiPH. Methods In this prospective cohort study, consecutive adults with exercise intolerance after chronic thromboembolic disease, increased tricuspid regurgitation velocity on transthoracic echocardiography, or previously documented mildly elevated pulmonary artery pressure were enrolled. All participants underwent comprehensive clinical assessment, resting and exercise echocardiography, CPET, and invasive exercise right heart catheterization as the reference standard. Feature selection was performed using Spearman’s correlation analysis and regression with the Least Absolute Shrinkage and Selection (LASSO) operator. Single-modality and multimodal prediction models based on clinical, echocardiographic, and CPET variables were constructed. Model performance was evaluated using receiver operating characteristic (ROC) analysis, net reclassification improvement and integrated discriminant improvement. Prespecified subgroup analyses were performed. Associations between selected predictors and invasive hemodynamic parameters were analyzed to explore underlying pathophysiology. Results This study included a total of 86 participants, comprising 45 patients with EiPH and 41 patients without EiPH. Compared with the non-EiPH group, EiPH patients had a poorer WHO functional class; during exercise, their peak mPAP was significantly elevated, accompanied by impaired right ventricular reserve and reduced RV-PA coupling efficiency. Six predictors were selected by correlation analysis and LASSO regression: peak heart rate, age, peak tricuspid annular plane systolic excursion to pulmonary artery systolic pressure ratio, ventilatory equivalent for oxygen, maximal oxygen uptake, and change in tricuspid regurgitation velocity. The combined Clinical+Echo+CPET model achieved the best diagnostic performance (AUC: 0.906), with significant improvement in reclassification compared with single- or dual-modality models. Model performance remained consistent across subgroups stratified by resting mPAP, age, and sex. Mechanistic analysis demonstrated strong correlations between selected predictors and invasive haemodynamic parameters. Conclusions A noninvasive multimodal model integrating clinical variables, exercise echocardiography, and cardiopulmonary metabolic parameters enables robust identification of exercise-induced pulmonary hypertension and reflects underlying abnormalities in RV–PA coupling and ventilatory efficiency. This framework may facilitate early detection and risk stratification of occult pulmonary vascular disease. Exercise-induced pulmonary hypertension Cardiopulmonary exercise testing Exercise echocardiography Right ventricular-pulmonary arterial coupling Multimodal diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Pulmonary hypertension (PH) is a progressive and highly heterogeneous pulmonary vascular disorder characterized by a sustained increase in right ventricular (RV) afterload, ultimately leading to right heart failure and death [ 1 ]. Despite substantial advances in targeted therapies for PH in recent years, the long-term prognosis remains poor, largely because irreversible structural and functional remodeling of the right ventricle has often occurred by the time of clinical diagnosis [ 2 , 3 ]. Accordingly, current research in PH has increasingly shifted toward the early stages of pulmonary vascular disease. Exercise-induced pulmonary hypertension (EiPH) represents an early disease phenotype in which resting hemodynamics remain within normal limits but abnormal responses become evident during physiologic stress such as exercise [ 4 ]. The 2022 European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines formally reintroduced the hemodynamic definition of EiPH based on an abnormal increase in the slope of mean pulmonary arterial pressure relative to cardiac output (mPAP/CO slope) during exercise [ 5 , 6 ]. This revised framework emphasizes the dynamic nature of the pressure–flow relationship in the pulmonary circulation and highlights early exhaustion of pulmonary vascular reserve together with stress-induced impairment of right ventricle–pulmonary artery (RV–PA) coupling [ 7 ]. Accurate identification of EiPH remains challenging in routine clinical practice. Invasive exercise right heart catheterization is considered the reference standard for diagnosing EiPH; however, its invasive nature, technical complexity, and high resource utilization limit its use as a screening tool [ 8 , 9 ]. Conventional noninvasive methods also have important limitations when used alone. Resting transthoracic echocardiography often fails to detect subtle abnormalities in RV–PA coupling, and nonspecific symptoms such as exertional dyspnea may overlap with a wide range of cardiopulmonary disorders [ 10 ]. Exercise stress echocardiography can directly assess right ventricular mechanical reserve and dynamic RV–PA coupling, whereas cardiopulmonary exercise testing (CPET) quantifies ventilatory efficiency and global cardiopulmonary metabolic reserve [ 11 , 12 ]. However, EiPH is a multidimensional pathophysiologic condition involving abnormal pulmonary hemodynamics, chronotropic incompetence, and impaired metabolic efficiency. A single noninvasive modality therefore provides only a partial view of this dynamic process and may result in underrecognition of subclinical pulmonary vascular dysfunction. Given the complexity of EiPH pathophysiology, a multidimensional noninvasive assessment strategy is needed. Recently, Martín et al demonstrated that a multiparametric noninvasive score based on CPET and exercise echocardiography showed promising performance for risk stratification in EiPH [ 10 ]. To further clarify the physiologic relationship between noninvasive predictors and invasive reference standards, the present study performed exercise echocardiography, CPET, and exercise right heart catheterization during the same evaluation period to minimize bias related to nonsynchronous measurements. In addition, right ventricular strain parameters and their dynamic changes during exercise were incorporated into the analysis. On this basis, we aimed to develop and internally validate a noninvasive multimodal prediction model for EiPH, with the goal of providing a reliable tool for early detection and risk stratification of pulmonary vascular dysfunction. Methods Study Design and Participants This was a prospective observational study. Adult patients were consecutively enrolled if they had exercise limitation after chronic pulmonary embolism, increased tricuspid regurgitation velocity on transthoracic echocardiography, or a prior history of mildly elevated pulmonary arterial pressure. All participants underwent a comprehensive evaluation in a clinically stable condition. Exercise right heart catheterization, exercise stress echocardiography, and cardiopulmonary exercise testing (CPET) were performed during the same evaluation period to minimize the influence of physiologic variability. A total of 104 patients were enrolled between January 2024 and December 2025. The study was approved by the Ethics Committee of Beijing Chaoyang Hospital, Capital Medical University (approval number 2023-KE-264), and all participants provided written informed consent. Inclusion criteria were age ≥ 18 years, presence of exertional dyspnea, and the ability to complete exercise testing. Exclusion criteria included significant left-sided heart disease (left ventricular ejection fraction < 50% or moderate-to-severe valvular disease), severe parenchymal lung disease, acute pulmonary embolism, severe arrhythmia, or contraindications to exercise testing. RHC and exercise haemodynamic assessment All participants underwent right heart catheterization via venous access using a Swan–Ganz catheter. A symptom-limited graded exercise test was performed in the supine position using a cycle ergometer. The exercise load is increased gradually, rising by 30 watts every 2 minutes while maintaining a cadence of 55–65 revolutions per minute. Hemodynamic data were recorded at each stage until symptom limitation or maximal tolerance was reached [ 13 ]. At rest and at each exercise stage, the following parameters were continuously measured and recorded simultaneously: central venous pressure, mPAP, pulmonary arterial wedge pressure, right atrial pressure, and right ventricular pressure. Cardiac output was measured using the thermodilution method, with at least three measurements obtained at each stage and averaged. When significant tricuspid regurgitation was present or thermodilution measurements were unreliable, the direct Fick method was used for correction. Pulmonary vascular resistance (PVR) and cardiac index (CI) were calculated at each workload. The mPAP/cardiac output (CO) slope was calculated using linear regression based on multipoint exercise data. Exercise-induced pulmonary hypertension (EiPH) was defined by the presence of at least one of the following invasive hemodynamic criteria: (1) peak exercise mPAP > 30 mmHg and peak total pulmonary resistance > 3 Wood units; (2) mPAP/CO slope > 3 mmHg/L/min calculated from multipoint exercise data; or (3) mPAP/CO slope > 3 mmHg/L/min calculated from resting and peak values [ 5 , 9 , 14 ]. Exercise right heart catheterization served as the reference standard for the diagnosis of EiPH. Exercise stress echocardiography All participants underwent exercise stress echocardiography on the same platform during the catheterization procedure. Echocardiography was performed using a Philips EPIQ 7C system with an X5-1 phased-array probe (1–5 MHz). Apical four-chamber views and continuous-wave Doppler recordings of tricuspid regurgitation were obtained at rest and at each exercise stage. All measurements were averaged over five consecutive cardiac cycles. Echocardiographic analysis was performed offline using TomTec software by investigators blinded to invasive hemodynamic data. Right heart parameters included right ventricular end-diastolic area, right ventricular end-systolic area, fractional area change, tricuspid annular plane systolic excursion (TAPSE), tissue Doppler S′ velocity, right ventricular free-wall longitudinal strain, right atrial conduit strain, right atrial reservoir strain, and right atrial contraction strain. Strain analysis was performed using two-dimensional speckle-tracking imaging with adequate frame rates. Measurements were independently performed by two experienced echocardiographers blinded to catheterization results. Peak tricuspid regurgitation velocity (TRV) was measured using continuous-wave Doppler during exercise, and systolic pulmonary arterial pressure (PASP) was estimated using the simplified Bernoulli equation. The TAPSE/PASP ratio was calculated at each stage as a noninvasive index of right ventricleRV–PA coupling. Dynamic reserve indices included peak exercise values and change values (Δ), defined as the difference between peak and resting measurements, reflecting right ventricular contractile reserve and adaptation to increased pulmonary vascular load. Cardiopulmonary exercise test All participants also underwent CPET on the same graded exercise platform with continuous gas exchange analysis and heart rate monitoring. Recorded variables included peak oxygen uptake (VO₂max), percent predicted peak oxygen uptake (% predicted VO₂peak), ventilatory equivalent for carbon dioxide (VE/VCO₂), ventilatory equivalent for oxygen (VE/VO₂), peak heart rate, and achieved workload relative to predicted workload. The anaerobic threshold was determined using the V-slope method combined with ventilatory parameters. CPET data were analyzed by certified personnel blinded to invasive hemodynamic and echocardiographic results. Feature Selection To construct a robust prediction model and minimize overfitting, a multistep feature selection strategy was applied. First, Spearman correlation analysis was performed for all candidate noninvasive variables to assess multicollinearity. For highly correlated variable pairs (|r| ≥ 0.70), the feature with weaker clinical relevance was removed. The least absolute shrinkage and selection operator (LASSO) regression algorithm was then applied to the remaining variables. Ten-fold cross-validation was used to determine the optimal penalty parameter (λ). The 1–standard error rule was applied to select the simplest model within one standard error of the minimum cross-validation error, allowing coefficients with minimal contribution to shrink to zero. Model Construction and Evaluation After feature selection, all variables were standardized using Z-score normalization. A binary logistic regression model was then constructed. Three hierarchical models were developed to evaluate incremental diagnostic value: single-modality models (clinical variables, exercise echocardiography, or CPET), dual-modality model (clinical + exercise echocardiography), and a multimodal model (clinical + exercise echocardiography + CPET). Subgroup analyses were performed to assess model robustness across predefined strata, including baseline resting mPAP ≤ 20 vs 21–24 mmHg, sex, and age < 60 vs ≥ 60 years. Interaction P values were calculated to evaluate heterogeneity in predictive performance. Mechanistic Analysis To explore the relationship between predictors and hemodynamic mechanisms, a bubble correlation matrix based on Spearman rank correlation coefficients was constructed to evaluate associations between model features and four key hemodynamic variables (peak mPAP, total pulmonary resistance, ΔmPAP/ΔCO, and mPAP/CO slope). A feature association network was generated using Spearman rank correlation coefficients to assess nonlinear relationships among predictors and between predictors and the outcome variable. Statistical Analysis Continuous variables are presented as mean ± SD or median (interquartile range), depending on distribution. Categorical variables are expressed as counts and percentages. Normality was assessed using the Shapiro–Wilk test. Between-group comparisons were performed using the independent-samples t test or Mann–Whitney U test, and categorical variables were compared using the χ² test or Fisher exact test. Correlations were evaluated using the Spearman method. Prediction models were constructed using logistic regression without regularization after Z-score standardization. The optimal cutoff value was determined by maximizing the Youden index. The 95% confidence interval of the area under the receiver operating characteristic curve (AUC) was calculated using bootstrap resampling (1,000 iterations) to assess model stability. Discrimination was evaluated using the AUC. Continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to quantify the incremental value of adding modalities. Calibration was assessed using calibration plots and the Brier score. Model calibration was further evaluated using the Hosmer–Lemeshow test. Subgroup consistency was tested by including interaction terms in multivariable models. All tests were two-sided, and P < 0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 25.0 (IBM) or PyCharm (Python 3.12). Results Study Population Characteristics Among all patients, only 19 patients with chronic thromboembolic pulmonary hypertension (CTEPH) had mildly elevated resting mPAP, whereas the remaining patients had normal pulmonary arterial pressure at rest. Compared with patients with resting mPAP ≤ 20 mmHg, those with mildly elevated mPAP (21–24 mmHg) had a higher proportion of men (63.2% vs. 34.3%, P = .046) and worse World Health Organization functional class (P = .005). Resting pulmonary function parameters did not differ significantly between the two groups. Resting echocardiography showed that the mildly elevated group had higher resting TRV (P = .003) and higher resting right atrial strain rate (RASr) (P = .018). During exercise echocardiographic assessment, the mildly elevated group showed higher resting TAPSE/PASP (P = .009), a greater increase in TRV (ΔTRV, P = .024), and a more pronounced decrease in ΔTAPSE/PASP (P = .003), suggesting impaired RV–PA coupling reserve. Cardiopulmonary exercise testing demonstrated lower percent predicted peak oxygen uptake (VO₂ peak % predicted, P = .003) and a lower ratio of achieved to predicted workload (P = .023), indicating reduced exercise capacity. Invasive hemodynamic measurements further confirmed these findings. Total pulmonary resistance (TPR), central venous pressure (CVP), right ventricular pressure (RVP), pulmonary arterial pressure (PAP), peak mPAP, and mPAP/cardiac output (CO) slope were all significantly higher in the mildly elevated group (all P ≤ .05), whereas resting CO and cardiac index did not differ significantly. Overall, even at the stage of mildly elevated resting mPAP, patients already exhibited early abnormalities, including increased pulmonary vascular resistance, impaired RV–PA coupling, and abnormal exercise hemodynamic responses (e-Table 1). A total of 86 participants were included in the final analysis, including 45 patients with exercise-induced pulmonary hypertension (EiPH) and 41 without EiPH (Fig. 1 ). At baseline, the EiPH group had a higher proportion of men and worse WHO functional class. Baseline pulmonary function parameters were similar between groups. During exercise, patients with EiPH showed more severe cardiopulmonary impairment, with higher peak mPAP (43.49 vs 35.88 mmHg, P < .001) and a steeper mPAP/CO slope (3.27 vs 2.05 mmHg/L/min, P < .001) (Table 1 ). Echocardiographic analysis further supported these hemodynamic findings. Although resting parameters were similar between groups, patients with EiPH demonstrated impaired right ventricular functional reserve during exercise, characterized by a smaller increase in TRV (ΔTRV: 119.50 vs 89.00, P = .003) and a lower TAPSE/PASP ratio during exercise (0.42 vs 0.65, P = .001) (Table 2 ). Overall, patients with EiPH showed marked reduction in pulmonary vascular reserve and decreased RV–PA coupling efficiency under exercise stress, which was consistently demonstrated by both invasive hemodynamic assessment and noninvasive exercise echocardiography. Table 1 Comparison of baseline demographics, pulmonary function, and resting/exercise hemodynamic characteristics between patients with and without exercise-induced pulmonary hypertension Variables ALL (N = 86) No Ex PH (N = 41) Ex PH (N = 45) P Demographics Sex, male 35 (37.1%) 23 (34.3%) 12 (63.2%) 0.046 Diagnosis CTEPD 21 (24.4%) 14 (34.1%) 7 (15.6%) 0.007 CTEPH 53 (61.6%) 18 (43.9%) 35 (77.8%) CTD-PAH 8 (9.3%) 7 (17.1%) 1 (2.2%) Resting TRV ≥ 2.8 m/s 4 (4.7%) 2 (4.9%) 2 (4.4%) WHO, FC 0.005 Class I 31 (36.0%) 30 (44.8%) 1 (5.3%) Class II 54 (62.8%) 36 (53.7%) 18 (94.7%) Class III 3 (1.2%) 1 (1.5%) 0 (0.0%) Age, years 1 (1.2%) 0 (0.0%) 1 (1.9%) 0.542 BMI, kg/m 2 25.53 ± 3.63 25.14 ± 3.57 26.47 ± 3.76 0.161 BSA, m² 1.77 ± 0.19 1.74 ± 0.18 1.86 ± 0.19 0.012 6MWD, m 466.81 ± 66.25 461.86 ± 67.18 484.28 ± 61.36 0.195 NT-proBNP, pmol/L 57.65 (40.05, 93.70) 64.60 (41.20, 94.75) 48.10 (38.90, 75.85) 0.263 HR, bpm 75.00 (66.00, 84.00) 75.00 (66.50, 83.50) 75.00 (65.00, 86.00) 0.002 sBP, mmHg 77.00 (71.00, 85.00) 76.00 (71.00, 85.00) 77.00 (71.50, 84.50) 0.011 dBP, mmHg 93.00 (86.00, 98.75) 93.00 (87.00, 98.50) 93.00 (86.00, 98.50) 0.147 SpO 2, % 96.00 (95.00, 98.00) 96.00 (95.00, 98.00) 96.00 (94.00, 97.50) 0.187 Pulmonary Function Test FVC, % pred 107.67 ± 16.30 109.19 ± 15.25 102.29 ± 19.07 0.104 FEV 1, % pred 97.19 ± 15.05 98.76 ± 14.70 91.68 ± 15.38 0.07 FEV 1 %FVC, % 89.47 ± 8.01 89.99 ± 7.55 87.62 ± 9.45 0.258 DLCOc SB, mL/min/mmHg 83.15 (76.42, 91.38) 83.90 (77.75, 89.90) 82.10 (69.50, 95.70) 0.414 DLCOc/VA, mL/min/mmHg/L 90.30 (81.00, 94.70) 89.00 (81.00, 93.56) 90.59 (80.81, 101.00) 0.31 TLC, % pred 98.40 ± 13.10 99.41 ± 12.80 94.82 ± 13.85 0.179 Rest and Exercise Right Heart Catheterization SvO2 72.79 ± 5.52 74.53 ± 5.22 71.21 ± 5.36 0.005 TPR, Wood Units 2.40 (1.84, 3.16) 2.30 (1.76, 2.83) 3.15 (2.26, 3.53) 0.016 Resting mPAP, mmHg 18.00 (15.00, 22.00) 16.00 (14.00, 19.00) 20.00 (16.75, 23.00) 0.002 CVP, mmHg 3.00 (2.00, 5.00) 3.00 (2.00, 4.00) 5.00 (3.50, 5.00) 0.006 RAP, mmHg 3.00 (2.00, 4.00) 3.00 (2.00, 4.00) 3.00 (2.50, 4.50) 0.099 RVP, mmHg 10.38 ± 2.29 9.79 ± 2.11 12.47 ± 1.58 < 0.001 PCWP, mmHg 7.00 (5.25, 8.00) 7.00 (5.00, 8.00) 7.00 (6.00, 9.00) 0.381 CO, L/min 5.48 (4.76, 6.30) 5.43 (4.59, 6.29) 5.90 (5.37, 6.55) 0.115 CI, L/min/m² 3.07 (2.73, 3.60) 3.09 (2.70, 3.63) 3.04 (2.75, 3.56) 0.673 Peak mPAP, mmHg 39.86 ± 10.11 35.88 ± 9.17 43.49 ± 9.63 < 0.001 ΔmPAP/ΔCO, mmHg/L/min 2.98 (2.30;3.52) 2.18 (1.56;2.61) 3.49 (3.16;4.06) < 0.001 mPAP/CO Slope, mmHg/L/min 2.73 (2.06;3.29) 2.05 (1.49;2.41) 3.27 (3.01;3.95) < 0.001 6MWD: 6, minute walk distance; BMI: body mass index; BSA: body surface area; CI: cardiac index; CO: cardiac output; CVP: central venous pressure; CTD-PAH: Connective Tissue Disease-associated Pulmonary Arterial Hypertension; CTEPD: Chronic Thromboembolic Pulmonary Disease; CTEPH: Chronic Thromboembolic Pulmonary Hypertension; dBP: diastolic blood pressure; DLCOc SB: diffusing capacity of the lung for carbon monoxide corrected for hemoglobin, single breath; DLCOc/VA: transfer coefficient of the lung for carbon monoxide corrected for hemoglobin; FEV1: forced expiratory volume in 1 second; FEV1/FVC: ratio of forced expiratory volume in 1 second to forced vital capacity; FVC: forced vital capacity; HR: heart rate; mPAP: mean pulmonary artery pressure; mPAP/CO slope: slope of the mean pulmonary artery pressure–cardiac output relationship; NT, proBNP: N, terminal pro, brain natriuretic peptide; PCWP: pulmonary capillary wedge pressure; RAP: right atrial pressure; RVP: right ventricular pressure; sBP: systolic blood pressure; SpO2: peripheral oxygen saturation; SvO 2 : Mixed Venous Oxygen Saturation; TLC: total lung capacity; TPR: total pulmonary resistance; WHO, FC: World Health Organization functional class; Δ: change from rest to peak exercise. Table 2 Comparison of transthoracic echocardiographic, stress echocardiographic, and cardiopulmonary exercise testing parameters between patients with and without exercise-induced pulmonary hypertension Variables ALL (N = 86) No Ex PH (N = 41) Ex PH (N = 45) P Transthoracic Echocardiography EDV, mL 98.18 ± 14.74 97.65 ± 15.22 100.05 ± 13.12 0.533 ESV, mL 31.31 ± 6.54 30.79 ± 6.56 33.16 ± 6.30 0.165 LVEF, % 68.12 ± 4.71 68.48 ± 4.70 66.84 ± 4.63 0.183 E’, cm/s 7.00 (6.00, 8.00) 7.00 (6.10, 8.00) 6.50 (5.65, 9.00) 0.867 RV basal diameter, mm 33.00 (31.00, 35.00) 33.00 (31.00, 35.00) 35.00 (30.50, 36.50) 0.649 MPA, mm 27.00 (24.00, 30.00) 26.00 (24.00, 28.50) 29.00 (25.00, 31.50) 0.123 RVEDA, cm² 22.12 ± 4.78 22.04 ± 4.93 22.42 ± 4.32 0.759 RVESA, cm² 13.87 ± 3.63 13.77 ± 3.78 14.19 ± 3.11 0.663 FAC, % 0.37 ± 0.10 0.37 ± 0.11 0.37 ± 0.06 0.811 MPI 0.50 (0.41, 0.57) 0.48 (0.40, 0.56) 0.52 (0.45, 0.57) 0.525 Stress Echocardiography Resting TAPSE, mm 21.00 (19.79, 22.79) 21.20 (19.82, 23.00) 20.79 (19.82, 21.50) 0.715 Resting S’, cm/s 13.00 (12.00, 15.38) 13.00 (11.95, 15.95) 12.80 (12.00, 14.35) 0.531 Resting TRV, m/s 213.83 ± 57.40 223.50 ± 55.84 179.70 ± 50.47 0.003 Resting RVFWSL, % -22.14 ± 4.06 -21.89 ± 4.15 -23.05 ± 3.68 0.271 Resting RV4CSL, % -18.95 (-21.35, 15.70) -18.60 (-20.55, 15.60) -19.15 (-23.95, 17.45) 0.079 Resting RASr, % 21.75 (18.30, 28.57) 20.30 (17.90, 25.70) 24.00 (21.70, 35.30) 0.018 Resting RAScd, % -12.55 (-17.62, 9.03) -11.40 (-18.25, 8.65) -14.00 (-16.65, 11.70) 0.096 Resting RASct, % -10.18 ± 4.72 -9.48 ± 4.36 -12.66 ± 5.19 0.009 Resting TAPSE/PASP, mm/mmHg 1.09 (0.81–1.82) 1.04 (0.77–1.60) 1.74 (0.99–2.41) 0.009 Peak TAPSE, mm 23.79 ± 3.23 23.83 ± 3.18 23.64 ± 3.49 0.823 Peak S’, cm/s 19.41 ± 4.34 19.56 ± 4.34 18.88 ± 4.38 0.551 Peak TRV, m/s 336.53 (256.50, 378.70) 343.00 (250.35, 379.50) 307.00 (270.77, 363.15) 0.929 Peak RVFWSL, % -24.25 ± 5.03 -24.56 ± 5.03 -23.17 ± 5.01 0.292 Peak RV4CSL, % -20.49 (-23.70, 17.15) -20.38 (-24.15, 17.30) -20.60 (-23.15, 17.70) 1 Peak RASr, % 26.05 (20.92, 30.32) 25.90 (20.95, 30.25) 26.30 (21.30, 33.70) 0.815 Peak RAScd, % -14.07 (-16.00, 10.50) -14.30 (-16.10, 10.50) -12.50 (-14.10, 10.30) 0.423 Peak RASct, % -12.20 (-16.00, 9.62) -11.80 (-15.00, 9.80) -13.02 (-17.85, 9.35) 0.371 Peak TAPSE/PASP, mm/mmHg 0.51 (0.40, 0.83) 0.51 (0.40, 0.96) 0.65 (0.40, 0.78) 0.967 ΔTRV, m/s 104.89 (64.75, 131.23) 95.30 (62.00, 124.15) 128.00 (85.05, 189.05) 0.024 ΔTAPSE, mm 3.00 (0.19, 5.00) 3.00 (-0.20, 5.00) 2.76 (1.27, 4.73) 0.929 ΔRASct, % -2.95 ± 6.26 -3.36 ± 5.74 -1.53 ± 7.82 0.262 ΔRAScd, % 0.03 ± 8.33 -0.52 ± 8.81 1.98 ± 6.17 0.251 ΔRASr, % 4.45 (-0.90, 7.50) 5.10 (0.10, 8.25) 4.00 (-4.70, 5.15) 0.07 ΔRV4CSL, % -1.19 (-6.00, 2.10) -1.50 (-6.40, 2.10) -1.08 (-3.90, 3.05) 0.385 ΔRVFWSL, % -2.11 ± 6.10 -2.67 ± 6.05 -0.12 ± 6.04 0.108 ΔTAPSE/PASP, mm/mmHg -0.44 (-0.95, 0.28) -0.39 (-0.82, 0.24) -0.87 (-1.56, 0.54) 0.003 ΔS’, cm/s 5.81 ± 4.43 5.82 ± 4.44 5.76 ± 4.54 0.956 Cardiopulmonary Exercise Testing PRP, % 0.93 (0.75, 1.05) 0.98 (0.83, 1.07) 0.80 (0.73, 0.95) 0.023 VO 2 max, mL/min 1098.00 (915.25, 1334.75) 1048.00 (905.00, 1314.50) 1159.00 (989.00, 1342.00) 0.441 VO 2 peak %pred, % 70.64 ± 13.36 72.85 ± 12.79 62.84 ± 12.65 0.003 VO 2 (AT) %pred, % 44.62 ± 10.85 45.70 ± 10.84 40.79 ± 10.26 0.081 Peak HR, bpm 133.59 ± 21.24 145.17 ± 18.79 123.04 ± 17.67 < 0.001 VE/VCO 2 52.19 (47.60, 61.38) 52.30 (47.20, 59.45) 51.50 (47.70, 67.95) 0.338 VE/VO 2 0.93 (0.75, 1.05) 0.98 (0.83, 1.07) 0.80 (0.73, 0.95) 0.023 %pred: percentage of predicted; AT: anaerobic threshold; E’: early diastolic mitral annular velocity; EDV: end-diastolic volume; ESV: end-systolic volume; FAC: fractional area change; HR: heart rate; LVEF: left ventricular ejection fraction; MPA: main pulmonary artery; MPI: myocardial performance index; PASP: pulmonary artery systolic pressure; PRP: Predicted Work Rate Percentage; RAScd: right atrial conduit strain; RASct: right atrial contractile strain; RASr: right atrial reservoir strain; RV: right ventricular; RV4CSL: right ventricular 4-chamber longitudinal strain; RVEDA: right ventricular end-diastolic area; RVESA: right ventricular end-systolic area; RVFWSL: right ventricular free wall longitudinal strain; S’: peak systolic tricuspid annular velocity; TAPSE: tricuspid annular plane systolic excursion; TRV: tricuspid regurgitation velocity; VE/VCO2: ventilatory equivalent for carbon dioxide; VE/VO2: ventilatory equivalent for oxygen; VO2max: maximal oxygen consumption; Δ: change from rest to peak exercise. Feature Selection After Spearman correlation analysis to remove highly correlated variables, 48 candidate features were retained for model development. According to the absolute values of the least absolute shrinkage and selection operator (LASSO) coefficients, the six most important predictors were peak heart rate, age, VE/VO₂, peak TAPSE/PASP, ΔTRV, and VO₂max (e-Figure 1–4). Peak heart rate showed the strongest negative association, whereas age was the most important positive predictor. The selected variables covered clinical characteristics, echocardiographic structural and functional indices, and cardiopulmonary metabolic parameters, suggesting that structural remodeling, impaired RV–PA coupling, and reduced exercise cardiopulmonary reserve jointly contributed to the prediction of EiPH. Model Performance Evaluation With stepwise integration of multimodal parameters, diagnostic performance for EiPH improved significantly and consistently (Fig. 2 A–F). Single-modality models showed only moderate discrimination (area under the curve [AUC] 0.794–0.807). After adding exercise echocardiographic parameters (clinical + echocardiography), diagnostic accuracy improved, with an AUC of 0.800. The full multimodal model (clinical + echocardiography + CPET) demonstrated the best performance, with the highest AUC (0.882; 95% CI, 0.820–0.964) and overall accuracy (0.867), along with the lowest Brier score (0.137), confirming excellent discrimination and calibration for identification of EiPH (Table 3 ). Calibration plots showed that the combined clinical + echocardiography + CPET model was closest to the ideal calibration line, indicating optimal agreement between predicted probability and observed outcomes (e-Figure 5). e-Table 2 summarizes the use of all models. As shown in Fig. 3 , the multimodal model generated individualized probability scores based on noninvasive parameters, accurately stratifying risk and correctly distinguishing a true-positive EiPH case (P = 0.971) from a true-negative case (P = 0.162). Table 3 Diagnostic performance of single-modality and multi-modality models for predicting exercise-induced pulmonary hypertension Model Cutoff AUC (95% CI) Sensitivity Specificity Accuracy PPV NPV Brier Score Clinical 0.571 0.794 (0.696, 0.887) 0.756 0.780 0.767 0.791 0.744 0.182 Echo 0.618 0.717 (0.584, 0.827) 0.622 0.780 0.698 0.757 0.653 0.217 CPET 0.544 0.807 (0.721, 0.895) 0.756 0.780 0.767 0.791 0.744 0.177 Clinical+Echo 0.543 0.819 (0.756, 0.912) 0.800 0.756 0.779 0.783 0.775 0.168 Clin+Echo+CPET 0.539 0.882 (0.820, 0.964) 0.867 0.805 0.837 0.830 0.846 0.137 AUC: area under the receiver operating characteristic curve; CI: confidence interval; Clin: clinical; CPET: cardiopulmonary exercise testing; Echo: echocardiography; NPV: Negative Predictive Value; PPV: Positive Predictive Value. Reclassification analysis confirmed the incremental value of multimodal integration (Table 4 ). Adding exercise echocardiographic parameters to the baseline clinical model improved risk classification (net reclassification improvement [NRI] 0.411, P = .098; integrated discrimination improvement [IDI] 0.053, P = .006). Further addition of CPET variables resulted in an additional significant improvement in predictive accuracy (NRI 0.886, P = .014; IDI 0.126, P < .001). Table 4 Incremental value of echocardiography and cardiopulmonary exercise testing parameters in predicting exercise-induced pulmonary hypertension Comparison Step NRI (95% CI) P IDI (95% CI) P Clinical vs. Clinical+Echo Adding Echo 0.411 (-0.080, 0.991) 0.098 0.053 (0.005, 0.182) 0.006 Clinical+Echo vs. Clin+Echo+CPET Adding CPET 0.886 (0.211, 1.267) 0.014 0.126 (0.029, 0.297) < 0.001 CI: confidence interval; Clin: clinical; CPET: cardiopulmonary exercise testing; Echo: echocardiography; IDI: integrated discrimination improvement; NRI: net reclassification improvement. To evaluate the generalizability and robustness of the full multimodal model (clinical + echocardiography + CPET), prespecified subgroup analyses were performed according to resting mPAP, sex, and age (Table 5 ). The model maintained consistently high diagnostic performance across all subgroups, with AUC values ranging from 0.821 to 0.928. No significant interaction was observed between model performance and resting mPAP category (≤ 20 vs 21–24 mmHg, P = .895), sex (P = .404), or age (< 60 vs ≥ 60 years, P = .207). Notably, the model showed excellent discrimination in patients with normal resting mPAP (≤ 20 mmHg; AUC 0.899), in women (AUC 0.901), and in older individuals (AUC 0.856). Table 5 Subgroup analysis of the multimodal model’s performance for predicting exercise-induced pulmonary hypertension Subgroup N AUC (95% CI) P Resting mPAP 21-24mmHg 19 0.888 (0.700, 1.000) 0.895 Resting mPAP ≤ 20mmHg 67 0.899 (0.818, 0.962) Male 35 0.879 (0.737, 0.977) 0.404 Female 51 0.901 (0.805, 0.973) <60 years 41 0.804 (0.650, 0.936) 0.207 ≥ 60 years 45 0.856 (0.705, 0.972) AUC: area under the receiver operating characteristic curve; CI: confidence interval; mPAP: mean pulmonary artery pressure. Mechanistic Analysis To explore the pathophysiologic relevance of the selected multimodal predictors, correlations between these features and invasive hemodynamic parameters obtained from right heart catheterization were analyzed (Fig. 4 ). The correlation matrix demonstrated that the predictors were closely linked to the mechanical abnormalities underlying pulmonary hypertension. Peak heart rate and age showed the strongest associations with key indicators of pulmonary vascular reserve, including mPAP/CO slope and ΔmPAP/ΔCO (P < .001). ΔTRV was strongly correlated with peak mPAP (P < .001). VE/VO₂ was significantly associated with multiple hemodynamic variables, including TPR, peak mPAP, and mPAP/CO slope, suggesting that ventilatory efficiency reflects the overall integrity of RV–PA coupling. Spearman correlation network analysis was performed to evaluate interactions among predictors. The results revealed a highly integrated physiologic network in which peak TAPSE/PASP, age, and peak heart rate served as central nodes (Fig. 5 ). A significant negative correlation was observed between peak TAPSE/PASP and peak heart rate, indicating an intrinsic link between impaired RV–PA coupling (reduced functional reserve) and chronotropic incompetence during exercise. Age showed a distinct association pattern, with a positive correlation with VO₂max and negative correlations with peak TAPSE/PASP and peak heart rate. These findings suggest that the predictive performance of the final multimodal model results from synergistic integration of multiple physiologic domains, including myocardial mechanical efficiency, ventilatory inefficiency (VE/VO₂), and exercise reserve, rather than from any single parameter alone. Discussion This study demonstrates that a novel noninvasive multimodal prediction model integrating clinical characteristics, exercise echocardiography, and cardiopulmonary exercise testing provides excellent diagnostic accuracy for identifying exercise-induced pulmonary hypertension. Through systematic screening of a large number of candidate variables, six key predictors were ultimately selected, collectively reflecting the progressive physiologic deterioration inherent to EiPH. Stepwise integration of different modalities significantly improved predictive performance, and the final combined model showed excellent discrimination and calibration. In addition, a clear dose–response relationship was observed between the predicted probability generated by the model and the number of invasive diagnostic criteria fulfilled by each patient, further supporting the validity of the model across the continuous spectrum of pulmonary vascular disease severity. The core predictors identified in this study span clinical, structural, and metabolic domains, providing important insight into the pathophysiology of EiPH. During exercise, the healthy right ventricle augments contractile function to accommodate increased venous return and mild elevation in pulmonary vascular resistance, thereby maintaining RV–PA coupling [ 15 ]. In contrast, patients with EiPH in our cohort demonstrated early exhaustion of contractile reserve. This impairment was detectable noninvasively as a reduced change in right ventricular free-wall longitudinal strain (ΔRVFWSL) together with an abnormal increase in tricuspid regurgitation velocity during exercise (ΔTRV) [ 16 , 17 ]. More importantly, a marked reduction in the TAPSE/PASP ratio at peak exercise indicated an inability of the right ventricle to adapt to disproportionate increases in afterload, reflecting dynamic RV–PA uncoupling [ 18 ]. These noninvasive structural and functional indices were strongly correlated with invasive hemodynamic abnormalities, particularly the mPAP/CO slope and total pulmonary resistance, and are consistent with recent concepts emphasizing the identification of maladaptive right ventricular responses during exercise right heart catheterization [ 19 , 20 ]. Beyond structural uncoupling, this study highlights the important role of autonomic dysfunction and impaired cardiopulmonary metabolic regulation in EiPH. Peak heart rate showed the strongest negative predictive effect in the model, indicating that chronotropic incompetence is common in patients with EiPH. In the presence of a stiff pulmonary vascular bed, an inadequate heart rate response limits the ability to increase cardiac output during exercise and consequently reduces systemic oxygen delivery [ 21 ]. Failure of an appropriate chronotropic response leads to insufficient circulatory adaptation and worsens the mismatch between circulatory demand and pulmonary vascular capacity. The combined effects of autonomic dysregulation and right ventricular dysfunction were reflected metabolically by a significant reduction in maximal oxygen uptake (VO₂max) [ 22 , 23 ]. In addition, a decrease in ventilatory equivalent for oxygen (VE/VO₂) indicated increased ineffective ventilation, a typical feature of early pulmonary vascular disease [ 24 ]. Network analysis further revealed a closely interconnected physiologic cluster consisting of chronotropic incompetence, right ventricular systolic dysfunction, and reduced ventilatory efficiency, supporting the concept that EiPH represents a disorder of the entire cardiopulmonary unit rather than an isolated vascular abnormality. The clinical implications of this multimodal strategy are particularly relevant for early screening and risk stratification in high-risk populations. Identifying pulmonary vascular disease before resting mPAP exceeds 20 mmHg remains a major challenge in clinical practice. Similar to previous reports, patients with EiPH already demonstrate abnormal RV–PA coupling and impaired exercise hemodynamic reserve [ 10 , 25 ]. Notably, in the subgroup analysis of the present study, the multimodal model maintained stable diagnostic performance across different age groups, including patients ≥ 60 years of age. In older individuals, reduced pulmonary vascular compliance, impaired left ventricular diastolic function, and lower exercise tolerance make the interpretation of exercise-induced increases in pulmonary arterial pressure more complex and may hinder early detection of pulmonary vascular disease [ 26 , 27 ]. However, the good performance of the present model in older patients suggests that integration of clinical characteristics, exercise echocardiography, and exercise physiology data may help distinguish age-related physiologic changes from true impairment of pulmonary vascular reserve, thereby improving the feasibility and accuracy of early screening in elderly populations. Differences in feature associations under different hemodynamic definitions may reflect distinct physiologic manifestations of early pulmonary vascular dysfunction during exercise, particularly those related to RV–PA interaction and cardiopulmonary reserve. During normal exercise, the pulmonary circulation adapts to increased cardiac output through vascular recruitment and distension, maintaining relatively low afterload and preserving RV–PA coupling [ 6 , 28 ]. When pulmonary vascular reserve is impaired, the increase in mPAP becomes disproportionate to flow, resulting in an elevated mPAP/CO slope that can be detected by exercise right heart catheterization [ 29 ]. In this context, the mPAP/CO slope derived from multipoint exercise measurements reflects pulmonary vascular behavior across a wide range of flow conditions, whereas peak mPAP and total pulmonary resistance primarily represent hemodynamics at maximal workload and may fail to detect early abnormalities5. From the perspective of RV–PA coupling, an increased pressure–flow slope indicates excessive afterload during exercise, requiring greater augmentation of right ventricular contractility to maintain forward flow [ 30 , 31 ]. When right ventricular contractile reserve is limited, abnormal hemodynamic slopes may occur even when peak pressures remain within the normal range [ 32 ]. Therefore, different exercise hemodynamic criteria may identify different stages along the continuum of pulmonary vascular dysfunction, ranging from early loss of vascular compliance to overt RV–PA uncoupling and impaired right ventricular reserve. Several limitations should be acknowledged. First, this was a single-center study with a relatively small sample size, and the generalizability of the findings requires validation in larger, multicenter cohorts. Second, the three diagnostic criteria used for exercise-induced pulmonary hypertension were based on peak hemodynamic parameters and pressure–flow relationships, and these definitions may reflect different physiologic stages of pulmonary vascular abnormality. A universally accepted standard is still lacking, which may influence evaluation of model performance. Third, the multimodal prediction strategy incorporated clinical variables, exercise echocardiographic parameters, and exercise physiology data; although the model performed well in the present cohort, its applicability across different age groups, especially in elderly patients, may be affected by comorbid cardiopulmonary disease, diastolic dysfunction, and differences in physical capacity. Finally, this was a cross-sectional analysis without long-term follow-up, and it remains unclear whether the exercise hemodynamic abnormalities predicted by the model are associated with future development of resting pulmonary hypertension or adverse clinical outcomes. Conclusion This study confirms that a noninvasive multimodal model integrating clinical characteristics, exercise echocardiography, and cardiopulmonary exercise testing can accurately and reliably identify exercise-induced pulmonary hypertension, with particular advantage in patients with borderline resting pulmonary arterial pressure. The model captures the continuous pathophysiologic progression from increased pulmonary vascular load to impaired RV–PA coupling and provides a reliable framework for early screening and risk stratification of pulmonary vascular dysfunction. Abbreviations AUC Area under the curve CI Cardiac index CO Cardiac output CPET Cardiopulmonary exercise testing CTEPH Chronic thromboembolic pulmonary hypertension Echo Echocardiography EiPH Exercise-induced pulmonary hypertension FAC Fractional area change HR Heart rate IDI Integrated discrimination improvement LASSO Least absolute shrinkage and selection operator mPAP Mean pulmonary arterial pressure NRI Net reclassification improvement PASP Pulmonary arterial systolic pressure PH Pulmonary hypertension PVR Pulmonary vascular resistance RAScd Right atrial conduit strain RASct Right atrial contraction strain RASr Right atrial reservoir strain RHC Right heart catheterization RV Right ventricle RVEDA Right ventricular end-diastolic area RVESA Right ventricular end-systolic area RVFWS Right ventricular free-wall strain RV–PA Right ventricle–pulmonary artery SD Standard deviation TAPSE Tricuspid annular plane systolic excursion TPR Total pulmonary resistance TRV Tricuspid regurgitation velocity VE/VCO₂ Ventilatory equivalent for carbon dioxide VE/VO₂ Ventilatory equivalent for oxygen VO₂max Peak oxygen uptake WHO World Health Organization Declarations Ethics approval and consent to participate This prospective study was approved by the Institutional Ethics Committee of Beijing Chaoyang Hospital, and all patients signed informed consent forms (approval number 2023-KE-264). The study was conducted in strict accordance with the ethical standards of the institutional research committee, the 1964 Declaration of Helsinki and its later amendments, or comparable ethical standards. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding Supported by Beijing Research Ward Excellence Program (No.BRWEP2024W112030103);the National Natural Science Foundation of China (No.82572244)༛Beijing Municipal Administration of Hospitals Incubating Program (No. Z221100007422028). Author Contribution 1. Conception and design: XPD, YDL. (II) Administrative support: YDL, XPD. (III) Provision of study materials or patients: JNG, SQY, YHY,QMX. (IV) Collection and assembly of data: XPD, RF, JWZ. (V) Data analysis and interpretation: XPD, YDL, JYH, RF, DCG, RF. (VI) Manuscript writing: All authors. (VII) Final approval of manuscript: All authors. 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Diagnostic, prognostic and differential-diagnostic relevance of pulmonary haemodynamic parameters during exercise: a systematic review. Eur Respir J. 2022;60(4):2103181. 10.1183/13993003.03181-2021 . Wright SP, Granton JT, Esfandiari S, Goodman JM, Mak S. The relationship of pulmonary vascular resistance and compliance to pulmonary artery wedge pressure during submaximal exercise in healthy older adults. J Physiol. 2016;594(12):3307–15. 10.1113/JP271788 . Westerhof N, Lankhaar J-W, Westerhof BE. The arterial Windkessel. Med Biol Eng Comput. 2009;47(2):131–41. 10.1007/s11517-008-0359-2 . Kovacs G, Herve P, Barbera JA, et al. An official European Respiratory Society statement: pulmonary haemodynamics during exercise. Eur Respir J. 2017;50(5):1700578. 10.1183/13993003.00578-2017 . Martens P, Herbots L, Timmermans P, et al. Cardiopulmonary Exercise Testing with Echocardiography to Identify Mechanisms of Unexplained Dyspnea. 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Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 27 Mar, 2026 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. 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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-9243345","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617878963,"identity":"1714f839-0714-47ce-867a-c5852f7a2e51","order_by":0,"name":"Xinpeng Dai","email":"","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinpeng","middleName":"","lastName":"Dai","suffix":""},{"id":617878967,"identity":"4a2da63c-50c5-4d68-a5c0-ca5c6c1dbe78","order_by":1,"name":"Rui Fan","email":"","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Fan","suffix":""},{"id":617878971,"identity":"56ce56d1-e621-4128-b74f-99a2cf7ae10c","order_by":2,"name":"Junwei Zhang","email":"","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junwei","middleName":"","lastName":"Zhang","suffix":""},{"id":617878972,"identity":"763e18ba-d182-489e-a1db-dc8898a3a266","order_by":3,"name":"Dichen Guo","email":"","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dichen","middleName":"","lastName":"Guo","suffix":""},{"id":617878978,"identity":"693409c6-ec51-4dc0-aaa0-2a46f2911fad","order_by":4,"name":"Qiumeng Xi","email":"","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiumeng","middleName":"","lastName":"Xi","suffix":""},{"id":617878982,"identity":"c0165773-30f3-4546-bc9c-0a74d8a1bd31","order_by":5,"name":"Jiayi He","email":"","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"He","suffix":""},{"id":617878984,"identity":"0c879332-7d82-4c85-88a1-10c48ffca0b2","order_by":6,"name":"Juanni Gong","email":"","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juanni","middleName":"","lastName":"Gong","suffix":""},{"id":617878985,"identity":"08ff10fe-b7cb-409d-992c-a03439e48d85","order_by":7,"name":"Suqiao Yang","email":"","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Suqiao","middleName":"","lastName":"Yang","suffix":""},{"id":617878986,"identity":"18c82ba7-3097-4e86-ac2e-a1a371ba1c6f","order_by":8,"name":"Yuanhua Yang","email":"","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanhua","middleName":"","lastName":"Yang","suffix":""},{"id":617878990,"identity":"8d0f5ef1-a327-493c-9608-918ee3f3dd7b","order_by":9,"name":"Yidan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYDACZgYDIGnBw8/MfPgBKVokeCTb2dIMiLUHrIXB4DyPggRx6o8zb/xcwCAhY3yYB6i5xiaasJbDbMXSM4AOMzvMe+ABw7G03AbCWngMpHnAWvgSDBgbDhOlxfg3SItxM4+BBLFazMC2GDATq0XyMFuZNUiLxGFgICcQ4xe+84c33+ZhsLHn7z98+MGHGhvCWhQOAAnGf1BeAiHlICBP0NBRMApGwSgYBQDnxTLIYa0LgQAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Chaoyang Hospital Affiliated to Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yidan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-03-27 10:09:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9243345/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9243345/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106533990,"identity":"caa91839-7738-49c3-953f-3de4f0a7579f","added_by":"auto","created_at":"2026-04-09 15:01:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98450,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study population selection and grouping according to mPAP and EiPH status. EiPH: exercise-induced pulmonary hypertension; mPAP: mean pulmonary artery pressure;\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9243345/v1/421fca57def883476b10e446.jpeg"},{"id":106724771,"identity":"9ae0c518-bc26-47a4-839b-1209b12001d5","added_by":"auto","created_at":"2026-04-12 18:29:39","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84500,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic performance and predicted probability distributions for the identification of exercise-induced pulmonary hypertension. A, Receiver operating characteristic (ROC) curves illustrating the diagnostic performance of models. B–F, Raincloud plots showing the predicted probability of EiPH across different models: Clinical (B), Echo (C), CPET (D), Clinical + Echo (E), and the full combined model (F). CPET: cardiopulmonary exercise testing; Echo: echocardiography; EiPH: exercise-induced pulmonary hypertension; AUC: area under the curve; CI: confidence interval.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9243345/v1/e9b9cde515b72a0d516c307d.jpeg"},{"id":106533993,"identity":"c38b746e-0ef9-4b6a-98e7-576382503210","added_by":"auto","created_at":"2026-04-09 15:01:19","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254496,"visible":true,"origin":"","legend":"\u003cp\u003eClinical Translation and Representative Case Illustrations of the Multimodal EiPH Predictive Model. (A) The stepwise diagnostic inference workflow. The model calculates the log-odds (Logit(P)) using six non-invasive predictors (Age, SvO2, Peak TAPSE/PASP, ΔTRV, Peak HR, and VE/VO2), converts it to a predicted probability (P), and classifies the patient using an optimal threshold of 0.539. (B) A true-positive case (Patient 1). The multimodal inputs generated a high predicted probability (P = 0.971), correctly predicting EiPH, which was consistent with the invasive gold standard.. (C) A true-negative case (Patient 2). The multimodal inputs generated a low predicted probability (P = 0.162), correctly excluding EiPH, which was consistent with the invasive gold standard. ΔTRV: Change in tricuspid regurgitation velocity from rest to peak exercise; bpm: Beats per minute; cm/s: Centimeters per second; EiPH: Exercise-induced pulmonary hypertension; Exp: Exponential function;\u003c/p\u003e\n\u003cp\u003eHR: Heart rate; Logit(P): Natural logarithm of the odds; mmHg: Millimeters of mercury; \u003cem\u003eP\u003c/em\u003e: Predicted probability; PASP: Pulmonary artery systolic pressure; Peak HR: Peak heart rate during exercise; TAPSE: Tricuspid annular plane systolic excursion; VE/VO2: Ventilatory equivalent for oxygen; VO2max: maximal oxygen consumption.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9243345/v1/ed0d8440f78fa5c0aa11de25.jpeg"},{"id":106533994,"identity":"fc598a06-85ea-4855-8365-0ef83cae4b55","added_by":"auto","created_at":"2026-04-09 15:01:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129943,"visible":true,"origin":"","legend":"\u003cp\u003eBubble plot showing the significance (–log₁₀(P)) of associations between hemodynamic parameters (mPAP/CO Slope, ΔmPAP/ΔCO, TPR, Peak mPAP) and clinical/functional variables. Circle size and color intensity reflect statistical significance; *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001. Δ: change from rest to peak exercise; CO: cardiac output; HR: heart rate; mPAP: mean pulmonary artery pressure; PASP: pulmonary artery systolic pressure; Peak HR: peak heart rate; TAPSE: tricuspid annular plane systolic excursion; TPR: total pulmonary resistance; TRV: tricuspid regurgitation velocity; VE/VO2: ventilatory equivalent for oxygen; VO2max: maximal oxygen consumption.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9243345/v1/18fdcc4cb4944a0cc4e988ce.png"},{"id":106533995,"identity":"08d38aa3-8961-438f-8a35-df2073f716d2","added_by":"auto","created_at":"2026-04-09 15:01:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":113704,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagram showing feature correlations and their associations with target variables. Node color indicates target correlation (r value), node size reflects target correlation strength, edge thickness represents feature correlation magnitude, and line style denotes statistical significance (solid: P \u0026lt; 0.05; dashed: P ≥ 0.05). Bold node borders indicate significant nodes (P \u0026lt; 0.05). Δ: change from rest to peak exercise; HR: heart rate; PASP: pulmonary artery systolic pressure; Peak HR: peak heart rate; TAPSE: tricuspid annular plane systolic excursion; TRV: tricuspid regurgitation velocity; VE/VO₂: ventilatory equivalent for oxygen; VO2max: maximal oxygen consumption.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9243345/v1/eadef5fafbcb1ef62db21891.png"},{"id":107868464,"identity":"8b1b0d19-57f1-4aac-af50-b8bb07d5dd22","added_by":"auto","created_at":"2026-04-27 07:17:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1249547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9243345/v1/ebaf8eb7-7bf7-4069-994e-1a12445d0e01.pdf"},{"id":106724931,"identity":"22018365-08b9-4aea-a138-314d6e3e2da2","added_by":"auto","created_at":"2026-04-12 18:30:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":952475,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9243345/v1/b34d6c133fc4eb2d9a9cbc5d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multimodal Model for Predicting Exercise-induced pulmonary hypertension Validated by Invasive Exercise Hemodynamics: A Prospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary hypertension (PH) is a progressive and highly heterogeneous pulmonary vascular disorder characterized by a sustained increase in right ventricular (RV) afterload, ultimately leading to right heart failure and death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite substantial advances in targeted therapies for PH in recent years, the long-term prognosis remains poor, largely because irreversible structural and functional remodeling of the right ventricle has often occurred by the time of clinical diagnosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Accordingly, current research in PH has increasingly shifted toward the early stages of pulmonary vascular disease. Exercise-induced pulmonary hypertension (EiPH) represents an early disease phenotype in which resting hemodynamics remain within normal limits but abnormal responses become evident during physiologic stress such as exercise [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The 2022 European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines formally reintroduced the hemodynamic definition of EiPH based on an abnormal increase in the slope of mean pulmonary arterial pressure relative to cardiac output (mPAP/CO slope) during exercise [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This revised framework emphasizes the dynamic nature of the pressure\u0026ndash;flow relationship in the pulmonary circulation and highlights early exhaustion of pulmonary vascular reserve together with stress-induced impairment of right ventricle\u0026ndash;pulmonary artery (RV\u0026ndash;PA) coupling [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccurate identification of EiPH remains challenging in routine clinical practice. Invasive exercise right heart catheterization is considered the reference standard for diagnosing EiPH; however, its invasive nature, technical complexity, and high resource utilization limit its use as a screening tool [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Conventional noninvasive methods also have important limitations when used alone. Resting transthoracic echocardiography often fails to detect subtle abnormalities in RV\u0026ndash;PA coupling, and nonspecific symptoms such as exertional dyspnea may overlap with a wide range of cardiopulmonary disorders [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Exercise stress echocardiography can directly assess right ventricular mechanical reserve and dynamic RV\u0026ndash;PA coupling, whereas cardiopulmonary exercise testing (CPET) quantifies ventilatory efficiency and global cardiopulmonary metabolic reserve [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, EiPH is a multidimensional pathophysiologic condition involving abnormal pulmonary hemodynamics, chronotropic incompetence, and impaired metabolic efficiency. A single noninvasive modality therefore provides only a partial view of this dynamic process and may result in underrecognition of subclinical pulmonary vascular dysfunction.\u003c/p\u003e \u003cp\u003eGiven the complexity of EiPH pathophysiology, a multidimensional noninvasive assessment strategy is needed. Recently, Mart\u0026iacute;n et al demonstrated that a multiparametric noninvasive score based on CPET and exercise echocardiography showed promising performance for risk stratification in EiPH [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To further clarify the physiologic relationship between noninvasive predictors and invasive reference standards, the present study performed exercise echocardiography, CPET, and exercise right heart catheterization during the same evaluation period to minimize bias related to nonsynchronous measurements. In addition, right ventricular strain parameters and their dynamic changes during exercise were incorporated into the analysis. On this basis, we aimed to develop and internally validate a noninvasive multimodal prediction model for EiPH, with the goal of providing a reliable tool for early detection and risk stratification of pulmonary vascular dysfunction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eThis was a prospective observational study. Adult patients were consecutively enrolled if they had exercise limitation after chronic pulmonary embolism, increased tricuspid regurgitation velocity on transthoracic echocardiography, or a prior history of mildly elevated pulmonary arterial pressure. All participants underwent a comprehensive evaluation in a clinically stable condition. Exercise right heart catheterization, exercise stress echocardiography, and cardiopulmonary exercise testing (CPET) were performed during the same evaluation period to minimize the influence of physiologic variability. A total of 104 patients were enrolled between January 2024 and December 2025. The study was approved by the Ethics Committee of Beijing Chaoyang Hospital, Capital Medical University (approval number 2023-KE-264), and all participants provided written informed consent.\u003c/p\u003e \u003cp\u003eInclusion criteria were age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, presence of exertional dyspnea, and the ability to complete exercise testing. Exclusion criteria included significant left-sided heart disease (left ventricular ejection fraction\u0026thinsp;\u0026lt;\u0026thinsp;50% or moderate-to-severe valvular disease), severe parenchymal lung disease, acute pulmonary embolism, severe arrhythmia, or contraindications to exercise testing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRHC and exercise haemodynamic assessment\u003c/h3\u003e\n\u003cp\u003eAll participants underwent right heart catheterization via venous access using a Swan\u0026ndash;Ganz catheter. A symptom-limited graded exercise test was performed in the supine position using a cycle ergometer. The exercise load is increased gradually, rising by 30 watts every 2 minutes while maintaining a cadence of 55\u0026ndash;65 revolutions per minute. Hemodynamic data were recorded at each stage until symptom limitation or maximal tolerance was reached [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. At rest and at each exercise stage, the following parameters were continuously measured and recorded simultaneously: central venous pressure, mPAP, pulmonary arterial wedge pressure, right atrial pressure, and right ventricular pressure. Cardiac output was measured using the thermodilution method, with at least three measurements obtained at each stage and averaged. When significant tricuspid regurgitation was present or thermodilution measurements were unreliable, the direct Fick method was used for correction. Pulmonary vascular resistance (PVR) and cardiac index (CI) were calculated at each workload. The mPAP/cardiac output (CO) slope was calculated using linear regression based on multipoint exercise data.\u003c/p\u003e \u003cp\u003eExercise-induced pulmonary hypertension (EiPH) was defined by the presence of at least one of the following invasive hemodynamic criteria: (1) peak exercise mPAP\u0026thinsp;\u0026gt;\u0026thinsp;30 mmHg and peak total pulmonary resistance\u0026thinsp;\u0026gt;\u0026thinsp;3 Wood units; (2) mPAP/CO slope\u0026thinsp;\u0026gt;\u0026thinsp;3 mmHg/L/min calculated from multipoint exercise data; or (3) mPAP/CO slope\u0026thinsp;\u0026gt;\u0026thinsp;3 mmHg/L/min calculated from resting and peak values [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Exercise right heart catheterization served as the reference standard for the diagnosis of EiPH.\u003c/p\u003e\n\u003ch3\u003eExercise stress echocardiography\u003c/h3\u003e\n\u003cp\u003eAll participants underwent exercise stress echocardiography on the same platform during the catheterization procedure. Echocardiography was performed using a Philips EPIQ 7C system with an X5-1 phased-array probe (1\u0026ndash;5 MHz). Apical four-chamber views and continuous-wave Doppler recordings of tricuspid regurgitation were obtained at rest and at each exercise stage. All measurements were averaged over five consecutive cardiac cycles. Echocardiographic analysis was performed offline using TomTec software by investigators blinded to invasive hemodynamic data. Right heart parameters included right ventricular end-diastolic area, right ventricular end-systolic area, fractional area change, tricuspid annular plane systolic excursion (TAPSE), tissue Doppler S\u0026prime; velocity, right ventricular free-wall longitudinal strain, right atrial conduit strain, right atrial reservoir strain, and right atrial contraction strain. Strain analysis was performed using two-dimensional speckle-tracking imaging with adequate frame rates. Measurements were independently performed by two experienced echocardiographers blinded to catheterization results. Peak tricuspid regurgitation velocity (TRV) was measured using continuous-wave Doppler during exercise, and systolic pulmonary arterial pressure (PASP) was estimated using the simplified Bernoulli equation. The TAPSE/PASP ratio was calculated at each stage as a noninvasive index of right ventricleRV\u0026ndash;PA coupling. Dynamic reserve indices included peak exercise values and change values (Δ), defined as the difference between peak and resting measurements, reflecting right ventricular contractile reserve and adaptation to increased pulmonary vascular load.\u003c/p\u003e\n\u003ch3\u003eCardiopulmonary exercise test\u003c/h3\u003e\n\u003cp\u003eAll participants also underwent CPET on the same graded exercise platform with continuous gas exchange analysis and heart rate monitoring. Recorded variables included peak oxygen uptake (VO₂max), percent predicted peak oxygen uptake (% predicted VO₂peak), ventilatory equivalent for carbon dioxide (VE/VCO₂), ventilatory equivalent for oxygen (VE/VO₂), peak heart rate, and achieved workload relative to predicted workload. The anaerobic threshold was determined using the V-slope method combined with ventilatory parameters. CPET data were analyzed by certified personnel blinded to invasive hemodynamic and echocardiographic results.\u003c/p\u003e\n\u003ch3\u003eFeature Selection\u003c/h3\u003e\n\u003cp\u003eTo construct a robust prediction model and minimize overfitting, a multistep feature selection strategy was applied. First, Spearman correlation analysis was performed for all candidate noninvasive variables to assess multicollinearity. For highly correlated variable pairs (|r| \u0026ge; 0.70), the feature with weaker clinical relevance was removed. The least absolute shrinkage and selection operator (LASSO) regression algorithm was then applied to the remaining variables. Ten-fold cross-validation was used to determine the optimal penalty parameter (λ). The 1\u0026ndash;standard error rule was applied to select the simplest model within one standard error of the minimum cross-validation error, allowing coefficients with minimal contribution to shrink to zero.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel Construction and Evaluation\u003c/h2\u003e \u003cp\u003eAfter feature selection, all variables were standardized using Z-score normalization. A binary logistic regression model was then constructed. Three hierarchical models were developed to evaluate incremental diagnostic value: single-modality models (clinical variables, exercise echocardiography, or CPET), dual-modality model (clinical\u0026thinsp;+\u0026thinsp;exercise echocardiography), and a multimodal model (clinical\u0026thinsp;+\u0026thinsp;exercise echocardiography\u0026thinsp;+\u0026thinsp;CPET).\u003c/p\u003e \u003cp\u003eSubgroup analyses were performed to assess model robustness across predefined strata, including baseline resting mPAP\u0026thinsp;\u0026le;\u0026thinsp;20 vs 21\u0026ndash;24 mmHg, sex, and age\u0026thinsp;\u0026lt;\u0026thinsp;60 vs\u0026thinsp;\u0026ge;\u0026thinsp;60 years. Interaction P values were calculated to evaluate heterogeneity in predictive performance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMechanistic Analysis\u003c/h3\u003e\n\u003cp\u003eTo explore the relationship between predictors and hemodynamic mechanisms, a bubble correlation matrix based on Spearman rank correlation coefficients was constructed to evaluate associations between model features and four key hemodynamic variables (peak mPAP, total pulmonary resistance, ΔmPAP/ΔCO, and mPAP/CO slope). A feature association network was generated using Spearman rank correlation coefficients to assess nonlinear relationships among predictors and between predictors and the outcome variable.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (interquartile range), depending on distribution. Categorical variables are expressed as counts and percentages. Normality was assessed using the Shapiro\u0026ndash;Wilk test. Between-group comparisons were performed using the independent-samples t test or Mann\u0026ndash;Whitney U test, and categorical variables were compared using the χ\u0026sup2; test or Fisher exact test. Correlations were evaluated using the Spearman method. Prediction models were constructed using logistic regression without regularization after Z-score standardization. The optimal cutoff value was determined by maximizing the Youden index. The 95% confidence interval of the area under the receiver operating characteristic curve (AUC) was calculated using bootstrap resampling (1,000 iterations) to assess model stability. Discrimination was evaluated using the AUC. Continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to quantify the incremental value of adding modalities. Calibration was assessed using calibration plots and the Brier score. Model calibration was further evaluated using the Hosmer\u0026ndash;Lemeshow test. Subgroup consistency was tested by including interaction terms in multivariable models.\u003c/p\u003e \u003cp\u003eAll tests were two-sided, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 25.0 (IBM) or PyCharm (Python 3.12).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population Characteristics\u003c/h2\u003e \u003cp\u003eAmong all patients, only 19 patients with chronic thromboembolic pulmonary hypertension (CTEPH) had mildly elevated resting mPAP, whereas the remaining patients had normal pulmonary arterial pressure at rest. Compared with patients with resting mPAP\u0026thinsp;\u0026le;\u0026thinsp;20 mmHg, those with mildly elevated mPAP (21\u0026ndash;24 mmHg) had a higher proportion of men (63.2% vs. 34.3%, P = .046) and worse World Health Organization functional class (P = .005). Resting pulmonary function parameters did not differ significantly between the two groups. Resting echocardiography showed that the mildly elevated group had higher resting TRV (P = .003) and higher resting right atrial strain rate (RASr) (P = .018). During exercise echocardiographic assessment, the mildly elevated group showed higher resting TAPSE/PASP (P = .009), a greater increase in TRV (ΔTRV, P = .024), and a more pronounced decrease in ΔTAPSE/PASP (P = .003), suggesting impaired RV\u0026ndash;PA coupling reserve. Cardiopulmonary exercise testing demonstrated lower percent predicted peak oxygen uptake (VO₂ peak % predicted, P = .003) and a lower ratio of achieved to predicted workload (P = .023), indicating reduced exercise capacity. Invasive hemodynamic measurements further confirmed these findings. Total pulmonary resistance (TPR), central venous pressure (CVP), right ventricular pressure (RVP), pulmonary arterial pressure (PAP), peak mPAP, and mPAP/cardiac output (CO) slope were all significantly higher in the mildly elevated group (all P \u0026le; .05), whereas resting CO and cardiac index did not differ significantly. Overall, even at the stage of mildly elevated resting mPAP, patients already exhibited early abnormalities, including increased pulmonary vascular resistance, impaired RV\u0026ndash;PA coupling, and abnormal exercise hemodynamic responses (e-Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eA total of 86 participants were included in the final analysis, including 45 patients with exercise-induced pulmonary hypertension (EiPH) and 41 without EiPH (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At baseline, the EiPH group had a higher proportion of men and worse WHO functional class. Baseline pulmonary function parameters were similar between groups. During exercise, patients with EiPH showed more severe cardiopulmonary impairment, with higher peak mPAP (43.49 vs 35.88 mmHg, P \u0026lt; .001) and a steeper mPAP/CO slope (3.27 vs 2.05 mmHg/L/min, P \u0026lt; .001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Echocardiographic analysis further supported these hemodynamic findings. Although resting parameters were similar between groups, patients with EiPH demonstrated impaired right ventricular functional reserve during exercise, characterized by a smaller increase in TRV (ΔTRV: 119.50 vs 89.00, P = .003) and a lower TAPSE/PASP ratio during exercise (0.42 vs 0.65, P = .001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, patients with EiPH showed marked reduction in pulmonary vascular reserve and decreased RV\u0026ndash;PA coupling efficiency under exercise stress, which was consistently demonstrated by both invasive hemodynamic assessment and noninvasive exercise echocardiography.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline demographics, pulmonary function, and resting/exercise hemodynamic characteristics between patients with and without exercise-induced pulmonary hypertension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL (N\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Ex PH (N\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEx PH (N\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTEPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTEPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (61.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTD-PAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting TRV\u0026thinsp;\u0026ge;\u0026thinsp;2.8 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO, FC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (62.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (94.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.53\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSA, m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6MWD, m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e466.81\u0026thinsp;\u0026plusmn;\u0026thinsp;66.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461.86\u0026thinsp;\u0026plusmn;\u0026thinsp;67.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e484.28\u0026thinsp;\u0026plusmn;\u0026thinsp;61.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP, pmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.65 (40.05, 93.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.60 (41.20, 94.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.10 (38.90, 75.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR, bpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.00 (66.00, 84.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.00 (66.50, 83.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.00 (65.00, 86.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.00 (71.00, 85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.00 (71.00, 85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.00 (71.50, 84.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.00 (86.00, 98.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.00 (87.00, 98.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.00 (86.00, 98.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO\u003csub\u003e2,\u003c/sub\u003e %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.00 (95.00, 98.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.00 (95.00, 98.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.00 (94.00, 97.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePulmonary Function Test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC, % pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107.67\u0026thinsp;\u0026plusmn;\u0026thinsp;16.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.19\u0026thinsp;\u0026plusmn;\u0026thinsp;15.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.29\u0026thinsp;\u0026plusmn;\u0026thinsp;19.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1,\u003c/sub\u003e % pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.19\u0026thinsp;\u0026plusmn;\u0026thinsp;15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.76\u0026thinsp;\u0026plusmn;\u0026thinsp;14.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.68\u0026thinsp;\u0026plusmn;\u0026thinsp;15.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e%FVC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.99\u0026thinsp;\u0026plusmn;\u0026thinsp;7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLCOc SB, mL/min/mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.15 (76.42, 91.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.90 (77.75, 89.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.10 (69.50, 95.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLCOc/VA, mL/min/mmHg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.30 (81.00, 94.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.00 (81.00, 93.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.59 (80.81, 101.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLC, % pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.40\u0026thinsp;\u0026plusmn;\u0026thinsp;13.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.41\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.82\u0026thinsp;\u0026plusmn;\u0026thinsp;13.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRest and Exercise Right Heart Catheterization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSvO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.79\u0026thinsp;\u0026plusmn;\u0026thinsp;5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.53\u0026thinsp;\u0026plusmn;\u0026thinsp;5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPR, Wood Units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40 (1.84, 3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.30 (1.76, 2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15 (2.26, 3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting mPAP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.00 (15.00, 22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.00 (14.00, 19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.00 (16.75, 23.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00 (2.00, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00 (3.50, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00 (2.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00 (2.50, 4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCWP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.00 (5.25, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00 (5.00, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00 (6.00, 9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO, L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.48 (4.76, 6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.43 (4.59, 6.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.90 (5.37, 6.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI, L/min/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.07 (2.73, 3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.09 (2.70, 3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04 (2.75, 3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak mPAP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.86\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.88\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.49\u0026thinsp;\u0026plusmn;\u0026thinsp;9.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔmPAP/ΔCO, mmHg/L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.98 (2.30;3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18 (1.56;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.49 (3.16;4.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emPAP/CO Slope, mmHg/L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73 (2.06;3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05 (1.49;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.27 (3.01;3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e6MWD: 6, minute walk distance; BMI: body mass index; BSA: body surface area; CI: cardiac index; CO: cardiac output; CVP: central venous pressure; CTD-PAH: Connective Tissue Disease-associated Pulmonary Arterial Hypertension; CTEPD: Chronic Thromboembolic Pulmonary Disease; CTEPH: Chronic Thromboembolic Pulmonary Hypertension; dBP: diastolic blood pressure; DLCOc SB: diffusing capacity of the lung for carbon monoxide corrected for hemoglobin, single breath; DLCOc/VA: transfer coefficient of the lung for carbon monoxide corrected for hemoglobin; FEV1: forced expiratory volume in 1 second; FEV1/FVC: ratio of forced expiratory volume in 1 second to forced vital capacity; FVC: forced vital capacity; HR: heart rate; mPAP: mean pulmonary artery pressure; mPAP/CO slope: slope of the mean pulmonary artery pressure\u0026ndash;cardiac output relationship; NT, proBNP: N, terminal pro, brain natriuretic peptide; PCWP: pulmonary capillary wedge pressure; RAP: right atrial pressure; RVP: right ventricular pressure; sBP: systolic blood pressure; SpO2: peripheral oxygen saturation; SvO\u003csub\u003e2\u003c/sub\u003e: Mixed Venous Oxygen Saturation; TLC: total lung capacity; TPR: total pulmonary resistance; WHO, FC: World Health Organization functional class; Δ: change from rest to peak exercise.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of transthoracic echocardiographic, stress echocardiographic, and cardiopulmonary exercise testing parameters between patients with and without exercise-induced pulmonary hypertension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL (N\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Ex PH (N\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEx PH (N\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTransthoracic Echocardiography\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDV, mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.18\u0026thinsp;\u0026plusmn;\u0026thinsp;14.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.65\u0026thinsp;\u0026plusmn;\u0026thinsp;15.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.05\u0026thinsp;\u0026plusmn;\u0026thinsp;13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESV, mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.79\u0026thinsp;\u0026plusmn;\u0026thinsp;6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.16\u0026thinsp;\u0026plusmn;\u0026thinsp;6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u0026rsquo;, cm/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.00 (6.00, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00 (6.10, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.50 (5.65, 9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRV basal diameter, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.00 (31.00, 35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.00 (31.00, 35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.00 (30.50, 36.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.00 (24.00, 30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.00 (24.00, 28.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.00 (25.00, 31.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVEDA, cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.04\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVESA, cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.41, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48 (0.40, 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.45, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eStress Echocardiography\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting TAPSE, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.00 (19.79, 22.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.20 (19.82, 23.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.79 (19.82, 21.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting S\u0026rsquo;, cm/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.00 (12.00, 15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00 (11.95, 15.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.80 (12.00, 14.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting TRV, m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213.83\u0026thinsp;\u0026plusmn;\u0026thinsp;57.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223.50\u0026thinsp;\u0026plusmn;\u0026thinsp;55.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179.70\u0026thinsp;\u0026plusmn;\u0026thinsp;50.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting RVFWSL, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-22.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-21.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-23.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting RV4CSL, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-18.95 (-21.35, 15.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-18.60 (-20.55, 15.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-19.15 (-23.95, 17.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting RASr, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.75 (18.30, 28.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.30 (17.90, 25.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.00 (21.70, 35.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting RAScd, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-12.55 (-17.62, 9.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.40 (-18.25, 8.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.00 (-16.65, 11.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting RASct, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting TAPSE/PASP, mm/mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.81\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.77\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74 (0.99\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak TAPSE, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak S\u0026rsquo;, cm/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak TRV, m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336.53 (256.50, 378.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e343.00 (250.35, 379.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e307.00 (270.77, 363.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak RVFWSL, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-24.56\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-23.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak RV4CSL, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-20.49 (-23.70, 17.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-20.38 (-24.15, 17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20.60 (-23.15, 17.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak RASr, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.05 (20.92, 30.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.90 (20.95, 30.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.30 (21.30, 33.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak RAScd, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-14.07 (-16.00, 10.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-14.30 (-16.10, 10.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.50 (-14.10, 10.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak RASct, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-12.20 (-16.00, 9.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.80 (-15.00, 9.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.02 (-17.85, 9.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak TAPSE/PASP, mm/mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.40, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51 (0.40, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.40, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔTRV, m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.89 (64.75, 131.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.30 (62.00, 124.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128.00 (85.05, 189.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔTAPSE, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00 (0.19, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (-0.20, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.76 (1.27, 4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔRASct, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔRAScd, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔRASr, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.45 (-0.90, 7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.10 (0.10, 8.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 (-4.70, 5.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔRV4CSL, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.19 (-6.00, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.50 (-6.40, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.08 (-3.90, 3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔRVFWSL, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔTAPSE/PASP, mm/mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.44 (-0.95, 0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.39 (-0.82, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.87 (-1.56, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔS\u0026rsquo;, cm/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCardiopulmonary Exercise Testing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRP, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.75, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.83, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.73, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003emax, mL/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1098.00 (915.25, 1334.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1048.00 (905.00, 1314.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1159.00 (989.00, 1342.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003e peak %pred, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.64\u0026thinsp;\u0026plusmn;\u0026thinsp;13.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.84\u0026thinsp;\u0026plusmn;\u0026thinsp;12.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003e(AT) %pred, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.70\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.79\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak HR, bpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.59\u0026thinsp;\u0026plusmn;\u0026thinsp;21.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.17\u0026thinsp;\u0026plusmn;\u0026thinsp;18.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.04\u0026thinsp;\u0026plusmn;\u0026thinsp;17.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVE/VCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.19 (47.60, 61.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.30 (47.20, 59.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.50 (47.70, 67.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVE/VO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.75, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.83, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.73, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e%pred: percentage of predicted; AT: anaerobic threshold; E\u0026rsquo;: early diastolic mitral annular velocity; EDV: end-diastolic volume; ESV: end-systolic volume; FAC: fractional area change; HR: heart rate; LVEF: left ventricular ejection fraction; MPA: main pulmonary artery; MPI: myocardial performance index; PASP: pulmonary artery systolic pressure; PRP: Predicted Work Rate Percentage; RAScd: right atrial conduit strain; RASct: right atrial contractile strain; RASr: right atrial reservoir strain; RV: right ventricular; RV4CSL: right ventricular 4-chamber longitudinal strain; RVEDA: right ventricular end-diastolic area; RVESA: right ventricular end-systolic area; RVFWSL: right ventricular free wall longitudinal strain; S\u0026rsquo;: peak systolic tricuspid annular velocity; TAPSE: tricuspid annular plane systolic excursion; TRV: tricuspid regurgitation velocity; VE/VCO2: ventilatory equivalent for carbon dioxide; VE/VO2: ventilatory equivalent for oxygen; VO2max: maximal oxygen consumption; Δ: change from rest to peak exercise.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFeature Selection\u003c/h2\u003e \u003cp\u003eAfter Spearman correlation analysis to remove highly correlated variables, 48 candidate features were retained for model development. According to the absolute values of the least absolute shrinkage and selection operator (LASSO) coefficients, the six most important predictors were peak heart rate, age, VE/VO₂, peak TAPSE/PASP, ΔTRV, and VO₂max (e-Figure 1\u0026ndash;4). Peak heart rate showed the strongest negative association, whereas age was the most important positive predictor. The selected variables covered clinical characteristics, echocardiographic structural and functional indices, and cardiopulmonary metabolic parameters, suggesting that structural remodeling, impaired RV\u0026ndash;PA coupling, and reduced exercise cardiopulmonary reserve jointly contributed to the prediction of EiPH.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModel Performance Evaluation\u003c/h2\u003e \u003cp\u003eWith stepwise integration of multimodal parameters, diagnostic performance for EiPH improved significantly and consistently (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;F). Single-modality models showed only moderate discrimination (area under the curve [AUC] 0.794\u0026ndash;0.807). After adding exercise echocardiographic parameters (clinical\u0026thinsp;+\u0026thinsp;echocardiography), diagnostic accuracy improved, with an AUC of 0.800. The full multimodal model (clinical\u0026thinsp;+\u0026thinsp;echocardiography\u0026thinsp;+\u0026thinsp;CPET) demonstrated the best performance, with the highest AUC (0.882; 95% CI, 0.820\u0026ndash;0.964) and overall accuracy (0.867), along with the lowest Brier score (0.137), confirming excellent discrimination and calibration for identification of EiPH (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Calibration plots showed that the combined clinical\u0026thinsp;+\u0026thinsp;echocardiography\u0026thinsp;+\u0026thinsp;CPET model was closest to the ideal calibration line, indicating optimal agreement between predicted probability and observed outcomes (e-Figure 5). e-Table\u0026nbsp;2 summarizes the use of all models. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the multimodal model generated individualized probability scores based on noninvasive parameters, accurately stratifying risk and correctly distinguishing a true-positive EiPH case (P\u0026thinsp;=\u0026thinsp;0.971) from a true-negative case (P\u0026thinsp;=\u0026thinsp;0.162).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of single-modality and multi-modality models for predicting exercise-induced pulmonary hypertension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBrier Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.794 (0.696, 0.887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.717 (0.584, 0.827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.807 (0.721, 0.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical+Echo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.819 (0.756, 0.912)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClin+Echo+CPET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.882 (0.820, 0.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAUC: area under the receiver operating characteristic curve; CI: confidence interval; Clin: clinical; CPET: cardiopulmonary exercise testing; Echo: echocardiography; NPV: Negative Predictive Value; PPV: Positive Predictive Value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eReclassification analysis confirmed the incremental value of multimodal integration (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Adding exercise echocardiographic parameters to the baseline clinical model improved risk classification (net reclassification improvement [NRI] 0.411, P = .098; integrated discrimination improvement [IDI] 0.053, P = .006). Further addition of CPET variables resulted in an additional significant improvement in predictive accuracy (NRI 0.886, P = .014; IDI 0.126, P \u0026lt; .001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncremental value of echocardiography and cardiopulmonary exercise testing parameters in predicting exercise-induced pulmonary hypertension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRI (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIDI (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical vs. Clinical+Echo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdding Echo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.411 (-0.080, 0.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053 (0.005, 0.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical+Echo vs. Clin+Echo+CPET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdding CPET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.886 (0.211, 1.267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.126 (0.029, 0.297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCI: confidence interval; Clin: clinical; CPET: cardiopulmonary exercise testing; Echo: echocardiography; IDI: integrated discrimination improvement; NRI: net reclassification improvement.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the generalizability and robustness of the full multimodal model (clinical\u0026thinsp;+\u0026thinsp;echocardiography\u0026thinsp;+\u0026thinsp;CPET), prespecified subgroup analyses were performed according to resting mPAP, sex, and age (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The model maintained consistently high diagnostic performance across all subgroups, with AUC values ranging from 0.821 to 0.928. No significant interaction was observed between model performance and resting mPAP category (\u0026le;\u0026thinsp;20 vs 21\u0026ndash;24 mmHg, P = .895), sex (P = .404), or age (\u0026lt;\u0026thinsp;60 vs\u0026thinsp;\u0026ge;\u0026thinsp;60 years, P = .207). Notably, the model showed excellent discrimination in patients with normal resting mPAP (\u0026le;\u0026thinsp;20 mmHg; AUC 0.899), in women (AUC 0.901), and in older individuals (AUC 0.856).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analysis of the multimodal model\u0026rsquo;s performance for predicting exercise-induced pulmonary hypertension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting mPAP 21-24mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.888 (0.700, 1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResting mPAP \u0026le;\u0026thinsp;20mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.899 (0.818, 0.962)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.879 (0.737, 0.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.901 (0.805, 0.973)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.804 (0.650, 0.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.856 (0.705, 0.972)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAUC: area under the receiver operating characteristic curve; CI: confidence interval; mPAP: mean pulmonary artery pressure.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMechanistic Analysis\u003c/h2\u003e \u003cp\u003eTo explore the pathophysiologic relevance of the selected multimodal predictors, correlations between these features and invasive hemodynamic parameters obtained from right heart catheterization were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The correlation matrix demonstrated that the predictors were closely linked to the mechanical abnormalities underlying pulmonary hypertension. Peak heart rate and age showed the strongest associations with key indicators of pulmonary vascular reserve, including mPAP/CO slope and ΔmPAP/ΔCO (P \u0026lt; .001). ΔTRV was strongly correlated with peak mPAP (P \u0026lt; .001). VE/VO₂ was significantly associated with multiple hemodynamic variables, including TPR, peak mPAP, and mPAP/CO slope, suggesting that ventilatory efficiency reflects the overall integrity of RV\u0026ndash;PA coupling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpearman correlation network analysis was performed to evaluate interactions among predictors. The results revealed a highly integrated physiologic network in which peak TAPSE/PASP, age, and peak heart rate served as central nodes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A significant negative correlation was observed between peak TAPSE/PASP and peak heart rate, indicating an intrinsic link between impaired RV\u0026ndash;PA coupling (reduced functional reserve) and chronotropic incompetence during exercise. Age showed a distinct association pattern, with a positive correlation with VO₂max and negative correlations with peak TAPSE/PASP and peak heart rate. These findings suggest that the predictive performance of the final multimodal model results from synergistic integration of multiple physiologic domains, including myocardial mechanical efficiency, ventilatory inefficiency (VE/VO₂), and exercise reserve, rather than from any single parameter alone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that a novel noninvasive multimodal prediction model integrating clinical characteristics, exercise echocardiography, and cardiopulmonary exercise testing provides excellent diagnostic accuracy for identifying exercise-induced pulmonary hypertension. Through systematic screening of a large number of candidate variables, six key predictors were ultimately selected, collectively reflecting the progressive physiologic deterioration inherent to EiPH. Stepwise integration of different modalities significantly improved predictive performance, and the final combined model showed excellent discrimination and calibration. In addition, a clear dose\u0026ndash;response relationship was observed between the predicted probability generated by the model and the number of invasive diagnostic criteria fulfilled by each patient, further supporting the validity of the model across the continuous spectrum of pulmonary vascular disease severity.\u003c/p\u003e \u003cp\u003eThe core predictors identified in this study span clinical, structural, and metabolic domains, providing important insight into the pathophysiology of EiPH. During exercise, the healthy right ventricle augments contractile function to accommodate increased venous return and mild elevation in pulmonary vascular resistance, thereby maintaining RV\u0026ndash;PA coupling [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In contrast, patients with EiPH in our cohort demonstrated early exhaustion of contractile reserve. This impairment was detectable noninvasively as a reduced change in right ventricular free-wall longitudinal strain (ΔRVFWSL) together with an abnormal increase in tricuspid regurgitation velocity during exercise (ΔTRV) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. More importantly, a marked reduction in the TAPSE/PASP ratio at peak exercise indicated an inability of the right ventricle to adapt to disproportionate increases in afterload, reflecting dynamic RV\u0026ndash;PA uncoupling [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These noninvasive structural and functional indices were strongly correlated with invasive hemodynamic abnormalities, particularly the mPAP/CO slope and total pulmonary resistance, and are consistent with recent concepts emphasizing the identification of maladaptive right ventricular responses during exercise right heart catheterization [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond structural uncoupling, this study highlights the important role of autonomic dysfunction and impaired cardiopulmonary metabolic regulation in EiPH. Peak heart rate showed the strongest negative predictive effect in the model, indicating that chronotropic incompetence is common in patients with EiPH. In the presence of a stiff pulmonary vascular bed, an inadequate heart rate response limits the ability to increase cardiac output during exercise and consequently reduces systemic oxygen delivery [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Failure of an appropriate chronotropic response leads to insufficient circulatory adaptation and worsens the mismatch between circulatory demand and pulmonary vascular capacity. The combined effects of autonomic dysregulation and right ventricular dysfunction were reflected metabolically by a significant reduction in maximal oxygen uptake (VO₂max) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, a decrease in ventilatory equivalent for oxygen (VE/VO₂) indicated increased ineffective ventilation, a typical feature of early pulmonary vascular disease [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Network analysis further revealed a closely interconnected physiologic cluster consisting of chronotropic incompetence, right ventricular systolic dysfunction, and reduced ventilatory efficiency, supporting the concept that EiPH represents a disorder of the entire cardiopulmonary unit rather than an isolated vascular abnormality.\u003c/p\u003e \u003cp\u003eThe clinical implications of this multimodal strategy are particularly relevant for early screening and risk stratification in high-risk populations. Identifying pulmonary vascular disease before resting mPAP exceeds 20 mmHg remains a major challenge in clinical practice. Similar to previous reports, patients with EiPH already demonstrate abnormal RV\u0026ndash;PA coupling and impaired exercise hemodynamic reserve [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Notably, in the subgroup analysis of the present study, the multimodal model maintained stable diagnostic performance across different age groups, including patients\u0026thinsp;\u0026ge;\u0026thinsp;60 years of age. In older individuals, reduced pulmonary vascular compliance, impaired left ventricular diastolic function, and lower exercise tolerance make the interpretation of exercise-induced increases in pulmonary arterial pressure more complex and may hinder early detection of pulmonary vascular disease [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the good performance of the present model in older patients suggests that integration of clinical characteristics, exercise echocardiography, and exercise physiology data may help distinguish age-related physiologic changes from true impairment of pulmonary vascular reserve, thereby improving the feasibility and accuracy of early screening in elderly populations.\u003c/p\u003e \u003cp\u003eDifferences in feature associations under different hemodynamic definitions may reflect distinct physiologic manifestations of early pulmonary vascular dysfunction during exercise, particularly those related to RV\u0026ndash;PA interaction and cardiopulmonary reserve. During normal exercise, the pulmonary circulation adapts to increased cardiac output through vascular recruitment and distension, maintaining relatively low afterload and preserving RV\u0026ndash;PA coupling [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. When pulmonary vascular reserve is impaired, the increase in mPAP becomes disproportionate to flow, resulting in an elevated mPAP/CO slope that can be detected by exercise right heart catheterization [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this context, the mPAP/CO slope derived from multipoint exercise measurements reflects pulmonary vascular behavior across a wide range of flow conditions, whereas peak mPAP and total pulmonary resistance primarily represent hemodynamics at maximal workload and may fail to detect early abnormalities5. From the perspective of RV\u0026ndash;PA coupling, an increased pressure\u0026ndash;flow slope indicates excessive afterload during exercise, requiring greater augmentation of right ventricular contractility to maintain forward flow [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. When right ventricular contractile reserve is limited, abnormal hemodynamic slopes may occur even when peak pressures remain within the normal range [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, different exercise hemodynamic criteria may identify different stages along the continuum of pulmonary vascular dysfunction, ranging from early loss of vascular compliance to overt RV\u0026ndash;PA uncoupling and impaired right ventricular reserve.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, this was a single-center study with a relatively small sample size, and the generalizability of the findings requires validation in larger, multicenter cohorts. Second, the three diagnostic criteria used for exercise-induced pulmonary hypertension were based on peak hemodynamic parameters and pressure\u0026ndash;flow relationships, and these definitions may reflect different physiologic stages of pulmonary vascular abnormality. A universally accepted standard is still lacking, which may influence evaluation of model performance. Third, the multimodal prediction strategy incorporated clinical variables, exercise echocardiographic parameters, and exercise physiology data; although the model performed well in the present cohort, its applicability across different age groups, especially in elderly patients, may be affected by comorbid cardiopulmonary disease, diastolic dysfunction, and differences in physical capacity. Finally, this was a cross-sectional analysis without long-term follow-up, and it remains unclear whether the exercise hemodynamic abnormalities predicted by the model are associated with future development of resting pulmonary hypertension or adverse clinical outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms that a noninvasive multimodal model integrating clinical characteristics, exercise echocardiography, and cardiopulmonary exercise testing can accurately and reliably identify exercise-induced pulmonary hypertension, with particular advantage in patients with borderline resting pulmonary arterial pressure. The model captures the continuous pathophysiologic progression from increased pulmonary vascular load to impaired RV\u0026ndash;PA coupling and provides a reliable framework for early screening and risk stratification of pulmonary vascular dysfunction.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiac index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiac output\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiopulmonary exercise testing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTEPH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic thromboembolic pulmonary hypertension\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEcho\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEchocardiography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEiPH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExercise-induced pulmonary hypertension\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFractional area change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated discrimination improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast absolute shrinkage and selection operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emPAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean pulmonary arterial pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNet reclassification improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePASP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulmonary arterial systolic pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulmonary hypertension\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePVR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulmonary vascular resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAScd\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight atrial conduit strain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRASct\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight atrial contraction strain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRASr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight atrial reservoir strain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight heart catheterization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight ventricle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRVEDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight ventricular end-diastolic area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRVESA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight ventricular end-systolic area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRVFWS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight ventricular free-wall strain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRV\u0026ndash;PA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight ventricle\u0026ndash;pulmonary artery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAPSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTricuspid annular plane systolic excursion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTPR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal pulmonary resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTRV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTricuspid regurgitation velocity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVE/VCO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVentilatory equivalent for carbon dioxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVE/VO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVentilatory equivalent for oxygen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVO₂max\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeak oxygen uptake\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis prospective study was approved by the Institutional Ethics Committee of Beijing Chaoyang Hospital, and all patients signed informed consent forms (approval number 2023-KE-264). The study was conducted in strict accordance with the ethical standards of the institutional research committee, the 1964 Declaration of Helsinki and its later amendments, or comparable ethical standards.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eSupported by Beijing Research Ward Excellence Program (No.BRWEP2024W112030103);the National Natural Science Foundation of China (No.82572244)༛Beijing Municipal Administration of Hospitals Incubating Program (No. Z221100007422028).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1. Conception and design: XPD, YDL. (II) Administrative support: YDL, XPD. (III) Provision of study materials or patients: JNG, SQY, YHY,QMX. (IV) Collection and assembly of data: XPD, RF, JWZ. (V) Data analysis and interpretation: XPD, YDL, JYH, RF, DCG, RF. (VI) Manuscript writing: All authors. (VII) Final approval of manuscript: All authors.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe thank all the patients for their participation and the clinical and research teams for their contributions to data collection and study coordination.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data used and analyzed during this study are included in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMocumbi A, Humbert M, Saxena A, et al. Pulmonary hypertension. 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Hemodynamic function of the right ventricular-pulmonary vascular-left atrial unit: normal responses to exercise in healthy adults. Am J Physiol Heart Circ Physiol. 2021;320(3):H923\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/ajpheart.00720.2020\u003c/span\u003e\u003cspan address=\"10.1152/ajpheart.00720.2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Exercise-induced pulmonary hypertension, Cardiopulmonary exercise testing, Exercise echocardiography, Right ventricular-pulmonary arterial coupling, Multimodal diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-9243345/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9243345/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExercise-induced pulmonary hypertension (EiPH) represents an early stage of pulmonary vascular disease that remains challenging to identify noninvasively, particularly in patients with borderline resting haemodynamics. We aimed to develop and validate a multimodal non-invasive model integrating clinical characteristics, exercise echocardiography, and cardiopulmonary exercise testing (CPET) to accurately predict invasively confirmed EiPH.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this prospective cohort study, consecutive adults with exercise intolerance after chronic thromboembolic disease, increased tricuspid regurgitation velocity on transthoracic echocardiography, or previously documented mildly elevated pulmonary artery pressure were enrolled. All participants underwent comprehensive clinical assessment, resting and exercise echocardiography, CPET, and invasive exercise right heart catheterization as the reference standard. Feature selection was performed using Spearman\u0026rsquo;s correlation analysis and regression with the Least Absolute Shrinkage and Selection (LASSO) operator. Single-modality and multimodal prediction models based on clinical, echocardiographic, and CPET variables were constructed. Model performance was evaluated using receiver operating characteristic (ROC) analysis, net reclassification improvement and integrated discriminant improvement. Prespecified subgroup analyses were performed. Associations between selected predictors and invasive hemodynamic parameters were analyzed to explore underlying pathophysiology.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study included a total of 86 participants, comprising 45 patients with EiPH and 41 patients without EiPH. Compared with the non-EiPH group, EiPH patients had a poorer WHO functional class; during exercise, their peak mPAP was significantly elevated, accompanied by impaired right ventricular reserve and reduced RV-PA coupling efficiency. Six predictors were selected by correlation analysis and LASSO regression: peak heart rate, age, peak tricuspid annular plane systolic excursion to pulmonary artery systolic pressure ratio, ventilatory equivalent for oxygen, maximal oxygen uptake, and change in tricuspid regurgitation velocity. The combined Clinical+Echo+CPET model achieved the best diagnostic performance (AUC: 0.906), with significant improvement in reclassification compared with single- or dual-modality models. Model performance remained consistent across subgroups stratified by resting mPAP, age, and sex. Mechanistic analysis demonstrated strong correlations between selected predictors and invasive haemodynamic parameters.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA noninvasive multimodal model integrating clinical variables, exercise echocardiography, and cardiopulmonary metabolic parameters enables robust identification of exercise-induced pulmonary hypertension and reflects underlying abnormalities in RV\u0026ndash;PA coupling and ventilatory efficiency. This framework may facilitate early detection and risk stratification of occult pulmonary vascular disease.\u003c/p\u003e","manuscriptTitle":"Multimodal Model for Predicting Exercise-induced pulmonary hypertension Validated by Invasive Exercise Hemodynamics: A Prospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 15:01:12","doi":"10.21203/rs.3.rs-9243345/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T10:20:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T08:27:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T20:56:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T09:57:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174699679095671690003658691222954718212","date":"2026-04-05T20:43:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73470325109364210225680629661077163443","date":"2026-04-04T21:21:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268566219225967610660387170105453207927","date":"2026-04-02T22:51:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103409262893971937298084631321280240724","date":"2026-04-02T16:58:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T11:55:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T17:44:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T07:07:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2026-03-27T09:55:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3e27bf6-0aeb-4820-a6aa-031d5a49bc8d","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T17:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 15:01:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9243345","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9243345","identity":"rs-9243345","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}