Pulmonary arterial compliance as a long-term prognostic indicator in pulmonary arterial hypertension associated with adult congenital heart disease: results from a national multicenter prospective registry

Pulmonary arterial compliance as a long-term prognostic indicator in pulmonary arterial hypertension associated with adult congenital heart disease: results from a national multicenter prospective registry | 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 Pulmonary arterial compliance as a long-term prognostic indicator in pulmonary arterial hypertension associated with adult congenital heart disease: results from a national multicenter prospective registry Qimou Li, Hongrui Yu, Changming Xiong, Qing Gu, Zaixin Yu, Yuhao Liu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7036244/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background Pulmonary arterial compliance (PAC) reflects the pulsatile load and predicts outcome in pulmonary arterial hypertension (PAH). The prognostic role of PAC in the heterogeneous patient population of PAH associated with congenital heart disease (PAH-CHD) is poorly defined. This study aimed to explore the prognostic value of PAC in patients with PAH-CHD. Methods Adult patients diagnosed with PAH-CHD were collected from a PAH multicenter prospective registry between August 2009 and December 2019. The primary endpoint was all-cause mortality. Multivariable Cox regression and restricted cubic spline (RCS) analysis were used to evaluate the association between PAC and the primary endpoint. Subgroup and interaction analysis between PAC and shunts or defect characteristics were explored. Incremental predictive performance was evaluated by calculating the C-index, continuous net reclassification improvement, and integrated discrimination improvement. Results A total of 434 adult PAH-CHD patients were enrolled. The median follow-up time was 52.2 months. The survival rate of patients in the lower PAC group was significantly worse than those in the higher PAC group (Log-rank P < 0.001). Multivariable Cox regression analysis showed that PAC independently predicted all-cause mortality after adjustment for other prognostic factors, whether as a continuous variable (HR = 0.665, 95%CI 0.503–0.878, P = 0.004) or a dichotomous variable (HR = 0.251, 95%CI 0.124–0.507, P < 0.001). A linear relationship between PAC and all-cause mortality was identified by RCS analysis. Subgroup analysis revealed that the impact of PAC might be affected by the presence of post-tricuspid shunt. Incorporating PAC into the validated risk models significantly improved the reclassification and discrimination ability for all-cause mortality. Conclusion PAC was significantly associated with all-cause mortality in patients with PAH-CHD and provided additional value on risk assessment. The role of PAC may vary across different clinical subgroups. Trial registration ClinicalTrials.gov (NCT01417338), registered 16th August 2011. Pulmonary arterial compliance congenital heart disease Eisenmenger Syndrome pulmonary arterial hypertension right heart catheterization prognosis risk stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pulmonary arterial hypertension (PAH) is characterized by progressive pulmonary vascular remodeling and elevated pulmonary artery pressure, culminating in right heart failure and death[ 1 , 2 ], which caused approximately 22,000 deaths globally in 2021[ 3 ]. Congenital heart disease (CHD) is one of the most common causes of PAH, accounting for 45.2% of PAH cases in China[ 4 ]. The development of PAH in CHD patients is associated with severe symptoms, poor clinical course and worse prognosis[ 5 – 7 ], with a 10-year survival rate of 73.0%[ 8 ]. Moreover, PAH-CHD is a heterogeneous disease and exhibits significant difference in pathophysiology and survival within different shunt directions and defect types[ 5 , 9 ]. A comprehensive assessment of the pulmonary arterial load is critical to the mortality prediction in PAH and may help to the risk assessment[ 10 – 12 ]. It consists of both static and pulsatile component, evaluated by pulmonary vascular resistance and pulmonary artery compliance (PAC) respectively[ 13 ]. PAC reflects the distensibility of pulmonary vessels and right ventricular (RV) function and changes considerably in CHD patients at an early stage of developing pulmonary hypertension, and it can be estimated empirically using a simpler and accepted method[ 14 , 15 ]. There has been growing evidence of the prognostic value of PAC in idiopathic PAH and PAH associated with connective tissue disease[ 12 , 15 – 18 ]. However, the relation of PAC to the long-term prognosis of PAH-CHD is poorly defined, given the complexity and heterogeneity of the clinical characteristics and pathophysiology of different PAH-CHD subgroups. Early studies reported that decreased PAC related to worse exercise capacity and poor outcome in pediatric and infant populations of PAH-CHD[ 19 – 21 ]. A previous prospective study explored the role of PAC in a PAH-CHD cohort and concluded that lower PAC was associated with poor prognosis[ 22 ]. But they were limited by the concentration on pediatric patients or specific PAH-CHD subgroups, small number of events and single-center design. Moreover, how PAC interacts with different shunts condition and defect characteristics has not been explored yet. Thus, there is a lack of a multicenter cohort study with a larger sample size enrolling different subgroups of PAH-CHD patients that could help to explore the long-term predictive value of PAC across the entire PAH-CHD population. To fill the research gap, based on a national multicenter prospective registry with a larger sample size from China, this study aimed to investigate the long-term prognostic value of PAC in adult PAH-CHD patients and, additionally, its incremental value over validated risk assessment models. Methods Study design and population The details of the study design of the PAH multicenter prospective registry have been described previously[ 4 ]. In brief, patients diagnosed with PAH by right heart catheterization (RHC) were consecutively enrolled in the registry from 34 medical centers in China between August 2009 and December 2019. All enrolled patients had provided written informed consent. The study adhered to the ethical guidelines of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Fuwai Hospital (Approval No.2009 − 208) and was registered on ClinicalTrials.gov (NCT01417338). In this study, the inclusion criteria for the analysis cohort were as follow: (1) patients ≥ 18 years at diagnosis; (2) patients with PAH associated with CHD diagnosed by RHC according to contemporary guidelines[ 23 , 24 ], defined as mean pulmonary arterial pressure ≥ 25mmHg, and pulmonary arterial wedge pressure ≤ 15mmHg at rest by RHC and/or absence of evidence of left heart disease, including enlarged left heart and left heart systolic and/or diastolic dysfunction. Patients meeting any of the exclusion criteria were excluded: (1) absence of PAC data; (2) missing of follow-up information. The primary endpoint of the study was all-cause mortality. Patients were followed up every 6 months by telephone calls, outpatient or inpatient visits until 28 February 2021 or the occurrence of an endpoint. Survival status, treatment strategies and hospitalizations were collected at each follow-up. Follow-up time was recorded from the date of enrollment until the occurrence of death, or the date of study cut-off. Patients who could not be contacted by any of aforementioned methods were defined as lost to follow-up and the censor time was the date of last contact. Measurements and definitions Baseline information for the enrolled patients was collected, including demographics (age, sex), the World Health Organization functional class (WHO-FC), six-minute walking distance (6MWD), laboratory examinations, arterial blood gas analysis, echocardiographic parameters, hemodynamic parameters measured by RHC, and treatment strategies. Details of data collection and quality control have been reported previously[ 4 ]. PAC was calculated as RV stroke volume/(systolic pulmonary arterial pressure – diastolic pulmonary arterial pressure), where stroke volume was equal to pulmonary blood flow/heart rate [ 15 , 19 – 21 ]. Pulmonary vascular resistance (PVR) was calculated as (mean pulmonary arterial pressure – pulmonary arterial wedge pressure)/pulmonary blood flow. Cardiac index (CI) was calculated by dividing the systemic blood flow by the body surface area. Stroke volume index (SVI) was calculated as cardiac index/heart rate. The clinical classifications of patients with PAH-CHD based on the direction of shunting were as follows: (1) Eisenmenger Syndrome: characterized by right-to-left or bidirectional shunt; (2) PAH associated with systemic-to-pulmonary shunts: left-to-right shunting remains predominant; (3) PAH after defect correction: congenital heart disease had been repaired, but PAH either persisted immediately or recurred more than one year after interventional or surgical correction, in the absence of significant post-operative hemodynamic abnormalities[ 4 ]. Monotherapy was defined as the usage of single PAH-targeted drug, while combination therapy was defined as the usage of two or more PAH-targeted drugs. Risk assessment models The baseline three-strata risk model of 2022 European Society of Cardiology (ESC)/European Respiratory Society (ERS) guidelines was applied according to the Kylhammar approach[ 2 , 25 ] using parameters including WHO-FC, 6MWD, NT-proBNP level, mean right atrial pressure (mRAP), CI, SVI, and mixed-venous oxygen saturation (S v O 2 ). The Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) registry three- and four-strata models were applied as described by Hoeper et al.[ 26 ] using WHO-FC, 6MWD, and NT-proBNP level. At least two of the three above variables were available when calculating individual risk scores. If BNP and NT-proBNPwere both available, NT‑proBNP was used. Each variable was assigned a grade, and the mean was calculated by dividing the sum of all grades by the number of variables and rounding to the next integer to determine the risk group. The Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL) 2.0 score was calculated as described by Benza et al.[ 27 ] using the following variables: male > 60 years old, systolic blood pressure, heart rate, WHO-FC, 6MWD, NT-proBNP, estimated glomerular filtration rate, presence of pericardial effusion, and mRAP. The REVEAL scores were used as a continuous scoring system, and missing values were substituted by a score of zero[ 27 ]. REVEAL Lite 2.0 risk was calculated by incorporating NT-proBNP, 6MWD, WHO-FC, systolic blood pressure, heart rate, and estimated glomerular filtration rate[ 28 ]. Statistical analysis Continuous variables were expressed as mean ± standard deviation or median (25th percentile, 75th percentile) and analyzed using independent-sample t test (normally distributed) or Mann-Whitney U test (non-normally distributed). Categorical variables were presented as counts (percentages) and were compared with the chi-square test. Random forest method was used to impute missing data. Restricted cubic spline (RCS) was used to evaluate the relationship between continuous PAC and all-cause mortality. The optimal survival cut-off value of PAC was determined by maximally selected rank statistics as 1.47mL/mmHg, and patients were divided into the higher PAC group (≥ 1.47mL/mmHg) and the lower PAC group (< 1.47mL/mmHg). The cumulative incidence of death of two groups was estimated using Kaplan-Meier curves and compared using the log-rank test. Univariable Cox proportional-hazards regression analyses were used to examine the prognostic relevance of variables including PAC, age, sex, PAH-targeted treatment, 6MWD, WHO-FC, NT-proBNP, mRAP, CI, and S v O 2 , which were selected based on clinical significance and guideline recommendations[ 2 ]. Variables with P < 0.05 were entered into the multivariable Cox regression models using the stepwise selection method based on Akaike information criterion (AIC). PAC was entered into the stepwise multivariable analysis either as a continuous variable in Model 1or as a dichotomous variable (</≥1.47mL/mmHg) in Model 2. NT-proBNP levels were logarithmically transformed to ln (NT-proBNP) in the Cox regression analyses. The multicollinearity of the included variables was tested using the variance inflation factor (VIF) method and VIF < 5 indicated no multicollinearity. Subgroup analyses were conducted to determine the interaction effects, stratified by sex, age, clinical classification, defect types, presence of post-tricuspid shunt, and PAH-targeted treatment strategy, and the P value for the interaction was calculated. The incremental predictive values of PAC over the validated risk assessment models including ESC/ERS 2022 three-strata model, COMPERA three- and four-strata models, REVEAL 2.0, and REVEAL lite 2 score were evaluated by calculating the changes of C-statistic, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The goodness-of-fit of the different risk models after incorporating PAC were assessed using the AIC. All tests were two sided and P < 0.05 was considered statistically significant. All statistical analysis was performed with R 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). Results Baseline characteristics A total of 434 adult patients diagnosed with PAH-CHD were included into analysis (Fig. 1 ). The mean age of included patients was 34.2 years, and 69.6% were female (Table 1 ). Eisenmenger syndrome was the most common clinical classification (45.9%), followed by systemic-to-pulmonary shunts (36.4%) and PAH after defect correction (17.7%). Atrial septal defect was the most common cause (42.4%). 238 patients were classified into the lower PAC group (< 1.47mL/mmHg), while 196 were into the higher PAC group (≥ 1.47mL/mmHg). Compared with those with higher PAC, patients with lower PAC group had a slightly higher proportion of Eisenmenger syndrome and PAH after defect correction, and showed a worse WHO-FC, higher NT-proBNP levels and shorter 6MWD. Echocardiographic RV size was consistent between two groups, whereas a smaller left chamber was overserved in the lower PAC group. Generally, patients in the lower PAC group showed a lower arterial blood oxygen saturation and more deteriorated hemodynamics, including higher pulmonary arterial pressure, PVR and lower CI. 85.9% of patients of the entire cohort received PAH-targeted treatment and the treatment rate in the lower PAC group was significantly higher than that of the higher PAC group (91.1% vs. 79.5%, P = 0.001). Table 1 Baseline characteristics of patients with PAH-CHD. Variable Total (n = 434) PAC < 1.47mL/mmHg (n = 238) PAC ≥ 1.47 mL/mmHg (n = 196) P Sex, n (%) 0.013 Male 132 (30.4%) 60 (25.2%) 72 (36.7%) Female 302 (69.6%) 178 (74.8%) 124 (63.3%) Age (years) 34.2 ± 11.6 33.0 ± 11.2 35.6 ± 12.0 0.024 Systolic blood pressure, mmHg 110 (100, 120) 110 (100, 120) 110 (100, 120) 0.158 Heart rate, bpm 80 (72, 89) 80 (73, 90) 78 (70, 86) 0.010 WHO-FC, n (%) 0.014 I or II 255 (60.3%) 127 (54.7%) 128 (67.0%) III or IV 168 (39.7%) 105 (45.3%) 63 (33.0%) 6MWD, m 419 ± 95.4 401 ± 98.1 440 ± 87.8 < 0.001 NT-proBNP, ng/L 627 (187, 1752) 855 (394, 2436) 348 (123, 1290) < 0.001 eGFR, mL/min/1.73m 2 101.0 ± 25.4 99.1 ± 23.8 103.4 ± 27.1 0.082 Clinical classification, n (%) 0.003 Eisenmenger syndrome 199 (45.9%) 118 (49.6%) 81 (41.3%) Systemic-to-pulmonary shunts 158 (36.4%) 70 (29.4%) 88 (44.9%) PAH after defect correction 77 (17.7%) 50 (21.0%) 27 (13.8%) Defect type, n (%) < 0.001 VSD 132 (30.4%) 78 (32.8%) 54 (27.6%) PDA 57 (13.1%) 45 (18.9%) 12 (6.12%) ASD 184 (42.4%) 89 (37.4%) 95 (48.5%) Other defects 61 (14.1%) 26 (10.9%) 35 (17.9%) Arterial blood gas analysis S a O 2 , % 90.5 ± 8.3 89.2 ± 10.1 92.0 ± 4.9 < 0.001 P a O 2 , mmHg 65.6 ± 16.5 63.2 ± 16.6 68.5 ± 15.9 0.002 P a CO 2 , mmHg 34.7 ± 5.5 34.6 ± 6.4 34.9 ± 4.3 0.496 Echocardiography LAAPD, mm 33.7 ± 7.0 31.9 ± 5.7 35.9 ± 7.8 < 0.001 LVEDD, mm 41.0 ± 8.7 38.9 ± 7.9 43.5 ± 9.0 < 0.001 LVEF, % 64.0 ± 7.0 63.9 ± 8.0 64.1 ± 5.5 0.814 RVAPD, mm 33.0 ± 8.46 33.1 ± 8.0 32.9 ± 9.0 0.871 Presence of pericardial effusion, n (%) 24 (5.8%) 12 (5.3%) 12 (6.2%) 0.630 Right heart catheterization PAC, mL/mmHg 1.36 (0.93, 2.02) 0.97 (0.69, 1.20) 2.14 (1.74, 3.07) < 0.001 S v O 2 , % 71.4 ± 7.7 69.0 ± 8.4 74.3 ± 5.6 < 0.001 mRAP, mmHg 6.36 ± 4.36 5.98 ± 4.01 6.8 ± 4.7 0.061 sPAP, mmHg 100.0 ± 26.6 113.0 ± 22.2 84.8 ± 23.2 < 0.001 dPAP, mmHg 46.2 ± 17.0 52.1 ± 16.8 38.9 ± 14.4 < 0.001 mPAP, mmHg 65.7 ± 19.9 73.8 ± 18.0 55.8 ± 17.6 < 0.001 Qp, L/min 5.80 (4.28, 8.20) 4.46 (3.52, 5.48) 8.50 (6.25, 11.0) < 0.001 Qs, L/min 5.17 (4.20, 6.30) 4.73 (3.85, 5.77) 5.63 (4.58, 7.00) < 0.001 Qp/Qs 1.06 (0.88, 1.50) 0.93 (0.80, 1.10) 1.39 (1.05, 2.02) < 0.001 CI, L/min/m 2 3.72 ± 1.50 3.20 ± 1.04 4.35 ± 1.70 < 0.001 SVI, mL/m 2 46.0 ± 19.6 38.0 ± 13.1 55.5 ± 21.8 < 0.001 PVR, dyn·s/cm 5 824 (599, 1438) 1066 (739, 1591) 506 (332, 646) < 0.001 PAWP, mmHg 8.12 ± 3.38 8.38 ± 3.28 7.35 ± 3.59 0.141 PAH-targeted treatment, n (%) 0.001 None 61 (14.2%) 21 (8.90%) 40 (20.5%) Monotherapy 174 (40.4%) 94 (39.8%) 80 (41.0%) Combination 196 (45.5%) 121 (51.3%) 75 (38.5%) ESC/ERS 2022 three-strata, n (%) < 0.001 Low risk 242 (55.8%) 106 (45.9%) 136 (72.7%) Intermediate risk 171 (39.4%) 120 (51.9%) 51 (27.3%) High risk 5 (1.2%) 5 (2.2%) 0 (0%) COMPERA three-strata, n (%) < 0.001 Low risk 138 (31.8%) 56 (26.3%) 82 (44.8%) Intermediate risk 252 (58.1%) 153 (71.8%) 99 (54.1%) High risk 6 (1.4%) 4 (1.9%) 2 (1.1%) COMPERA four-strata, n (%) < 0.001 Low risk 115 (29.0%) 46 (21.6%) 69 (37.7%) Intermediate-low risk 177 (44.7%) 93 (43.7%) 84 (45.9%) Intermediate-high risk 92 (23.2%) 67 (31.5%) 25 (13.7%) High risk 12 (3.1%) 7 (3.3%) 5 (2.7%) REVEAL 2.0 6 (4, 8) 7 (5, 8) 5.5 (3, 7) < 0.001 REVEAL lite 2 6 (4, 8) 7 (5, 8) 5 (3.3, 7) < 0.001 Data are presented as mean ± standard deviation, median (range) or number (percentage). PAH-CHD, pulmonary arterial hypertension associated with congenital heart disease; WHO-FC, World Health Organization functional class; 6MWD, 6 minute walking distance; NT-proBNP, N-terminal pro-B-type natriuretic peptide; eGFR, estimated glomerular filtration rate; VSD, ventricular septal defect; ASD, atrial septal defect; PDA, patent ductus arteriosus; S a O 2 , saturation of oxygen in arterial blood; P a O 2 , partial pressure of oxygen in arterial blood; P a CO2, partial pressure of carbon dioxide in arterial blood; LAAPD, left atrium anteroposterior diameter; LVEDD, left ventricular end diastolic diameter; LVEF, left ventricular ejection fraction; RVAPD, right ventricular anteroposterior diameter; PAC, pulmonary arterial compliance; S v O 2 , mixed venous oxygen saturation; mRAP, mean right atrial pressure; sPAP, systolic pulmonary arterial pressure; dPAP, diastolic pulmonary arterial pressure; mPAP, mean pulmonary arterial pressure; Qp/Qs, pulmonary blood flow/systemic blood flow; CI, cardiac index; PVR, pulmonary vascular resistance; PAWP, pulmonary arterial wedge pressure. As for the risk stratification, 55.8% of patients were at low risk profile according to the ESC/ERS 2022 three-strata model. The median scores of REVEAL 2.0 and REVEAL lite 2 of the entire cohort were both 6 points. Patients with lower PAC had a higher rate of worse risk profile and score (Table 1 ). Relationship between PAC and long-term outcome of PAH-CHD During a median follow-up period of 52.2 months, 54 patients died, and 16 patients were lost to follow-up. Kaplan-Meier curve analysis revealed that the survival rate of the lower PAC group was significantly lower than that of the higher PAC group (log-rank P < 0.001, Fig. 2 ). Cox regression models were established to evaluate the predictive value of PAC for all-cause death (Table 2 ). PAC was analyzed either as a continuous variable or as a dichotomous variable (</≥1.47mL/mmHg). In the univariable analysis, PAC, WHO-FC III or IV, ln (NT-proBNP), 6MWD, mRAP, CI, S v O 2 and PAH-targeted treatment strategy were significant prognostic indicators (all P < 0.05). Per 1mL/mmHg increase in PAC reducing the all-cause mortality rate by 30.2% (HR = 0.698, 95%CI 0.513–0.948, P = 0.022). PAC ≥ 1.47mL/mmHg was associated with a reduced mortality rate by 72.1% (HR = 0.269, 95%CI 0.136–0.536, P < 0.001). Variables with P < 0.05 in the univariable Cox analysis were then entered into the multivariable models using the stepwise selection method based on AIC. PAC was entered into the Model 1 as a continuous variable and Model 2 as a dichotomous variable (</≥1.47mL/mmHg). In Model 1, continuous PAC, PAH-targeted treatment and ln (NT-proBNP) remained as independent factors after stepwise selection, where each 1mL/mmHg increase in PAC was associated with a 33.5% reduction in the risk of all-cause death (HR = 0.665, 95%CI 0.503–0.878, P = 0.004). In Model 2, after adjusting PAH-targeted treatment and ln (NT-proBNP), PAC ≥ 1.47mL/mmHg was independently associated with a reduced mortality (HR = 0.251, 95%CI 0.124–0.507, P < 0.001). No multicollinearity was detected in the Cox regression analysis given all VIFs < 5. The RCS curve revealed a linear relationship between PAC and all-cause mortality after adjusting for covariates in Model 1 (P for non-linear = 0.919, Fig. 3 ). Table 2 Univariable and stepwise multivariable Cox regression analysis for all-cause mortality. Univariable Model 1 * Model 2 * HR (95% CI) P HR (95% CI) P HR (95% CI) P PAC, mL/mmHg 0.698 (0.513–0.948) 0.022 0.665 (0.503–0.878) 0.004 PAC ≥ 1.47mL/mmHg 0.267 (0.116–0.618) 0.002 0.251 (0.124–0.507) < 0.001 Sex: female 1.057 (0.583–1.919) 0.855 Age, years 0.987 (0.962–1.012) 0.294 PAH-targeted treatment 0.552 (0.374–0.815) 0.003 0.458 (0.299–0.703) < 0.001 0.486 (0.322–0.734) 0.001 6MWD, m 0.999 (0.996–1.002) 0.438 WHO-FC III/IV 1.747 (1.019–2.997) 0.043 ln (NT-proBNP) 1.577 (1.208–2.059) 0.001 1.394 (1.063–1.829) 0.016 1.362 (1.037–1.789) 0.026 S v O 2 , % 0.978 (0.960–0.997) 0.022 mRAP, mmHg 1.015 (0.958–1.076) 0.609 CI, L/min/m 2 0.799 (0.654–0.975) 0.027 * PAC was entered in the stepwise multivariable analysis either as a continuous variable in Model 1or as a dichotomous variable in Model 2. PAC, pulmonary arterial compliance; PAH, pulmonary arterial hypertension; WHO-FC, World Health Organization functional class; 6MWD, 6-minute walking distance; ln logarithmically transformed; NT-proBNP, N-terminal pro-brain natriuretic peptide; S v O 2 , mixed venous oxygen saturation; mRAP, mean right atrial pressure; CI, cardiac index; HR, hazard ratio; 95%CI, 95% confidence interval. Subgroup analysis Subgroup analyses were performed according to sex, age, clinical classification, defect types, presence of post-tricuspid shunt, and PAH-targeted treatment strategy (Fig. 4 ). PAC was analyzed as a continuous variable and each subgroup was adjusted for PAH target treatment and ln (NT-proBNP) as in Model 1. All P values for interaction were less than 0.05, and the associations between PAC and all-cause mortality were consistent across different sex, age and treatment strategy. Interestingly, P for the interaction between PAC and post-tricuspid shunt was statistically marginal (P = 0.061), suggesting that prognostic value of PAC might be affected by the presence of post-tricuspid shunt. Moreover, the hazard ratio range of PAC in the subgroups with atrial septal defect (HR = 0.487, 95%CI 0.267–0.890, P = 0.019) or PAH after defect correction (HR = 0.195, 95%CI 0.046–0.830, P = 0.027) slightly deviated from the others although the P for interaction was not statistically significant. Incremental value of PAC for predicting all-cause mortality Bivariable Cox regression models were conducted to examine the predicting value of PAC over validated risk assessment models (Table 3 ). Continuous PAC remained as a significant prognostic factor after adjusting for COMPERA three-strata, COMPERA four-strata model, REVEAL 2.0, or REVEAL lite 2 score separately (all P < 0.05), but not for ESC/ERS 2022 three-strata model (P = 0.075). When entered as a dichotomous variable, PAC ≥ 1.47mL/mmHg independently predicted the outcome adjusting for every risk model (all P < 0.001). Incremental value of PAC over validated risk models was assessed using the C-statistics, NRI and IDI (Table 4 ). Compared with every risk model alone, the addition of PAC, whether as a continuous variable or a dichotomous variable, significantly improved the C-statistics for predicting all-cause mortality (all P < 0.05). The reclassification and discrimination ability of risk models were also improved suggested by significant NRI and IDI (all P < 0.05). Furthermore, the addition of PAC to the risk model improved the model’s goodness-of-fit. Among the models evaluated, the combination of the REVEAL lite 2 score and dichotomous PAC provided the best-fit, as indicated by the lowest AIC values, and the largest C-statistic (0.725, 95%CI 0.655–0.794, P < 0.001). Table 3 Hazard ratio and 95% confidence intervals of PAC adjusted for validated risk models. HR (95% CI) P PAC (mL/mmHg) +ESC/ERS 2022 three-strata 0.771 (0.579–1.027) 0.075 +COMPERA three-strata 0.718 (0.533–0.966) 0.029 +COMPERA four-strata 0.727 (0.543–0.974) 0.032 +REVEAL 2.0, per 1point 0.730 (0.548–0.971) 0.031 +REVEAL lite 2, per 1point 0.743 (0.559–0.987) 0.041 PAC ≥ 1.47mL/mmHg +ESC/ERS 2022 three-strata 0.307 (0.152–0.620) < 0.001 +COMPERA three-strata 0.281 (0.141–0.560) < 0.001 +COMPERA four-strata 0.288 (0.144–0.575) < 0.001 +REVEAL 2.0, per 1point 0.294 (0.147–0.585) < 0.001 +REVEAL lite 2, per 1point 0.303 (0.152–0.606) < 0.001 PAC, pulmonary arterial compliance. Table 4 Incremental value for prediction and the goodness-of-fit of the validated risk models after adding PAC. Models AIC C-statistic (95% CI) Model performance compared to standard model Δ C-statistic (95% CI) P NRI (95% CI) P IDI (95% CI) P ESC/ERS 2022 three-strata 579.41 0.626 (0.555–0.697) Ref. Ref. Ref. Ref. Ref. Ref. +PAC (continuous variable) 577.23 0.677 (0.613–0.741) 0.051 (0.011–0.092) 0.0134 0.398 (0.162–0.634) 0.001 0.010 (0.000-0.020) 0.0388 +PAC (≥ 1.47mL/mmHg) 568.24 0.706 (0.645–0.767) 0.079 (0.027–0.132) 0.0029 0.609 (0.378–0.839) < 0.001 0.028 (0.014–0.042) 0.0001 COMPERA three-strata 583.29 0.561 (0.507–0.615) Ref. Ref. Ref. Ref. Ref. Ref. +PAC (continuous variable) 578.79 0.656 (0.586–0.727) 0.095 (0.045–0.145) 0.0002 0.530 (0.308–0.752) < 0.001 0.015 (0.005–0.026) 0.0047 +PAC (≥ 1.47mL/mmHg) 568.96 0.680 (0.623–0.738) 0.119 (0.068–0.170) < 0.001 0.609 (0.378–0.839) < 0.001 0.035 (0.020–0.050) < 0.001 COMPERA four-strata 582.10 0.608 (0.529–0.688) Ref. Ref. Ref. Ref. Ref. Ref. +PAC (continuous variable) 577.80 0.668 (0.593–0.743) 0.060 (0.021–0.099) 0.0028 0.561 (0.338–0.784) < 0.001 0.016 (0.005–0.027) 0.0038 +PAC (≥ 1.47mL/mmHg) 568.57 0.704 (0.635–0.772) 0.095 (0.046–0.145) 0.0002 0.609 (0.378–0.839) < 0.001 0.034 (0.020–0.048) < 0.001 REVEAL 2.0 578.26 0.645 (0.560–0.729) Ref. Ref. Ref. Ref. Ref. Ref. +PAC (continuous variable) 573.96 0.687 (0.611–0.763) 0.043 (0.006–0.079) 0.0213 0.556 (0.334–0.779) < 0.001 0.016 (0.005–0.026) 0.0045 +PAC (≥ 1.47mL/mmHg) 565.14 0.725 (0.653–0.797) 0.080 (0.030–0.131) 0.0018 0.609 (0.378–0.839) < 0.001 0.034 (0.020–0.048) < 0.001 REVEAL lite 2 576.46 0.650 (0.567–0.734) Ref. Ref. Ref. Ref. Ref. Ref. +PAC (continuous variable) 572.85 0.687 (0.611–0.763) 0.036 (0.004–0.069) 0.0280 0.477 (0.255–0.699) < 0.001 0.013 (0.003–0.023) 0.0148 +PAC (≥ 1.47mL/mmHg) 564.37 0.725 (0.655–0.794) 0.075 (0.023–0.126) 0.0044 0.609 (0.378–0.839) < 0.001 0.030 (0.016–0.044) < 0.001 PAC, pulmonary arterial compliance; AIC, Akaike information criterion; NRI, net reclassification improvement; IDI, integrated discrimination improvement. Discussion This study investigated the relationships of PAC with long-term outcomes in patients with PAH-CHD with the largest sample size to date and especially across the subgroups of different pathophysiology which was not explored before. Our study found that: (1) Lower PAC was associated with worse functional status and hemodynamics in PAH-CHD; (2) PAC was independently associated with the all-cause mortality of PAH-CHD patients, which might be affected by the presence of post-tricuspid shunt; (3) Incorporating PAC into the validated risk assessment models significantly improved the discrimination ability. The combination of REVEAL lite 2 score and PAC showed the best predictive capacity and goodness-of-fit. Increased afterload is a critical factor in the progression of RV dysfunction and leads to poor prognosis in PAH, which is often composed of steady and pulsatile component[ 29 ]. PAC reflects the pulsatile component and is associated with distensibility and elasticity of pulmonary arterial wall[ 30 , 31 ]. The ratio of stroke volume to pulmonary pulse pressure is a simple and accepted method to derive PAC surrogate which has been validated in clinical studies[ 15 ]. A decrease in PAC usually implies elevated afterload, which reflects the progression of the entire pulmonary vascular tree remodeling, cumulation of collagen and reduction of elastin in pulmonary arteries as the arterial compliance mainly distributed over the distal vessels throughout the pulmonary circulation in contrast with systemic circulation[ 32 – 34 ]. Moreover, the change of PAC is sensitive in patients with mild pulmonary vascular disease owing to an inverse hyperbolic relationship between PAC and PVR[ 29 ]. As pulmonary arterial stiffness and PAC deteriorate, the pulmonary pulse pressure throughout the cardiac cycle will be dramatically elevated, which further promotes the pulmonary vascular lesion and increases the static component of the afterload, ultimately causing right heart failure and death[ 35 , 36 ]. The prognostic value of PAC has been explored in patient populations of idiopathic and connective tissue disease associated PAH[ 12 , 15 – 17 ]. In those studies, the additive value of PAC was attributed to a more precise assessment of the RV afterload along with PVR, especially in the patients with connective tissue disease where the myocardial fibrosis and arteritis played an important role[ 16 , 17 ]. However, there was also controversial reports that PAC was similar between different forms of pulmonary hypertension and did not predict outcome in PAH after adjustment for other risk factors[ 37 ]. Unlike other groups of PAH, PAH-CHD is a heterogenous population of multiple defect types, clinical classifications and pathophysiology. The pathogenesis of PAH-CHD involves the pressure and/or volume overload in the pulmonary circulation caused by intracardiac or extracardiac shunts, resulting in high shear stress and endothelial damage[ 38 ]. Moreover, it was also reported that patients in the PAH-CHD subgroup had better cardiac functional status and 10-year survival compared with other PAH subgroups, despite with the highest PVR and mPAP[ 4 ]. These factors may influence the performance of PAC in the afterload assessment and long-term prediction in PAH-CHD which needs further investigation. Prior studies have assessed the association between PAC and the risk of all-cause mortality in PAH-CHD patients. Sajan et al.[ 39 ] and Douwes et al.[ 21 ] reported that elevated PAC reduced the risk of death/lung transplantation in pediatric patients with PAH-CHD. However, both of studies were retrospective and limited to pediatric population with mixed etiologies of idiopathic and congenital heart disease (28 and 20 pediatric patients of PAH-CHD, respectively), leaving the role of PAC in the isolated PAH-CHD population unclear. Iwaya et al.[ 19 ] retrospectively enrolled 22 infants with PAH and atrial septal defect and found that PAC showed a significant relation to all-cause death in univariable logistic regression (OR = 0.03, 95%CI 0.001–0.73, P = 0.031) but did not in multivariable analysis due to the limited number of subjects. Another prospective single-center study enrolled 175 patients with PAH-CHD and found that lower PAC was correlated with worse exercise tolerance, biomarkers, hemodynamics and prognosis[ 22 ]. Notably, patients with prevalent systemic-to-pulmonary shunts, a common classification of PAH-CHD, was excluded in this study, and this limited the extrapolation of the findings. Furthermore, none of previous studies have ever explored the value of PAC within different subgroups of the heterogenous PAH-CHD population; for instance, within different shunts condition and defect types. To fill the research gap about the prognostic relevance of PAC in PAH-CHD patients, this prospective study included 434 adult patients from 15 medical centers in China with the largest sample size to date, covering a wide range of different subgroups and defect types. In the overall population of PAH-CHD, PAC independently predicted all-cause mortality after a stepwise multivariable analysis. Here, a cut-off value of 1.47mL/mmHg was reported of the largest discriminative capacity of death. Two prior studies on idiopathic PAH or specific patients with systemic lupus erythematosus reported cut-off values of 0.90 and 1.39mL/mmHg respectively[ 15 , 16 ]. A higher cut-off of current study may be due to the milder pulmonary vascular lesion and presence of cardiac shunts in some patients, which can relieve the RV load through the defect and, thereby, leads to a higher PAC but a lower load that RV actually confronted with. In another cohort of PAH-CHD, Cheng et al.[ 22 ] reported that PAC < 1.04mL/mmHg was associated with a poor prognosis in PAH-CHD, which was lower than that of current study and may be attributed to the extra inclusion of younger patients aged 14–18 years in this study. In comparison to adults, children typically exhibit lower PAC values[ 13 ]. Pathophysiology and prognosis varies among the different subgroups in PAH-CHD[ 8 ]. In the subgroup analysis of current study, PAC significantly predicted outcome in the subgroup without the post-tricuspid shunt but did not in the other, which differed from the results by Douwes et al.[ 21 ] although their study population were children. The assumption that PAC reflects the pulsatile component of load relies on that the chamber and vessels through RV to the distal pulmonary capillary bed are intact without defect, thus the presence of post-tricuspid shunt may have a negative impact on the precise assessment of the afterload by PAC. Therefore, the current study may indicate that PAC predicted outcome mainly though representing RV function instead of a function of pulmonary vascular disease[ 21 ]. This finding also concorded with a more significant hazard ratio range in the subgroup with atrial septal defect or PAH after correction, as in those two groups, there was no presence of post-tricuspid shunt. The additive value of PAC was ever investigated in other PAH subgroups. A recent study reported that inclusion of hemodynamics at follow-up showed additional value to non-invasive parameters for the end-point in PAH, where PAC > 1.5mL/mmHg, similar with the cut-off of the current study, defined a low risk status[ 11 ]. In another study on population of systemic sclerosis associated PAH, PAC was incorporated into a hemodynamic risk assessment tool for prognostic significance[ 12 ]. Meanwhile, McCormick et al.[ 37 ] reported that PAC did not add prognostic value after adjustment for REVEAL 2.0 score in PAH patients while PAH-CHD patients was not included in this study either. In contrast, the current study supported the incremental prognostic value of integrating PAC with other validated risk models in PAH-CHD patients. PAC </≥1.47mL/mmHg remained as a significant predictor after adjustment for ESC/ERS three-strata model, COMPERA models or REVEAL score, and the reclassification and discrimination abilities of these models were significantly improved. In a recent comparison research of contemporary risk scores in pulmonary hypertension, Yogeswaran et al.[ 40 ] reported that continuous REVEAL scores demonstrated the highest statistical prognostic power. In addition to this finding, we reported a combination model of REVEAL lite 2 score and PAC </≥1.47mL/mmHg to achieve both the best predictive capacity and goodness-of-fit in patients with PAH-CHD, which highlighted the importance of assessing pulsatile afterload in the risk stratification of this population. Limitations Several limitations in our study should be acknowledged. First, all patients were enrolled from tertiary hospitals in China, which typically enrolled more complex and severe cases, potentially introducing selection bias. But it should be noted that the study included patients from 15 centers in various areas of China, which helps to ensure the generalizability of the findings. Second, a portion of patients without PAC data were excluded from the cohort. Nevertheless, our study had the largest sample size to date for PAH-CHD patients with a broad inclusion criteria, allowing subgroup analysis and ensuring the robustness of the findings. Finally, PAC was measured only at baseline, without monitoring longitudinal changes. This restricted the assessment that how changes of PAC during follow-up might influence prognosis, which needs further investigation. Conclusion PAC was an independent prognostic factor of long-term prognosis in adult patients of PAH-CHD. The role of PAC varied across different clinical classifications, defect types and shunts characteristics, and the presence of post-tricuspid shunt may have a negative impact on the prognostic value of PAC. In addition, PAC provided an incremental predictive value on the validated risk assessment models. A combination model of REVEAL lite 2 score and PAC </≥1.47mL/mmHg showed the best predictive capacity and goodness-of-fit, highlighting the importance of assessing RV pulsatile afterload in the risk stratification of PAH-CHD population. Abbreviations 6MWD six-minute walking distance BNP brain natriuretic peptide CHD congenital heart disease CI cardiac index IDI integrated discrimination improvement mRAP mean right atrial pressure NRI net reclassification improvement NT-proBNP N-terminal pro-brain natriuretic peptide PAC pulmonary arterial compliance PAH pulmonary arterial hypertension PAH-CHD pulmonary arterial hypertension associated with congenital heart disease PVR pulmonary vascular resistance RCS restricted cubic spline RHC right heart catheterization RV right ventricle S v O 2 mixed venous oxygen saturation VIF variance inflation factor WHO-FC World Health Organization functional class. Declarations Ethics approval and consent to participate All enrolled patients had provided written informed consent. The study adhered to the ethical guidelines of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Fuwai Hospital (Approval No.2009-208). Consent for publication Not applicable. Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Competing interest None. Funding The study was supported by the National Key Research and Development Program of China (No. 2016YFC1304400), the China Key Research Projects of the 11th National Five-Year Development Plan (Project Number: 2006BAI01A07) and the China Key Research Projects of the 12th National Five-Year Development Plan (No. 2011BAI11B15). Authors' contributions JH contributed to the conception and design of the study. QL and HY performed the data analyses and were the major contributors in writing the manuscript. QG and CX contributed to the data interpretation. ZY, YL, WW, XZ and HH contributed to data acquisition. All authors read and approved the final manuscript. Acknowledgements We thank all the study individuals for their participation and all the staff members for their contributions to this study including: Gangcheng Zhang (Department of Cardiology, Wuhan Asia Heart Hospital, Wuhan 430022, P.R. China), Jichun Liu (Department of Cardiac Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, China), Zhi Zeng (Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China), Xiulong Zhu (Department of Cardiovascular Medicine, The People’s Hospital of Gaozhou, Maoming, China), Lianjun Huang (Interventional Department, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China), Daxin Zhou (Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Disease, Shanghai 200032, P.R. 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Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 27 Aug, 2025 Reviews received at journal 25 Aug, 2025 Reviews received at journal 01 Aug, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers invited by journal 11 Jul, 2025 Editor invited by journal 10 Jul, 2025 Editor assigned by journal 08 Jul, 2025 Submission checks completed at journal 08 Jul, 2025 First submitted to journal 03 Jul, 2025 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-7036244","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484227258,"identity":"f504149a-6b24-4d63-9a1e-3e7bc303c667","order_by":0,"name":"Qimou Li","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qimou","middleName":"","lastName":"Li","suffix":""},{"id":484227260,"identity":"dfc2c0c1-4237-4b42-84be-40ce48923be4","order_by":1,"name":"Hongrui Yu","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongrui","middleName":"","lastName":"Yu","suffix":""},{"id":484227263,"identity":"9e1102e4-0acb-4713-9d07-313a894f8793","order_by":2,"name":"Changming Xiong","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Changming","middleName":"","lastName":"Xiong","suffix":""},{"id":484227267,"identity":"5cdb055c-4ff3-4bf3-b8e1-ca962a623dc9","order_by":3,"name":"Qing Gu","email":"","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Gu","suffix":""},{"id":484227268,"identity":"99d5efeb-3f71-4f50-a231-94745834efa1","order_by":4,"name":"Zaixin Yu","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zaixin","middleName":"","lastName":"Yu","suffix":""},{"id":484227270,"identity":"0ac4c272-199d-404e-99f5-b840ffdcf4a8","order_by":5,"name":"Yuhao Liu","email":"","orcid":"","institution":"Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yuhao","middleName":"","lastName":"Liu","suffix":""},{"id":484227272,"identity":"9d9a7f8b-b3cf-4d9d-852d-842b52d52ed6","order_by":6,"name":"Weifeng Wu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Wu","suffix":""},{"id":484227273,"identity":"e24e510d-c381-45c5-9501-31d965e6be56","order_by":7,"name":"Xianyang Zhu","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Xianyang","middleName":"","lastName":"Zhu","suffix":""},{"id":484227275,"identity":"511c5be9-37bb-40a1-8595-d401fcb85b4d","order_by":8,"name":"Huijun Han","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Huijun","middleName":"","lastName":"Han","suffix":""},{"id":484227278,"identity":"e6f447e8-c83b-41cd-8f20-3f82bc81ca27","order_by":9,"name":"Jianguo He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3PMQuCQBTA8RfCtSiuBtlnEIKib/OO5qDRwaIhzqHE2W/hJI4Xgi3X7pZ+gIZoqSFIJWhT24LuNzzu4P3hDkCSfpAFgLw+KbyXo+18kxBUrFyknZI3gmRQbJX2ZNrfFPwRr6hP1IlNNwR0d4eNyWzP8eCJIw2YOsloPARDnMLmh2WIXGMpDc9elFFBwDIWLck5x8OzSlItWlKmdEgywERjTp1At0QgJibj44DpNwNFqrb/5Sjm1wtbmz4h9Hq3nZHues0JgIrlSD7XlvVKn5dj3WFRkiTpb70AAGxT0YaZ9PoAAAAASUVORK5CYII=","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-07-03 09:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7036244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7036244/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-05230-5","type":"published","date":"2025-10-14T15:58:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87030848,"identity":"d88a3559-685d-4452-abdd-efe899ce7ab1","added_by":"auto","created_at":"2025-07-18 12:46:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32308,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population.\u003c/p\u003e\n\u003cp\u003ePAC, pulmonary arterial compliance; PAH-CHD, pulmonary arterial hypertension associated with congenital heart disease; RHC, right heart catheterization.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7036244/v1/74a873bf733483c8309c1fa4.png"},{"id":87030851,"identity":"6502146d-f9aa-4cf0-879e-f5f1f8af5a08","added_by":"auto","created_at":"2025-07-18 12:46:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61619,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for PAH-CHD patients grouped by PAC.\u003c/p\u003e\n\u003cp\u003eThe log-rank test was used to compare the survival curves between the low PAC group and the high PAC group. Shaded area represents the 95% confidence interval of survival rates. PAC, pulmonary arterial compliance; PAH-CHD, pulmonary arterial hypertension associated with congenital heart disease.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7036244/v1/ce5a70ce6f8158894d300801.png"},{"id":87030849,"identity":"3ae7d90d-bd72-45f0-917d-67899c9aa50e","added_by":"auto","created_at":"2025-07-18 12:46:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41134,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between baseline PAC and the hazard ratio of all-cause mortality.\u003c/p\u003e\n\u003cp\u003ePAC as a continuous variable fitted the adjusted Model 1 using restricted cubic spline (RCS) regression where 1.47mL/mmHg was served as the reference point. The red line represents the hazard ratio, and the shaded area represents the 95% confidence interval. PAC, pulmonary arterial compliance.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7036244/v1/b849e4f3884163cd807f6780.png"},{"id":87030852,"identity":"dbfbbf01-4f53-4a75-8fdf-1e2a295eed9d","added_by":"auto","created_at":"2025-07-18 12:46:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":108384,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of hazard ratios by patient subgroups.\u003c/p\u003e\n\u003cp\u003eEach subgroup was adjusted for PAH target treatment and ln (NT-proBNP) as in Model 1. VSD, ventricular septal defect; ASD, atrial septal defect; PDA, patent ductus arteriosus; PAH, pulmonary arterial hypertension; ln logarithmically transformed; NT-proBNP, N-terminal pro-brain natriuretic peptide.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7036244/v1/ee9307164d0083da83267cf4.png"},{"id":93956678,"identity":"0c6c5471-1987-45d7-9c7c-af4175b652ba","added_by":"auto","created_at":"2025-10-20 16:11:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1507520,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7036244/v1/7e66b79f-de5c-4b27-b003-1126e03c44e0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pulmonary arterial compliance as a long-term prognostic indicator in pulmonary arterial hypertension associated with adult congenital heart disease: results from a national multicenter prospective registry","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary arterial hypertension (PAH) is characterized by progressive pulmonary vascular remodeling and elevated pulmonary artery pressure, culminating in right heart failure and death[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], which caused approximately 22,000 deaths globally in 2021[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Congenital heart disease (CHD) is one of the most common causes of PAH, accounting for 45.2% of PAH cases in China[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The development of PAH in CHD patients is associated with severe symptoms, poor clinical course and worse prognosis[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with a 10-year survival rate of 73.0%[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, PAH-CHD is a heterogeneous disease and exhibits significant difference in pathophysiology and survival within different shunt directions and defect types[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA comprehensive assessment of the pulmonary arterial load is critical to the mortality prediction in PAH and may help to the risk assessment[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It consists of both static and pulsatile component, evaluated by pulmonary vascular resistance and pulmonary artery compliance (PAC) respectively[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. PAC reflects the distensibility of pulmonary vessels and right ventricular (RV) function and changes considerably in CHD patients at an early stage of developing pulmonary hypertension, and it can be estimated empirically using a simpler and accepted method[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. There has been growing evidence of the prognostic value of PAC in idiopathic PAH and PAH associated with connective tissue disease[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the relation of PAC to the long-term prognosis of PAH-CHD is poorly defined, given the complexity and heterogeneity of the clinical characteristics and pathophysiology of different PAH-CHD subgroups. Early studies reported that decreased PAC related to worse exercise capacity and poor outcome in pediatric and infant populations of PAH-CHD[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A previous prospective study explored the role of PAC in a PAH-CHD cohort and concluded that lower PAC was associated with poor prognosis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. But they were limited by the concentration on pediatric patients or specific PAH-CHD subgroups, small number of events and single-center design. Moreover, how PAC interacts with different shunts condition and defect characteristics has not been explored yet. Thus, there is a lack of a multicenter cohort study with a larger sample size enrolling different subgroups of PAH-CHD patients that could help to explore the long-term predictive value of PAC across the entire PAH-CHD population.\u003c/p\u003e\u003cp\u003eTo fill the research gap, based on a national multicenter prospective registry with a larger sample size from China, this study aimed to investigate the long-term prognostic value of PAC in adult PAH-CHD patients and, additionally, its incremental value over validated risk assessment models.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design and population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe details of the study design of the PAH multicenter prospective registry have been described previously[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In brief, patients diagnosed with PAH by right heart catheterization (RHC) were consecutively enrolled in the registry from 34 medical centers in China between August 2009 and December 2019. All enrolled patients had provided written informed consent. The study adhered to the ethical guidelines of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Fuwai Hospital (Approval No.2009\u0026thinsp;\u0026minus;\u0026thinsp;208) and was registered on ClinicalTrials.gov (NCT01417338).\u003c/p\u003e\u003cp\u003eIn this study, the inclusion criteria for the analysis cohort were as follow: (1) patients\u0026thinsp;\u0026ge;\u0026thinsp;18 years at diagnosis; (2) patients with PAH associated with CHD diagnosed by RHC according to contemporary guidelines[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], defined as mean pulmonary arterial pressure\u0026thinsp;\u0026ge;\u0026thinsp;25mmHg, and pulmonary arterial wedge pressure\u0026thinsp;\u0026le;\u0026thinsp;15mmHg at rest by RHC and/or absence of evidence of left heart disease, including enlarged left heart and left heart systolic and/or diastolic dysfunction. Patients meeting any of the exclusion criteria were excluded: (1) absence of PAC data; (2) missing of follow-up information.\u003c/p\u003e\u003cp\u003eThe primary endpoint of the study was all-cause mortality. Patients were followed up every 6 months by telephone calls, outpatient or inpatient visits until 28 February 2021 or the occurrence of an endpoint. Survival status, treatment strategies and hospitalizations were collected at each follow-up. Follow-up time was recorded from the date of enrollment until the occurrence of death, or the date of study cut-off. Patients who could not be contacted by any of aforementioned methods were defined as lost to follow-up and the censor time was the date of last contact.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurements and definitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBaseline information for the enrolled patients was collected, including demographics (age, sex), the World Health Organization functional class (WHO-FC), six-minute walking distance (6MWD), laboratory examinations, arterial blood gas analysis, echocardiographic parameters, hemodynamic parameters measured by RHC, and treatment strategies. Details of data collection and quality control have been reported previously[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePAC was calculated as RV stroke volume/(systolic pulmonary arterial pressure \u0026ndash; diastolic pulmonary arterial pressure), where stroke volume was equal to pulmonary blood flow/heart rate [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Pulmonary vascular resistance (PVR) was calculated as (mean pulmonary arterial pressure \u0026ndash; pulmonary arterial wedge pressure)/pulmonary blood flow. Cardiac index (CI) was calculated by dividing the systemic blood flow by the body surface area. Stroke volume index (SVI) was calculated as cardiac index/heart rate.\u003c/p\u003e\u003cp\u003eThe clinical classifications of patients with PAH-CHD based on the direction of shunting were as follows: (1) Eisenmenger Syndrome: characterized by right-to-left or bidirectional shunt; (2) PAH associated with systemic-to-pulmonary shunts: left-to-right shunting remains predominant; (3) PAH after defect correction: congenital heart disease had been repaired, but PAH either persisted immediately or recurred more than one year after interventional or surgical correction, in the absence of significant post-operative hemodynamic abnormalities[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Monotherapy was defined as the usage of single PAH-targeted drug, while combination therapy was defined as the usage of two or more PAH-targeted drugs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk assessment models\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe baseline three-strata risk model of 2022 European Society of Cardiology (ESC)/European Respiratory Society (ERS) guidelines was applied according to the Kylhammar approach[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] using parameters including WHO-FC, 6MWD, NT-proBNP level, mean right atrial pressure (mRAP), CI, SVI, and mixed-venous oxygen saturation (S\u003csub\u003ev\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e). The Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) registry three- and four-strata models were applied as described by Hoeper et al.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] using WHO-FC, 6MWD, and NT-proBNP level. At least two of the three above variables were available when calculating individual risk scores. If BNP and NT-proBNPwere both available, NT‑proBNP was used. Each variable was assigned a grade, and the mean was calculated by dividing the sum of all grades by the number of variables and rounding to the next integer to determine the risk group.\u003c/p\u003e\u003cp\u003eThe Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL) 2.0 score was calculated as described by Benza et al.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] using the following variables: male\u0026thinsp;\u0026gt;\u0026thinsp;60 years old, systolic blood pressure, heart rate, WHO-FC, 6MWD, NT-proBNP, estimated glomerular filtration rate, presence of pericardial effusion, and mRAP. The REVEAL scores were used as a continuous scoring system, and missing values were substituted by a score of zero[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. REVEAL Lite 2.0 risk was calculated by incorporating NT-proBNP, 6MWD, WHO-FC, systolic blood pressure, heart rate, and estimated glomerular filtration rate[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (25th percentile, 75th percentile) and analyzed using independent-sample t test (normally distributed) or Mann-Whitney U test (non-normally distributed). Categorical variables were presented as counts (percentages) and were compared with the chi-square test. Random forest method was used to impute missing data. Restricted cubic spline (RCS) was used to evaluate the relationship between continuous PAC and all-cause mortality. The optimal survival cut-off value of PAC was determined by maximally selected rank statistics as 1.47mL/mmHg, and patients were divided into the higher PAC group (\u0026ge;\u0026thinsp;1.47mL/mmHg) and the lower PAC group (\u0026lt;\u0026thinsp;1.47mL/mmHg). The cumulative incidence of death of two groups was estimated using Kaplan-Meier curves and compared using the log-rank test.\u003c/p\u003e\u003cp\u003eUnivariable Cox proportional-hazards regression analyses were used to examine the prognostic relevance of variables including PAC, age, sex, PAH-targeted treatment, 6MWD, WHO-FC, NT-proBNP, mRAP, CI, and S\u003csub\u003ev\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, which were selected based on clinical significance and guideline recommendations[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were entered into the multivariable Cox regression models using the stepwise selection method based on Akaike information criterion (AIC). PAC was entered into the stepwise multivariable analysis either as a continuous variable in Model 1or as a dichotomous variable (\u0026lt;/\u0026ge;1.47mL/mmHg) in Model 2. NT-proBNP levels were logarithmically transformed to ln (NT-proBNP) in the Cox regression analyses. The multicollinearity of the included variables was tested using the variance inflation factor (VIF) method and VIF\u0026thinsp;\u0026lt;\u0026thinsp;5 indicated no multicollinearity. Subgroup analyses were conducted to determine the interaction effects, stratified by sex, age, clinical classification, defect types, presence of post-tricuspid shunt, and PAH-targeted treatment strategy, and the P value for the interaction was calculated.\u003c/p\u003e\u003cp\u003eThe incremental predictive values of PAC over the validated risk assessment models including ESC/ERS 2022 three-strata model, COMPERA three- and four-strata models, REVEAL 2.0, and REVEAL lite 2 score were evaluated by calculating the changes of C-statistic, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The goodness-of-fit of the different risk models after incorporating PAC were assessed using the AIC.\u003c/p\u003e\u003cp\u003eAll tests were two sided and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analysis was performed with R 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 434 adult patients diagnosed with PAH-CHD were included into analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of included patients was 34.2 years, and 69.6% were female (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Eisenmenger syndrome was the most common clinical classification (45.9%), followed by systemic-to-pulmonary shunts (36.4%) and PAH after defect correction (17.7%). Atrial septal defect was the most common cause (42.4%). 238 patients were classified into the lower PAC group (\u0026lt;\u0026thinsp;1.47mL/mmHg), while 196 were into the higher PAC group (\u0026ge;\u0026thinsp;1.47mL/mmHg). Compared with those with higher PAC, patients with lower PAC group had a slightly higher proportion of Eisenmenger syndrome and PAH after defect correction, and showed a worse WHO-FC, higher NT-proBNP levels and shorter 6MWD. Echocardiographic RV size was consistent between two groups, whereas a smaller left chamber was overserved in the lower PAC group. Generally, patients in the lower PAC group showed a lower arterial blood oxygen saturation and more deteriorated hemodynamics, including higher pulmonary arterial pressure, PVR and lower CI. 85.9% of patients of the entire cohort received PAH-targeted treatment and the treatment rate in the lower PAC group was significantly higher than that of the higher PAC group (91.1% vs. 79.5%, P\u0026thinsp;=\u0026thinsp;0.001).\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\u003eBaseline characteristics of patients with PAH-CHD.\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;434)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePAC\u0026thinsp;\u0026lt;\u0026thinsp;1.47mL/mmHg\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;238)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePAC\u0026thinsp;\u0026ge;\u0026thinsp;1.47 mL/mmHg\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;196)\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=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (36.7%)\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\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e302 (69.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178 (74.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124 (63.3%)\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\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (100, 120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (100, 120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (100, 120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate, bpm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (72, 89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (73, 90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78 (70, 86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eWHO-FC, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI or II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255 (60.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127 (54.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128 (67.0%)\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\u003eIII or IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105 (45.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (33.0%)\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\u003e6MWD, m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e419\u0026thinsp;\u0026plusmn;\u0026thinsp;95.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e401\u0026thinsp;\u0026plusmn;\u0026thinsp;98.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e440\u0026thinsp;\u0026plusmn;\u0026thinsp;87.8\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\u003eNT-proBNP, ng/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e627 (187, 1752)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e855 (394, 2436)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e348 (123, 1290)\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\u003eeGFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101.0\u0026thinsp;\u0026plusmn;\u0026thinsp;25.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.1\u0026thinsp;\u0026plusmn;\u0026thinsp;23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103.4\u0026thinsp;\u0026plusmn;\u0026thinsp;27.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eClinical classification, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEisenmenger syndrome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e199 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (49.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (41.3%)\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\u003eSystemic-to-pulmonary shunts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (29.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88 (44.9%)\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\u003ePAH after defect correction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (21.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eDefect type, n (%)\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\u003eVSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (32.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (27.6%)\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\u003ePDA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (6.12%)\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\u003eASD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e184 (42.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (37.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95 (48.5%)\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\u003eOther defects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eArterial blood gas analysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u003csub\u003ea\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\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\u003eP\u003csub\u003ea\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003ea\u003c/sub\u003eCO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eEchocardiography\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAAPD, mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\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\u003eLVEDD, mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\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\u003eLVEF, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.814\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRVAPD, mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence of pericardial effusion, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eRight heart catheterization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAC, mL/mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.36 (0.93, 2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97 (0.69, 1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.14 (1.74, 3.07)\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\u003eS\u003csub\u003ev\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\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\u003emRAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esPAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;26.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.0\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.8\u0026thinsp;\u0026plusmn;\u0026thinsp;23.2\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\u003edPAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\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\u003emPAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\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\u003eQp, L/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.80 (4.28, 8.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.46 (3.52, 5.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.50 (6.25, 11.0)\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\u003eQs, L/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.17 (4.20, 6.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.73 (3.85, 5.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.63 (4.58, 7.00)\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\u003eQp/Qs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06 (0.88, 1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93 (0.80, 1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39 (1.05, 2.02)\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\u003eCI, L/min/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\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\u003eSVI, mL/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.8\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\u003ePVR, dyn\u0026middot;s/cm\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e824 (599, 1438)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1066 (739, 1591)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e506 (332, 646)\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\u003ePAWP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ePAH-targeted treatment, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (14.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (8.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (20.5%)\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\u003eMonotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94 (39.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (41.0%)\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\u003eCombination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196 (45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (51.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (38.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eESC/ERS 2022 three-strata, n (%)\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\u003eLow risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e242 (55.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (72.7%)\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\u003eIntermediate risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171 (39.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 (51.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51 (27.3%)\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\u003eHigh risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCOMPERA three-strata, n (%)\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\u003eLow risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (31.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (26.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (44.8%)\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\u003eIntermediate risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e252 (58.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153 (71.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99 (54.1%)\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\u003eHigh risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCOMPERA four-strata, n (%)\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\u003eLow risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (29.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (37.7%)\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\u003eIntermediate-low risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177 (44.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (43.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (45.9%)\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\u003eIntermediate-high risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (23.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (13.7%)\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\u003eHigh risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (2.7%)\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\u003eREVEAL 2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (4, 8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (5, 8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.5 (3, 7)\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\u003eREVEAL lite 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (4, 8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (5, 8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (3.3, 7)\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, median (range) or number (percentage). PAH-CHD, pulmonary arterial hypertension associated with congenital heart disease; WHO-FC, World Health Organization functional class; 6MWD, 6 minute walking distance; NT-proBNP, N-terminal pro-B-type natriuretic peptide; eGFR, estimated glomerular filtration rate; VSD, ventricular septal defect; ASD, atrial septal defect; PDA, patent ductus arteriosus; S\u003csub\u003ea\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, saturation of oxygen in arterial blood; P\u003csub\u003ea\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, partial pressure of oxygen in arterial blood; P\u003csub\u003ea\u003c/sub\u003eCO2, partial pressure of carbon dioxide in arterial blood; LAAPD, left atrium anteroposterior diameter; LVEDD, left ventricular end diastolic diameter; LVEF, left ventricular ejection fraction; RVAPD, right ventricular anteroposterior diameter; PAC, pulmonary arterial compliance; S\u003csub\u003ev\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, mixed venous oxygen saturation; mRAP, mean right atrial pressure; sPAP, systolic pulmonary arterial pressure; dPAP, diastolic pulmonary arterial pressure; mPAP, mean pulmonary arterial pressure; Qp/Qs, pulmonary blood flow/systemic blood flow; CI, cardiac index; PVR, pulmonary vascular resistance; PAWP, pulmonary arterial wedge pressure.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs for the risk stratification, 55.8% of patients were at low risk profile according to the ESC/ERS 2022 three-strata model. The median scores of REVEAL 2.0 and REVEAL lite 2 of the entire cohort were both 6 points. Patients with lower PAC had a higher rate of worse risk profile and score (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelationship between PAC and long-term outcome of PAH-CHD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring a median follow-up period of 52.2 months, 54 patients died, and 16 patients were lost to follow-up. Kaplan-Meier curve analysis revealed that the survival rate of the lower PAC group was significantly lower than that of the higher PAC group (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCox regression models were established to evaluate the predictive value of PAC for all-cause death (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PAC was analyzed either as a continuous variable or as a dichotomous variable (\u0026lt;/\u0026ge;1.47mL/mmHg). In the univariable analysis, PAC, WHO-FC III or IV, ln (NT-proBNP), 6MWD, mRAP, CI, S\u003csub\u003ev\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and PAH-targeted treatment strategy were significant prognostic indicators (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Per 1mL/mmHg increase in PAC reducing the all-cause mortality rate by 30.2% (HR\u0026thinsp;=\u0026thinsp;0.698, 95%CI 0.513\u0026ndash;0.948, P\u0026thinsp;=\u0026thinsp;0.022). PAC\u0026thinsp;\u0026ge;\u0026thinsp;1.47mL/mmHg was associated with a reduced mortality rate by 72.1% (HR\u0026thinsp;=\u0026thinsp;0.269, 95%CI 0.136\u0026ndash;0.536, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariable Cox analysis were then entered into the multivariable models using the stepwise selection method based on AIC. PAC was entered into the Model 1 as a continuous variable and Model 2 as a dichotomous variable (\u0026lt;/\u0026ge;1.47mL/mmHg). In Model 1, continuous PAC, PAH-targeted treatment and ln (NT-proBNP) remained as independent factors after stepwise selection, where each 1mL/mmHg increase in PAC was associated with a 33.5% reduction in the risk of all-cause death (HR\u0026thinsp;=\u0026thinsp;0.665, 95%CI 0.503\u0026ndash;0.878, P\u0026thinsp;=\u0026thinsp;0.004). In Model 2, after adjusting PAH-targeted treatment and ln (NT-proBNP), PAC\u0026thinsp;\u0026ge;\u0026thinsp;1.47mL/mmHg was independently associated with a reduced mortality (HR\u0026thinsp;=\u0026thinsp;0.251, 95%CI 0.124\u0026ndash;0.507, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No multicollinearity was detected in the Cox regression analysis given all VIFs\u0026thinsp;\u0026lt;\u0026thinsp;5. The RCS curve revealed a linear relationship between PAC and all-cause mortality after adjusting for covariates in Model 1 (P for non-linear\u0026thinsp;=\u0026thinsp;0.919, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eUnivariable and stepwise multivariable Cox regression analysis for all-cause mortality.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 2\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAC, mL/mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.698 (0.513\u0026ndash;0.948)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.665 (0.503\u0026ndash;0.878)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAC\u0026thinsp;\u0026ge;\u0026thinsp;1.47mL/mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.267 (0.116\u0026ndash;0.618)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.251 (0.124\u0026ndash;0.507)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003eSex: female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.057 (0.583\u0026ndash;1.919)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e0.987 (0.962\u0026ndash;1.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAH-targeted treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.552 (0.374\u0026ndash;0.815)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.458 (0.299\u0026ndash;0.703)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.486 (0.322\u0026ndash;0.734)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\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\u003e0.999 (0.996\u0026ndash;1.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHO-FC III/IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.747 (1.019\u0026ndash;2.997)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eln (NT-proBNP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.577 (1.208\u0026ndash;2.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.394 (1.063\u0026ndash;1.829)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.362 (1.037\u0026ndash;1.789)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u003csub\u003ev\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.978 (0.960\u0026ndash;0.997)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.015 (0.958\u0026ndash;1.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCI, L/min/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.799 (0.654\u0026ndash;0.975)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e PAC was entered in the stepwise multivariable analysis either as a continuous variable in Model 1or as a dichotomous variable in Model 2.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ePAC, pulmonary arterial compliance; PAH, pulmonary arterial hypertension; WHO-FC, World Health Organization functional class; 6MWD, 6-minute walking distance; ln logarithmically transformed; NT-proBNP, N-terminal pro-brain natriuretic peptide; S\u003csub\u003ev\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, mixed venous oxygen saturation; mRAP, mean right atrial pressure; CI, cardiac index; HR, hazard ratio; 95%CI, 95% confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSubgroup analyses were performed according to sex, age, clinical classification, defect types, presence of post-tricuspid shunt, and PAH-targeted treatment strategy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). PAC was analyzed as a continuous variable and each subgroup was adjusted for PAH target treatment and ln (NT-proBNP) as in Model 1. All P values for interaction were less than 0.05, and the associations between PAC and all-cause mortality were consistent across different sex, age and treatment strategy. Interestingly, P for the interaction between PAC and post-tricuspid shunt was statistically marginal (P\u0026thinsp;=\u0026thinsp;0.061), suggesting that prognostic value of PAC might be affected by the presence of post-tricuspid shunt. Moreover, the hazard ratio range of PAC in the subgroups with atrial septal defect (HR\u0026thinsp;=\u0026thinsp;0.487, 95%CI 0.267\u0026ndash;0.890, P\u0026thinsp;=\u0026thinsp;0.019) or PAH after defect correction (HR\u0026thinsp;=\u0026thinsp;0.195, 95%CI 0.046\u0026ndash;0.830, P\u0026thinsp;=\u0026thinsp;0.027) slightly deviated from the others although the P for interaction was not statistically significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncremental value of PAC for predicting all-cause mortality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBivariable Cox regression models were conducted to examine the predicting value of PAC over validated risk assessment models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Continuous PAC remained as a significant prognostic factor after adjusting for COMPERA three-strata, COMPERA four-strata model, REVEAL 2.0, or REVEAL lite 2 score separately (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but not for ESC/ERS 2022 three-strata model (P\u0026thinsp;=\u0026thinsp;0.075). When entered as a dichotomous variable, PAC\u0026thinsp;\u0026ge;\u0026thinsp;1.47mL/mmHg independently predicted the outcome adjusting for every risk model (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Incremental value of PAC over validated risk models was assessed using the C-statistics, NRI and IDI (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Compared with every risk model alone, the addition of PAC, whether as a continuous variable or a dichotomous variable, significantly improved the C-statistics for predicting all-cause mortality (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The reclassification and discrimination ability of risk models were also improved suggested by significant NRI and IDI (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the addition of PAC to the risk model improved the model\u0026rsquo;s goodness-of-fit. Among the models evaluated, the combination of the REVEAL lite 2 score and dichotomous PAC provided the best-fit, as indicated by the lowest AIC values, and the largest C-statistic (0.725, 95%CI 0.655\u0026ndash;0.794, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eHazard ratio and 95% confidence intervals of PAC adjusted for validated risk models.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAC (mL/mmHg)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ESC/ERS 2022 three-strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.771 (0.579\u0026ndash;1.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+COMPERA three-strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.718 (0.533\u0026ndash;0.966)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+COMPERA four-strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.727 (0.543\u0026ndash;0.974)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+REVEAL 2.0, per 1point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.730 (0.548\u0026ndash;0.971)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+REVEAL lite 2, per 1point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.743 (0.559\u0026ndash;0.987)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAC\u0026thinsp;\u0026ge;\u0026thinsp;1.47mL/mmHg\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ESC/ERS 2022 three-strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.307 (0.152\u0026ndash;0.620)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003e+COMPERA three-strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.281 (0.141\u0026ndash;0.560)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003e+COMPERA four-strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.288 (0.144\u0026ndash;0.575)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003e+REVEAL 2.0, per 1point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.294 (0.147\u0026ndash;0.585)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003e+REVEAL lite 2, per 1point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.303 (0.152\u0026ndash;0.606)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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=\"3\"\u003ePAC, pulmonary arterial compliance.\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=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIncremental value for prediction and the goodness-of-fit of the validated risk models after adding PAC.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eC-statistic (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u003cp\u003eModel performance compared to standard model\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔ C-statistic (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNRI (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eIDI (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESC/ERS 2022 three-strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e579.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.626 (0.555\u0026ndash;0.697)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (continuous variable)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e577.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.677 (0.613\u0026ndash;0.741)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.051 (0.011\u0026ndash;0.092)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.398 (0.162\u0026ndash;0.634)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.010 (0.000-0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0388\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (\u0026ge;\u0026thinsp;1.47mL/mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e568.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.706 (0.645\u0026ndash;0.767)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.079 (0.027\u0026ndash;0.132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.609 (0.378\u0026ndash;0.839)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.028 (0.014\u0026ndash;0.042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOMPERA three-strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e583.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.561 (0.507\u0026ndash;0.615)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (continuous variable)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e578.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.656 (0.586\u0026ndash;0.727)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.095 (0.045\u0026ndash;0.145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.530 (0.308\u0026ndash;0.752)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.015 (0.005\u0026ndash;0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (\u0026ge;\u0026thinsp;1.47mL/mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e568.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.680 (0.623\u0026ndash;0.738)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.119 (0.068\u0026ndash;0.170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.609 (0.378\u0026ndash;0.839)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.035 (0.020\u0026ndash;0.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eCOMPERA four-strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e582.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.608 (0.529\u0026ndash;0.688)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (continuous variable)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e577.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.668 (0.593\u0026ndash;0.743)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.060 (0.021\u0026ndash;0.099)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.561 (0.338\u0026ndash;0.784)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.016 (0.005\u0026ndash;0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (\u0026ge;\u0026thinsp;1.47mL/mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e568.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.704 (0.635\u0026ndash;0.772)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.095 (0.046\u0026ndash;0.145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.609 (0.378\u0026ndash;0.839)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.034 (0.020\u0026ndash;0.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eREVEAL 2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e578.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.645 (0.560\u0026ndash;0.729)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (continuous variable)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e573.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.687 (0.611\u0026ndash;0.763)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.043 (0.006\u0026ndash;0.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.556 (0.334\u0026ndash;0.779)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.016 (0.005\u0026ndash;0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (\u0026ge;\u0026thinsp;1.47mL/mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e565.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.725 (0.653\u0026ndash;0.797)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.080 (0.030\u0026ndash;0.131)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.609 (0.378\u0026ndash;0.839)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.034 (0.020\u0026ndash;0.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eREVEAL lite 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e576.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.650 (0.567\u0026ndash;0.734)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (continuous variable)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e572.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.687 (0.611\u0026ndash;0.763)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.036 (0.004\u0026ndash;0.069)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.477 (0.255\u0026ndash;0.699)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.013 (0.003\u0026ndash;0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+PAC (\u0026ge;\u0026thinsp;1.47mL/mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e564.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.725 (0.655\u0026ndash;0.794)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.075 (0.023\u0026ndash;0.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.609 (0.378\u0026ndash;0.839)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.030 (0.016\u0026ndash;0.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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=\"9\"\u003ePAC, pulmonary arterial compliance; AIC, Akaike information criterion; NRI, net reclassification improvement; IDI, integrated discrimination improvement.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the relationships of PAC with long-term outcomes in patients with PAH-CHD with the largest sample size to date and especially across the subgroups of different pathophysiology which was not explored before. Our study found that: (1) Lower PAC was associated with worse functional status and hemodynamics in PAH-CHD; (2) PAC was independently associated with the all-cause mortality of PAH-CHD patients, which might be affected by the presence of post-tricuspid shunt; (3) Incorporating PAC into the validated risk assessment models significantly improved the discrimination ability. The combination of REVEAL lite 2 score and PAC showed the best predictive capacity and goodness-of-fit.\u003c/p\u003e\u003cp\u003eIncreased afterload is a critical factor in the progression of RV dysfunction and leads to poor prognosis in PAH, which is often composed of steady and pulsatile component[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. PAC reflects the pulsatile component and is associated with distensibility and elasticity of pulmonary arterial wall[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The ratio of stroke volume to pulmonary pulse pressure is a simple and accepted method to derive PAC surrogate which has been validated in clinical studies[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A decrease in PAC usually implies elevated afterload, which reflects the progression of the entire pulmonary vascular tree remodeling, cumulation of collagen and reduction of elastin in pulmonary arteries as the arterial compliance mainly distributed over the distal vessels throughout the pulmonary circulation in contrast with systemic circulation[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, the change of PAC is sensitive in patients with mild pulmonary vascular disease owing to an inverse hyperbolic relationship between PAC and PVR[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. As pulmonary arterial stiffness and PAC deteriorate, the pulmonary pulse pressure throughout the cardiac cycle will be dramatically elevated, which further promotes the pulmonary vascular lesion and increases the static component of the afterload, ultimately causing right heart failure and death[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe prognostic value of PAC has been explored in patient populations of idiopathic and connective tissue disease associated PAH[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In those studies, the additive value of PAC was attributed to a more precise assessment of the RV afterload along with PVR, especially in the patients with connective tissue disease where the myocardial fibrosis and arteritis played an important role[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, there was also controversial reports that PAC was similar between different forms of pulmonary hypertension and did not predict outcome in PAH after adjustment for other risk factors[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Unlike other groups of PAH, PAH-CHD is a heterogenous population of multiple defect types, clinical classifications and pathophysiology. The pathogenesis of PAH-CHD involves the pressure and/or volume overload in the pulmonary circulation caused by intracardiac or extracardiac shunts, resulting in high shear stress and endothelial damage[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, it was also reported that patients in the PAH-CHD subgroup had better cardiac functional status and 10-year survival compared with other PAH subgroups, despite with the highest PVR and mPAP[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These factors may influence the performance of PAC in the afterload assessment and long-term prediction in PAH-CHD which needs further investigation.\u003c/p\u003e\u003cp\u003ePrior studies have assessed the association between PAC and the risk of all-cause mortality in PAH-CHD patients. Sajan et al.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and Douwes et al.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] reported that elevated PAC reduced the risk of death/lung transplantation in pediatric patients with PAH-CHD. However, both of studies were retrospective and limited to pediatric population with mixed etiologies of idiopathic and congenital heart disease (28 and 20 pediatric patients of PAH-CHD, respectively), leaving the role of PAC in the isolated PAH-CHD population unclear. Iwaya et al.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] retrospectively enrolled 22 infants with PAH and atrial septal defect and found that PAC showed a significant relation to all-cause death in univariable logistic regression (OR\u0026thinsp;=\u0026thinsp;0.03, 95%CI 0.001\u0026ndash;0.73, P\u0026thinsp;=\u0026thinsp;0.031) but did not in multivariable analysis due to the limited number of subjects. Another prospective single-center study enrolled 175 patients with PAH-CHD and found that lower PAC was correlated with worse exercise tolerance, biomarkers, hemodynamics and prognosis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Notably, patients with prevalent systemic-to-pulmonary shunts, a common classification of PAH-CHD, was excluded in this study, and this limited the extrapolation of the findings. Furthermore, none of previous studies have ever explored the value of PAC within different subgroups of the heterogenous PAH-CHD population; for instance, within different shunts condition and defect types.\u003c/p\u003e\u003cp\u003eTo fill the research gap about the prognostic relevance of PAC in PAH-CHD patients, this prospective study included 434 adult patients from 15 medical centers in China with the largest sample size to date, covering a wide range of different subgroups and defect types. In the overall population of PAH-CHD, PAC independently predicted all-cause mortality after a stepwise multivariable analysis. Here, a cut-off value of 1.47mL/mmHg was reported of the largest discriminative capacity of death. Two prior studies on idiopathic PAH or specific patients with systemic lupus erythematosus reported cut-off values of 0.90 and 1.39mL/mmHg respectively[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A higher cut-off of current study may be due to the milder pulmonary vascular lesion and presence of cardiac shunts in some patients, which can relieve the RV load through the defect and, thereby, leads to a higher PAC but a lower load that RV actually confronted with. In another cohort of PAH-CHD, Cheng et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported that PAC\u0026thinsp;\u0026lt;\u0026thinsp;1.04mL/mmHg was associated with a poor prognosis in PAH-CHD, which was lower than that of current study and may be attributed to the extra inclusion of younger patients aged 14\u0026ndash;18 years in this study. In comparison to adults, children typically exhibit lower PAC values[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePathophysiology and prognosis varies among the different subgroups in PAH-CHD[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the subgroup analysis of current study, PAC significantly predicted outcome in the subgroup without the post-tricuspid shunt but did not in the other, which differed from the results by Douwes et al.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] although their study population were children. The assumption that PAC reflects the pulsatile component of load relies on that the chamber and vessels through RV to the distal pulmonary capillary bed are intact without defect, thus the presence of post-tricuspid shunt may have a negative impact on the precise assessment of the afterload by PAC. Therefore, the current study may indicate that PAC predicted outcome mainly though representing RV function instead of a function of pulmonary vascular disease[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This finding also concorded with a more significant hazard ratio range in the subgroup with atrial septal defect or PAH after correction, as in those two groups, there was no presence of post-tricuspid shunt.\u003c/p\u003e\u003cp\u003eThe additive value of PAC was ever investigated in other PAH subgroups. A recent study reported that inclusion of hemodynamics at follow-up showed additional value to non-invasive parameters for the end-point in PAH, where PAC\u0026thinsp;\u0026gt;\u0026thinsp;1.5mL/mmHg, similar with the cut-off of the current study, defined a low risk status[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In another study on population of systemic sclerosis associated PAH, PAC was incorporated into a hemodynamic risk assessment tool for prognostic significance[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Meanwhile, McCormick et al.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] reported that PAC did not add prognostic value after adjustment for REVEAL 2.0 score in PAH patients while PAH-CHD patients was not included in this study either. In contrast, the current study supported the incremental prognostic value of integrating PAC with other validated risk models in PAH-CHD patients. PAC \u0026lt;/\u0026ge;1.47mL/mmHg remained as a significant predictor after adjustment for ESC/ERS three-strata model, COMPERA models or REVEAL score, and the reclassification and discrimination abilities of these models were significantly improved. In a recent comparison research of contemporary risk scores in pulmonary hypertension, Yogeswaran et al.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] reported that continuous REVEAL scores demonstrated the highest statistical prognostic power. In addition to this finding, we reported a combination model of REVEAL lite 2 score and PAC \u0026lt;/\u0026ge;1.47mL/mmHg to achieve both the best predictive capacity and goodness-of-fit in patients with PAH-CHD, which highlighted the importance of assessing pulsatile afterload in the risk stratification of this population.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral limitations in our study should be acknowledged. First, all patients were enrolled from tertiary hospitals in China, which typically enrolled more complex and severe cases, potentially introducing selection bias. But it should be noted that the study included patients from 15 centers in various areas of China, which helps to ensure the generalizability of the findings. Second, a portion of patients without PAC data were excluded from the cohort. Nevertheless, our study had the largest sample size to date for PAH-CHD patients with a broad inclusion criteria, allowing subgroup analysis and ensuring the robustness of the findings. Finally, PAC was measured only at baseline, without monitoring longitudinal changes. This restricted the assessment that how changes of PAC during follow-up might influence prognosis, which needs further investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePAC was an independent prognostic factor of long-term prognosis in adult patients of PAH-CHD. The role of PAC varied across different clinical classifications, defect types and shunts characteristics, and the presence of post-tricuspid shunt may have a negative impact on the prognostic value of PAC. In addition, PAC provided an incremental predictive value on the validated risk assessment models. A combination model of REVEAL lite 2 score and PAC \u0026lt;/\u0026ge;1.47mL/mmHg showed the best predictive capacity and goodness-of-fit, highlighting the importance of assessing RV pulsatile afterload in the risk stratification of PAH-CHD population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e6MWD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esix-minute walking distance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBNP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebrain natriuretic peptide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econgenital heart disease\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\"\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\"\u003emRAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean right atrial 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\"\u003eNT-proBNP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eN-terminal pro-brain natriuretic peptide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epulmonary arterial compliance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epulmonary arterial hypertension\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAH-CHD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epulmonary arterial hypertension associated with congenital heart disease\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\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erestricted cubic spline\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\"\u003eS\u003csub\u003ev\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emixed venous oxygen saturation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003evariance inflation factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO-FC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization functional class.\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\u003c/p\u003e\n\u003cp\u003eAll enrolled patients had provided written informed consent. The study adhered to the ethical guidelines of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Fuwai Hospital (Approval No.2009-208).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the National Key Research and Development Program of China (No. 2016YFC1304400), the China Key Research Projects of the 11th National Five-Year Development Plan (Project Number: 2006BAI01A07) and the China Key Research Projects of the 12th National Five-Year Development Plan (No. 2011BAI11B15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJH contributed to the conception and design of the study. QL and HY performed the data analyses and were the major contributors in writing the manuscript. QG and CX contributed to the data interpretation.\u0026nbsp;ZY, YL, WW, XZ and HH\u0026nbsp;contributed to data acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the study individuals for their participation and all the staff members for their contributions to this study including: Gangcheng Zhang (Department of Cardiology, Wuhan Asia Heart Hospital, Wuhan 430022, P.R. China),\u0026nbsp;Jichun Liu (Department of Cardiac Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, China), Zhi Zeng (Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China), Xiulong Zhu (Department of Cardiovascular Medicine, The People’s Hospital of Gaozhou, Maoming, China), Lianjun Huang (Interventional Department, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China), Daxin Zhou (Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Disease, Shanghai 200032, P.R. China), Xiangqian Shen (Department of Cardiology, the Second Xiangya Hospital, Central South University, Changsha 410008, P.R. China), Guishuang Li (Department of Cardiology, Qilu Hospital of Shandong University, Jinan 250063, P. R. China),\u0026nbsp;Zhonghe Zhang (Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China), Long Yang (Cardiology Department, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China), and Xiaoqiao Liu (Department of Cardiology, Guizhou Provincial People's Hospital, Guiyang 550002, China).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMocumbi A, Humbert M, Saxena A, Jing ZC, Sliwa K, Thienemann F, Archer SL, Stewart S. Pulmonary hypertension. Nat Rev Dis Primers. 2024;10(1):1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHumbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, Carlsen J, Coats AJS, Escribano-Subias P, Ferrari P, et al. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. 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Eur J Heart Fail. 2020;22(1):32\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThenappan T, Prins KW, Pritzker MR, Scandurra J, Volmers K, Weir EK. The Critical Role of Pulmonary Arterial Compliance in Pulmonary Hypertension. Ann Am Thorac Soc. 2016;13(2):276\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOoi CY, Wang Z, Tabima DM, Eickhoff JC, Chesler NC. The role of collagen in extralobar pulmonary artery stiffening in response to hypoxia-induced pulmonary hypertension. Am J Physiol Heart Circ Physiol. 2010;299(6):H1823\u0026ndash;1831.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLammers SR, Kao PH, Qi HJ, Hunter K, Lanning C, Albietz J, Hofmeister S, Mecham R, Stenmark KR, Shandas R. Changes in the structure-function relationship of elastin and its impact on the proximal pulmonary arterial mechanics of hypertensive calves. 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(2013 Grover Conference series). \u003cem\u003ePulm Circ\u003c/em\u003e 2014, 4(4):560\u0026ndash;580.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCormick A, Krishnan A, Badesch D, Benza RL, Bull TM, De Marco T, Feldman J, Hemnes AR, Hirsch R, Horn E, et al. Pulmonary artery compliance in different forms of pulmonary hypertension. Heart. 2023;109(14):1098\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahmoud AK, Abbas MT, Kamel MA, Farina JM, Pereyra M, Scalia IG, Barry T, Chao CJ, Marcotte F, Ayoub C et al. Current Management and Future Directions for Pulmonary Arterial Hypertension Associated with Congenital Heart Disease. J Pers Med 2023, 14(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSajan I, Manlhiot C, Reyes J, McCrindle BW, Humpl T, Friedberg MK. Pulmonary arterial capacitance in children with idiopathic pulmonary arterial hypertension and pulmonary arterial hypertension associated with congenital heart disease: relation to pulmonary vascular resistance, exercise capacity, and survival. Am Heart J. 2011;162(3):562\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYogeswaran A, Gall H, Funderich M, Wilkins MR, Howard L, Kiely DG, Lawrie A, Hassoun PM, Sirenklo Y, Torbas O, et al. Comparison of Contemporary Risk Scores in All Groups of Pulmonary Hypertension: A Pulmonary Vascular Research Institute GoDeep Meta-Registry Analysis. Chest. 2024;166(3):585\u0026ndash;603.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary arterial compliance, congenital heart disease, Eisenmenger Syndrome, pulmonary arterial hypertension, right heart catheterization, prognosis, risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-7036244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7036244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePulmonary arterial compliance (PAC) reflects the pulsatile load and predicts outcome in pulmonary arterial hypertension (PAH). The prognostic role of PAC in the heterogeneous patient population of PAH associated with congenital heart disease (PAH-CHD) is poorly defined. This study aimed to explore the prognostic value of PAC in patients with PAH-CHD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdult patients diagnosed with PAH-CHD were collected from a PAH multicenter prospective registry between August 2009 and December 2019. The primary endpoint was all-cause mortality. Multivariable Cox regression and restricted cubic spline (RCS) analysis were used to evaluate the association between PAC and the primary endpoint. Subgroup and interaction analysis between PAC and shunts or defect characteristics were explored. Incremental predictive performance was evaluated by calculating the C-index, continuous net reclassification improvement, and integrated discrimination improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 434 adult PAH-CHD patients were enrolled. The median follow-up time was 52.2 months. The survival rate of patients in the lower PAC group was significantly worse than those in the higher PAC group (Log-rank P \u0026lt; 0.001). Multivariable Cox regression analysis showed that PAC independently predicted all-cause mortality after adjustment for other prognostic factors, whether as a continuous variable (HR = 0.665, 95%CI 0.503–0.878, P = 0.004) or a dichotomous variable (HR = 0.251, 95%CI 0.124–0.507, P \u0026lt; 0.001). A linear relationship between PAC and all-cause mortality was identified by RCS analysis. Subgroup analysis revealed that the impact of PAC might be affected by the presence of post-tricuspid shunt. Incorporating PAC into the validated risk models significantly improved the reclassification and discrimination ability for all-cause mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePAC was significantly associated with all-cause mortality in patients with PAH-CHD and provided additional value on risk assessment. The role of PAC may vary across different clinical subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinicalTrials.gov (NCT01417338), registered 16th August 2011.\u003c/p\u003e","manuscriptTitle":"Pulmonary arterial compliance as a long-term prognostic indicator in pulmonary arterial hypertension associated with adult congenital heart disease: results from a national multicenter prospective registry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 12:46:38","doi":"10.21203/rs.3.rs-7036244/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-27T06:51:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T01:54:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T05:10:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335207414202369350288664385509263960376","date":"2025-07-26T10:28:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298027634723255241917759470092656987972","date":"2025-07-11T18:11:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-11T17:52:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-10T05:46:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-08T08:36:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-08T08:35:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-07-03T09:11:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d26d54b9-07f2-4a88-9278-747138f71431","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:08:28+00:00","versionOfRecord":{"articleIdentity":"rs-7036244","link":"https://doi.org/10.1186/s12872-025-05230-5","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-10-14 15:58:45","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-07-18 12:46:38","video":"","vorDoi":"10.1186/s12872-025-05230-5","vorDoiUrl":"https://doi.org/10.1186/s12872-025-05230-5","workflowStages":[]},"version":"v1","identity":"rs-7036244","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7036244","identity":"rs-7036244","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}