Association of Pre-Transplant Pulmonary Hypertension and Post-Transplant Major Adverse Cardiovascular Events in Kidney Transplant Recipients

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DeLonais-Parker, Krista L. Lentine, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8014688/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2026 Read the published version in BMC Nephrology → Version 1 posted 16 You are reading this latest preprint version Abstract Background: Pulmonary hypertension (PH) frequently complicates chronic kidney disease and end-stage kidney disease, contributing significantly to cardiovascular morbidity and mortality. We investigated the association between pre-transplant PH and major adverse cardiovascular events (MACE) after kidney transplantation (KT). Methods: This retrospective cohort study included 468 adult KT recipients from an academic medical center between January 2015 and December 2024. We excluded patients who did not follow up at our institution. Patients were stratified based on the presence of pre-transplant PH (defined as pulmonary artery systolic pressure >35 mmHg on echocardiography or mean pulmonary artery pressure >20 mmHg via right heart catheterization. The primary outcome was the occurrence of MACE, defined as cardiovascular death, myocardial infarction, stroke, or hospitalization for heart failure. Multivariable Cox proportional hazards models were used to evaluate the independent association between pre-transplant PH and post-KT MACE. Results: Of the 468 recipients who qualified for the study, 86 (18.4%) had pre-transplant PH. Over a mean follow-up of 54.7 ± 28.4 months, 89 patients (19.0%) experienced MACE. The incidence of MACE was significantly higher in recipients with pre-KT PH compared with those without at one-year (8.1% vs 2.9%, p=0.031) and five-year (22.0% vs 11.0%, p=0.008). After adjusting for age, sex, and confounding variables, PH remained independently associated with MACE (adjusted HR 2.16; 95% CI 1.31–3.55; p=0.003). Conclusions: In this retrospective single-center study, pre-transplant PH was independently associated with an increased risk for MACE following KT. These findings highlight the importance of identifying PH in KT candidates. More intensified risk mitigation measures might be needed in this population. Pulmonary hypertension Major Adverse Cardiovascular Events Transplant End Stage Kidney Disease Figures Figure 1 Introduction Pulmonary hypertension (PH) is prevalent among patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD), where it is linked to increased cardiovascular morbidity and mortality ( 1 ). In patients with CKD and ESKD, PH is most commonly attributed to World Symposium on Pulmonary Hypertension (WSPH) Group 2 (left-sided cardiac dysfunction) and Group 5 (multifactorial including kidney disease-related). Multiple mechanisms are involved including, volume overload, anemia, endothelial dysfunction, and increased AV fistula flow ( 2 – 4 ). The reported prevalence of PH in ESKD varies significantly between studies by the diagnostic criteria used and modality employed ( 5 – 8 ). Previous studies have consistently identified elevated pulmonary pressures as predictors of adverse cardiovascular outcomes in this patient population, largely attributed to chronic volume overload, arteriovenous fistulas for dialysis, anemia, and underlying left ventricular dysfunction ( 2 – 4 ). Kidney transplantation (KT) significantly improves cardiovascular profiles in ESKD patients by addressing key pathophysiological mechanisms ( 9 – 11 ); however, the persistence and impact of pre-existing PH on long-term post-transplant outcomes remain inadequately understood. Prior investigations into KT outcomes in PH patients have primarily examined perioperative outcomes, all-cause mortality, and graft dysfunction without a detailed exploration of long-term cardiovascular events post-transplantation ( 12 – 14 ). In the current study, we investigated the association between pre-transplant PH and the incidence of long-term major adverse cardiovascular events (MACE), in a well-characterized cohort of KT recipients. Methods Study sample and exposure After obtaining approval from our Institutional Review Board, we constructed a retrospective database of adult (age \(\:\ge\:\) 18 years) KT recipients at SSM Health Saint Louis University Hospital over a 9-year period (2015–2024). Electronic medical records were reviewed for demographic, clinical, laboratory values, and medications. Appendix 2 contains a dictionary of the variables recorded and their definitions. We excluded patients who did not have follow-up visits within our healthcare system. Patients without either a transthoracic echocardiogram (TTE) or right heart catheterization (RHC) were also excluded. The primary exposure was the presence of pre-transplant PH, defined using echocardiographic or invasive hemodynamic criteria. PH was considered present if one of the following criteria was met: 1) Estimated pulmonary artery systolic pressure (PASP) > 35 mmHg on TTE; 2) Mean pulmonary artery pressure (mPAP) > 20 mmHg on RHC ( 15 – 17 ). Other echocardiographic findings were documented for statistical adjustment ( 18 ). The TTE or RHC performed closest to the date of KT was used. Patients were categorized into two groups: those with (PH group) and those without (NoPH group) PH. Outcomes The primary outcome of the study was the occurrence of MACE after KT, defined as a composite of cardiovascular death, non-fatal myocardial infarction (MI), non-fatal stroke, or hospitalization for heart failure (HF) after KT until the end of the study (12/31/2024). Outcomes were assessed at 1 year and 5 years following KT. Outcome data were obtained from electronic medical record review by investigators who were blinded to PH status. HF admissions were adjudicated by the reviewers confirming that the admission was a true HF event, consistent with the 2022 AHA/ACC/HFSA criteria for HF hospitalization. Other potential causes for the symptoms (such as pneumonia or other lung diseases) should have been considered and ruled out as the primary reason for admission. Statistical analysis Continuous variables were reported as mean ± standard deviation (SD). We used Student’s T-test for continuous variables and chi-square test or Fisher exact test for categorical variables as appropriate based on event count. Univariate analysis was conducted using logistic regression model. Kaplan-Meier survival analysis curves were constructed for both groups, and differences between groups were compared utilizing log-rank test. Multivariate analyses were performed using cox proportional hazards regression model and reported as hazardous ratio (HR) with 95% confidence interval (95% CI). The analysis was adjusted for age, sex and confounding variables identified from unadjusted regression. Proportional hazard assumptions were verified using Schoenfeld residuals ( 19 ). All statistical analyses were conducted using R 2024 version (R Foundation for Statistical Computing, Vienna, Austria). A two-tailed p-value < 0.05 was considered statistically significant. Results Among 468 KT recipients qualifying for the study cohort, 86 patients (18.4%) had pre-transplant PH. Table 1 includes the baseline demographics, medical comorbidities, and cardiac medications in both groups. Mean age was comparable between both groups (53.8 vs 53.0 years). PH group patients had a significantly higher prevalence of diabetes mellitus (43.0% vs. 31.4%). Prevalence of pre-transplant coronary artery disease was higher in the PH group (34.9% vs. 27.7%) but this was not statistically significant. Table 1: Clinical and demographic characteristics by pre-transplant PH status Variables Total (N= 468) PH group (N=86) NoPH group (N=382) p value Age at transplant (mean ± SD) 53.8 ± 11.9 53.0 ± 13.3 0.57 Male, n (%) 265 (56.6) 49 (57.0) 216 (56.5) 1 Race, n (%) 0.36 White 215 (45.9) 33 (38.4) 182 (47.6) Black/ African American 225 (48.1) 49 (57.0) 176 (46.1) Asian 16 (3.4) 4 (4.7) 12 (3.1) Hispanic Latino 7 (1.5) 0 7 (1.8) BMI, kg/m 2 , n (%) 0.46 Underweight ( 35) 132 (28.2) 9 (10.4) 66 (17.3) Etiology of renal failure, n (%) Focal segmental glomerulosclerosis 31 (6.6) 8 (9.3) 23 (6.0) 0.39 Diabetes Type I 18 (3.8) 3 (3.5) 15 (3.9) 1 Diabetes Type II 138 (29.5) 34 (39.5) 104 (27.2) 0.03* Essential Hypertension 146 (31.2) 42 (48.8) 104 (27.2) 0.20 Renovascular hypertension 8 (1.7) 0 8 (2.1) 0.36 Glomerulonephritis 36 (7.7) 0 36 (9.4) 0.01* Interstitial nephritis/Pyelonephritis 5 (1.1) 1 (1.2) 4 (1) 1 Polycystic Kidney Disease 33 (7.1) 5 (5.8) 28 (7.3) 0.79 Neoplasm 4 (0.9) 2 (2.3) 2 (0.5) 0.32 Comorbidities, n (%) Hypertension 197 (42.1) 42 (48.8) 155 (40.6) 0.20 Coronary artery disease 136 (29.1) 30 (34.9) 106 (27.7) 0.16 Diabetes mellitus 157 (33.5) 37 (43.0) 120 (31.4) 0.04* Medications, n (%) Aspirin 195 (41.7) 47 (54.7) 148 (38.7) 0.01* Beta-blocker 298 (63.7) 62 (72.1) 236 (61.8) 0.09 ACEi/ARB 185 (39.5) 36 (41.9) 149 (39.0) 0.71 Calcium channel blocker 250 (53.4) 52 (60.5) 198 (51.8) 0.18 Hydralazine 82 (17.5) 24 (27.9) 58 (15.2) 0.01* Nitrates 22 (4.7) 6 (7.0) 16 (4.2) 0.37 Diuretics 134 (28.6) 27 (31.4) 107 (28.0) 0.62 Note: Continuous variables are reported as mean ± SD, N(%) are reported. Abbreviations: BMI: Body Mass Index; ACEi: Angiotensin-Converting Enzyme Inhibitor; ARB: Angiotensin Receptor Blockers; Statistical significance: p < 0.05 (*). Among those who underwent RHC, PH group had significantly higher mean pulmonary artery pressures (mean PAP 28.94 ± 7.45 mmHg vs. 16.21 ± 2.44 mmHg, p<0.001). Echocardiographic characteristics (Table 2) revealed significantly higher tricuspid regurgitation (TR) peak gradient (mean TR peak gradient 35.2 ± 8.17 mmHg vs. 22.4 ± 6.11mmHg, p<0.001) and larger left atrial volumes (83.4 ± 44.2 mL vs. 62.1 ± 26.1 mL, p<0.001) among patients with PH. Table 2: Echocardiographic features in kidney transplant recipients based on PH status Variable Total (n=468) PH (n=86) No PH (n=382) p value LV ejection fraction, % 62.9 ± 8.1 64.0 ± 10.5 62.7 ± 7.4 0.29 RV dysfunction, n (%) 4 (0.9) 1 (1.2) 3 (0.8) 0.12 TAPSE, cm 2.30 ± 0.66 2.46 ± 0.68 2.27 ± 0.65 0.04* TR peak gradient, mmHg 25.7 ± 9.05 35.2 ± 8.17 22.4 ± 6.11 < 0.001* Left atrial volume, mL 66.0 ± 31.3 83.4 ± 44.2 62.1 ± 26.1 < 0.001* IVS diastolic thickness, cm 1.30 ± 0.40 1.43 ± 0.43 1.27 ± 0.38 0.002* Posterior wall thickness, cm 1.18 ± 0.32 1.33 ± 0.37 1.15 ± 0.30 < 0.001* Pericardial effusion, n (%) 90 (19.2) 27 (33.8) 63 (17.3) 0.001* Mitral regurgitation, n (%) None or trace 308 (65.8) 45 (52.9) 263 (68.9) 0.005* Mild 140 (29.9) 31 (36.5) 109 (28.5) 0.21 Moderate 18 (3.8) 9 (10.6) 9 (2.46) 0.001* Severe 1 (0.2) 0 1 (0.3) 1 Tricuspid regurgitation, (%) None or trace 259 (55.3) 28 (33.73) 231 (60.47) 0.001* Mild 190 (40.6) 48 (57.83) 142 (37.17) 0.002* Moderate 15 (3.2) 7 (18.43) 8（2.10) 0.01* Severe 1 (0.2) 0 (0%) 1 (0.26) 1 Aortic stenosis, (%) None or trace 439 (93.8) 77 (92.87) 362 (96.8) 0.12 Mild 14 (3.0) 5 (6.0) 9 (2.4) 0.18 Moderate 4 (0.9) 1 (1.2) 3 (0.8) 1 Severe 0 0 0 Aortic regurgitation, (%) None or trace 399 (85.3) 64 (76.2) 335 (88.4) 0.003* Mild 57 (12.2) 15 (17.9) 42 (11.1) 0.14 Moderate 7 (1.5) 5 (6.0) 2 (0.5) 0.002* Severe 0 0 0 Average RV strain 20.54 ± 8.55 21.6 ± 7.34 20.3 ± 8.79 0.23 Note: Continuous variables are reported as mean ± SD, N (%) are given Abbreviations: LV: left ventricle, RV: right ventricular, TAPSE: tricuspid annular posterior systolic excursion, TR: tricuspid regurgitation, IVS: interventricular septum; Statistical significance: p < 0.05 (*). The mean follow-up duration from transplant was 54.7 ± 28.4 months. As shown in Table 3, at one year post transplant, patients with pre-transplant PH experienced significantly higher incidence of MACE compared to those without PH (8.1 vs. 2.9%; p=0.031). This difference remained significant at five-year post-transplant follow up with (22.0% vs 11.0%, p=0.008). This difference was driven by higher rates of non-fatal MI (8.1 vs 2.9%, p=0.031) and hospitalization for heart failure (13.0% vs 4.5%, p = 0.007) in the PH group. Table 3: One- and Five-Year All-Cause Mortality and MACE Stratified by Pre-KT Pulmonary Hypertension 1 Year 5 Years PH (n, %) No PH (n, %) p value PH (n, %) No PH (n, %) p value All-Cause Mortality 3 (3.5%) 6 (1.6%) 0.22 8 (9.3%) 34 (8.9%) 1 Total MACE 7 (8.1%) 11 (2.9%) 0.031* 19 (22.0%) 41 (11.0%) 0.008* Cardiovascular Death 1 (1.2%) 0 (0%) 0.18 3 (3.5%) 9 (2.4%) 0.47 Non-Fatal Myocardial Infarction 1 (1.2%) 4 (1%) 1 7 (8.1%) 11 (2.9%) 0.031* Hospitalization for Heart Failure 3 (3.5%) 8 (2.1%) 0.43 11 (13.0%) 17 (4.5%) 0.007* Non-Fatal Stroke 3 (3.5%) 2 (0.5%) 0.045* 6 (7.0%) 11 (2.9%) 0.1 Abbreviations: PH = pulmonary hypertension; MACE = major adverse cardiovascular events; Statistical significance: p < 0.05 (*). Kaplan- Meier survival analysis (Figure 1) demonstrated a significantly lower MACE-free survival in PH group when compared to the NoPH group at all time points from KT (log rank p=0.0011). Univariate logistic regression results are listed in Table 4. Table 4: Univariate logistic regression for Predictors of Major Adverse Cardiovascular Events in Kidney Transplant Recipients. Variable Odds ratio 95% CI p value Pulmonary hypertension 2.04 1.19–3.49 0.009* Age 1.03 1.01–1.05 0.005* Sex (male) 1.10 0.69–1.75 0.70 Left ventricular ejection fraction 0.98 0.95–1.01 0.11 Diabetes mellitus 1.83 1.15–2.92 0.011* Coronary artery disease 2.76 1.71–4.47 <0.001* Obstructive sleep apnea 1.20 0.73–1.97 0.47 Right ventricular dysfunction 1.44 0.15–14.03 0.75 Tricuspid annular plane systolic excursion 0.78 0.50–1.23 0.29 Tricuspid regurgitation peak gradient 1.01 0.98–1.04 0.44 Right ventricular strain 0.95 0.92–0.99 0.01* Abbreviations: CI = confidence interval; Statistical significance: p < 0.05 (*). In the Cox hazard regression analysis (Table 5), after adjusting for age, sex, diabetes mellitus, coronary artery disease, right ventricular dysfunction and left ventricular ejection fraction, pre-transplant PH remained strongly associated with increased risk of MACE (HR 2.16; 95% CI 1.31–3.55; p=0.003). Age and history of coronary artery disease also remained significantly associated with post-transplant MACE incidence. The proportional hazards assumption for all covariates in the Cox regression model was verified using Schoenfeld residuals, and no violations were identified. Table 5: Cox Regression Analysis for the Outcome of Major Adverse Cardiovascular Events Post-Kidney Transplant. Variable Hazard ratio 95% CI p value PH on Echocardiogram or RHC 2.16 1.31 -3.55 0.003* Age 1.03 1.01 -1.05 0.01* Sex (male) 1.03 0.65 -1.63 0.89 Left ventricular ejection fraction 0.99 0.96 -1.01 0.33 Diabetes mellitus 1.27 0.78 - 2.03 0.31 Coronary artery disease 1.92 1.21 - 3.04 0.006* Right ventricular dysfunction 1.79 0.24 - 13.3 0.57 Abbreviations: CI = confidence interval; PH = pulmonary hypertension; RHC = right heart catheterization; Statistical significance: p < 0.05 (*). Discussion In this retrospective cohort study of KT recipients, our main finding was that pre-transplant PH was independently associated with MACE following KT. Even though the tricuspid regurgitation peak gradient in the PH group in our study was only 35 mmHg (mild PH), patients in PH group had at least twice the risk of MACE. Our findings are consistent with prior studies demonstrating the negative prognostic implications of PH in patients with ESRD (4, 7, 9). Rabih et al. reported that pre-transplant PH, defined by echocardiography, was associated with increased risk of post-transplant death, graft dysfunction, and graft failure at 5-year follow-up(14). Obi et al. similarly observed increased mortality and reduced graft survival in patients with pre-transplant PH(20). Tang et al.'s meta-analysis of 16 studies reported a 2.2-fold increased risk of cardiovascular mortality in CKD or ESKD patients with PH (7). Although KT mitigates several contributors to PH, such as uremia, volume overload, and AV fistulas, it may not reverse longstanding pulmonary vascular and myocardial remodeling. This persistence of risk highlights the need for enhanced cardiovascular surveillance and potentially targeted management strategies for transplant recipients with pre-existing PH. Clinical Implications These results should not be interpreted as justification to exclude PH patients from transplant candidacy. Rather, they emphasize the importance of early recognition and stratification of PH during transplant evaluation. This may include prioritizing cardiology consultation, more aggressive optimization of volume and pulmonary pressure status, and consideration of RHC as clinically indicated. Strengths of our study include the relatively large sample size and the long-term follow up of cardiovascular outcomes post-KT. Additionally, our robust multivariable analysis carefully adjusted for key demographic and clinical confounders, enhancing the validity of our findings. However, several limitations must be acknowledged. This was a single-center, retrospective study with potential for various biases and limited generalizability. Most patients were stratified by TTE estimates of PASP rather than by invasive RHC which may confer a variable degree of accuracy in estimating pulmonary pressure compared to RHC (16). In addition, the lack of invasive hemodynamic data did not allow further classification of PH e.g. pre- versus post-capillary PH. Finally, the confounding effect from other commonly observed comorbidities in various groups of PH such as interstitial lung disease, dialysis status, severity of anemia, and pre-transplant medication regimen cannot be excluded. Future investigations should explore the evolution of PH before and after transplantation, the role of universal invasive hemodynamic testing for all candidates, and whether targeted interventions can modify cardiovascular risk in this high-risk group. In conclusion, in this retrospective study of KT recipients, the presence of -even mild- pre-transplant PH was independently associated with increased risk of post-transplant MACE. Clinicians should consider targeted risk modification strategies to improve outcomes in this vulnerable population. Abbreviations PH: pulmonary hypertension, ESKD: end-stage kidney disease, WSPH: World Symposium on Pulmonary Hypertension, KT: kidney transplantation, MACE: major adverse cardiovascular events, TTE: transthoracic echocardiogram, RHC: right heart catheterization, PASP: pulmonary artery systolic pressure, mPAP: mean pulmonary artery pressure, MI: myocardial infarction, HF: heart failure, SD: standard deviation, HR: hazardous ratio, CI: confidence interval, BMI: Body Mass Index; ACEi: Angiotensin-Converting Enzyme Inhibitor; ARB: Angiotensin Receptor Blockers, LV: left ventricle, RV: right ventricular, TAPSE: tricuspid annular posterior systolic excursion, TR: tricuspid regurgitation, IVS: interventricular septum Declarations Ethics approval and consent to participate Study was approved by Saint Louis University IRB protocol number 34091. All procedures were conducted in accordance with the 1964 Declaration of Helsinki. Consent to Participate declarations: Need for consent to participate was waived by Saint Louis University Institutional Review Board (IRB). IRB titled “Cardiac imaging predictors of outcomes in transplant patients.” IRB number is 34091 Consent for publication : Not applicable Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: No financial or non-financial competing interests to declare Funding: No funding was obtained to support this study. Authors’ contributions YL RN participated in data analysis, data interpretation, and writing the manuscript ADP participated in study design, regulatory approvals, data review, data entry, and manuscript writing KL BO MB participated in study design, acquisition of data, regulatory approvals, data analysis, data interpretation, and writing the manuscript. 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Effect of pulmonary hypertension on 5-year outcome of kidney transplantation. Pulm Circ . 2022;12(1):e12010. Published 2022 Jan 3. 10.1002/pul2.12010 Simonneau G, Montani D, Celermajer DS, Denton CP, Gatzoulis MA, Krowka M et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. Eur Respir J. 2019;53(1). Taleb M, Khuder S, Tinkel J, Khouri SJ. The diagnostic accuracy of Doppler echocardiography in assessment of pulmonary artery systolic pressure: a meta-analysis. Echocardiography. 2013;30(3):258–65. Arcasoy SM, Christie JD, Ferrari VA, Sutton MS, Zisman DA, Blumenthal NP, et al. Echocardiographic assessment of pulmonary hypertension in patients with advanced lung disease. Am J Respir Crit Care Med. 2003;167(5):735–40. Raymond RJ, Hinderliter AL, Willis PW, Ralph D, Caldwell EJ, Williams W, et al. Echocardiographic predictors of adverse outcomes in primary pulmonary hypertension. J Am Coll Cardiol. 2002;39(7):1214–9. Grambsch PMTT. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515–26. Obi C, Frost AE, Graviss EA, Nguyen DT, Gaber AO, Suki WN. The Association of Pretransplant Pulmonary Hypertension With Patient and Graft Survival After Kidney Transplantation: A Retrospective Cohort Study. Transplant Proc. 2020;52(10):3023–3032. 10.1016/j.transproceed.2020.05.003 Appendix 1. SLU Transplant Rx research Group. Ananta Sriram A, Bower E, Li G, Christy H, Wiseman. Julia Lieu, Khang Nguyen, Leighton Hope, Ravneet Nagra, Samantha Harrington, Sophia Heuer, Zalan Shah. Affiliation. Saint Louis University School of Medicine. Additional Declarations No competing interests reported. Supplementary Files Appendixs.docx Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2026 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 14 Jan, 2026 Reviews received at journal 14 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviews received at journal 28 Dec, 2025 Reviews received at journal 17 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers invited by journal 24 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Editor invited by journal 16 Nov, 2025 Submission checks completed at journal 14 Nov, 2025 First submitted to journal 14 Nov, 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. 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08:18:57","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163894,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8014688/v1/81500677ad7ea2ebb0f5cc9f.html"},{"id":97125517,"identity":"e7ef6735-0b4d-441b-b33a-399410d3ac69","added_by":"auto","created_at":"2025-12-01 08:18:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75827,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for freedom from major adverse cardiovascular events in kidney transplant recipients with and without pre-transplant pulmonary hypertension (PH)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8014688/v1/5f0cc5a8c51041f1435945b9.png"},{"id":103765714,"identity":"f8d5dfe5-f42c-4c35-a076-c30fd5549bcc","added_by":"auto","created_at":"2026-03-02 16:08:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":929630,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8014688/v1/0187ada8-a985-4016-af41-c0242b22cf5f.pdf"},{"id":97125513,"identity":"ee3cd870-6bc3-44a3-81be-3252beb49fd9","added_by":"auto","created_at":"2025-12-01 08:18:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":36750,"visible":true,"origin":"","legend":"","description":"","filename":"Appendixs.docx","url":"https://assets-eu.researchsquare.com/files/rs-8014688/v1/03498b60ef4675ad89c0bb02.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Pre-Transplant Pulmonary Hypertension and Post-Transplant Major Adverse Cardiovascular Events in Kidney Transplant Recipients","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary hypertension (PH) is prevalent among patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD), where it is linked to increased cardiovascular morbidity and mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In patients with CKD and ESKD, PH is most commonly attributed to World Symposium on Pulmonary Hypertension (WSPH) Group 2 (left-sided cardiac dysfunction) and Group 5 (multifactorial including kidney disease-related). Multiple mechanisms are involved including, volume overload, anemia, endothelial dysfunction, and increased AV fistula flow (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The reported prevalence of PH in ESKD varies significantly between studies by the diagnostic criteria used and modality employed (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious studies have consistently identified elevated pulmonary pressures as predictors of adverse cardiovascular outcomes in this patient population, largely attributed to chronic volume overload, arteriovenous fistulas for dialysis, anemia, and underlying left ventricular dysfunction (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Kidney transplantation (KT) significantly improves cardiovascular profiles in ESKD patients by addressing key pathophysiological mechanisms (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e); however, the persistence and impact of pre-existing PH on long-term post-transplant outcomes remain inadequately understood. Prior investigations into KT outcomes in PH patients have primarily examined perioperative outcomes, all-cause mortality, and graft dysfunction without a detailed exploration of long-term cardiovascular events post-transplantation (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In the current study, we investigated the association between pre-transplant PH and the incidence of long-term major adverse cardiovascular events (MACE), in a well-characterized cohort of KT recipients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy sample and exposure\u003c/p\u003e\u003cp\u003eAfter obtaining approval from our Institutional Review Board, we constructed a retrospective database of adult (age \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 18 years) KT recipients at SSM Health Saint Louis University Hospital over a 9-year period (2015\u0026ndash;2024). Electronic medical records were reviewed for demographic, clinical, laboratory values, and medications. Appendix 2 contains a dictionary of the variables recorded and their definitions. We excluded patients who did not have follow-up visits within our healthcare system. Patients without either a transthoracic echocardiogram (TTE) or right heart catheterization (RHC) were also excluded.\u003c/p\u003e\u003cp\u003eThe primary exposure was the presence of pre-transplant PH, defined using echocardiographic or invasive hemodynamic criteria. PH was considered present if one of the following criteria was met: 1) Estimated pulmonary artery systolic pressure (PASP)\u0026thinsp;\u0026gt;\u0026thinsp;35 mmHg on TTE; 2) Mean pulmonary artery pressure (mPAP)\u0026thinsp;\u0026gt;\u0026thinsp;20 mmHg on RHC (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Other echocardiographic findings were documented for statistical adjustment (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The TTE or RHC performed closest to the date of KT was used. Patients were categorized into two groups: those with (PH group) and those without (NoPH group) PH.\u003c/p\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003cp\u003eThe primary outcome of the study was the occurrence of MACE after KT, defined as a composite of cardiovascular death, non-fatal myocardial infarction (MI), non-fatal stroke, or hospitalization for heart failure (HF) after KT until the end of the study (12/31/2024). Outcomes were assessed at 1 year and 5 years following KT. Outcome data were obtained from electronic medical record review by investigators who were blinded to PH status. HF admissions were adjudicated by the reviewers confirming that the admission was a true HF event, consistent with the 2022 AHA/ACC/HFSA criteria for HF hospitalization. Other potential causes for the symptoms (such as pneumonia or other lung diseases) should have been considered and ruled out as the primary reason for admission.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). We used Student\u0026rsquo;s T-test for continuous variables and chi-square test or Fisher exact test for categorical variables as appropriate based on event count. Univariate analysis was conducted using logistic regression model. Kaplan-Meier survival analysis curves were constructed for both groups, and differences between groups were compared utilizing log-rank test. Multivariate analyses were performed using cox proportional hazards regression model and reported as hazardous ratio (HR) with 95% confidence interval (95% CI). The analysis was adjusted for age, sex and confounding variables identified from unadjusted regression. Proportional hazard assumptions were verified using Schoenfeld residuals (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). All statistical analyses were conducted using R 2024 version (R Foundation for Statistical Computing, Vienna, Austria). A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong 468 KT recipients qualifying for the study cohort, 86 patients (18.4%) had pre-transplant PH. Table 1 includes the baseline demographics, medical comorbidities, and cardiac medications in both groups. Mean age was comparable between both groups (53.8 vs 53.0 years). PH group patients had a significantly higher prevalence of diabetes mellitus (43.0% vs. 31.4%). Prevalence of pre-transplant coronary artery disease was higher in the PH group (34.9% vs. 27.7%) but this was not statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Clinical and demographic characteristics by pre-transplant PH status\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N= 468)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePH group (N=86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNoPH group (N=382)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at transplant (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e53.8 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e53.0 \u0026plusmn; 13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e265 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e49 (57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e216 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e215 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e33 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e182 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eBlack/ African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e225 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e49 (57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e176 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n 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(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eUnderweight (\u0026lt;18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e3 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eNormal (18.6 - 24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e95 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e20 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e75 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eOverweight (25 - 34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e294 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e57 (66.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e237 (62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eObese (\u0026gt; 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e132 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e9 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e66 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEtiology of renal failure, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eFocal segmental glomerulosclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e31 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e8 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e23 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eDiabetes Type I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e18 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e3 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e15 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eDiabetes Type II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e138 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e34 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e104 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eEssential Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e146 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e42 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e104 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eRenovascular hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e8 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e8 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eGlomerulonephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e36 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e36 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eInterstitial nephritis/Pyelonephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e5 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e1 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e4 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003ePolycystic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e33 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e5 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e28 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eNeoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e4 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e2 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e197 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e42 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e155 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e136 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e30 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e106 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e157 (33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e37 (43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e120 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedications, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eAspirin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e195 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e47 (54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e148 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eBeta-blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e298 (63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e62 (72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e236 (61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eACEi/ARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e185 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e36 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e149 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eCalcium channel blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e250 (53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e52 (60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e198 (51.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eHydralazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e82 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e24 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e58 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eNitrates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e22 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e6 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e16 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.4615%;\"\u003e\n \u003cp\u003eDiuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.8289%;\"\u003e\n \u003cp\u003e134 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.9254%;\"\u003e\n \u003cp\u003e27 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8772%;\"\u003e\n \u003cp\u003e107 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.907%;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: Continuous variables are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, N(%) are reported.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: BMI: Body Mass Index; ACEi: Angiotensin-Converting Enzyme Inhibitor; ARB: Angiotensin Receptor Blockers; Statistical significance: p \u0026lt; 0.05 (*).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong those who underwent RHC, PH group had significantly higher mean pulmonary artery pressures (mean PAP 28.94 \u0026plusmn; 7.45 mmHg vs. 16.21 \u0026plusmn; 2.44 mmHg, p\u0026lt;0.001). Echocardiographic characteristics (Table 2) revealed significantly higher tricuspid regurgitation (TR) peak gradient (mean TR peak gradient 35.2 \u0026plusmn; 8.17 mmHg vs. 22.4 \u0026plusmn; 6.11mmHg, p\u0026lt;0.001) and larger left atrial volumes (83.4 \u0026plusmn; 44.2 mL vs. 62.1 \u0026plusmn; 26.1 mL, p\u0026lt;0.001) among patients with PH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Echocardiographic features in kidney transplant recipients based on PH status\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=468)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePH\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo PH\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=382)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eLV ejection fraction, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e62.9 \u0026plusmn; 8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e64.0 \u0026plusmn; 10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e62.7 \u0026plusmn; 7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eRV dysfunction, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eTAPSE, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2.30 \u0026plusmn; 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2.46 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2.27 \u0026plusmn; 0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eTR peak gradient, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25.7 \u0026plusmn; 9.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e35.2 \u0026plusmn; 8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e22.4 \u0026plusmn; 6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eLeft atrial volume, mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e66.0 \u0026plusmn; 31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e83.4 \u0026plusmn; 44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e62.1 \u0026plusmn; 26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eIVS diastolic thickness, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.30 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1.43 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.27 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003ePosterior wall thickness, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.18 \u0026plusmn; 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1.33 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.15 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003ePericardial effusion, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e90 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e27 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e63 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eMitral regurgitation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eNone or trace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e308 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e45 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e263 (68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e140 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e31 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e109 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e9 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e9 (2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eTricuspid regurgitation, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eNone or trace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e259 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e28 (33.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e231 (60.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e190 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e48 (57.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e142 (37.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e7 (18.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e8（2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eAortic stenosis, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eNone or trace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e439 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e77 (92.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e362 (96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e14 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e5 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e9 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eAortic regurgitation, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eNone or trace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e399 (85.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e64 (76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e335 (88.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e57 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e15 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e42 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e5 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003eAverage RV strain\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20.54 \u0026plusmn; 8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e21.6 \u0026plusmn; 7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e20.3 \u0026plusmn; 8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: Continuous variables are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, N (%) are given\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: LV: left ventricle, RV: right ventricular, TAPSE: tricuspid annular posterior systolic excursion, TR: tricuspid regurgitation, IVS: interventricular septum; Statistical significance: p \u0026lt; 0.05 (*).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe mean follow-up duration from transplant was 54.7 \u0026plusmn; 28.4 months. As shown in Table 3, at one year post transplant, patients with pre-transplant PH experienced significantly higher incidence of MACE compared to those without PH (8.1 vs. 2.9%; p=0.031). This difference remained significant at five-year post-transplant follow up with (22.0% vs 11.0%, p=0.008). This difference was driven by higher rates of non-fatal MI (8.1 vs 2.9%, p=0.031) and hospitalization for heart failure (13.0% vs 4.5%, p = 0.007) in the PH group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e One- and Five-Year All-Cause Mortality and MACE Stratified by Pre-KT Pulmonary Hypertension\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 Year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 247px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 Years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePH (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo PH (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePH (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo PH (n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll-Cause Mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e34 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal MACE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e11 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e19 (22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e41 (11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eCardiovascular Death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e9 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eNon-Fatal Myocardial Infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e4 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e11 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eHospitalization for Heart Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e8 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e17 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eNon-Fatal Stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e2 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.045*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e11 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: PH = pulmonary hypertension; MACE = major adverse cardiovascular events; Statistical significance: p \u0026lt; 0.05 (*).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eKaplan- Meier survival analysis (Figure 1) demonstrated a significantly lower MACE-free survival in PH group when compared to the NoPH group at all time points from KT (log rank p=0.0011). Univariate logistic regression results are listed in Table 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Univariate logistic regression for Predictors of Major Adverse Cardiovascular Events in Kidney Transplant Recipients. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003ePulmonary hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.19\u0026ndash;3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.01\u0026ndash;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSex (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.69\u0026ndash;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eLeft ventricular ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.95\u0026ndash;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.15\u0026ndash;2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.71\u0026ndash;4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eObstructive sleep apnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.73\u0026ndash;1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eRight ventricular dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.15\u0026ndash;14.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eTricuspid annular plane systolic excursion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.50\u0026ndash;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eTricuspid regurgitation peak gradient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.98\u0026ndash;1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eRight ventricular strain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.92\u0026ndash;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: CI = confidence interval; Statistical significance: p \u0026lt; 0.05 (*).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the Cox hazard regression analysis (Table 5), after adjusting for age, sex, diabetes mellitus, coronary artery disease, right ventricular dysfunction and left ventricular ejection fraction, pre-transplant PH remained strongly associated with increased risk of MACE (HR 2.16; 95% CI 1.31\u0026ndash;3.55; p=0.003). Age and history of coronary artery disease also remained significantly associated with post-transplant MACE incidence. The proportional hazards assumption for all covariates in the Cox regression model was verified using Schoenfeld residuals, and no violations were identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003eCox Regression Analysis for the Outcome of Major Adverse Cardiovascular Events Post-Kidney Transplant. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 228px;\"\u003e\n \u003cp\u003ePH on Echocardiogram or RHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.31 -3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 228px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.01 -1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 228px;\"\u003e\n \u003cp\u003eSex (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.65 -1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 228px;\"\u003e\n \u003cp\u003eLeft ventricular ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.96 -1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 228px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.78 - 2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 228px;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.21 - 3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 228px;\"\u003e\n \u003cp\u003eRight ventricular dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.24 - 13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: CI = confidence interval; PH = pulmonary hypertension; RHC = right heart catheterization; Statistical significance: p \u0026lt; 0.05 (*).\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study of KT recipients, our main finding was that pre-transplant PH was independently associated with MACE following KT. Even though the tricuspid regurgitation peak gradient in the PH group in our study was only 35 mmHg (mild PH), patients in PH group had at least twice the risk of MACE. Our findings are consistent with prior studies demonstrating the negative prognostic implications of PH in patients with ESRD (4, 7, 9). Rabih et al. reported that pre-transplant PH, defined by echocardiography, was associated with increased risk of post-transplant death, graft dysfunction, and graft failure at 5-year follow-up(14). Obi et al. similarly observed increased mortality and reduced graft survival in patients with pre-transplant PH(20). Tang et al.\u0026apos;s meta-analysis of 16 studies reported a 2.2-fold increased risk of cardiovascular mortality in CKD or ESKD patients with PH (7). Although KT mitigates several contributors to PH, such as uremia, volume overload, and AV fistulas, it may not reverse longstanding pulmonary vascular and myocardial remodeling. This persistence of risk highlights the need for enhanced cardiovascular surveillance and potentially targeted management strategies for transplant recipients with pre-existing PH.\u003c/p\u003e\n\u003cp\u003eClinical Implications\u003c/p\u003e\n\u003cp\u003eThese results should not be interpreted as justification to exclude PH patients from transplant candidacy. Rather, they emphasize the importance of early recognition and stratification of PH during transplant evaluation. This may include prioritizing cardiology consultation, more aggressive optimization of volume and pulmonary pressure status, and consideration of RHC as clinically indicated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStrengths of our study include the relatively large sample size and the long-term follow up of cardiovascular outcomes post-KT. Additionally, our robust multivariable analysis carefully adjusted for key demographic and clinical confounders, enhancing the validity of our findings. However, several limitations must be acknowledged. This was a single-center, retrospective study with potential for various biases and limited generalizability. Most patients were stratified by TTE estimates of PASP rather than by invasive RHC which may confer a variable degree of accuracy in estimating pulmonary pressure compared to RHC (16). In addition, the lack of invasive hemodynamic data did not allow further classification of PH e.g. pre- versus post-capillary PH. Finally, the confounding effect from other commonly observed comorbidities in various groups of PH such as interstitial lung disease, dialysis status, severity of anemia, and pre-transplant medication regimen cannot be excluded.\u003c/p\u003e\n\u003cp\u003eFuture investigations should explore the evolution of PH before and after transplantation, the role of universal invasive hemodynamic testing for all candidates, and whether targeted interventions can modify cardiovascular risk in this high-risk group. In conclusion, in this retrospective study of KT recipients, the presence of -even mild- pre-transplant PH was independently associated with increased risk of post-transplant MACE. Clinicians should consider targeted risk modification strategies to improve outcomes in this vulnerable population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePH: pulmonary hypertension, ESKD: end-stage kidney disease, WSPH: World Symposium on Pulmonary Hypertension, KT: kidney transplantation, MACE: major adverse cardiovascular events, TTE: transthoracic echocardiogram, RHC: right heart catheterization, PASP: pulmonary artery systolic pressure, mPAP: mean pulmonary artery pressure, MI: myocardial infarction, HF: heart failure, SD: standard deviation, HR: hazardous ratio, CI: confidence interval, BMI: Body Mass Index; ACEi: Angiotensin-Converting Enzyme Inhibitor; ARB: Angiotensin Receptor Blockers, LV: left ventricle, RV: right ventricular, TAPSE: tricuspid annular posterior systolic excursion, TR: tricuspid regurgitation, IVS: interventricular septum\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy was approved by Saint Louis University IRB protocol number 34091.\u003c/p\u003e\n\u003cp\u003eAll procedures were conducted in accordance with the 1964 Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent to Participate declarations: Need for consent to participate was waived by Saint Louis University Institutional Review Board (IRB). IRB titled “Cardiac imaging predictors of outcomes in transplant patients.” IRB number is 34091\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNo financial or non-financial competing interests to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding was obtained to support this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL RN participated in data analysis, data interpretation, and writing the manuscript\u003c/p\u003e\n\u003cp\u003eADP participated in study design, regulatory approvals, data review, data entry, and manuscript writing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKL BO MB participated in study design, acquisition of data, regulatory approvals, data analysis, data interpretation, and writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors thank the SLU Transplant Rx research Group (Appendix 1), and the Transplant care team at SSM Health Saint Louis University Hospital.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShroff GR, Benjamin MM, Rangaswami J, Lentine KL. Risk and management of cardiac disease in kidney and liver transplant recipients. Heart. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHumbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, et al. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J. 2022;43(38):3618\u0026ndash;731.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYigla M, Nakhoul F, Sabag A, Tov N, Gorevich B, Abassi Z, et al. Pulmonary hypertension in patients with end-stage renal disease. Chest. 2003;123(5):1577\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO'Leary JM, Assad TR, Xu M, Birdwell KA, Farber-Eger E, Wells QS, et al. Pulmonary hypertension in patients with chronic kidney disease: invasive hemodynamic etiology and outcomes. Pulm Circ. 2017;7(3):674\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLentine KL, Villines TC, Axelrod D, Kaviratne S, Weir MR, Costa SP. Evaluation and Management of Pulmonary Hypertension in Kidney Transplant Candidates and Recipients: Concepts and Controversies. Transplantation. 2017;101(1):166\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKosmadakis G, Aguilera D, Carceles O, Da Costa Correia E, Boletis I. Pulmonary hypertension in dialysis patients. Ren Fail. 2013;35(4):514\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang M, Batty JA, Lin C, Fan X, Chan KE, Kalim S. Pulmonary Hypertension, Mortality, and Cardiovascular Disease in CKD and ESRD Patients: A Systematic Review and Meta-analysis. Am J Kidney Dis. 2018;72(1):75\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolignano D, Rastelli S, Agarwal R, Fliser D, Massy Z, Ortiz A, et al. Pulmonary hypertension in CKD. Am J Kidney Dis. 2013;61(4):612\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeier-Kriesche HU, Schold JD, Srinivas TR, Reed A, Kaplan B. Kidney transplantation halts cardiovascular disease progression in patients with end-stage renal disease. Am J Transpl. 2004;4(10):1662\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarcia-Covarrubias L, Hernandez K, Castro I, Hinojosa H, Molina L, Bazan O et al. Cardiac Remodeling in Structure and Function Six Months After Kidney Transplantation. Transplant Proc. 2018;50(2):454-7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalerno MP, Rossi E, Favi E et al. The reduction of left ventricular hypertrophy after renal transplantation is not influenced by the immunosuppressive regimen. \u003cem\u003eTransplant Proc\u003c/em\u003e. 2013;45(7):2660\u0026ndash;2662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.transproceed.2013.07.045\u003c/span\u003e\u003cspan address=\"10.1016/j.transproceed.2013.07.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLentine KL, Costa SP, Weir MR, Robb JF, Fleisher LA, Kasiske BL, et al. Cardiac disease evaluation and management among kidney and liver transplantation candidates: a scientific statement from the American Heart Association and the American College of Cardiology Foundation: endorsed by the American Society of Transplant Surgeons, American Society of Transplantation, and National Kidney Foundation. Circulation. 2012;126(5):617\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerzog CA, Mangrum JM, Passman R. Sudden cardiac death and dialysis patients. Semin Dial. 2008;21(4):300\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRabih F, Holden RL, Vasanth P, Pastan SO, Fisher MR, Trammell AW. Effect of pulmonary hypertension on 5-year outcome of kidney transplantation. \u003cem\u003ePulm Circ\u003c/em\u003e. 2022;12(1):e12010. Published 2022 Jan 3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/pul2.12010\u003c/span\u003e\u003cspan address=\"10.1002/pul2.12010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimonneau G, Montani D, Celermajer DS, Denton CP, Gatzoulis MA, Krowka M et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. Eur Respir J. 2019;53(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaleb M, Khuder S, Tinkel J, Khouri SJ. The diagnostic accuracy of Doppler echocardiography in assessment of pulmonary artery systolic pressure: a meta-analysis. Echocardiography. 2013;30(3):258\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArcasoy SM, Christie JD, Ferrari VA, Sutton MS, Zisman DA, Blumenthal NP, et al. Echocardiographic assessment of pulmonary hypertension in patients with advanced lung disease. Am J Respir Crit Care Med. 2003;167(5):735\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaymond RJ, Hinderliter AL, Willis PW, Ralph D, Caldwell EJ, Williams W, et al. Echocardiographic predictors of adverse outcomes in primary pulmonary hypertension. J Am Coll Cardiol. 2002;39(7):1214\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrambsch PMTT. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eObi C, Frost AE, Graviss EA, Nguyen DT, Gaber AO, Suki WN. The Association of Pretransplant Pulmonary Hypertension With Patient and Graft Survival After Kidney Transplantation: A Retrospective Cohort Study. Transplant Proc. 2020;52(10):3023\u0026ndash;3032. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.transproceed.2020.05.003\u003c/span\u003e\u003cspan address=\"10.1016/j.transproceed.2020.05.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAppendix 1. SLU Transplant Rx research Group.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnanta Sriram A, Bower E, Li G, Christy H, Wiseman. Julia Lieu, Khang Nguyen, Leighton Hope, Ravneet Nagra, Samantha Harrington, Sophia Heuer, Zalan Shah.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAffiliation. Saint Louis University School of Medicine.\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-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary hypertension, Major Adverse Cardiovascular Events, Transplant, End Stage Kidney Disease","lastPublishedDoi":"10.21203/rs.3.rs-8014688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8014688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003ePulmonary hypertension (PH) frequently complicates chronic kidney disease and end-stage kidney disease, contributing significantly to cardiovascular morbidity and mortality. We investigated the association between pre-transplant PH and major adverse cardiovascular events (MACE) after kidney transplantation (KT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis retrospective cohort study included 468 adult KT recipients from an academic medical center between January 2015 and December 2024. We excluded patients who did not follow up at our institution. Patients were stratified based on the presence of pre-transplant PH (defined as pulmonary artery systolic pressure \u0026gt;35 mmHg on echocardiography or mean pulmonary artery pressure \u0026gt;20 mmHg via right heart catheterization. The primary outcome was the occurrence of MACE, defined as cardiovascular death, myocardial infarction, stroke, or hospitalization for heart failure. Multivariable Cox proportional hazards models were used to evaluate the independent association between pre-transplant PH and post-KT MACE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eOf the 468 recipients who qualified for the study, 86 (18.4%) had pre-transplant PH. Over a mean follow-up of 54.7 ± 28.4 months, 89 patients (19.0%) experienced MACE. The incidence of MACE was significantly higher in recipients with pre-KT PH compared with those without at one-year (8.1% vs 2.9%, p=0.031) and five-year (22.0% vs 11.0%, p=0.008). After adjusting for age, sex, and confounding variables, PH remained independently associated with MACE (adjusted HR 2.16; 95% CI 1.31–3.55; p=0.003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eIn this retrospective single-center study, pre-transplant PH was independently associated with an increased risk for MACE following KT. These findings highlight the importance of identifying PH in KT candidates. More intensified risk mitigation measures might be needed in this population.\u003c/p\u003e","manuscriptTitle":"Association of Pre-Transplant Pulmonary Hypertension and Post-Transplant Major Adverse Cardiovascular Events in Kidney Transplant Recipients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:18:52","doi":"10.21203/rs.3.rs-8014688/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-14T19:30:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T15:30:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306723661929331446160618515680640625701","date":"2026-01-14T14:51:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82469958305083908137712590027375647593","date":"2026-01-13T16:37:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160046200886991274147616522629802062962","date":"2026-01-12T19:25:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112490649381346487185103748758613637181","date":"2026-01-12T18:56:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T22:50:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T17:55:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151773640028110958030459401716034639880","date":"2025-12-17T17:00:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192285396351252379685995901040373650801","date":"2025-12-16T19:50:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278978125281969042979843356268334604743","date":"2025-12-16T19:41:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-24T20:30:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T18:53:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-17T04:28:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-14T12:20:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-11-14T12:17:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c4b68da8-d833-451d-b5b0-00e5ae8eada9","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:05:16+00:00","versionOfRecord":{"articleIdentity":"rs-8014688","link":"https://doi.org/10.1186/s12882-026-04832-1","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2026-02-27 15:58:29","publishedOnDateReadable":"February 27th, 2026"},"versionCreatedAt":"2025-12-01 08:18:52","video":"","vorDoi":"10.1186/s12882-026-04832-1","vorDoiUrl":"https://doi.org/10.1186/s12882-026-04832-1","workflowStages":[]},"version":"v1","identity":"rs-8014688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8014688","identity":"rs-8014688","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}