Predictive Value of Fatigue, Diastolic Dysfunction, and Right Ventricular Function for Mortality in Hemodialysis Patients with Preserved Ejection Fraction: A 4-Year Prospective Follow- Up Study

Predictive Value of Fatigue, Diastolic Dysfunction, and Right Ventricular Function for Mortality in Hemodialysis Patients with Preserved Ejection Fraction: A 4-Year Prospective Follow- Up Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive Value of Fatigue, Diastolic Dysfunction, and Right Ventricular Function for Mortality in Hemodialysis Patients with Preserved Ejection Fraction: A 4-Year Prospective Follow- Up Study Ertan AKBAY, Sinan AKINCI, Gultekin GENCTOY, Fahri CAKAN, Abdullah SUKUN, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8957047/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic kidney disease (CKD) is a significant global cause of mortality, particularly due to cardiovascular complications. In patients with end-stage kidney disease (ESKD), fatigue is a highly prevalent symptom that has been linked to diastolic and right ventricular (RV) dysfunction. While left ventricular assessment is routine, the prognostic significance of RV function, specifically in patients with preserved left ventricular ejection fraction (LVEF), remains insufficiently explored. This study aims to investigate the impact of fatigue, diastolic dysfunction, and RV function on the survival of hemodialysis (HD) patients with preserved LVEF. Methods In this prospective cohort study, 94 HD patients with preserved LVEF were followed for 48 months. Baseline assessments included fatigue severity measured by the Visual Analog Scale for Fatigue (VAS-F) and echocardiographic parameters, including the E/e' ratio for diastolic function and Tricuspid Annular Plane Systolic Excursion (TAPSE) for RV function. Predictors of mortality were identified using univariate and multivariate Cox proportional hazards regression models. Results During the 4-year follow-up, all-cause mortality occurred in 41 patients (43.6%). Non-survivors had significantly higher E/e' ratios (p = 0.017) and lower TAPSE values (p = 0.003) compared to survivors. While VAS-F scores were higher in the mortality group, the difference was not statistically significant (p = 0.138). In the multivariate Cox regression model, TAPSE was identified as the only independent predictor of mortality (Hazard Ratio [HR]: 0.87; 95% Confidence Interval [CI]: 0.78–0.97; p = 0.012), with each 1 mm decrease in TAPSE increasing the risk of death by 13%. LVEF, E/e' ratio, and VAS-F score were not independent predictors in the multivariate analysis. Conclusion In HD patients with preserved LVEF, right ventricular function ‘as measured by TAPSE’ is a more powerful independent predictor of mortality than diastolic dysfunction or clinical fatigue severity. Routine monitoring of TAPSE may be critical for identifying high-risk patients and improving cardiovascular risk assessment in this population. Hemodialysis Mortality Right Ventricular Function TAPSE Fatigue Diastolic Dysfunction Preserved Ejection Fraction Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chronic kidney disease (CKD) remains one of the leading causes of mortality and morbidity globally [ 1 ]. Although technological advancements in dialysis techniques, improvements in patient care standards, and increased accessibility to dialysis centers have enhanced survival expectations, the disease is still characterized by high mortality rates. Furthermore, CKD is a significant risk factor for cardiovascular diseases, particularly coronary artery disease and hypertension [ 1 ]. Fatigue is among the most common symptoms in patients with end-stage kidney disease (ESKD) and directly impacts their quality of life [ 2 ]. In hemodialysis patients, fatigue occurs at rates of up to 80% as a result of a complex interaction between physiological, psychological, and sociocultural factors [ 3 , 4 ]. Additionally, fatigue in ESKD patients is associated with diastolic dysfunction and right ventricular (RV) impairment [ 4 ]. The association between fatigue severity and mortality has been previously established within the ESKD population [ 5 ]. While cardiovascular assessments in hemodialysis (HD) patients with ESKD have traditionally prioritized left ventricular ejection fraction (LVEF), the critical role of the RV in hemodynamic adaptation has gained increasing prominence in recent years [ 6 ]. The RV is a sensitive structure directly affected by factors such as chronic volume overload during the dialysis process, arteriovenous fistula dynamics, and pulmonary hypertension (PH) [ 7 ]. Tricuspid Annular Plane Systolic Excursion (TAPSE), an indicator of RV function, is known to be associated with mortality and morbidity in many cardiac and non-cardiac conditions, but studies in CKD patients are limited [ 8 – 10 ]. Notably, it has been recorded that the decline in TAPSE values is more pronounced in hemodialysis patients compared to pre-dialysis CKD patients [ 11 ] While current literature emphasizes the prognostic utility of diastolic dysfunction in predicting renal disease progression and mortality within the CKD and ESKD populations, there is a lack of comprehensive research comparing the effects of fatigue, diastolic dysfunction, and RV parameters on mortality in patients with preserved LVEF [ 12 ]. The objective of this research is to perform a comparative analysis of fatigue, diastolic dysfunction, and RV function as mortality predictors in hemodialysis patients with preserved LVEF. Materials and Methods Research Design and Participants This study is a prospective cohort study evaluating patients receiving HD treatment at the Başkent University Alanya Hospital Dialysis Unit. The study analyzed mortality outcomes over a 4-year follow-up period, alongside baseline echocardiographic parameters and fatigue severity, in a cohort of 94 patients previously enrolled in a study investigating the relationship between HD and fatigue. The original study inclusion criteria were: being over 18 years of age and receiving regular HD treatment for at least three months [4]. Exclusion criteria were defined as follows: LVEF <50%, moderate and/or severe valvular heart disease, angina pectoris or known significant coronary stenosis, arrhythmias other than sinus rhythm, development of intradialytic hypotension, diagnosis of depression or use of antidepressants, poor dialysis compliance or unstable patients failing to achieve dry weight, uncontrolled hypertension, pregnancy, immobility, malignancy, chronic liver disease, and sepsis or active severe infection. Ethical Approval and Protocol The study was conducted at a single center, and written informed consent was obtained from all participants after they were fully informed. The protocol was approved by the Başkent University Institutional Review Board and Ethics Committee (Project No: KA25/436) and was supported by the Başkent University Research Fund. Follow-up and Endpoints Patients were followed for 48 months; death, newly diagnosed illnesses, and interventions performed were recorded during this period. Reasons for early termination were identified as death, insufficient medical records, and kidney transplantation. Patients who underwent kidney transplantation were monitored until the date of transplantation, and the period during which they did not receive hemodialysis was excluded from the analysis. The survival status of patients whose hospital admissions ceased before 48 months was queried through state databases. Evaluations were made based on data from the date of the last hospital admission. Clinical and Laboratory Assessments Patients demographic data and cardiovascular risk factors were obtained from the hospital database. Body Mass Index (BMI) was calculated, and the glomerular filtration rate (GFR) was determined using the "Chronic Kidney Disease Epidemiology Collaboration" (CKD-EPI) formula [13]. All blood samples were collected prior to HD sessions. On the baseline date (a non-dialysis day), all patients underwent a comprehensive evaluation by a single physician, including cardiological physical examination, electrocardiography, and transthoracic echocardiography. Hemodialysis Technique Dialysis procedures were performed using Nikkiso DDB-06/09 (Japan) machines and 1.8-2 m² Allmed Polypure (Germany) dialyzers, with a dialysate flow rate of 500-800 ml/min. Ultrafiltration volume-controlled Nipro machines and polysulfone filters (1.6-2 m²) were utilized. Dialysate sodium levels were maintained between 135-145 mEq/L, and the temperature was kept within the 36.0-36.7°C range. Echocardiographic Evaluation Echocardiographic measurements were performed using a GE Vivid E (Norway, 3.5-MHz) device. In accordance with the American Society of Echocardiography guidelines, 2D, M-mode, pulse-wave (PW), and color Doppler examinations were conducted. LVEF was calculated using the Teichholz method, and left ventricular mass (LVM) was determined using the Devereux equation [14]. For right heart assessment, TAPSE was measured via M-mode from the apical 4-chamber view [15]. Systolic pulmonary artery pressure (sPAP) was estimated using the modified Bernoulli equation by incorporating the tricuspid regurgitation jet velocity and right atrial pressure [16]. Tissue Doppler Imaging (TDI) measurements were performed at high frame rates (>150 fps). Following clinical guidelines, the average of e' velocities obtained from the medial and lateral basal walls of the left ventricle was calculated [17]. All measurements were recorded as the mean of five consecutive cycles. Assessment of Fatigue Fatigue severity were assessed at the beginning of the study using the Visual Analog Scale for Fatigue [18]. This scale, as defined by Lee et al., consists of 18 items [18]. Scoring is performed on 10-cm lines; items 6-10 evaluate energy levels, while the remaining items assess fatigue [19]. High scores on the fatigue subscale and low scores on the energy subscale indicate severe fatigue. The Turkish validity and reliability study of the scale was conducted by Yurtsever et al. [20]. Statistical Analysis Statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA) and Python software libraries (Scikit-learn, and Lifelines). The normality of the data distribution was assessed using the Shapiro-Wilk test and visual inspection of histograms. Continuous variables were expressed as mean ± standard deviation (SD) for normally distributed data, and as median and 25th–75th percentiles for non-normally distributed data. Categorical variables were presented as frequencies (n) and percentages (%). Baseline characteristics were compared between the survivor and non-survivor groups. For continuous variables, the Student’s t-test was used for normally distributed data, and the Mann-Whitney U test was used for non-normally distributed data. Comparisons of categorical variables were performed using the Chi-square (χ²) test or Fisher’s exact test, as appropriate. Survival curves were estimated using the Kaplan-Meier method, and differences in survival rates between groups were assessed using the Log-rank test. Patients were stratified into groups based on median values. To identify predictors of all-cause mortality, Cox proportional hazards regression models were employed. First, a univariate Cox regression analysis was conducted to screen for potential risk factors among demographic, clinical, laboratory, and echocardiographic parameters. Variables with a p-value ≤0.10 in the univariate analysis were included in the multivariate Cox regression model to determine independent predictors using a stepwise selection approach. The results were presented as Hazard Ratios (HR) with 95% Confidence Intervals (CI). Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the predictive performance of TAPSE for all-cause mortality. The Area Under the Curve (AUC), sensitivity, and specificity were calculated, and the optimal cut-off value was determined using the Youden index (J= Sensitivity + Specificity - 1). A two-sided p-value of <0.05 was considered statistically significant for all analyses. Results Study Population and Baseline Characteristics A total of 94 HD patients were included in the study. At the end of the four-year follow-up period, 53 patients (56.4%) survived, while all-cause mortality was observed in 41 patients (43.6%). No statistically significant differences were found between the groups in terms of age (p = 0.224), gender (p = 1.000), and BMI (p = 0.643). Dialysis vintage was significantly higher in survivors compared to non-survivors (p = 0.019) (Table 1 ). Table 1 Baseline characteristics of the study population stratified by all-cause mortality Variable All Patients (n = 94) Survivors (n = 53) Non-Survivors (n = 41) P Value Demographics & Clinical Age (years) 66.0 (57.0-75.8) 66.0 (57.0–72.0) 64.0 (57.0–80.0) 0.224 Gender (Male) 54 (57.4%) 30 (56.6%) 24 (58.5%) 1.000 Dialysis Vintage (months) 83.0 (59.3-118.8) 85.8 (65.6-125.3) 66.1 (44.1-106.5) 0.019 Follow-up Duration (months) 46.8 (22.1–48.6) 48.4 (47.9–49.3) 21.3 (12.3–31.3) < 0.001 VAS-F Score 68.5 (34.0–91.0) 64.0 (34.0–87.0) 79.0 (35.0-103.0) 0.138 BMI (kg/m²) 25.0 ± 4.1 25.2 ± 3.9 24.8 ± 4.4 0.643 Diabetes Mellitus 20 (21.3%) 8 (15.1%) 12 (29.3%) 0.158 Hypertension 70 (74.5%) 37 (69.8%) 33 (80.5%) 0.348 History of CAD 17 (18.1%) 10 (18.9%) 7 (17.1%) 1.000 Laboratory Parameters Hemoglobin (g/dL) 11.3 ± 1.4 11.2 ± 1.5 11.4 ± 1.4 0.536 WBC (×10³/µL) 7.5 ± 2.2 7.4 ± 2.3 7.6 ± 2.1 0.621 Platelets (×10³/µL) 184.4 (139.7-222.3) 175.9 (136.4-222.6) 186.4 (143.3-221.4) 0.706 Albumin (g/dL) 37.9 (35.7–39.2) 37.5 (35.7–38.5) 38.3 (35.7–39.3) 0.327 Serum Creatinine (mg/dL) 7.4 ± 2.3 7.5 ± 2.3 7.3 ± 2.3 0.676 GFR (mL/min) 6.3 (4.8–8.2) 6.2 (4.8–8.3) 6.4 (5.3-8.0) 0.743 Echocardiography LVEDD (mm) 47.2 ± 4.8 47.0 ± 4.9 47.5 ± 4.7 0.596 LVMI (g/m²) 113.1 (91.0-144.6) 112.8 (91.5-143.1) 113.8 (90.7-148.4) 0.766 LVEF (%) 62.4 (58.6–65.1) 62.0 (56.6–65.1) 63.5 (60.7–65.2) 0.127 Aorta (mm) 33.5 ± 3.5 33.4 ± 3.6 33.6 ± 3.5 0.824 Left Atrium (mm) 39.2 ± 5.1 39.0 ± 5.2 39.5 ± 4.9 0.631 E/A Ratio 0.8 (0.7-1.0) 0.8 (0.7–1.1) 0.8 (0.7–0.9) 0.951 Average e' (cm/s) 7.6 ± 2.0 7.9 ± 2.2 7.3 ± 1.7 0.137 E/e' Ratio 9.4 (7.7–12.2) 8.9 (7.1–10.8) 10.7 (8.8–13.3) 0.017 Right Ventricle (mm) 36.4 ± 4.2 35.8 ± 4.1 37.0 ± 4.2 0.161 TAPSE (mm) 21.0 ± 3.0 21.8 ± 3.0 20.0 ± 2.9 0.003 sPAP (mmHg) 25.0 (20.0–35.0) 25.0 (20.0–30.0) 28.0 (25.0–35.0) 0.104 TAPSE/sPAP Ratio 0.73 (0.60–1.04) 0.82 (0.63–1.05) 0.68 (0.54–0.80) 0.023 Data are presented as percentage, mean ± standard deviation or median (interquartile range). A; late diastolic flow velocities, CAD; coronary artery disease, ACE; angiotensin converting enzyme, ARB; angiotensin receptor blockers, BMI; the body mass index, E; early diastolic flow velocities, e'; peak myocardial velocity during early diastole, GFR; glomerular filtration rate, LVEDD; left ventricular end-diastolic diameter, LVMI; left ventricular mass index, LVEF; left ventricular ejection fraction, sPAP; Pulmonary artery peak systolic pressure, TAPSE; Tricuspid annular plane systolic excursion, VAS-F; Visual Analogue Scale to Evaluate Fatigue Severity, WBC; white blood cell count, Regarding laboratory parameters, no significant differences were observed between the groups in hemoglobin, creatinine, albumin, or GFR levels (p > 0.05). In terms of echocardiographic data, LVEF (p = 0.127) and LVMI (p = 0.766) were similar between groups. However, the E/e' ratio was significantly higher in the non-survivor group compared to survivors (10.7 vs. 8.9; p = 0.017), while the TAPSE value was significantly lower (20.0 ± 2.9 mm vs. 21.8 ± 3.0 mm; p = 0.003). Additionally, the TAPSE/sPAP ratio was significantly lower in the mortality group (0.68 vs. 0.82; p = 0.023). Although VAS-F scores assessing fatigue were higher in the mortality group, this difference did not reach statistical significance (79.0 vs. 64.0; p = 0.138) (Table 1 ). Clinical Events During Follow-up During the four-year follow-up period, 50.0% (n = 47) of the patients experienced a non-fatal composite cardiovascular event. The distribution of recorded events was as follows: non-fatal myocardial infarction in 17.0% (n = 16), coronary revascularization in 31.9% (n = 30), non-fatal stroke in 13.8% (n = 13), new-onset atrial fibrillation in 14.9% (n = 14), and peripheral artery revascularization in 9.6% (n = 9). One patient (1.1%) underwent kidney transplantation (Table 2 ). Table 2 Frequency of clinical events during 4-year follow-up Event n % All-cause Mortality 41 43.6% Composite Cardiovascular Events 47 50.0% Non-fatal Myocardial Infarction 16 17.0% Coronary Revascularization 30 31.9% Non-fatal Stroke 13 13.8% New-onset Atrial Fibrillation 14 14.9% Peripheral Artery Revascularization 9 9.6% Kidney Transplantation 1 1.1% Survival and ROC Analysis According to the Kaplan-Meier survival analysis, patients with low TAPSE values (≤ 21.0 mm) had a significantly lower survival rate compared to those with high TAPSE values (p = 0.016) (Fig. 1 ). In survival analyses based on E/e' ratio (p = 0.055), VAS-F score (p = 0.142), and systolic pulmonary artery pressure (sPAP) (p = 0.278), the differences between groups did not achieve statistical significance (Fig. 1 ). Predictors of Mortality (Cox Regression Analysis) In the univariate Cox regression analysis, variables found to be associated with mortality included TAPSE (HR: 0.84; p < 0.001), E/e' ratio (HR: 1.08; p = 0.054), Diabetes Mellitus (HR: 1.83; p = 0.079), LVEF (HR: 1.06; p = 0.083), and VAS-F score (HR: 1.01; p = 0.087) (Table 3 ) (Fig. 2 ). Table 3 Univariate Cox Regression Analysis for All-Cause Mortality Variable Hazard Ratio (HR) 95% Confidence Interval P Value TAPSE (mm) 0.84 0.76–0.93 < 0.001 E/e' Ratio 1.08 1.00–1.16 0.054 Diabetes Mellitus 1.83 0.93–3.59 0.079 LVEF (%) 1.06 0.99–1.13 0.083 VAS-F Score 1.01 1.00–1.01 0.087 Right Ventricle (mm) 1.06 0.98–1.14 0.134 Age (years) 1.02 0.99–1.04 0.145 sPAP (mmHg) 1.02 0.99–1.06 0.146 Average e' (cm/s) 0.91 0.78–1.07 0.256 Hypertension 1.47 0.68–3.18 0.328 Hemoglobin (g/dL) 1.10 0.88–1.36 0.415 GFR 1.03 0.95–1.12 0.424 E/A Ratio 1.32 0.53–3.25 0.552 BMI (kg/m²) 0.98 0.91–1.05 0.571 LVEDD (mm) 1.01 0.95–1.08 0.679 LVMI (g/m²) 1.00 0.99–1.01 0.699 Left Atrium (mm) 1.01 0.95–1.07 0.741 History of CAD 0.91 0.40–2.06 0.828 Gender (Male) 1.05 0.56–1.95 0.885 A; late diastolic flow velocities, CAD; coronary artery disease, ACE; angiotensin converting enzyme, ARB; angiotensin receptor blockers, BMI; the body mass index, E; early diastolic flow velocities, e'; peak myocardial velocity during early diastole, GFR; glomerular filtration rate, LVEDD; left ventricular end-diastolic diameter, LVMI; left ventricular mass index, LVEF; left ventricular ejection fraction, sPAP; Pulmonary artery peak systolic pressure, TAPSE; Tricuspid annular plane systolic excursion, VAS-F; Visual Analogue Scale to Evaluate Fatigue Severity. A multivariate Cox regression model was constructed including variables with a p-value ≤ 0.1 in the univariate analysis. The model revealed that each 1 mm decrease in TAPSE independently increased the risk of all-cause mortality by 13% (HR: 0.87; 95% CI: 0.78–0.97; p = 0.012) (Table 3 ). In the same model, LVEF (p = 0.092), Diabetes Mellitus (p = 0.151), E/e' ratio (p = 0.380), and VAS-F score (p = 0.617) were not found to be independent predictors of mortality (Table 4 ) (Fig. 3 ). Table 4 Multivariate Cox Regression Analysis for predictors of all-cause mortality Variable Hazard Ratio (HR) 95% Confidence Interval P Value TAPSE (mm) 0.87 0.78–0.97 0.012 LVEF (%) 1.07 0.99–1.15 0.092 Diabetes Mellitus 1.71 0.82–3.55 0.151 E/e' Ratio 1.04 0.95–1.13 0.380 VAS-F Score 1.00 0.99–1.01 0.617 Data are presented as percentage, mean ± standard deviation or median (interquartile range). E; early diastolic flow velocities, e'; peak myocardial velocity during early diastole, LVEF; left ventricular ejection fraction, TAPSE; Tricuspid annular plane systolic excursion, VAS-F; Visual Analogue Scale to Evaluate Fatigue Severity. Receiver Operating Characteristic (ROC) analysis for predicting all-cause mortality yielded an area under the curve (AUC) for TAPSE of 0.681 (95% CI: 0.565–0.783; p = 0.001) (Fig. 4 ). The optimal cut-off value for mortality was determined as ≤ 20.0 mm, with a sensitivity of 61.0% and a specificity of 69.8% (Fig. 4 ). Discussion The main finding of this study is that in ESKD patients with preserved left ventricular ejection fraction (LVEF), the TAPSE value, an indicator of RV systolic function, is the strongest and independent predictor of all-cause mortality. Our four-year prospective follow-up revealed that every 1 mm decrease in TAPSE is associated with a 13% increase in mortality risk. This finding suggests that in ESKD patients, the traditional left ventricle-focused approach to cardiovascular risk assessment should be expanded, and that RV function assessment may play a significant role in this process. RV dysfunction is known to be associated with poor prognosis in ESKD patients [ 8 – 10 ]. Tanasa et al. and Wang et al. previously reported strong associations between RV impairment and cardiovascular events or death [ 21 , 22 ]. In our study, the statistically significant cutoff value of ≤ 20.0 mm for TAPSE in predicting mortality supports the emphasis made by Grabysa et al. on the prognostic value of TAPSE in dialysis patients [ 7 ]. In hemodialysis patients, RV damage is attributed to the fact that arteriovenous fistulas, used as vascular access routes, reduce systemic vascular resistance and chronically increase venous return and cardiac output [ 7 ]. This high-output situation increases right ventricular preload, leading to dilation and hypertrophy in the right heart chambers [ 23 ]. In addition, the TAPSE/sPAP ratio is known to be associated with RV dysfunction and is lower in individuals with high mortality [ 24 ]. PH is frequently seen in hemodialysis patients and is classified within the group of diseases with unclear or multifactorial pathogenesis in current guidelines [ 25 ]. The pathophysiology of the change in TAPSE is thought to be similar to the development of PH, and it is known that the use of TAPSE in the early diagnosis of PH has an effect on diagnosis and prognosis [ 7 ]. Floccari et al. showed a negative correlation between TAPSE and sPAP in a study conducted on stage 3 CKD patients [ 26 ]. Although no significant increase in mortality and sPAP was detected in our study, sPAP was found to be higher in patients who died. This situation, where TAPSE was significantly lower without a difference between the groups in terms of PH, supports the use of TAPSE in the early detection of PH [ 7 ]. In addition, heart failure and valvular diseases, which cause an increase in sPAP and are frequently observed in CKD patients, are secondary causes of PH [ 25 ]. The exclusion of these diseases from the study may have altered the sPAP value and its relationship with mortality between the groups. The significantly higher E/e' ratio in the mortality group confirms the impact of increased left ventricular filling pressures on survival. Pedersen et al. and Kim et al. have shown that a high E/e' ratio predicts poor survival in CKD patients [ 27 , 28 ]. However, the loss of independence for the E/e' ratio in our multivariate analysis suggests that right ventricular parameters may possess superior prognostic power in this specific patient cohort. While fatigue has been reported as a mortality predictor elsewhere, it did not reach independent significance in our study [ 5 ]. We previously noted that fatigue correlates with diastolic dysfunction and TAPSE in this group [ 4 ]. In the four-year follow-up, although the higher E/e’ ratio and VAS-F score in absolute terms in patients with high mortality indicated a clinical trend, it did not reach statistical significance. We believe that the difference between the studies is mainly due to the fact that patients in Watanabe et al. had higher VAS-F scores and our study had stricter exclusion criteria [ 5 ]. Additionally, the fact that fatigue was measured only at the beginning of the study during a single visit may be due to the failure to record dynamic changes in symptoms throughout the dialysis process. The evaluation of a younger patient population with less fatigue and shorter dialysis duration may have reduced the power of the VAS-F score. However, this trend, which approached significance with p = 0.087 in univariate analysis, suggests that fatigue may also gain prognostic value in a larger sample size. One of the key findings of our study is that the dialysis duration of the surviving patients was significantly longer than that of the group with mortality. This difference in duration, observed independently of age, may be due to the surviving patients having a longer dialysis history, which could be an example of "reverse epidemiology" specific to the dialysis population, or it may be dependent on the research population [ 29 ]. This difference can be explained by the fact that the surviving group successfully navigated the high-risk first years of dialysis and were a subgroup that was biologically and hemodynamically more resistant to treatment, while those who died were more vulnerable to uremic toxins or acute hemodynamic changes at the onset of dialysis. Additionally, it may be due to the fact that we conducted our research in a group with strict exclusion criteria, who were on dialysis for a short time and nearly half of whom died. The E/e ratio, VAS-F score, and sPAP appear to follow an inverse relationship with TAPSE, as can be seen from the survival curves. Although we could not detect a correlation between the increase in E/e ratio, VAS-F score, and sPAP and mortality in the survival curves of our study, we believe that it may become significant in larger patient groups and/or with longer follow-up periods. This study has several limitations. First, the fact that the research was conducted in a single center and on a relatively small sample group may limit the generalization of the results to the entire ESKD population. Second, the use of two-dimensional and M-mode parameters such as TAPSE, which are more readily available in routine clinical practice, instead of cardiac magnetic resonance imaging or three-dimensional echocardiography, which are considered the gold standard in evaluating RV function, can be considered a limitation. Third, a subjective self-report scale, which may be affected by psychological states or sociocultural differences, was used to assess fatigue severity. In addition, the fact that echocardiographic measurements and fatigue scores were taken only once at the beginning of the study prevented the analysis of the effect of changes in these parameters on mortality during the follow-up period. Finally, the lack of cause-specific mortality data prevents us from distinguishing between cardiovascular and non-cardiovascular deaths, though the strong link between TAPSE and all-cause mortality remains clinically robust. Conclusion RV function may be as strong a predictor of mortality in hemodialysis patients with preserved LVEF as left ventricular parameters and clinical symptoms. Including TAPSE measurement in routine echocardiographic examinations during the follow-up of dialysis patients may be useful in identifying high-risk patients. Declarations Conflicts of interest: The authors declare that there is no conflict of interest Funding: This study was approved by Baskent University Institutional Review Board and Ethics Committee (Project no: KA25/436) and supported by Baskent University Research Fund. Author Contribution EA: Responsible for the conceptualization, design, literature search, and completion of the manuscript. GG: Contributed to data acquisition, evaluation of results, and determination of the final version of the manuscript. SA: Involved in the research conception stage and performed the statistical evaluation. FC: Contributed to the interpretation of data and drafting the manuscript. AS: Participated in the conception and analysis of the research. AA: Contributed to the evaluation of research data and the writing of the research article. All authors reviewed the manuscript. Data Availability The datasets generated during and/or analyzed during the current study are not publicly available due to patient privacy and institutional data protection policies but are available from the corresponding author on reasonable request. References Richardson T, Gardner M, Salani M (2025) Cardiovascular Disease and Dialysis: A Review of the Underlying Mechanisms, Methods of Risk Stratification, and Impact of Dialysis Modality Selection on Cardiovascular Outcomes. Kidney Dialysis 5(1):5. 10.3390/kidneydial5010005 Koyama H, Fukuda S, Shoji T, Inaba M, Tsujimoto Y, Tabata T et al (2010) Fatigue is a predictor for cardiovascular outcomes in patients undergoing hemodialysis. Clin J Am Soc Nephrol 5(4):659–666 Horigan AE (2012) Fatigue in hemodialysis patients: a review of current knowledge. J Pain Symptom Manage 44(5):715–724 Akbay E, Akinci S, Coner A, Adar A, Genctoy G, Demir AR (2022) New perspective on fatigue in hemodialysis patients with preserved ejection fraction: diastolic dysfunction: Fatigue and diastolic dysfunction. Int J Cardiovasc Imaging 38(10):2143–2153 Watanabe G, Tanaka K, Saito H, Kimura H, Tani Y, Asai J et al (2025) Post-dialysis fatigue predicts all-cause mortality in patients on chronic hemodialysis. Ther Apher Dial 29(1):12–22 Logu K, Balakrishnan K, Sahay M, George M, Kandadai SD, Elumalai DV et al (2026) Chronic kidney disease and right ventricular dysfunction; an echocardiographic assessment in hemodialysis patients. J Nephropharmacol 15(1):e12704–e04 Grabysa R, Wańkowicz Z (2015) Can Echocardiography, Especially Tricuspid Annular Plane Systolic Excursion Measurement, Predict Pulmonary Hypertension and Improve Prognosis in Patients on Long-Term Dialysis? Med Sci Monit 21:4015–4022 Foschi M, Di Mauro M, Tancredi F, Capparuccia C, Petroni R, Leonzio L et al (2017) The Dark Side of the Moon: The Right Ventricle. J Cardiovasc Dev Dis. ;4(4) Reinecke A, Dißmann P, Frey N, Müller OJ, Seoudy H, Frank J et al (2025) In heart failure, echocardiographic parameters of right ventricular function are powerful tools to predict renal failure. ESC Heart Fail 12(3):2310–2320 Perencin A, Curreri C, Zanforlini BM, Bertocco A, Ceolin C, Papa MV et al (2026) Beyond APACHE II: the role of TAPSE in predicting mortality among septic patients and septic shock; a systematic review and metanalysis Right heart, right prognosis: TAPSE, a new tool for predicting mortality among septic patients and septic shock; a systematic review and metanalysis. Clin Res Cardiol 115(3):383–394 Demirci DE, Demirci D, İnci A (2022) Long-term impacts of different dialysis modalities on right ventricular function in patients with end-stage renal disease. Echocardiography 39(10):1316–1323 Han SS, Cho GY, Park YS, Baek SH, Ahn SY, Kim S et al (2015) Predictive value of echocardiographic parameters for clinical events in patients starting hemodialysis. J Korean Med Sci 30(1):44–53 Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150(9):604–612 Lang RM, Bierig M, Devereux RB, Flachskampf FA, Foster E, Pellikka PA et al (2005) Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology. J Am Soc Echocardiogr 18(12):1440–1463 Ghio S, Recusani F, Klersy C, Sebastiani R, Laudisa ML, Campana C et al (2000) Prognostic usefulness of the tricuspid annular plane systolic excursion in patients with congestive heart failure secondary to idiopathic or ischemic dilated cardiomyopathy. Am J Cardiol 85(7):837–842 Steckelberg RC, Tseng AS, Nishimura R, Ommen S, Sorajja P (2013) Derivation of mean pulmonary artery pressure from noninvasive parameters. J Am Soc Echocardiogr 26(5):464–468 Galderisi M, Cosyns B, Edvardsen T, Cardim N, Delgado V, Di Salvo G et al (2017) Standardization of adult transthoracic echocardiography reporting in agreement with recent chamber quantification, diastolic function, and heart valve disease recommendations: an expert consensus document of the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging 18(12):1301–1310 Shahid A, Wilkinson K, Marcu S, Shapiro C (2011) Visual Analogue Scale to Evaluate Fatigue Severity (VAS-F) Lee KA, Hicks G, Nino-Murcia G (1991) Validity and reliability of a scale to assess fatigue. Psychiatry Res 36(3):291–298 Yurtsever S, Bedük T (2003) Evaluation of fatigue on hemodialysis patients. Turkish J Res Dev Nurs 5(2):3–12 Tanasa A, Burlacu A, Popa IV, Covic A (2021) Right Ventricular Functionality Following Hemodialysis Initiation in End-Stage Kidney Disease-A Single-Center, Prospective, Cohort Study. Med (Kaunas). ;57(7) Wang C, Meng L, Cheng XY, Chen YQ (2024) Assessment of right ventricular dysfunction and its association with excess risk of cardiovascular events in patients undergoing maintenance hemodialysis. Ren Fail 46(2):2364766 Volk MC, Honnekeri B, Ghobrial J, Hanna M, Bhattacharya S, Kirksey L et al (2025) High-output heart failure from arteriovenous dialysis access: A structured approach to diagnosis and management. Cleve Clin J Med 92(6):362–371 Guazzi M, Bandera F, Pelissero G, Castelvecchio S, Menicanti L, Ghio S et al (2013) Tricuspid annular plane systolic excursion and pulmonary arterial systolic pressure relationship in heart failure: an index of right ventricular contractile function and prognosis. Am J Physiol Heart Circ Physiol 305(9):H1373–H1381 Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M et al (2022) 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J 43(38):3618–3731 Floccari F, Granata A, Rivera R, Marrocco F, Santoboni A, Malaguti M et al (2012) Echocardiography and right ventricular function in NKF stage III cronic kidney disease: Ultrasound nephrologists' role. J Ultrasound 15(4):252–256 Kim MK, Kim B, Lee JY, Kim JS, Han BG, Choi SO et al (2013) Tissue Doppler-derived E/e' ratio as a parameter for assessing diastolic heart failure and as a predictor of mortality in patients with chronic kidney disease. Korean J Intern Med 28(1):35–44 Pedersen MZ, Skaarup KG, Landler NE, Olsen FJ, Christensen J, Johansen ND et al (2026) Diastolic dysfunction and the risk of end-stage kidney disease among patients with non-dialysis-dependent chronic kidney disease. Int J Cardiovasc Imaging. Jan 9 Vashistha T, Mehrotra R, Park J, Streja E, Dukkipati R, Nissenson AR et al (2014) Effect of age and dialysis vintage on obesity paradox in long-term hemodialysis patients. Am J Kidney Dis 63(4):612–622 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8957047","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601428777,"identity":"17f26149-24d0-4bbe-b7b1-9a541ec76ff9","order_by":0,"name":"Ertan AKBAY","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDCCA1BaQgJIfABiNnZitByAamGcAdLCTIoWZh6QCCEtfLcPH/v8oeaOvOTs5mfSNr+2yfMxMzB++JiDW4vkubTkGQeOPTOcLXPMTDq377ZhGzMDs+TMbbi1GJzhMWY4wHaYcZ5EAlBLz21GoBY2Zl68Wvg/Mxz4d9h+nkT6N2nLntv2RGjhYWY42HY4cbZEjpk0w4/biQS1SJ5hM2Y423c4eeacM8WWvQ23k9uYGZvx+oXvDPNjhopvh21n3G7feOPHn9u289ubD374iEcLMmCRYGwD0YwNxKkHAuYPDH+IVjwKRsEoGAUjCAAAvFhU4K7SeiUAAAAASUVORK5CYII=","orcid":"","institution":"Baskent University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ertan","middleName":"","lastName":"AKBAY","suffix":""},{"id":601428778,"identity":"c609cfc1-b12b-40a0-b664-511511157603","order_by":1,"name":"Sinan AKINCI","email":"","orcid":"","institution":"Baskent University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sinan","middleName":"","lastName":"AKINCI","suffix":""},{"id":601428779,"identity":"a6b3f4cd-3242-470b-949b-1730e8852701","order_by":2,"name":"Gultekin GENCTOY","email":"","orcid":"","institution":"Baskent University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gultekin","middleName":"","lastName":"GENCTOY","suffix":""},{"id":601428780,"identity":"e1d63008-1656-414f-9dfa-e9e4fa21816b","order_by":3,"name":"Fahri CAKAN","email":"","orcid":"","institution":"Alanya Alaaddin Keykubat University","correspondingAuthor":false,"prefix":"","firstName":"Fahri","middleName":"","lastName":"CAKAN","suffix":""},{"id":601428781,"identity":"9822dc82-4e1d-45f0-b3ae-96b8b5c1dd34","order_by":4,"name":"Abdullah SUKUN","email":"","orcid":"","institution":"Baskent University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"SUKUN","suffix":""},{"id":601428782,"identity":"9eda4e59-ece6-4ec9-b280-1e721b665754","order_by":5,"name":"Adem ADAR","email":"","orcid":"","institution":"Baskent University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Adem","middleName":"","lastName":"ADAR","suffix":""}],"badges":[],"createdAt":"2026-02-24 11:53:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8957047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8957047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104404316,"identity":"16199859-7497-494e-8be8-7f2b645a9eae","added_by":"auto","created_at":"2026-03-11 12:20:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169927,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of mortality predictors in Kaplan-Meier survival analysis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8957047/v1/6742e21ea12b001b4baa80c5.png"},{"id":104207157,"identity":"ff53eddd-9bcb-4079-8de5-702a47d73037","added_by":"auto","created_at":"2026-03-09 07:07:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":205316,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate Cox Regression Analysis for All-Cause Mortality\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8957047/v1/7749e08ab80ab9f43def73c9.png"},{"id":104207160,"identity":"dbcc5ef4-6c6f-45ae-b39d-43e1c7bc6d14","added_by":"auto","created_at":"2026-03-09 07:07:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144769,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate Cox Regression Analysis for predictors of all-cause mortality\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8957047/v1/e0dbbbdcd6c5e47205dca3ed.png"},{"id":104207156,"identity":"0158445f-c273-4f7f-b5a1-7c6b8e0a3381","added_by":"auto","created_at":"2026-03-09 07:07:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122596,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve Analysis of TAPSE for Prediction of All-Cause Mortality\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8957047/v1/f086a257e056fcea025619fa.png"},{"id":104835221,"identity":"2a0deeb5-a322-4f33-bd80-f3685171bcb0","added_by":"auto","created_at":"2026-03-17 17:42:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1345833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8957047/v1/da502ae4-58d0-414b-b965-164e1f15479c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of Fatigue, Diastolic Dysfunction, and Right Ventricular Function for Mortality in Hemodialysis Patients with Preserved Ejection Fraction: A 4-Year Prospective Follow- Up Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) remains one of the leading causes of mortality and morbidity globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although technological advancements in dialysis techniques, improvements in patient care standards, and increased accessibility to dialysis centers have enhanced survival expectations, the disease is still characterized by high mortality rates. Furthermore, CKD is a significant risk factor for cardiovascular diseases, particularly coronary artery disease and hypertension [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFatigue is among the most common symptoms in patients with end-stage kidney disease (ESKD) and directly impacts their quality of life [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In hemodialysis patients, fatigue occurs at rates of up to 80% as a result of a complex interaction between physiological, psychological, and sociocultural factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additionally, fatigue in ESKD patients is associated with diastolic dysfunction and right ventricular (RV) impairment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The association between fatigue severity and mortality has been previously established within the ESKD population [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile cardiovascular assessments in hemodialysis (HD) patients with ESKD have traditionally prioritized left ventricular ejection fraction (LVEF), the critical role of the RV in hemodynamic adaptation has gained increasing prominence in recent years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The RV is a sensitive structure directly affected by factors such as chronic volume overload during the dialysis process, arteriovenous fistula dynamics, and pulmonary hypertension (PH) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Tricuspid Annular Plane Systolic Excursion (TAPSE), an indicator of RV function, is known to be associated with mortality and morbidity in many cardiac and non-cardiac conditions, but studies in CKD patients are limited [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Notably, it has been recorded that the decline in TAPSE values is more pronounced in hemodialysis patients compared to pre-dialysis CKD patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWhile current literature emphasizes the prognostic utility of diastolic dysfunction in predicting renal disease progression and mortality within the CKD and ESKD populations, there is a lack of comprehensive research comparing the effects of fatigue, diastolic dysfunction, and RV parameters on mortality in patients with preserved LVEF [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective of this research is to perform a comparative analysis of fatigue, diastolic dysfunction, and RV function as mortality predictors in hemodialysis patients with preserved LVEF.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eResearch Design and Participants\u003c/p\u003e\n\u003cp\u003eThis study is a prospective cohort study evaluating patients receiving HD treatment at the Başkent University Alanya Hospital Dialysis Unit.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe study analyzed mortality outcomes over a 4-year follow-up period, alongside baseline echocardiographic parameters and fatigue severity, in a cohort of 94 patients previously enrolled in a study investigating the relationship between HD and fatigue. The original study inclusion criteria were: being over 18 years of age and receiving regular HD treatment for at least three months [4].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExclusion criteria were defined as follows: LVEF \u0026lt;50%, moderate and/or severe valvular heart disease, angina pectoris or known significant coronary stenosis, arrhythmias other than sinus rhythm, development of intradialytic hypotension, diagnosis of depression or use of antidepressants, poor dialysis compliance or unstable patients failing to achieve dry weight, uncontrolled hypertension, pregnancy, immobility, malignancy, chronic liver disease, and sepsis or active severe infection.\u003c/p\u003e\n\u003cp\u003eEthical Approval and Protocol\u003c/p\u003e\n\u003cp\u003eThe study was conducted at a single center, and written informed consent was obtained from all participants after they were fully informed. The protocol was approved by the Başkent University Institutional Review Board and Ethics Committee (Project No: KA25/436) and was supported by the Başkent University Research Fund.\u003c/p\u003e\n\u003cp\u003eFollow-up and Endpoints\u003c/p\u003e\n\u003cp\u003ePatients were followed for 48 months; death, newly diagnosed illnesses, and interventions performed were recorded during this period. Reasons for early termination were identified as death, insufficient medical records, and kidney transplantation. Patients who underwent kidney transplantation were monitored until the date of transplantation, and the period during which they did not receive hemodialysis was excluded from the analysis. The survival status of patients whose hospital admissions ceased before 48 months was queried through state databases. Evaluations were made based on data from the date of the last hospital admission.\u003c/p\u003e\n\u003cp\u003eClinical and Laboratory Assessments\u003c/p\u003e\n\u003cp\u003ePatients demographic data and cardiovascular risk factors were obtained from the hospital database. Body Mass Index (BMI) was calculated, and the glomerular filtration rate (GFR) was determined using the \"Chronic Kidney Disease Epidemiology Collaboration\" (CKD-EPI) formula [13]. All blood samples were collected prior to HD sessions. On the baseline date (a non-dialysis day), all patients underwent a comprehensive evaluation by a single physician, including cardiological physical examination, electrocardiography, and transthoracic echocardiography.\u003c/p\u003e\n\u003cp\u003eHemodialysis Technique\u003c/p\u003e\n\u003cp\u003eDialysis procedures were performed using Nikkiso DDB-06/09 (Japan) machines and 1.8-2 m² Allmed Polypure (Germany) dialyzers, with a dialysate flow rate of 500-800 ml/min. Ultrafiltration volume-controlled Nipro machines and polysulfone filters (1.6-2 m²) were utilized. Dialysate sodium levels were maintained between 135-145 mEq/L, and the temperature was kept within the 36.0-36.7°C range.\u003c/p\u003e\n\u003cp\u003eEchocardiographic Evaluation\u003c/p\u003e\n\u003cp\u003eEchocardiographic measurements were performed using a GE Vivid E (Norway, 3.5-MHz) device. In accordance with the American Society of Echocardiography guidelines, 2D, M-mode, pulse-wave (PW), and color Doppler examinations were conducted. LVEF was calculated using the Teichholz method, and left ventricular mass (LVM) was determined using the Devereux equation [14]. For right heart assessment, TAPSE was measured via M-mode from the apical 4-chamber view [15]. Systolic pulmonary artery pressure (sPAP) was estimated using the modified Bernoulli equation by incorporating the tricuspid regurgitation jet velocity and right atrial pressure [16]. Tissue Doppler Imaging (TDI) measurements were performed at high frame rates (\u0026gt;150 fps). Following clinical guidelines, the average of e' velocities obtained from the medial and lateral basal walls of the left ventricle was calculated [17]. All measurements were recorded as the mean of five consecutive cycles.\u003c/p\u003e\n\u003cp\u003eAssessment of Fatigue\u003c/p\u003e\n\u003cp\u003eFatigue severity were assessed at the beginning of the study using the Visual Analog Scale for Fatigue [18]. This scale, as defined by Lee et al., consists of 18 items [18]. Scoring is performed on 10-cm lines; items 6-10 evaluate energy levels, while the remaining items assess fatigue [19]. High scores on the fatigue subscale and low scores on the energy subscale indicate severe fatigue. The Turkish validity and reliability study of the scale was conducted by Yurtsever et al. [20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA) and Python software libraries (Scikit-learn, and Lifelines). The normality of the data distribution was assessed using the Shapiro-Wilk test and visual inspection of histograms. Continuous variables were expressed as mean ± standard deviation (SD) for normally distributed data, and as median and 25th–75th percentiles for non-normally distributed data. Categorical variables were presented as frequencies (n) and percentages (%).\u003c/p\u003e\n\u003cp\u003eBaseline characteristics were compared between the survivor and non-survivor groups. For continuous variables, the Student’s t-test was used for normally distributed data, and the Mann-Whitney U test was used for non-normally distributed data. Comparisons of categorical variables were performed using the Chi-square (χ²) test or Fisher’s exact test, as appropriate.\u003c/p\u003e\n\u003cp\u003eSurvival curves were estimated using the Kaplan-Meier method, and differences in survival rates between groups were assessed using the Log-rank test. Patients were stratified into groups based on median values.\u003c/p\u003e\n\u003cp\u003eTo identify predictors of all-cause mortality, Cox proportional hazards regression models were employed. First, a univariate Cox regression analysis was conducted to screen for potential risk factors among demographic, clinical, laboratory, and echocardiographic parameters. Variables with a p-value ≤0.10 in the univariate analysis were included in the multivariate Cox regression model to determine independent predictors using a stepwise selection approach. The results were presented as Hazard Ratios (HR) with 95% Confidence Intervals (CI).\u003c/p\u003e\n\u003cp\u003eReceiver Operating Characteristic (ROC) curve analysis was performed to evaluate the predictive performance of TAPSE for all-cause mortality. The Area Under the Curve (AUC), sensitivity, and specificity were calculated, and the optimal cut-off value was determined using the Youden index (J= Sensitivity + Specificity - 1).\u003c/p\u003e\n\u003cp\u003eA two-sided p-value of \u0026lt;0.05 was considered statistically significant for all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eStudy Population and Baseline Characteristics\u003c/p\u003e \u003cp\u003eA total of 94 HD patients were included in the study. At the end of the four-year follow-up period, 53 patients (56.4%) survived, while all-cause mortality was observed in 41 patients (43.6%). No statistically significant differences were found between the groups in terms of age (p\u0026thinsp;=\u0026thinsp;0.224), gender (p\u0026thinsp;=\u0026thinsp;1.000), and BMI (p\u0026thinsp;=\u0026thinsp;0.643). Dialysis vintage was significantly higher in survivors compared to non-survivors (p\u0026thinsp;=\u0026thinsp;0.019) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population stratified by all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll Patients (n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvivors (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-Survivors (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics \u0026amp; Clinical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.0 (57.0-75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.0 (57.0\u0026ndash;72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.0 (57.0\u0026ndash;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (58.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis Vintage (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.0 (59.3-118.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.8 (65.6-125.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.1 (44.1-106.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up Duration (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.8 (22.1\u0026ndash;48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.4 (47.9\u0026ndash;49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.3 (12.3\u0026ndash;31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS-F Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.5 (34.0\u0026ndash;91.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.0 (34.0\u0026ndash;87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.0 (35.0-103.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (74.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (80.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of CAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184.4 (139.7-222.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175.9 (136.4-222.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186.4 (143.3-221.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.9 (35.7\u0026ndash;39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.5 (35.7\u0026ndash;38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.3 (35.7\u0026ndash;39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Creatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFR (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3 (4.8\u0026ndash;8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2 (4.8\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4 (5.3-8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEchocardiography\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVMI (g/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.1 (91.0-144.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112.8 (91.5-143.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.8 (90.7-148.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.4 (58.6\u0026ndash;65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.0 (56.6\u0026ndash;65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.5 (60.7\u0026ndash;65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAorta (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Atrium (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.7-1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.7\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.7\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage e' (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eE/e' Ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4 (7.7\u0026ndash;12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9 (7.1\u0026ndash;10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.7 (8.8\u0026ndash;13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Ventricle (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTAPSE (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esPAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0 (20.0\u0026ndash;35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0 (20.0\u0026ndash;30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.0 (25.0\u0026ndash;35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAPSE/sPAP Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.60\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.63\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.54\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as percentage, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eA; late diastolic flow velocities, CAD; coronary artery disease, ACE; angiotensin converting enzyme, ARB; angiotensin receptor blockers, BMI; the body mass index, E; early diastolic flow velocities, e'; peak myocardial velocity during early diastole, GFR; glomerular filtration rate, LVEDD; left ventricular end-diastolic diameter, LVMI; left ventricular mass index, LVEF; left ventricular ejection fraction, sPAP; Pulmonary artery peak systolic pressure, TAPSE; Tricuspid annular plane systolic excursion, VAS-F; Visual Analogue Scale to Evaluate Fatigue Severity, WBC; white blood cell count,\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding laboratory parameters, no significant differences were observed between the groups in hemoglobin, creatinine, albumin, or GFR levels (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In terms of echocardiographic data, LVEF (p\u0026thinsp;=\u0026thinsp;0.127) and LVMI (p\u0026thinsp;=\u0026thinsp;0.766) were similar between groups. However, the E/e' ratio was significantly higher in the non-survivor group compared to survivors (10.7 vs. 8.9; p\u0026thinsp;=\u0026thinsp;0.017), while the TAPSE value was significantly lower (20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 mm vs. 21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 mm; p\u0026thinsp;=\u0026thinsp;0.003). Additionally, the TAPSE/sPAP ratio was significantly lower in the mortality group (0.68 vs. 0.82; p\u0026thinsp;=\u0026thinsp;0.023). Although VAS-F scores assessing fatigue were higher in the mortality group, this difference did not reach statistical significance (79.0 vs. 64.0; p\u0026thinsp;=\u0026thinsp;0.138) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClinical Events During Follow-up\u003c/p\u003e \u003cp\u003eDuring the four-year follow-up period, 50.0% (n\u0026thinsp;=\u0026thinsp;47) of the patients experienced a non-fatal composite cardiovascular event. The distribution of recorded events was as follows: non-fatal myocardial infarction in 17.0% (n\u0026thinsp;=\u0026thinsp;16), coronary revascularization in 31.9% (n\u0026thinsp;=\u0026thinsp;30), non-fatal stroke in 13.8% (n\u0026thinsp;=\u0026thinsp;13), new-onset atrial fibrillation in 14.9% (n\u0026thinsp;=\u0026thinsp;14), and peripheral artery revascularization in 9.6% (n\u0026thinsp;=\u0026thinsp;9). One patient (1.1%) underwent kidney transplantation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of clinical events during 4-year follow-up\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposite Cardiovascular Events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-fatal Myocardial Infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary Revascularization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-fatal Stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew-onset Atrial Fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral Artery Revascularization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney Transplantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSurvival and ROC Analysis\u003c/p\u003e \u003cp\u003eAccording to the Kaplan-Meier survival analysis, patients with low TAPSE values (\u0026le;\u0026thinsp;21.0 mm) had a significantly lower survival rate compared to those with high TAPSE values (p\u0026thinsp;=\u0026thinsp;0.016) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In survival analyses based on E/e' ratio (p\u0026thinsp;=\u0026thinsp;0.055), VAS-F score (p\u0026thinsp;=\u0026thinsp;0.142), and systolic pulmonary artery pressure (sPAP) (p\u0026thinsp;=\u0026thinsp;0.278), the differences between groups did not achieve statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePredictors of Mortality (Cox Regression Analysis)\u003c/p\u003e \u003cp\u003eIn the univariate Cox regression analysis, variables found to be associated with mortality included TAPSE (HR: 0.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), E/e' ratio (HR: 1.08; p\u0026thinsp;=\u0026thinsp;0.054), Diabetes Mellitus (HR: 1.83; p\u0026thinsp;=\u0026thinsp;0.079), LVEF (HR: 1.06; p\u0026thinsp;=\u0026thinsp;0.083), and VAS-F score (HR: 1.01; p\u0026thinsp;=\u0026thinsp;0.087) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Cox Regression Analysis for All-Cause Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard Ratio (HR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAPSE (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/e' Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.054\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026ndash;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.079\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.083\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS-F Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.087\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Ventricle (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esPAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage e' (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026ndash;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u0026ndash;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVMI (g/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Atrium (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of CAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u0026ndash;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u0026ndash;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eA; late diastolic flow velocities, CAD; coronary artery disease, ACE; angiotensin converting enzyme, ARB; angiotensin receptor blockers, BMI; the body mass index, E; early diastolic flow velocities, e'; peak myocardial velocity during early diastole, GFR; glomerular filtration rate, LVEDD; left ventricular end-diastolic diameter, LVMI; left ventricular mass index, LVEF; left ventricular ejection fraction, sPAP; Pulmonary artery peak systolic pressure, TAPSE; Tricuspid annular plane systolic excursion, VAS-F; Visual Analogue Scale to Evaluate Fatigue Severity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA multivariate Cox regression model was constructed including variables with a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.1 in the univariate analysis. The model revealed that each 1 mm decrease in TAPSE independently increased the risk of all-cause mortality by 13% (HR: 0.87; 95% CI: 0.78\u0026ndash;0.97; p\u0026thinsp;=\u0026thinsp;0.012) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the same model, LVEF (p\u0026thinsp;=\u0026thinsp;0.092), Diabetes Mellitus (p\u0026thinsp;=\u0026thinsp;0.151), E/e' ratio (p\u0026thinsp;=\u0026thinsp;0.380), and VAS-F score (p\u0026thinsp;=\u0026thinsp;0.617) were not found to be independent predictors of mortality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox Regression Analysis for predictors of all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard Ratio (HR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAPSE (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/e' Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS-F Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as percentage, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eE; early diastolic flow velocities, e'; peak myocardial velocity during early diastole, LVEF; left ventricular ejection fraction, TAPSE; Tricuspid annular plane systolic excursion, VAS-F; Visual Analogue Scale to Evaluate Fatigue Severity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eReceiver Operating Characteristic (ROC) analysis for predicting all-cause mortality yielded an area under the curve (AUC) for TAPSE of 0.681 (95% CI: 0.565\u0026ndash;0.783; p\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The optimal cut-off value for mortality was determined as \u0026le;\u0026thinsp;20.0 mm, with a sensitivity of 61.0% and a specificity of 69.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main finding of this study is that in ESKD patients with preserved left ventricular ejection fraction (LVEF), the TAPSE value, an indicator of RV systolic function, is the strongest and independent predictor of all-cause mortality. Our four-year prospective follow-up revealed that every 1 mm decrease in TAPSE is associated with a 13% increase in mortality risk. This finding suggests that in ESKD patients, the traditional left ventricle-focused approach to cardiovascular risk assessment should be expanded, and that RV function assessment may play a significant role in this process.\u003c/p\u003e \u003cp\u003eRV dysfunction is known to be associated with poor prognosis in ESKD patients [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Tanasa et al. and Wang et al. previously reported strong associations between RV impairment and cardiovascular events or death [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In our study, the statistically significant cutoff value of \u0026le;\u0026thinsp;20.0 mm for TAPSE in predicting mortality supports the emphasis made by Grabysa et al. on the prognostic value of TAPSE in dialysis patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In hemodialysis patients, RV damage is attributed to the fact that arteriovenous fistulas, used as vascular access routes, reduce systemic vascular resistance and chronically increase venous return and cardiac output [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This high-output situation increases right ventricular preload, leading to dilation and hypertrophy in the right heart chambers [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, the TAPSE/sPAP ratio is known to be associated with RV dysfunction and is lower in individuals with high mortality [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePH is frequently seen in hemodialysis patients and is classified within the group of diseases with unclear or multifactorial pathogenesis in current guidelines [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The pathophysiology of the change in TAPSE is thought to be similar to the development of PH, and it is known that the use of TAPSE in the early diagnosis of PH has an effect on diagnosis and prognosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Floccari et al. showed a negative correlation between TAPSE and sPAP in a study conducted on stage 3 CKD patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although no significant increase in mortality and sPAP was detected in our study, sPAP was found to be higher in patients who died. This situation, where TAPSE was significantly lower without a difference between the groups in terms of PH, supports the use of TAPSE in the early detection of PH [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, heart failure and valvular diseases, which cause an increase in sPAP and are frequently observed in CKD patients, are secondary causes of PH [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The exclusion of these diseases from the study may have altered the sPAP value and its relationship with mortality between the groups.\u003c/p\u003e \u003cp\u003eThe significantly higher E/e' ratio in the mortality group confirms the impact of increased left ventricular filling pressures on survival. Pedersen et al. and Kim et al. have shown that a high E/e' ratio predicts poor survival in CKD patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the loss of independence for the E/e' ratio in our multivariate analysis suggests that right ventricular parameters may possess superior prognostic power in this specific patient cohort.\u003c/p\u003e \u003cp\u003eWhile fatigue has been reported as a mortality predictor elsewhere, it did not reach independent significance in our study [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. We previously noted that fatigue correlates with diastolic dysfunction and TAPSE in this group [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the four-year follow-up, although the higher E/e\u0026rsquo; ratio and VAS-F score in absolute terms in patients with high mortality indicated a clinical trend, it did not reach statistical significance. We believe that the difference between the studies is mainly due to the fact that patients in Watanabe et al. had higher VAS-F scores and our study had stricter exclusion criteria [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, the fact that fatigue was measured only at the beginning of the study during a single visit may be due to the failure to record dynamic changes in symptoms throughout the dialysis process. The evaluation of a younger patient population with less fatigue and shorter dialysis duration may have reduced the power of the VAS-F score. However, this trend, which approached significance with p\u0026thinsp;=\u0026thinsp;0.087 in univariate analysis, suggests that fatigue may also gain prognostic value in a larger sample size.\u003c/p\u003e \u003cp\u003eOne of the key findings of our study is that the dialysis duration of the surviving patients was significantly longer than that of the group with mortality. This difference in duration, observed independently of age, may be due to the surviving patients having a longer dialysis history, which could be an example of \"reverse epidemiology\" specific to the dialysis population, or it may be dependent on the research population [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This difference can be explained by the fact that the surviving group successfully navigated the high-risk first years of dialysis and were a subgroup that was biologically and hemodynamically more resistant to treatment, while those who died were more vulnerable to uremic toxins or acute hemodynamic changes at the onset of dialysis. Additionally, it may be due to the fact that we conducted our research in a group with strict exclusion criteria, who were on dialysis for a short time and nearly half of whom died.\u003c/p\u003e \u003cp\u003eThe E/e ratio, VAS-F score, and sPAP appear to follow an inverse relationship with TAPSE, as can be seen from the survival curves. Although we could not detect a correlation between the increase in E/e ratio, VAS-F score, and sPAP and mortality in the survival curves of our study, we believe that it may become significant in larger patient groups and/or with longer follow-up periods.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the fact that the research was conducted in a single center and on a relatively small sample group may limit the generalization of the results to the entire ESKD population. Second, the use of two-dimensional and M-mode parameters such as TAPSE, which are more readily available in routine clinical practice, instead of cardiac magnetic resonance imaging or three-dimensional echocardiography, which are considered the gold standard in evaluating RV function, can be considered a limitation. Third, a subjective self-report scale, which may be affected by psychological states or sociocultural differences, was used to assess fatigue severity. In addition, the fact that echocardiographic measurements and fatigue scores were taken only once at the beginning of the study prevented the analysis of the effect of changes in these parameters on mortality during the follow-up period. Finally, the lack of cause-specific mortality data prevents us from distinguishing between cardiovascular and non-cardiovascular deaths, though the strong link between TAPSE and all-cause mortality remains clinically robust.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRV function may be as strong a predictor of mortality in hemodialysis patients with preserved LVEF as left ventricular parameters and clinical symptoms. Including TAPSE measurement in routine echocardiographic examinations during the follow-up of dialysis patients may be useful in identifying high-risk patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest:\u003c/h2\u003e \u003cp\u003eThe authors declare that there is no conflict of interest\u003c/p\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003e This study was approved by Baskent University Institutional Review Board and Ethics Committee (Project no: KA25/436) and supported by Baskent University Research Fund.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEA: Responsible for the conceptualization, design, literature search, and completion of the manuscript. GG: Contributed to data acquisition, evaluation of results, and determination of the final version of the manuscript. SA: Involved in the research conception stage and performed the statistical evaluation. FC: Contributed to the interpretation of data and drafting the manuscript. AS: Participated in the conception and analysis of the research. AA: Contributed to the evaluation of research data and the writing of the research article. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available due to patient privacy and institutional data protection policies but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRichardson T, Gardner M, Salani M (2025) Cardiovascular Disease and Dialysis: A Review of the Underlying Mechanisms, Methods of Risk Stratification, and Impact of Dialysis Modality Selection on Cardiovascular Outcomes. Kidney Dialysis 5(1):5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/kidneydial5010005\u003c/span\u003e\u003cspan address=\"10.3390/kidneydial5010005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoyama H, Fukuda S, Shoji T, Inaba M, Tsujimoto Y, Tabata T et al (2010) Fatigue is a predictor for cardiovascular outcomes in patients undergoing hemodialysis. Clin J Am Soc Nephrol 5(4):659\u0026ndash;666\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorigan AE (2012) Fatigue in hemodialysis patients: a review of current knowledge. J Pain Symptom Manage 44(5):715\u0026ndash;724\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkbay E, Akinci S, Coner A, Adar A, Genctoy G, Demir AR (2022) New perspective on fatigue in hemodialysis patients with preserved ejection fraction: diastolic dysfunction: Fatigue and diastolic dysfunction. Int J Cardiovasc Imaging 38(10):2143\u0026ndash;2153\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe G, Tanaka K, Saito H, Kimura H, Tani Y, Asai J et al (2025) Post-dialysis fatigue predicts all-cause mortality in patients on chronic hemodialysis. Ther Apher Dial 29(1):12\u0026ndash;22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLogu K, Balakrishnan K, Sahay M, George M, Kandadai SD, Elumalai DV et al (2026) Chronic kidney disease and right ventricular dysfunction; an echocardiographic assessment in hemodialysis patients. J Nephropharmacol 15(1):e12704\u0026ndash;e04\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrabysa R, Wańkowicz Z (2015) Can Echocardiography, Especially Tricuspid Annular Plane Systolic Excursion Measurement, Predict Pulmonary Hypertension and Improve Prognosis in Patients on Long-Term Dialysis? Med Sci Monit 21:4015\u0026ndash;4022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoschi M, Di Mauro M, Tancredi F, Capparuccia C, Petroni R, Leonzio L et al (2017) The Dark Side of the Moon: The Right Ventricle. J Cardiovasc Dev Dis. ;4(4)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReinecke A, Di\u0026szlig;mann P, Frey N, M\u0026uuml;ller OJ, Seoudy H, Frank J et al (2025) In heart failure, echocardiographic parameters of right ventricular function are powerful tools to predict renal failure. 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Am J Cardiol 85(7):837\u0026ndash;842\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteckelberg RC, Tseng AS, Nishimura R, Ommen S, Sorajja P (2013) Derivation of mean pulmonary artery pressure from noninvasive parameters. J Am Soc Echocardiogr 26(5):464\u0026ndash;468\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalderisi M, Cosyns B, Edvardsen T, Cardim N, Delgado V, Di Salvo G et al (2017) Standardization of adult transthoracic echocardiography reporting in agreement with recent chamber quantification, diastolic function, and heart valve disease recommendations: an expert consensus document of the European Association of Cardiovascular Imaging. 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Am J Physiol Heart Circ Physiol 305(9):H1373\u0026ndash;H1381\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHumbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M et al (2022) 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J 43(38):3618\u0026ndash;3731\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFloccari F, Granata A, Rivera R, Marrocco F, Santoboni A, Malaguti M et al (2012) Echocardiography and right ventricular function in NKF stage III cronic kidney disease: Ultrasound nephrologists' role. J Ultrasound 15(4):252\u0026ndash;256\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim MK, Kim B, Lee JY, Kim JS, Han BG, Choi SO et al (2013) Tissue Doppler-derived E/e' ratio as a parameter for assessing diastolic heart failure and as a predictor of mortality in patients with chronic kidney disease. Korean J Intern Med 28(1):35\u0026ndash;44\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedersen MZ, Skaarup KG, Landler NE, Olsen FJ, Christensen J, Johansen ND et al (2026) Diastolic dysfunction and the risk of end-stage kidney disease among patients with non-dialysis-dependent chronic kidney disease. Int J Cardiovasc Imaging. Jan 9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVashistha T, Mehrotra R, Park J, Streja E, Dukkipati R, Nissenson AR et al (2014) Effect of age and dialysis vintage on obesity paradox in long-term hemodialysis patients. Am J Kidney Dis 63(4):612\u0026ndash;622\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hemodialysis, Mortality, Right Ventricular Function, TAPSE, Fatigue, Diastolic Dysfunction, Preserved Ejection Fraction","lastPublishedDoi":"10.21203/rs.3.rs-8957047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8957047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic kidney disease (CKD) is a significant global cause of mortality, particularly due to cardiovascular complications. In patients with end-stage kidney disease (ESKD), fatigue is a highly prevalent symptom that has been linked to diastolic and right ventricular (RV) dysfunction. While left ventricular assessment is routine, the prognostic significance of RV function, specifically in patients with preserved left ventricular ejection fraction (LVEF), remains insufficiently explored. This study aims to investigate the impact of fatigue, diastolic dysfunction, and RV function on the survival of hemodialysis (HD) patients with preserved LVEF.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this prospective cohort study, 94 HD patients with preserved LVEF were followed for 48 months. Baseline assessments included fatigue severity measured by the Visual Analog Scale for Fatigue (VAS-F) and echocardiographic parameters, including the E/e' ratio for diastolic function and Tricuspid Annular Plane Systolic Excursion (TAPSE) for RV function. Predictors of mortality were identified using univariate and multivariate Cox proportional hazards regression models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring the 4-year follow-up, all-cause mortality occurred in 41 patients (43.6%). Non-survivors had significantly higher E/e' ratios (p\u0026thinsp;=\u0026thinsp;0.017) and lower TAPSE values (p\u0026thinsp;=\u0026thinsp;0.003) compared to survivors. While VAS-F scores were higher in the mortality group, the difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.138). In the multivariate Cox regression model, TAPSE was identified as the only independent predictor of mortality (Hazard Ratio [HR]: 0.87; 95% Confidence Interval [CI]: 0.78\u0026ndash;0.97; p\u0026thinsp;=\u0026thinsp;0.012), with each 1 mm decrease in TAPSE increasing the risk of death by 13%. LVEF, E/e' ratio, and VAS-F score were not independent predictors in the multivariate analysis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn HD patients with preserved LVEF, right ventricular function \u0026lsquo;as measured by TAPSE\u0026rsquo; is a more powerful independent predictor of mortality than diastolic dysfunction or clinical fatigue severity. Routine monitoring of TAPSE may be critical for identifying high-risk patients and improving cardiovascular risk assessment in this population.\u003c/p\u003e","manuscriptTitle":"Predictive Value of Fatigue, Diastolic Dysfunction, and Right Ventricular Function for Mortality in Hemodialysis Patients with Preserved Ejection Fraction: A 4-Year Prospective Follow- Up Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 07:07:37","doi":"10.21203/rs.3.rs-8957047/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cebabc4e-2c3f-4158-80dd-f7bbcaf84ded","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-15T01:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 07:07:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8957047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8957047","identity":"rs-8957047","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}