Individual Components of Central Blood Pressure and Aortic Stiffness versus the Integration of Multiple Components in Predicting Cardiovascular Mortality in End-Stage Renal Disease

Individual Components of Central Blood Pressure and Aortic Stiffness versus the Integration of Multiple Components in Predicting Cardiovascular Mortality in End-Stage Renal Disease | 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 Article Individual Components of Central Blood Pressure and Aortic Stiffness versus the Integration of Multiple Components in Predicting Cardiovascular Mortality in End-Stage Renal Disease Mohsen Agharazii, Nadège Côté, Catherine Fortier, Louis-Charles Desbiens, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3170711/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2024 Read the published version in Journal of Human Hypertension → Version 1 posted 9 You are reading this latest preprint version Abstract Aortic stiffness, measured by carotid-femoral pulse-wave velocity (PWV), is a predictor of cardiovascular (CV) mortality in patients with end-stage renal disease (ESRD). Aortic stiffness increases aortic systolic and pulse pressures (cSBP, cPP) and augmentation index (AIx). In this study, we examined if the integration of multiple components of central blood pressure and aortic stiffness (ICPS) into risk score categories could improve CV mortality prediction in ESRD. In a prospective cohort of 311 patients with ESRD on dialysis who underwent vascular assessment at baseline, 118 CV deaths occurred after a medial follow-up of 3.1 years. The relationship between hemodynamic parameters and CV mortality was analyzed through Kaplan-Meier and Cox survival analysis. ICPS risk score from 0 to 5 points were calculated from points given to tertiles, and were regrouped into three risk categories (Average, High, Very High). A strong association was found between the ICPS risk categories and CV mortality (High risk HR = 2.20, 95%CI: 1.05–4.62, P = 0.036; Very High risk (HR = 4.44, 95%CI: 2.21–8.92, P < 0.001). The very high-risk category remained associated with CV mortality (HR = 3.55, 95% CI: 1.37–9.21, P = 0.009) after adjustment for traditional CV risk factors. While ICPS categories showed higher C-statistics (C: 0.627, 95%CI: 0.578–0.676, P = 0.001), it was not statistically superior to PWV, cPP or AIx. In conclusion, integration of multiple components of central blood pressure and aortic stiffness did not result in a significantly better prediction of CV mortality in this cohort. Figures Figure 1 Figure 2 Figure 3 Introduction Chronic kidney disease (CKD) is strongly associated with cardiovascular (CV) disease, even after adjustments for traditional risk factors ( 1 – 3 ). It has been proposed that non-traditional CV risk factors play a more dominant role in the context of CKD. Aortic stiffness is one of these non-traditional cardiovascular risk factors, which has become the focus of much research over the past two decades ( 4 – 8 ). Aortic stiffness is measured by determination of carotid-femoral pulse wave velocity (PWV) ( 9 ). Its hemodynamic consequences can be assessed by central systolic blood pressure (cSBP), central pulse pressure (cPP), and wave reflection, which is measured by heart rate adjusted augmentation index (AIx). These biomarkers of aortic stiffness have been mostly used individually to predict CV outcomes ( 5 , 10 , 11 ). Two studies in CKD patients, on conservative therapy or on dialysis therapy, suggest that integrating these biomarkers could potentially be better for prediction of CV outcomes ( 12 , 13 ). These studies used an integrated central blood pressure-aortic stiffness (ICPS) risk scoring, which was built based on the tertiles of PWV, cPP and cSBP. ICPS risk score was then used to generate ICPS risk categories (Average, High, and Very High risk). They found that ICPS risk categories were strongly associated with CV events, and even surpassing PWV and cSBP in CKD patients on conservative therapy ( 12 ), and cSBP in patients end-stage renal disease (ESRD) on hemodialysis therapy (HD) ( 13 ). Although the ICPS categories showed promising results in the prediction of cardiovascular events, PWV was still a better predictor of cardiovascular mortality in ESRD patients on HD as compared with the ICPS categories. Therefore, the aim of the present study was to examine, in an independent cohort, if the integration of multiple components of central blood pressure and aortic stiffness into risk score categories could improve CV mortality prediction in ESRD. Materials and methods Participants and Settings Patients with end-stage renal disease on dialysis were recruited at the Hôtel-Dieu de Québec Hospital, Canada. All patients were adults on peritoneal dialysis or hemodialysis with stable dry weight and stable medication for more than a month. Patients were excluded if they presented acute episodes of illness such as acute heart failure, infection, or active bleeding or if they had any clinical condition compromising hemodynamic measurements at baseline. Clinical, pharmacological and laboratory datas were obtained by health record review and vascular assessment was performed. All patients were followed prospectively for CV survival. A total of 311 patients underwent baseline assessment between April 2006 and February 2012, and the survival status was last evaluated in June 2022. Arterial Stiffness and Central Hemodynamics Hemodynamic measurements were obtained after a 10-minute rest in a supine position. In case of the presence of an arteriovenous fistula, measurements were obtained on the contralateral arm. Brachial blood pressure (BP) was recorded by an automatic oscillometric sphygmomanometer BPM-100 (BP-Tru, Coquitlam, Canada) ( 14 ) in triplicates with a 2-minute interval between each measurement. Immediately after BP measurements, PWV was obtained in triplicates by Complior® SP (Artech Medical, Pantin—France), using the maximal upstroke algorithm previously described ( 15 ). To be coherent with the previous studies on ICPS ( 12 , 13 ) and to respect the latest recommendations ( 9 ), 80% of the direct carotid-femoral distance was used to obtain PWV; our data was corrected accordingly. Radial artery waveforms were obtained by applanation tonometry and calibrated with brachial systolic blood pressure (pSBP) and brachial diastolic blood pressure (pDBP). Radial artery waveforms allowed to obtain cSBP, cPP and AIx via generalized transfer function (SphygmoCor system®, AtCor Medical Pty. Ltd., Sydney, Australia) ( 16 ). Outcome measurement The outcome was CV mortality defined as death due to cardiac arrhythmia, heart failure, myocardial infarction, cardiogenic shock, stroke (ischemic or hemorrhagic), bowel ischemia, critical limb ischemia, aortic dissection, and sudden death. Statistical Analysis The values are reported as mean ± SD or median (25th -75th percentiles) as appropriate. We used a similar approach to predict the risk of cardiovascular mortality as previously performed ( 12 , 13 ). First, we used Cox regression analysis of the standardized values of PWV, cPP, AIx and cSBP, with respect to CV mortality. We then used another approach to generate the ICPS risk score by using a Kaplan—Meier survival analysis for tertiles of each parameter of interest (PWV, cPP, AIx and cSBP), followed by Cox regression analysis. Polynomial and simple contrast analysis were performed to evaluate the best way to attribute points to each tertile. Based on these results, there appeared to be a linear association between PWV, cPP, and CV mortality. Accordingly, 0, 1 or 2 points were given to the consecutive tertiles. As the risk of CV mortality only significantly increased in the third tertile of AIx, 0 points were given to the first two tertiles and 1 point was given to the third tertile. There appeared to be a “U shaped” relationship between cSBP and CV mortality, the second tertile was therefore used as the reference group. However, none of the tertiles of cSBP was associated with increased risk. cSBP was not included in the ICPS risk score. An ICPS risk score (ranging from 0 to 5 points) was calculated for each patient by adding the points attributed for each parameter. The predictive value of the ICPS risk score was tested using Kaplan-Meier survival analysis and Cox survival regression analyses. The ICPS risk score were subsequently categorized into three risk categories: Average (ICPS risk score = 0), High (ICPS risk score = 1 or 2), and Very High risk (ICPS risk score ≥ 3). The predictive power of ICPS risk categories towards CV mortality was then tested. In Cox regression analysis, hazard ratios (HR) were reported as unadjusted and adjusted for age, sex, pSBP, LDL cholesterol, smoking, diabetes, body mass index (BMI) and history of CV disease. Finally, Harrell’s concordance index (C-statistics) was calculated to investigate and compare the discrimination of the risk categories and each of its component (PWV, cPP, AIx). C-statistics analyses were performed using Stata/SE 17.0 (StataCorp LLC, USA). All other analysis was performed using SPSS 28 (IBM, Ltd., USA). Results A total of 311 patients were included in this cohort. During a median follow-up of 3.1 (1.4–6.0) years, 64 (21%) patients were censored because they underwent renal transplantation, and 117 (38%) died from non-cardiovascular causes. Cardiovascular deaths occurred in 118 (38%) patients. Baseline data regarding clinical characteristics, traditional and non-traditional risk factors, metabolic and hemodynamic parameters are reported in Table 1 . Table 1 Baseline demographic, clinical, laboratory and hemodynamic characteristics of participants Subjects, n 311 Male sex 185 (60) Age (years) 64.7 ± 15.0 Dialysis duration (years) 1.5 (0.5–3.3) BMI (kg/m 2 ) 27.3 ± 5.6 Smoking (active or past) 122 (39) Diabetes 134 (43) Cardiovascular disease 162 (52) Hypertension 284 (91) Primary renal disease Diabetic 78 (25) Hypertensive 50 (16) Diabetic and hypertensive 13 (4) Chronic tubulointerstitial nephritis 30 (10) Glomerulonephritis 81 (26) Polycystic 23 (7) Other or unknown 36 (12) Antihypertensive medication ACE or ARBs 135 (43) Calcium channel blockers 106 (34) Diuretics 137 (44) β-receptor blockers 174 (56) α-receptor blockers 17 (6) Long-acting nitrate 50 (16) Centrally acting agents 27 (9) Laboratory results Cholesterol (mmol/l) 3.9 ± 1.0 HDL cholesterol (mmol/l) 1.1 ± 0.3 LDL cholesterol (mmol/l) 1.9 ± 0.8 Triglyceride (mmol/l) 2.0 ± 1.1 Sodium (mmol/l) Potassium (mmol/l) 4.4 ± 0.7 Calcium (mmol/l) 2.2 ± 0.2 Phosphate (mmol/l) 1.5 ± 0.4 Albumin (g/l) 37.7 ± 3.5 Parathormone (ng/l) 359 ± 284 C Reactive Protein (mg/l) 6.5 (2.6–14.2) Hemodynamic data Brachial SBP (mmHg) 131 ± 26 Brachial DBP (mmHg) 71 ± 13 Brachial MP (mmHg) 92 ± 17 Brachial Pulse pressure (mmHg) 61 ± 22 Heart rate (bpm) 69 ± 11 Central SBP (mmHg) 121 ± 25 Central DBP (mmHg) 72 ± 13 Central MP (mmHg) 92 ± 17 Central PP (mmHg) 46 (32.5– 62.8) PWV (m/s) 10.8 ± 3.2 Augmentation index for heart rate of 75 bpm (%) 27.7 (20.2–33.0) Categorical parameters are presented as number (%), continuous data are presented as mean (standard deviation) or median (interquartile range). ACE or ARBs = angiotensin-converting enzyme inhibitors or angiotensin receptor blockers Table 2 shows the association of each parameter of interest (PWV, cPP, AIx and cSBP) with the risk of CV mortality. Hazard ratios are presented for 1 SD increase in PWV, cPP, AIx, and SBP in unadjusted and adjusted models. After adjustments, only AIx remained statistically significant with regards to CV mortality. Table 2 Cox models with cardiovascular mortality as outcome and individual arterial stiffness and central hemodynamic parameters as predictors Variables Unadjusted Adjusted Hazard ratio 95% CI p-value Hazard ratio 95% CI p-value PWV (per 1 SD) 1.449 1.233 1.703 <0.001 1.186 0.944 1.491 0.144 cPP (per 1 SD) 1.317 1.116 1.555 0.002 1.594 0.993 2.558 0.054 AIx (per 1 SD) 1.609 1.303 1.988 <0.001 1.502 1.166 1.934 0.002 cSBP (per 1 SD) 1.038 0.868 1.242 0.683 0.526 0.150 1.839 0.314 Bold values demonstrate significance when p-value < 0.05. Adjusted model included age, sex, current smoking, diabetes, BMI, CV disease, pSBP and LDL cholesterol. PWV: pulse wave velocity; cSBP: central systolic blood pressure; cPP: central pulse pressure; AIx: augmentation index adjusted for a heart rate of 75 beats per minute. Figures 1 A to 1 D show the Kaplan-Meier survival curves related to tertiles of PWV, cPP, AIx, and cSBP. Table 3 shows the hazard ratio for each tertiles of PWV, cPP, AIx and cSBP. In the unadjusted analysis, there is an increased HR for the risk of CV mortality with the 2nd and 3rd tertiles of PWV, the 2nd and 3rd tertiles of cPP, and the 3rd tertile of AIx. In the adjusted model, only the 2nd and 3rd tertiles of cPP as well as the 3rd tertile of AIx remained significantly related to CV mortality. cSBP was not associated with increased risk of CV mortality in either unadjusted or adjusted model. Table 3 Cox models with cardiovascular mortality as outcome and individual arterial stiffness and central hemodynamic parameters by tertiles as predictors Unadjusted Adjusted Variables Tertiles N Range Hazard ratio 95% CI p-value Hazard ratio 95% CI p-value PWV 1st 104 3.9 − 9.1 1 (ref) 1 (ref) 2nd 104 9.1 − 11.7 1.842 1.121 3.025 0.016 1.195 0.684 2.086 0.532 3rd 103 11.8 − 24.8 2.541 1.564 4.128 <0.001 1.146 0.610 2.152 0.671 cPP 1st 104 13.0 − 36.7 1 (ref) 1 (ref) 2nd 103 37.0 − 53.3 2.201 1.327 3.650 0.002 1.979 1.108 3.533 0.021 3rd 104 56.3 − 133.3 2.531 1.543 4.151 <0.001 2.372 1.117 5.036 0.025 AIx 1st 104 -14.3 − 23.0 1 (ref) 1 (ref) 2nd 104 23.3 − 30.9 1.546 0.953 2.508 0.078 1.262 0.754 2.113 0.376 3rd 103 31.0 − 60.5 2.413 1.524 3.820 <0.001 2.342 1.349 4.065 0.002 cSBP 1st 104 63.3–107.5 1.317 0.829 2.090 0.243 0.998 0.565 1.765 0.995 2nd 104 107.7–130.6 1 (ref) 1 (ref) 3rd 103 130.7 − 235.5 1.394 0.880 2.209 0.156 1.380 0.735 2.588 0.316 Bold values demonstrate significance when p-value < 0.05. Adjusted model included age, sex, current smoking, diabetes, BMI, CV disease, pSBP and LDL cholesterol. PWV: pulse wave velocity; cSBP: central systolic blood pressure; cPP: central pulse pressure; AIx: augmentation index adjusted for a heart rate of 75 beats per minute ICPS risk score and Cardiovascular Mortality The ICPS risk score ranged from 0 to 5 points based on the points assigned to each tertiles of PWV, cPP and AIx, as described in the statistical analysis section. Figure 2 shows the Kaplan-Meier survival curves of ICPS risk scores with regard to CV mortality. As shown in the first portion of Table 4 , the higher risk scores (scores ≥3) were significantly associated with increased risk of CV mortality in both unadjusted and adjusted models of Cox regression analysis. Table 4 The relation between ICPS risk score and ICPS risk categories with CV based on Cox proportional hazard regression analyses N Hazard ratio 95% CI p-value ICPS risk scores Unadjusted 0 point 60 1 (ref.) 1 point 46 2.251 0.970 5.222 0.059 2 points 57 2.171 0.981 4.804 0.056 3 points 60 4.332 2.050 9.152 <0.001 4 points 56 4.450 2.097 9.443 <0.001 5 points 32 4.659 2.022 10.734 <0.001 Adjusted 0 point 60 1 (ref.) 1 point 46 1.565 0.613 3.991 0.349 2 points 57 1.719 0.694 4.259 0.242 3 points 60 3.592 1.365 9.455 0.010 4 points 56 3.904 1.357 11.231 0.012 5 points 32 4.478 1.300 15.425 0.018 ICPS risk categories Unadjusted Average (0 point) 60 1 (ref.) High (1–2 points) 103 2.203 1.052 4.615 0.036 Very High (3–5 points) 148 4.437 2.208 8.916 <0.001 Adjusted Average (0 point) 60 1 (ref.) High (1–2 points) 103 1.598 0.688 3.713 0.276 Very High (3–5 points) 148 3.550 1.369 9.205 0.009 Bold values demonstrate significance when p-value < 0.05. Adjusted model included age, sex, current smoking, diabetes, BMI, CV disease, pSBP and LDL cholesterol. PWV: pulse wave velocity; cSBP: central systolic blood pressure; cPP: central pulse pressure; AIx: augmentation index adjusted for a heart rate of 75 beats per minute. Figure 3 shows Kaplan-Meier survival curves of ICPS risk categories, which are Average risk (ICPS risk score = 0), High risk (ICPS risk score = 1 or 2 points), and Very High risk (ICPS risk score ≥3 points), with regards to CV mortality. The second portion of Table 4 presents the three ICPS risk categories in both unadjusted and adjusted models. Both the High and the Very High risk categories of ICPS risk were related to CV mortality in the unadjusted Cox survival regression analysis. However, only the Very High risk category remained statistically significant after adjustments for potential confounders. Table 5 presents C-statistics, and differences between C-statistics for ICPS risk categories and PWV, AIx, and cPP. All C-statistics presented a modest discrimination towards CV mortality. While ICPS categories showed a higher C-statistics, this was not statistically superior to PWV, cPP or AIx. Table 5 Harell’s C -statistics for ICPS risk categories and arterial stiffness parameters, and the differences in the C -statistics between ICPS risk categories and arterial stiffness parameters Variables Coefficient Standard error 95% CI p-value ICPS risk categories 0.627 0.025 0.578 0.676 <0.001 PWV 0.614 0.028 0.560 0.668 <0.001 cPP 0.594 0.028 0.538 0.650 <0.001 AIx 0.618 0.029 0.561 0.674 <0.001 ICPS risk categories 0.013 0.020 -0.027 0.052 0.526 vs. PWV ICPS risk categories 0.033 0.020 -0.005 0.071 0.091 vs. cPP ICPS risk categories 0.009 0.029 -0.048 0.067 0.748 vs. AIx Bold values demonstrate significance when p-value < 0.05. CI: confidence intervals; ICPS risk categories: integrated central pressure-stiffness risk categories; PWV: carotid-femoral pulse wave velocity; cPP: central pulse pressure and AIx: augmentation index adjusted for a heart rate of 75 beats per minute Table 6 Summary table What is known about topic What this study adds • Patients with end-stage renal disease are at extreme risk of cardiovascular disease and cardiovascular mortality. • Aortic stiffness, which increases systolic and pulse pressures and increases augmentation index, is associated with increased risk of cardiovascular mortality in patients with end-stage renal disease. • It was suggested that integration of multiple components of central blood pressure and aortic stiffness may improve prediction of cardiovascular mortality. • A risk categorization based on an integrated approach to central blood pressure and aortic stiffness was associated with increased risk of cardiovascular mortality even when adjusting for confounding risk factors. • However, this approach was not statistically better than the individual components of central blood pressure and aortic stiffness. Discussion This study demonstrates that an integrated risk scoring concept and the defined ICPS risk categories based on the one developed in previous studies ( 12 , 13 ) are statistically related to CV mortality in a larger cohort of ESRD patients on dialysis therapy. The predictive relationship between ICPS Very High risk category and CV mortality remained statistically significant even after adjustments for traditional cardiovascular risk factors such as age, sex, brachial systolic blood pressure, smoking, BMI, LDL cholesterol levels, CV disease and diabetes. The ICPS risk categories show a better, yet not statistically significant, predictive power towards CV mortality than each of the parameters used in the scoring (PWV, cPP and AIx) in our cohort of patients. Patients with chronic kidney disease suffer from high prevalence and incidence of cardiovascular disease ( 1 – 3 ). CV disease and events are generally classified according to atherosclerotic events, either progressive arterial stenosis or sudden occlusion of blood flow because of plaque rupture, thrombosis and/or emboli. Lipids play an important role in atherosclerotic vascular disease and many lipid lowering trials have shown their clinical efficacy both in primary and secondary prevention of cardiovascular disease ( 17 , 18 ). However, despite high rates of CV events in ESRD, lipid lowering trials have not shown a similar cardiovascular benefit in this population ( 19 – 21 ). As such, it has been proposed that arteriosclerosis may play a more dominant role in the pathophysiology of CKD-related cardiovascular events. Indeed, arteriosclerosis and medial vascular calcification lead to arterial stiffening which affects the aorta more predominantly. In physiological conditions, aortic elasticity is crucial for dampening of the systolic rise in blood pressure during systole and maintaining a continuous flow during diastole. In the process of aging, the aorta becomes stiffer, resulting in increased SBP and increased cardiac workload. When the aorta stiffens, aortic PWV increases, which in turn results in early and enhanced wave reflection (AIx) that occurs more predominantly during the systole. This process further increases central systolic BP and reduces central diastolic BP, increasing central PP. Aortic stiffness has been independently associated with increased risk of CV disease in various clinical conditions ( 22 – 25 ). In CKD, especially in dialysis patients, the accumulation of uremic toxins, endothelial dysfunction, accelerated elastin fragmentation, and vascular calcification conspire to increase aortic stiffness at a faster rate ( 26 – 28 ). While aortic PWV is the gold standard measure of aortic stiffness, hemodynamic consequences of aortic stiffness can also be used as an indirect measure of aortic stiffness. These include central SBP, central PP and central AIx. However, these parameters can also be influenced by other factors such as heart rate, height, sex, and antihypertensive medications ( 29 , 30 ). Finally, aortic stiffness increases pulsatile pressure and flow, which could be harmful to the microcirculation of highly perfused organs such as the brain and kidneys. There are potential theoretical advantages of using multiple parameters of the central blood pressure and aortic stiffness over an individual parameter for the prediction of CV mortality. Indeed, the integration of the parameters in a risk score could allow us to get the most out of the complementary information given by each individual parameter. These parameters can easily be obtained using most devices available for hemodynamic assessment. Therefore, it seems logical that an integrated risk score could have some potential to improve the prediction of CV mortality in cohorts of patients at increased CV risk. Two previous studies ( 12 , 13 ) proposed an integrated central pressure aortic stiffness score considering aortic stiffness as well as central blood pressure parameters. They first developed ICPS risk categories to predict CV events that they applied to 100 CKD patients on conservative therapy. Their ICPS risk categories showed statistically significant better predictive power towards CV events than PWV and cSBP taken individually ( 12 ). They then used the same scoring approach in 91 patients with ESRD on chronic hemodialysis therapy to predict CV mortality. In this second study, the ICPS risk categories were significantly better at predicting CV mortality than was cSBP alone. However, the ICPS risk categories did not surpass PWV and cPP in the prediction of CV mortality ( 13 ). The authors suggested that the superior predictive power of the ICPS risk categories in the non-HD CKD group might be related to a stronger role of PWV as a determinant of CV outcomes in patients on hemodialysis. The present study involves a larger cohort regrouping a mix of patient on hemodialysis and peritoneal dialysis, and reported almost four times more events compared with the previous study using ICPS risk score in HD patients ( 13 ). The ICPS risk score used in the present study is slightly different than the ones previously reported ( 12 , 13 ). First, we only attributed points to the tertiles that were significantly associated with CV mortality when creating the ICPS risk score. Second, we included PWV, cPP and AIx into the ICPS score, whereas in the previous studies they used PWV, cPP and cSBP in their scoring system. In the present study the ICPS risk categories provided a slightly and non-significant increase in C-statistics value than PWV, cPP or AIx, which was not the case for ICPS risk categories presented in the previous study on patients in ESRD on dialysis ( 13 ). The different technics used to perform hemodynamic measurements could potentially explain the difference in the association between stiffness parameters and CV mortality. In the previous studies ( 12 , 13 ), the PulsePen device (device; DiaTecne s.r.l. Milan, Italy) was used, which provides PWV automatically, but requires personal pulse wave analysis to calculate cPP, AIx and cSBP. In the present study, the Sphygmocor device was used providing all studied parameters without potential for interobserver variability. Our study has some limitations. First, the study includes a limited number of subjects and cardiovascular events, and therefore may be underpowered to detect statistically significant changes in C-statistics. Second, tertiles of each parameter were used to describe CV risk and create risk scores and categories. Therefore, the cut-off values of the parameters used in the present study may not be relevant to other populations to define ICPS risk scoring and risk categories. To make such a scoring system relevant and useful for clinical use, defined cut-off values need to be established by studying larger number of patients. In conclusion, the integrated score and categories inspired by the one previously developed ( 12 , 13 ) is associated with the risk of CV mortality in patients with ESRD on dialysis therapy, but this was not superior to individual components of central blood pressure and aortic stiffness. Declarations DATA AVAILABILITY STATEMENT The datasets analyzed during the current study are available from the corresponding author on a reasonable request. ACKNOWLEDGMENTS We are grateful to the dialysis personnel for their generous contribution and kind collaboration. AUTHOR CONTRIBUTION NC was involved in data analysis and in writing. CF was involved in data gathering and data analysis. LCD was involved in data analysis. JN was involved in study conceptualization and reviewing. MA was involved in study conceptualization, methodology, data analysis and writing. FUNDING This project was supported by the Canadian Institute of Health Research (CIHR), New Emerging Team Grant (NET-54008), the Heart and Stroke Foundation of Canada, the Kidney Foundation of Canada, and the Canadian Diabetes Association. CF is supported by the Research chair in nephrology from the Fondation de l’Université Laval . NC holds a scholarship from the Société québecoise d’hypertension artérielle . ETHICAL APPROUVAL The study had been approved by the Comité d’éthique de la recherche du CHU de Québec and was conducted in accordance with the Declaration of Helsinki. Institutional guidelines were followed, and each patient included provided informed written consent. 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Aortic pulse-wave velocity and its relationship to mortality in diabetes and glucose intolerance: an integrated index of vascular function? Circulation. 2002;106(16):2085-90. Karras A, Boutouyrie P, Briet M, Bozec E, Haymann JP, Legendre C, et al. Reversal of Arterial Stiffness and Maladaptative Arterial Remodeling After Kidney Transplantation. J Am Heart Assoc. 2017;6(9). Shroff RC, McNair R, Figg N, Skepper JN, Schurgers L, Gupta A, et al. Dialysis accelerates medial vascular calcification in part by triggering smooth muscle cell apoptosis. Circulation. 2008;118(17):1748-57. Guérin AP, Pannier B, Marchais SJ, London GM. Arterial structure and function in end-stage renal disease. Curr Hypertens Rep. 2008;10(2):107-11. Barenbrock M, Spieker C, Laske V, Heidenreich S, Hohage H, Bachmann J, et al. Studies of the vessel wall properties in hemodialysis patients. Kidney Int. 1994;45(5):1397-400. Davies JI, Struthers AD. Pulse wave analysis and pulse wave velocity: a critical review of their strengths and weaknesses. J Hypertens. 2003;21(3):463-72. Sharman JE, Davies JE, Jenkins C, Marwick TH. Augmentation index, left ventricular contractility, and wave reflection. Hypertension. 2009;54(5):1099-105. Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2024 Read the published version in Journal of Human Hypertension → Version 1 posted Editorial decision: revise 05 Sep, 2023 Review # 2 received at journal 01 Sep, 2023 Review # 1 received at journal 21 Aug, 2023 Reviewer # 2 agreed at journal 16 Aug, 2023 Reviewer # 1 agreed at journal 11 Aug, 2023 Reviewers invited by journal 11 Aug, 2023 Editor assigned by journal 09 Aug, 2023 Submission checks completed at journal 27 Jul, 2023 First submitted to journal 14 Jul, 2023 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3170711","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":225723563,"identity":"938a11a1-2e87-42f0-93d5-14ef06466c16","order_by":0,"name":"Mohsen Agharazii","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-7628-5757","institution":"CHU de Québec - Université Laval","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Agharazii","suffix":""},{"id":225723564,"identity":"e2324f61-494e-4832-b0d2-4f73e126c34b","order_by":1,"name":"Nadège Côté","email":"","orcid":"","institution":"CHU de Quebec - Université Laval","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nadège","middleName":"","lastName":"Côté","suffix":""},{"id":225723565,"identity":"93a0c4ca-5a20-43aa-abfa-f29ea0a5a728","order_by":2,"name":"Catherine Fortier","email":"","orcid":"","institution":"CRCHU de Quebec - Université Laval","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Fortier","suffix":""},{"id":225723566,"identity":"d96ecb1f-1eb4-4630-acc5-1db194f82b14","order_by":3,"name":"Louis-Charles Desbiens","email":"","orcid":"","institution":"Université de Montréal Montreal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Louis-Charles","middleName":"","lastName":"Desbiens","suffix":""},{"id":225723567,"identity":"c32882a0-422e-478a-b970-cf1f5071b2bc","order_by":4,"name":"János Nemcsik","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"János","middleName":"","lastName":"Nemcsik","suffix":""}],"badges":[],"createdAt":"2023-07-14 13:41:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3170711/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3170711/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41371-023-00888-w","type":"published","date":"2024-01-20T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":41734114,"identity":"6bca1370-e3ac-4909-a7dd-64b69aae25b9","added_by":"auto","created_at":"2023-08-17 23:27:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":870167,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves that illustrate the unadjusted associations between each parameter of interest and CV mortality. A: PWV, B: cPP, C: AIx, D: cSBP\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3170711/v1/f46fd1c83436d68bb2a8b8f3.png"},{"id":41734681,"identity":"164b56af-d166-46de-8d8a-a65d8903797f","added_by":"auto","created_at":"2023-08-17 23:35:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":522467,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival distribution function with CV mortality as the outcome for the ICPS risk score.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3170711/v1/e0a3cf892bca3898f68d4d26.png"},{"id":41734116,"identity":"1a8dfd66-9e96-4036-9d09-fa37b39a385c","added_by":"auto","created_at":"2023-08-17 23:27:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":490774,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival distribution function with CV mortality as the outcome for the ICPS risk categories.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3170711/v1/0330b70e53762732363678c4.png"},{"id":49925155,"identity":"7fc883a1-37fe-4789-bcda-651ad54a80c9","added_by":"auto","created_at":"2024-01-21 08:12:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1498555,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3170711/v1/8b961cb4-0dca-4054-9781-b7f746402173.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Individual Components of Central Blood Pressure and Aortic Stiffness versus the Integration of Multiple Components in Predicting Cardiovascular Mortality in End-Stage Renal Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is strongly associated with cardiovascular (CV) disease, even after adjustments for traditional risk factors (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It has been proposed that non-traditional CV risk factors play a more dominant role in the context of CKD. Aortic stiffness is one of these non-traditional cardiovascular risk factors, which has become the focus of much research over the past two decades (\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAortic stiffness is measured by determination of carotid-femoral pulse wave velocity (PWV) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Its hemodynamic consequences can be assessed by central systolic blood pressure (cSBP), central pulse pressure (cPP), and wave reflection, which is measured by heart rate adjusted augmentation index (AIx). These biomarkers of aortic stiffness have been mostly used individually to predict CV outcomes (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Two studies in CKD patients, on conservative therapy or on dialysis therapy, suggest that integrating these biomarkers could potentially be better for prediction of CV outcomes (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These studies used an integrated central blood pressure-aortic stiffness (ICPS) risk scoring, which was built based on the tertiles of PWV, cPP and cSBP. ICPS risk score was then used to generate ICPS risk categories (Average, High, and Very High risk). They found that ICPS risk categories were strongly associated with CV events, and even surpassing PWV and cSBP in CKD patients on conservative therapy (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and cSBP in patients end-stage renal disease (ESRD) on hemodialysis therapy (HD) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Although the ICPS categories showed promising results in the prediction of cardiovascular events, PWV was still a better predictor of cardiovascular mortality in ESRD patients on HD as compared with the ICPS categories.\u003c/p\u003e \u003cp\u003eTherefore, the aim of the present study was to examine, in an independent cohort, if the integration of multiple components of central blood pressure and aortic stiffness into risk score categories could improve CV mortality prediction in ESRD.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Settings\u003c/h2\u003e \u003cp\u003ePatients with end-stage renal disease on dialysis were recruited at the \u003cem\u003eH\u0026ocirc;tel-Dieu de Qu\u0026eacute;bec\u003c/em\u003e Hospital, Canada. All patients were adults on peritoneal dialysis or hemodialysis with stable dry weight and stable medication for more than a month. Patients were excluded if they presented acute episodes of illness such as acute heart failure, infection, or active bleeding or if they had any clinical condition compromising hemodynamic measurements at baseline. Clinical, pharmacological and laboratory datas were obtained by health record review and vascular assessment was performed. All patients were followed prospectively for CV survival. A total of 311 patients underwent baseline assessment between April 2006 and February 2012, and the survival status was last evaluated in June 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eArterial Stiffness and Central Hemodynamics\u003c/h2\u003e \u003cp\u003eHemodynamic measurements were obtained after a 10-minute rest in a supine position. In case of the presence of an arteriovenous fistula, measurements were obtained on the contralateral arm. Brachial blood pressure (BP) was recorded by an automatic oscillometric sphygmomanometer BPM-100 (BP-Tru, Coquitlam, Canada) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) in triplicates with a 2-minute interval between each measurement. Immediately after BP measurements, PWV was obtained in triplicates by Complior\u0026reg; SP (Artech Medical, Pantin\u0026mdash;France), using the maximal upstroke algorithm previously described (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). To be coherent with the previous studies on ICPS (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and to respect the latest recommendations (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), 80% of the direct carotid-femoral distance was used to obtain PWV; our data was corrected accordingly. Radial artery waveforms were obtained by applanation tonometry and calibrated with brachial systolic blood pressure (pSBP) and brachial diastolic blood pressure (pDBP). Radial artery waveforms allowed to obtain cSBP, cPP and AIx via generalized transfer function (SphygmoCor system\u0026reg;, AtCor Medical Pty. Ltd., Sydney, Australia) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome measurement\u003c/h2\u003e \u003cp\u003eThe outcome was CV mortality defined as death due to cardiac arrhythmia, heart failure, myocardial infarction, cardiogenic shock, stroke (ischemic or hemorrhagic), bowel ischemia, critical limb ischemia, aortic dissection, and sudden death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe values are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (25th -75th percentiles) as appropriate. We used a similar approach to predict the risk of cardiovascular mortality as previously performed (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). First, we used Cox regression analysis of the standardized values of PWV, cPP, AIx and cSBP, with respect to CV mortality. We then used another approach to generate the ICPS risk score by using a Kaplan\u0026mdash;Meier survival analysis for tertiles of each parameter of interest (PWV, cPP, AIx and cSBP), followed by Cox regression analysis. Polynomial and simple contrast analysis were performed to evaluate the best way to attribute points to each tertile. Based on these results, there appeared to be a linear association between PWV, cPP, and CV mortality. Accordingly, 0, 1 or 2 points were given to the consecutive tertiles. As the risk of CV mortality only significantly increased in the third tertile of AIx, 0 points were given to the first two tertiles and 1 point was given to the third tertile. There appeared to be a \u0026ldquo;U shaped\u0026rdquo; relationship between cSBP and CV mortality, the second tertile was therefore used as the reference group. However, none of the tertiles of cSBP was associated with increased risk. cSBP was not included in the ICPS risk score.\u003c/p\u003e \u003cp\u003eAn ICPS risk score (ranging from 0 to 5 points) was calculated for each patient by adding the points attributed for each parameter. The predictive value of the ICPS risk score was tested using Kaplan-Meier survival analysis and Cox survival regression analyses. The ICPS risk score were subsequently categorized into three risk categories: Average (ICPS risk score\u0026thinsp;=\u0026thinsp;0), High (ICPS risk score\u0026thinsp;=\u0026thinsp;1 or 2), and Very High risk (ICPS risk score\u0026thinsp;\u0026ge;\u0026thinsp;3). The predictive power of ICPS risk categories towards CV mortality was then tested. In Cox regression analysis, hazard ratios (HR) were reported as unadjusted and adjusted for age, sex, pSBP, LDL cholesterol, smoking, diabetes, body mass index (BMI) and history of CV disease.\u003c/p\u003e \u003cp\u003eFinally, Harrell\u0026rsquo;s concordance index (C-statistics) was calculated to investigate and compare the discrimination of the risk categories and each of its component (PWV, cPP, AIx). C-statistics analyses were performed using Stata/SE 17.0 (StataCorp LLC, USA). All other analysis was performed using SPSS 28 (IBM, Ltd., USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 311 patients were included in this cohort. During a median follow-up of 3.1 (1.4\u0026ndash;6.0) years, 64 (21%) patients were censored because they underwent renal transplantation, and 117 (38%) died from non-cardiovascular causes. Cardiovascular deaths occurred in 118 (38%) patients.\u003c/p\u003e \u003cp\u003eBaseline data regarding clinical characteristics, traditional and non-traditional risk factors, metabolic and hemodynamic parameters are reported in 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 demographic, clinical, laboratory and hemodynamic characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjects, \u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185 (60)\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.7 \u0026plusmn; 15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (0.5\u0026ndash;3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.3 \u0026plusmn; 5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (active or past)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 (43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (52)\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\u003e284 (91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary renal disease\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic and hypertensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic tubulointerstitial nephritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerulonephritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolycystic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther or unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAntihypertensive medication\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE or ARBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium channel blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-receptor blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174 (56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-receptor blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-acting nitrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentrally acting agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLaboratory results\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9 \u0026plusmn; 1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 \u0026plusmn; 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 \u0026plusmn; 0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 \u0026plusmn; 1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4 \u0026plusmn; 0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 \u0026plusmn; 0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.7 \u0026plusmn; 3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParathormone (ng/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359 \u0026plusmn; 284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC Reactive Protein (mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 (2.6\u0026ndash;14.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHemodynamic data\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrachial SBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 \u0026plusmn; 26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrachial DBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 \u0026plusmn; 13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrachial MP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 \u0026plusmn; 17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrachial Pulse pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 \u0026plusmn; 22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 \u0026plusmn; 11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral SBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 \u0026plusmn; 25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral DBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 \u0026plusmn; 13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral MP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 \u0026plusmn; 17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral PP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (32.5\u0026ndash; 62.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWV (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8 \u0026plusmn; 3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugmentation index for heart rate of 75 bpm (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.7 (20.2\u0026ndash;33.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eCategorical parameters are presented as number (%), continuous data are presented as mean (standard deviation) or median (interquartile range). ACE or ARBs\u0026thinsp;=\u0026thinsp;angiotensin-converting enzyme inhibitors or angiotensin receptor blockers\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the association of each parameter of interest (PWV, cPP, AIx and cSBP) with the risk of CV mortality. Hazard ratios are presented for 1 SD increase in PWV, cPP, AIx, and SBP in unadjusted and adjusted models. After adjustments, only AIx remained statistically significant with regards to CV mortality.\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\u003eCox models with cardiovascular mortality as outcome and individual arterial stiffness and central hemodynamic parameters as predictors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHazard ratio\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHazard ratio\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWV (per 1 SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecPP (per 1 SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIx (per 1 SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecSBP (per 1 SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eBold values demonstrate significance when \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Adjusted model included age, sex, current smoking, diabetes, BMI, CV disease, pSBP and LDL cholesterol. PWV: pulse wave velocity; cSBP: central systolic blood pressure; cPP: central pulse pressure; AIx: augmentation index adjusted for a heart rate of 75 beats per minute.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA to \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD show the Kaplan-Meier survival curves related to tertiles of PWV, cPP, AIx, and cSBP. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the hazard ratio for each tertiles of PWV, cPP, AIx and cSBP. In the unadjusted analysis, there is an increased HR for the risk of CV mortality with the 2nd and 3rd tertiles of PWV, the 2nd and 3rd tertiles of cPP, and the 3rd tertile of AIx. In the adjusted model, only the 2nd and 3rd tertiles of cPP as well as the 3rd tertile of AIx remained significantly related to CV mortality. cSBP was not associated with increased risk of CV mortality in either unadjusted or adjusted model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox models with cardiovascular mortality as outcome and individual arterial stiffness and central hemodynamic parameters by tertiles as predictors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTertiles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHazard ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003eHazard ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePWV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9 \u0026minus; 9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1 \u0026minus; 11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.8 \u0026minus; 24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ecPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 \u0026minus; 36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.0 \u0026minus; 53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.3 \u0026minus; 133.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAIx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.3 \u0026minus; 23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.3 \u0026minus; 30.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.0 \u0026minus; 60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.3\u0026ndash;107.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.7\u0026ndash;130.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130.7 \u0026minus; 235.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eBold values demonstrate significance when \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Adjusted model included age, sex, current smoking, diabetes, BMI, CV disease, pSBP and LDL cholesterol. PWV: pulse wave velocity; cSBP: central systolic blood pressure; cPP: central pulse pressure; AIx: augmentation index adjusted for a heart rate of 75 beats per minute\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eICPS risk score and Cardiovascular Mortality\u003c/h2\u003e \u003cp\u003eThe ICPS risk score ranged from 0 to 5 points based on the points assigned to each tertiles of PWV, cPP and AIx, as described in the \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003estatistical analysis\u003c/span\u003e section. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Kaplan-Meier survival curves of ICPS risk scores with regard to CV mortality. As shown in the first portion of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the higher risk scores (scores \u0026ge;3) were significantly associated with increased risk of CV mortality in both unadjusted and adjusted models of Cox regression analysis.\u003c/p\u003e \u003cp\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\u003eThe relation between ICPS risk score and ICPS risk categories with CV based on Cox proportional hazard regression analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eICPS risk scores\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e1 (ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e1 (ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003e5 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eICPS risk categories\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage \u003cem\u003e(0 point)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e1 (ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u003cem\u003e(1\u0026ndash;2 points)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High \u003cem\u003e(3\u0026ndash;5 points)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage \u003cem\u003e(0 point)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e1 (ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u003cem\u003e(1\u0026ndash;2 points)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High \u003cem\u003e(3\u0026ndash;5 points)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBold values demonstrate significance when \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Adjusted model included age, sex, current smoking, diabetes, BMI, CV disease, pSBP and LDL cholesterol. PWV: pulse wave velocity; cSBP: central systolic blood pressure; cPP: central pulse pressure; AIx: augmentation index adjusted for a heart rate of 75 beats per minute.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows Kaplan-Meier survival curves of ICPS risk categories, which are Average risk (ICPS risk score\u0026thinsp;=\u0026thinsp;0), High risk (ICPS risk score\u0026thinsp;=\u0026thinsp;1 or 2 points), and Very High risk (ICPS risk score \u0026ge;3 points), with regards to CV mortality. The second portion of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the three ICPS risk categories in both unadjusted and adjusted models. Both the High and the Very High risk categories of ICPS risk were related to CV mortality in the unadjusted Cox survival regression analysis. However, only the Very High risk category remained statistically significant after adjustments for potential confounders.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents C-statistics, and differences between C-statistics for ICPS risk categories and PWV, AIx, and cPP. All C-statistics presented a modest discrimination towards CV mortality. While ICPS categories showed a higher C-statistics, this was not statistically superior to PWV, cPP or AIx.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHarell\u0026rsquo;s \u003cem\u003eC\u003c/em\u003e-statistics for ICPS risk categories and arterial stiffness parameters, and the differences in the \u003cem\u003eC\u003c/em\u003e-statistics between ICPS risk categories and arterial stiffness parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eICPS risk categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPS risk categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evs. PWV\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPS risk categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evs. cPP\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPS risk categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evs. AIx\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBold values demonstrate significance when \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. CI: confidence intervals; ICPS risk categories: integrated central pressure-stiffness risk categories; PWV: carotid-femoral pulse wave velocity; cPP: central pulse pressure and AIx: augmentation index adjusted for a heart rate of 75 beats per minute\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhat is known about topic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhat this study adds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; Patients with end-stage renal disease are at extreme risk of cardiovascular disease and cardiovascular mortality.\u003c/p\u003e \u003cp\u003e\u0026bull; Aortic stiffness, which increases systolic and pulse pressures and increases augmentation index, is associated with increased risk of cardiovascular mortality in patients with end-stage renal disease.\u003c/p\u003e \u003cp\u003e\u0026bull; It was suggested that integration of multiple components of central blood pressure and aortic stiffness may improve prediction of cardiovascular mortality.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; A risk categorization based on an integrated approach to central blood pressure and aortic stiffness was associated with increased risk of cardiovascular mortality even when adjusting for confounding risk factors.\u003c/p\u003e \u003cp\u003e\u0026bull; However, this approach was not statistically better than the individual components of central blood pressure and aortic stiffness.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that an integrated risk scoring concept and the defined ICPS risk categories based on the one developed in previous studies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) are statistically related to CV mortality in a larger cohort of ESRD patients on dialysis therapy. The predictive relationship between ICPS Very High risk category and CV mortality remained statistically significant even after adjustments for traditional cardiovascular risk factors such as age, sex, brachial systolic blood pressure, smoking, BMI, LDL cholesterol levels, CV disease and diabetes. The ICPS risk categories show a better, yet not statistically significant, predictive power towards CV mortality than each of the parameters used in the scoring (PWV, cPP and AIx) in our cohort of patients.\u003c/p\u003e \u003cp\u003ePatients with chronic kidney disease suffer from high prevalence and incidence of cardiovascular disease (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). CV disease and events are generally classified according to atherosclerotic events, either progressive arterial stenosis or sudden occlusion of blood flow because of plaque rupture, thrombosis and/or emboli. Lipids play an important role in atherosclerotic vascular disease and many lipid lowering trials have shown their clinical efficacy both in primary and secondary prevention of cardiovascular disease (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, despite high rates of CV events in ESRD, lipid lowering trials have not shown a similar cardiovascular benefit in this population (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). As such, it has been proposed that arteriosclerosis may play a more dominant role in the pathophysiology of CKD-related cardiovascular events.\u003c/p\u003e \u003cp\u003eIndeed, arteriosclerosis and medial vascular calcification lead to arterial stiffening which affects the aorta more predominantly. In physiological conditions, aortic elasticity is crucial for dampening of the systolic rise in blood pressure during systole and maintaining a continuous flow during diastole. In the process of aging, the aorta becomes stiffer, resulting in increased SBP and increased cardiac workload. When the aorta stiffens, aortic PWV increases, which in turn results in early and enhanced wave reflection (AIx) that occurs more predominantly during the systole. This process further increases central systolic BP and reduces central diastolic BP, increasing central PP. Aortic stiffness has been independently associated with increased risk of CV disease in various clinical conditions (\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In CKD, especially in dialysis patients, the accumulation of uremic toxins, endothelial dysfunction, accelerated elastin fragmentation, and vascular calcification conspire to increase aortic stiffness at a faster rate (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). While aortic PWV is the gold standard measure of aortic stiffness, hemodynamic consequences of aortic stiffness can also be used as an indirect measure of aortic stiffness. These include central SBP, central PP and central AIx. However, these parameters can also be influenced by other factors such as heart rate, height, sex, and antihypertensive medications (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Finally, aortic stiffness increases pulsatile pressure and flow, which could be harmful to the microcirculation of highly perfused organs such as the brain and kidneys.\u003c/p\u003e \u003cp\u003eThere are potential theoretical advantages of using multiple parameters of the central blood pressure and aortic stiffness over an individual parameter for the prediction of CV mortality. Indeed, the integration of the parameters in a risk score could allow us to get the most out of the complementary information given by each individual parameter. These parameters can easily be obtained using most devices available for hemodynamic assessment. Therefore, it seems logical that an integrated risk score could have some potential to improve the prediction of CV mortality in cohorts of patients at increased CV risk.\u003c/p\u003e \u003cp\u003eTwo previous studies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) proposed an integrated central pressure aortic stiffness score considering aortic stiffness as well as central blood pressure parameters. They first developed ICPS risk categories to predict CV events that they applied to 100 CKD patients on conservative therapy. Their ICPS risk categories showed statistically significant better predictive power towards CV events than PWV and cSBP taken individually (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). They then used the same scoring approach in 91 patients with ESRD on chronic hemodialysis therapy to predict CV mortality. In this second study, the ICPS risk categories were significantly better at predicting CV mortality than was cSBP alone. However, the ICPS risk categories did not surpass PWV and cPP in the prediction of CV mortality (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The authors suggested that the superior predictive power of the ICPS risk categories in the non-HD CKD group might be related to a stronger role of PWV as a determinant of CV outcomes in patients on hemodialysis.\u003c/p\u003e \u003cp\u003eThe present study involves a larger cohort regrouping a mix of patient on hemodialysis and peritoneal dialysis, and reported almost four times more events compared with the previous study using ICPS risk score in HD patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The ICPS risk score used in the present study is slightly different than the ones previously reported (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). First, we only attributed points to the tertiles that were significantly associated with CV mortality when creating the ICPS risk score. Second, we included PWV, cPP and AIx into the ICPS score, whereas in the previous studies they used PWV, cPP and cSBP in their scoring system. In the present study the ICPS risk categories provided a slightly and non-significant increase in C-statistics value than PWV, cPP or AIx, which was not the case for ICPS risk categories presented in the previous study on patients in ESRD on dialysis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe different technics used to perform hemodynamic measurements could potentially explain the difference in the association between stiffness parameters and CV mortality. In the previous studies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), the PulsePen device (device; DiaTecne s.r.l. Milan, Italy) was used, which provides PWV automatically, but requires personal pulse wave analysis to calculate cPP, AIx and cSBP. In the present study, the Sphygmocor device was used providing all studied parameters without potential for interobserver variability.\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, the study includes a limited number of subjects and cardiovascular events, and therefore may be underpowered to detect statistically significant changes in C-statistics. Second, tertiles of each parameter were used to describe CV risk and create risk scores and categories. Therefore, the cut-off values of the parameters used in the present study may not be relevant to other populations to define ICPS risk scoring and risk categories. To make such a scoring system relevant and useful for clinical use, defined cut-off values need to be established by studying larger number of patients.\u003c/p\u003e \u003cp\u003eIn conclusion, the integrated score and categories inspired by the one previously developed (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) is associated with the risk of CV mortality in patients with ESRD on dialysis therapy, but this was not superior to individual components of central blood pressure and aortic stiffness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the dialysis personnel for their generous contribution and kind collaboration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNC was involved in data analysis and in writing. CF was involved in data gathering and data analysis. LCD was involved in data analysis. JN was involved in study conceptualization and reviewing. MA was involved in study conceptualization, methodology, data analysis and writing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the Canadian Institute of Health\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eResearch (CIHR), New Emerging Team Grant (NET-54008), the\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHeart and Stroke Foundation of Canada, the Kidney Foundation of\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCanada, and the Canadian Diabetes Association. CF is supported by the\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eResearch chair in nephrology from the \u003cem\u003eFondation de l\u0026rsquo;Universit\u0026eacute; Laval\u003c/em\u003e. NC holds a scholarship from the \u003cem\u003eSoci\u0026eacute;t\u0026eacute; qu\u0026eacute;becoise d\u0026rsquo;hypertension art\u0026eacute;rielle\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eETHICAL APPROUVAL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study had been approved by the \u003cem\u003eComité d\u0026rsquo;éthique de la recherche du CHU de Québec\u003c/em\u003e and was conducted in accordance with the Declaration of Helsinki. Institutional guidelines were followed, and each patient included provided informed written consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFoley RN, Parfrey PS, Sarnak MJ. Epidemiology of cardiovascular disease in chronic renal disease. J Am Soc Nephrol. 1998;9(12 Suppl):S16-23.\u003c/li\u003e\n\u003cli\u003eGo AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351(13):1296-305.\u003c/li\u003e\n\u003cli\u003eParfrey PS, Foley RN. The clinical epidemiology of cardiac disease in chronic renal failure. J Am Soc Nephrol. 1999;10(7):1606-15.\u003c/li\u003e\n\u003cli\u003eBlacher J, Guerin AP, Pannier B, Marchais SJ, London GM. Arterial calcifications, arterial stiffness, and cardiovascular risk in end-stage renal disease. Hypertension. 2001;38(4):938-42.\u003c/li\u003e\n\u003cli\u003eBlacher J, Guerin AP, Pannier B, Marchais SJ, Safar ME, London GM. Impact of aortic stiffness on survival in end-stage renal disease. Circulation. 1999;99(18):2434-9.\u003c/li\u003e\n\u003cli\u003ePannier B, Gu\u0026eacute;rin AP, Marchais SJ, Safar ME, London GM. 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Hypertension. 2009;54(5):1099-105.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-human-hypertension","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"jhh","sideBox":"Learn more about [Journal of Human Hypertension](http://www.nature.com/jhh/)","snPcode":"41371","submissionUrl":"https://mts-jhh.nature.com/cgi-bin/main.plex","title":"Journal of Human Hypertension","twitterHandle":"@jhhypertension","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3170711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3170711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAortic stiffness, measured by carotid-femoral pulse-wave velocity (PWV), is a predictor of cardiovascular (CV) mortality in patients with end-stage renal disease (ESRD). Aortic stiffness increases aortic systolic and pulse pressures (cSBP, cPP) and augmentation index (AIx). In this study, we examined if the integration of multiple components of central blood pressure and aortic stiffness (ICPS) into risk score categories could improve CV mortality prediction in ESRD.\u003c/p\u003e \u003cp\u003eIn a prospective cohort of 311 patients with ESRD on dialysis who underwent vascular assessment at baseline, 118 CV deaths occurred after a medial follow-up of 3.1 years. The relationship between hemodynamic parameters and CV mortality was analyzed through Kaplan-Meier and Cox survival analysis. ICPS risk score from 0 to 5 points were calculated from points given to tertiles, and were regrouped into three risk categories (Average, High, Very High). A strong association was found between the ICPS risk categories and CV mortality (High risk HR\u0026thinsp;=\u0026thinsp;2.20, 95%CI: 1.05\u0026ndash;4.62, P\u0026thinsp;=\u0026thinsp;0.036; Very High risk (HR\u0026thinsp;=\u0026thinsp;4.44, 95%CI: 2.21\u0026ndash;8.92, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The very high-risk category remained associated with CV mortality (HR\u0026thinsp;=\u0026thinsp;3.55, 95% CI: 1.37\u0026ndash;9.21, P\u0026thinsp;=\u0026thinsp;0.009) after adjustment for traditional CV risk factors. While ICPS categories showed higher C-statistics (C: 0.627, 95%CI: 0.578\u0026ndash;0.676, P\u0026thinsp;=\u0026thinsp;0.001), it was not statistically superior to PWV, cPP or AIx. In conclusion, integration of multiple components of central blood pressure and aortic stiffness did not result in a significantly better prediction of CV mortality in this cohort.\u003c/p\u003e","manuscriptTitle":"Individual Components of Central Blood Pressure and Aortic Stiffness versus the Integration of Multiple Components in Predicting Cardiovascular Mortality in End-Stage Renal Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-17 23:27:35","doi":"10.21203/rs.3.rs-3170711/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2023-09-05T14:04:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2023-09-01T07:43:20+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2023-08-21T07:53:02+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2023-08-17T03:58:06+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2023-08-11T09:34:27+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2023-08-11T09:19:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-09T22:52:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-07-27T14:21:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Human Hypertension","date":"2023-07-14T13:38:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-human-hypertension","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"jhh","sideBox":"Learn more about [Journal of Human Hypertension](http://www.nature.com/jhh/)","snPcode":"41371","submissionUrl":"https://mts-jhh.nature.com/cgi-bin/main.plex","title":"Journal of Human Hypertension","twitterHandle":"@jhhypertension","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"81aff0d3-c12e-4146-9069-18f182206e01","owner":[],"postedDate":"August 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-01-21T08:12:21+00:00","versionOfRecord":{"articleIdentity":"rs-3170711","link":"https://doi.org/10.1038/s41371-023-00888-w","journal":{"identity":"journal-of-human-hypertension","isVorOnly":false,"title":"Journal of Human Hypertension"},"publishedOn":"2024-01-20 05:00:00","publishedOnDateReadable":"January 20th, 2024"},"versionCreatedAt":"2023-08-17 23:27:35","video":"","vorDoi":"10.1038/s41371-023-00888-w","vorDoiUrl":"https://doi.org/10.1038/s41371-023-00888-w","workflowStages":[]},"version":"v1","identity":"rs-3170711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3170711","identity":"rs-3170711","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}