Autonomic Dysfunction Predicts Long-Term Mortality in Hypotensive Hemodialysis Patients: Prognostic Value of Heart Rate Variability and Turbulence

Autonomic Dysfunction Predicts Long-Term Mortality in Hypotensive Hemodialysis Patients: Prognostic Value of Heart Rate Variability and Turbulence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Autonomic Dysfunction Predicts Long-Term Mortality in Hypotensive Hemodialysis Patients: Prognostic Value of Heart Rate Variability and Turbulence Zafer Yalım, Sümeyra Alan Yalım This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8743385/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Autonomic dysfunction has been implicated in adverse outcomes in end-stage renal disease. However, the prognostic significance of heart rate variability (HRV) and turbulence (HRT) in chronically hypotensive hemodialysis (HD) patients remains unclear. Therefore, the present study aimed to investigate the association between baseline autonomic markers and all-cause mortality using time-to-event analyses. Methods A total of 44 hypotensive and 46 normotensive HD patients who underwent 24-h ambulatory blood pressure monitoring and Holter recording during the interdialytic period were included in this prospective observational cohort study. Moreover, their HRV and HRT parameters were analyzed. Patients were monitored for all-cause mortality outcomes, and survival analyses were performed. Results During follow-up, 39 patients (43.3%) died. Non-survivors had significantly lower standard deviation of the averages of normal-to-normal intervals in all 5-min segments (SDANN) (101.49 ± 11.03 ms vs. 105.80 ± 8.79 ms; p = 0.042) and markedly impaired turbulence onset (TO) (− 1.92 ± 0.42 vs. −2.40 ± 0.43; p < 0.001) compared with survivors. Kaplan–Meier analysis demonstrated significantly reduced survival among patients with abnormal autonomic parameters (standard deviation of all normal-to-normal intervals, SDANN, TO; p < 0.001). In multivariable Cox regression analysis, TO emerged as the strongest independent predictor of mortality (p < 0.001). Conclusion Impaired TO is a powerful and independent predictor of long-term mortality among hypotensive HD patients. These findings indicate that TO is a promising noninvasive marker for risk stratification and that autonomic failure plays a central mechanistic role in adverse outcomes in this high-risk population. arrhythmia heart rate turbulence heart rate variability hemodialysis hypotension Figures Figure 1 Background Hemodialysis (HD) is a life-sustaining therapy for patients with end-stage renal disease (ESRD). However, cardiovascular mortality remains disproportionately high in this population. Among HD patients, sudden cardiac death accounts for a substantial proportion of deaths and is predominantly attributed to malignant ventricular arrhythmias rather than progressive heart failure [ 1 – 3 ]. Despite improvements in dialysis techniques and cardiovascular management, identifying patients at the highest risk for fatal cardiac events remains a major clinical challenge. Intradialytic hypotension (a well-known complication of HD) has been associated with myocardial ischemia, repetitive myocardial stunning, and increased mortality [ 4 , 5 ]. Nevertheless, hypotensive episodes are not limited to dialysis sessions. A subset of HD patients experiences recurrent hypotension during interdialytic periods, frequently accompanied by impaired organ perfusion symptoms. Outside dialysis sessions, the long-term prognostic implications of this chronic hypotensive phenotype remain to be fully elucidated. In ESRD, autonomic nervous system dysfunction is highly prevalent and considered as an important pathophysiological mechanism linking hypotension, arrhythmogenesis, and sudden cardiac death [ 6 ]. Heart rate variability (HRV), a noninvasive marker of cardiac autonomic modulation, reflects the balance between sympathetic and parasympathetic activity and has been extensively studied in cardiovascular diseases [ 7 ]. Studies have shown that reduced HRV is associated with increased mortality in HD patients. However, the reported results remain inconsistent, partly because of heterogeneity in study populations, HRV assessment timing, and outcome definitions. Heart rate turbulence (HRT) evaluates baroreflex-mediated sinus rhythm responses following ventricular premature beats, providing complementary information regarding autonomic integrity. Impaired HRT parameters (specifically abnormal turbulence onset [TO] and turbulence slope [TS]) have been shown to be strong predictors of mortality after myocardial infarction and in other high-risk cardiac populations [ 8 – 10 ]. Despite the high burden of ventricular ectopy and autonomic neuropathy in HD patients, there are limited data regarding the prognostic value of HRT in this population. Moreover, its role in patients with chronic hypotension remains largely unexplored. In our previous study, we demonstrated that HD patients with frequent hypotensive episodes during interdialytic periods exhibit significantly impaired HRV and HRT parameters compared with normotensive HD patients, indicating advanced autonomic dysfunction [ 11 ]. However, whether these autonomic alterations translate into adverse long-term outcomes remains unknown. Addressing this gap is clinically important as the early identification of high-risk patients may allow closer cardiologic surveillance and the implementation of preventive strategies. Therefore, the present study was designed to extend our previous findings by evaluating the prognostic significance of HRV and HRT parameters in a larger cohort of hypotensive HD patients with extended follow-up. We aimed to investigate the association between baseline autonomic markers and all-cause mortality using time-to-event analyses. Methods This prospective observational cohort study was conducted among patients with ESRD receiving maintenance HD. Patients were recruited from multiple dialysis centers and followed longitudinally for clinical outcomes. Patients were considered eligible if they were undergoing thrice-weekly HD and had a history of recurrent hypotensive episodes during interdialytic periods. Hypotension was defined as systolic blood pressure < 90 mmHg and/or diastolic blood pressure < 60 mmHg, accompanied by impaired organ perfusion symptoms. Compared with our previous study, this study was conducted with an increased number of patients (44 hypotensive and 46 normotensive). Patients were followed up according to our previous study’s methodology. Each patient was followed up for mortality as the primary endpoint, and the follow-up time between baseline and death was recorded. Patients reporting at least two symptomatic hypotensive episodes per day during the interdialytic period were considered eligible for further evaluation. Importantly, patient selection relied on the presence of clinically meaningful symptoms following a documented reduction in blood pressure, rather than isolated blood pressure readings alone. All ambulatory blood pressure monitoring (ABPM) and 24-h rhythm Holter recordings were performed exclusively during the interdialytic period to ensure that hypotensive events were not attributable to the dialysis procedure itself. Dialysis schedules were temporarily adjusted to allow uninterrupted ambulatory monitoring. This approach ensured that autonomic and hemodynamic assessments were performed at least 24 h after the most recent dialysis session, thereby minimizing acute dialysis-related confounding. Exclusion Criteria Patients with atrial fibrillation; permanent pacemakers; significant valvular heart disease; hormonal disorders; serious infection and neurological diseases; known autonomic neuropathies unrelated to ESRD; active infection; malignancy; or use of medications affecting heart rate, autonomic function, and blood pressure (including β-blockers, antihypertensives, antiarrhythmic agents, and nitrates) were excluded. Ambulatory Blood Pressure and Rhythm Monitoring All patients underwent 24-h ABPM using a validated device (CardioSoft Diagnostic System Ambulatory Blood Pressure, General Electric, Boston, USA). Measurements were obtained at predefined intervals throughout the day to capture circadian blood pressure patterns. Simultaneously, 24-h ambulatory electrocardiographic Holter (Pathfinder Holter Software, version 8.255, Reynolds Medical, England) monitoring was performed to assess cardiac rhythm status, ventricular ectopy burden, and autonomic modulation. Holter data were analyzed for the presence of atrial and ventricular arrhythmias, and recordings with inadequate signal quality were excluded. In accordance with established guidelines, HRV analysis was performed using time-domain parameters, including standard deviation of all normal-to-normal intervals (SDNN), standard deviation of the averages of normal-to-normal intervals in all 5-min segments (SDANN), and SDNN index. Meanwhile, HRT analysis was conducted in patients with adequate ventricular premature beats suitable for analysis. TO and TS were calculated using validated algorithms. HRV and HRT analyses were evaluated in accordance with the methodology described in our previous study [ 11 ]. Laboratory Measurements Blood samples were obtained during the interdialytic period on the same day as ABPM and Holter recordings. Routine biochemical and hematological parameters were measured using standardized laboratory techniques. The laboratory results were evaluated in conjunction with HRV and HRT findings. Follow-Up and Outcome Assessment Patients were prospectively followed for the occurrence of all-cause mortality, which served as the primary endpoint of the study. The follow-up duration was calculated from the date of baseline autonomic assessment to the date of death or last clinical contact. Patients who were alive at the end of follow-up were censored. Statistical Analysis All statistical analyses were performed using SPSS software (version 23.0, IBM Corp., Armonk, NY, USA). Data distribution was assessed using visual and analytical tests for normality. Continuous variables were expressed as mean ± standard deviation for normally distributed data and median (interquartile range) for non-normally distributed data. Conversely, categorical variables were summarized as frequencies and percentages. Comparisons between categorical variables were performed using the Chi-square test. Continuous variables with normal distributions were compared using the independent samples T-test, whereas those with non-normal distributions were analyzed using the Mann–Whitney U test. Correlation analyses were performed using Pearson and Spearman correlation. HRV and HRT parameters were dichotomized according to median values. Survival analyses were performed using Kaplan–Meier curves and compared with log-rank tests. Binary logistic regression analyses were conducted to estimate factors that could predict mortality. Statistical significance was considered at a two-sided p-value < 0.05. Results Ninety hypotensive HD patients were included in the analysis. The median follow-up duration was 61 months (interquartile range: 49–83.5 months). During the follow-up period, 39 patients (43%) died. The baseline demographic and clinical characteristics of patients are presented in Table 1 . Table 1 Baseline demographic and clinical characteristics of study groups Variables Hypotensive HD (n = 44) Normotensive HD (n = 46) P-value Age (years) 57.02 ± 7.09 57.06 ± 10.8 0.983 Women, n (%) 19 (43.2%) 24 (52.2%) 0.393 BMI, kg/m 2 24.02 ± 2.5 24.9 ± 3.02 0.112 Diabetes mellitus, n (%) 21 (47.7%) 21 (45.7%) 0.844 Hyperlipidemia, n (%) 17 (38.6%) 18 (39.1%) 0.962 CAD, n (%) 26 (59.1%) 16 (34.8%) 0.021* Stroke or TIA, n (%) 7 (15.9%) 5 (10.9%) 0.482 Neuropathy, n (%) 17 (38.6%) 16 (34.8%) 0.705 HD duration (years) 9.3 ± 3.6 8.4 ± 2.8 0.209 HD time (hours) 3.91 ± 0.17 3.89 ± 0.14 0.509 HD volume removed (L) 1.79 ± 0.32 2.18 ± 0.4 < 0.001* EF (%) 54.2 ± 6.6 53.6 ± 4.61 0.634 Fasting glucose (mg/dL) 131.2 ± 29.9 135.4 ± 25.9 0.485 Creatinine (mg/dL) 6.6 ± 2.1 6.9 ± 1.7 0.385 Sodium (mmol/dL) 136.9 ± 2.4 137.6 ± 2.4 0.194 Potassium (mmol/dL) 4.9 ± 0.87 4.9 ± 0.57 0.965 Calcium (mg/dL) 8.4 ± 0.6 8.2 ± 0.3 0.079 Albumin 4.2 ± 0.44 4.12 ± 0.57 0.613 Hemoglobin (mg/dL) 11.4 ± 1.05 11.2 ± 1.57 0.634 MCV 83.9 ± 8.8 89.1 ± 6.7 0.002 Neutrophil 6.1 ± 1.2 5.5 ± 0.9 < 0.001* Lymphocyte 2.05 ± 0.6 2.19 ± 0.57 0.253 Monosit 0.97 ± 0.42 0.85 ± 0.35 0.138 Platelet 213.7 ± 16.8 232.5 ± 15.7 0.161 Trigliserid 172.1 ± 38.4 159.5 ± 25.8 0.072 LDL-C 126.7 ± 22.3 126.5 ± 17.7 0.970 HDL-C 38.1 ± 6.4 40.1 ± 5.7 0.189 Survival time (month) 49.8 ± 10.7 77.3 ± 15.8 < 0.001* Death, yes 24 (%54.5) 15 (%32.6) 0.036* *p < 0.05 statistically significant. Values are presented as the mean ± standard deviation; p < 0.05 is significant. Abbreviations: HD, hemodialysis; n, number; BMI, body mass index; CAD, coronary artery disease; TIA, transient ischemic attack; EF, ejection fraction, ± MCV, mean corpuscular volume; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol Hypotensive patients had significantly lower daytime, nighttime, pre-dialysis, and post-dialysis blood pressure values and a higher prevalence of a non-dipping pattern, indicating impaired circadian blood pressure regulation. With regard to autonomic function, hypotensive patients exhibited significantly reduced long-term HRV indices (SDNN, SDNN index, and SDANN). However, short-term parasympathetic markers (RMSSD and pNN50) did not differ between groups. HRT parameters showed marked impairment in hypotensive patients, with less negative TO and lower TS, consistent with baroreflex dysfunction (Table 2 ). Table 2 ABPM and HRV–HRT analyses of the study groups Variables Hypotensive HD (n = 40) Normotensive HD (n = 39) P-value ABPM day S (mmHg) 104.6 ± 9.7 123.6 ± 13.5 < 0.001* ABPM day D (mmHg) 65.1 ± 6.5 78.9 ± 10.3 < 0.001* ABPM night S (mmHg) 97.6 ± 10.6 105.2 ± 14.5 0.006* ABPM night D (mmHg) 64.7 ± 6.6 68.6 ± 9.8 0.072 ABPM 24 h S (mmHg) 100.4 ± 10 114.2 ± 13.8 0.098 ABPM 24 h D (mmHg) 63.5 ± 6.09 74.3 ± 10.2 0.011* Non-dipping pattern, yes 32 (%72.7) 19 (%41.3) 0.003* Frequency of hypotension in HD, n (%) 33 / %82.5 25 / %64.1 0.064 Pre-HD S (mmHg) 95.7 ± 8.02 121.6 ± 14.1 < 0.001* Pre-HD D (mmHg) 61.07 ± 5.8 73.4 ± 9.5 < 0.001* Post-HD S (mmHg) 85.16 ± 5.8 101.5 ± 13.1 < 0.001* Post-HD S (mmHg) 53.2 ± 6.2 67.1 ± 8.4 < 0.001* HRV and HRV analysis Mean heart rate, beats/min 78 ± 6.5 76 ± 5.1 0.204 SDNN, ms 105.5 ± 7.02 127.6 ± 6.2 < 0.001* SDNN index, ms 50.3 ± 2.7 52.7 ± 3.7 0.001* SDANN, ms 95.3 ± 5.7 112.1 ± 4.8 < 0.001* RMSSD, ms 26.45 ± 2.6 27.2 ± 2.7 0.180 pNN50, % 16.9 ± 1.69 17.5 ± 2.3 0.215 Triangular index 35.2 ± 3.1 34.9 ± 2.5 0.626 Turbulence onset, % −1.87 ± 0.36 −2.4 ± 0.37 < 0.001* Turbulence slope, ms/RR 6.94 ± 0.7 8.32 ± 0.98 < 0.001* *p < 0.05 statistically significant. Abbreviations: ABPM, ambulatory blood pressure monitoring; HRV, heart rate variability; HRT, heart rate turbulence; HD, hemodialysis; n, number; S, systole; D, diastole; SDNN, standard deviation of all normal-to-normal intervals; SDANN, standard deviation of the averages of normal-to-normal intervals in all 5-min segments; RMSSD, square root of the mean of the sum of the squares of differences between adjacent normal-to-normal intervals; pNN50, number of pairs of adjacent normal-to-normal intervals differing by more than 50 ms divided by the total number of all normal-to-normal intervals Compared with survivors, non-survivors had a significantly higher prevalence of coronary artery disease (66.7% vs. 31.4%, p = 0.021), lower ultrafiltration volumes (p < 0.001), and higher neutrophil counts (p < 0.001). The mean corpuscular volume was significantly lower among non-survivors than survivors (p = 0.002). Moreover, the follow-up duration was substantially shorter among non-survivors than survivors (p < 0.001). Among HRV indices, SDANN was significantly lower in non-survivors than in survivors (101.49 ± 11.03 ms vs. 105.80 ± 8.79 ms, p = 0.042), indicating impaired long-term autonomic modulation. SDNN showed a borderline association with mortality (p = 0.075), whereas the SDNN and triangular indexes did not significantly differ between groups. In addition, TO demonstrated the strongest association with mortality, with non-survivors exhibiting markedly impaired (less negative) TO values (− 1.92 ± 0.42 vs. −2.40 ± 0.43, p < 0.001). TS did not significantly differ between groups. The baseline demographic and clinical characteristics of survivors and non-survivors are presented in Table 3 . Table 3 Baseline demographic and clinical characteristics, and autonomic function analysis results of survivors and non-survivors Variables Death (n = 39) Survival (n = 51) P-value Age (years) 57.0 ± 7.1 57.1 ± 10.8 0.983 Female sex, n (%) 19 (48.7) 24 (47.1) 0.393 BMI (kg/m²) 24.0 ± 2.5 24.9 ± 3.0 0.112 Diabetes mellitus, n (%) 21 (53.8) 21 (41.2) 0.844 Hyperlipidemia, n (%) 17 (43.6) 18 (35.3) 0.962 CAD, n (%) 26 (66.7) 16 (31.4) 0.021* Stroke/TIA, n (%) 7 (17.9) 5 (9.8) 0.482 Neuropathy, n (%) 17 (43.6) 16 (31.4) 0.705 HD duration (years) 9.3 ± 3.6 8.4 ± 2.8 0.209 HD session duration (hours) 3.91 ± 0.17 3.89 ± 0.14 0.509 Ultrafiltration volume (L) 1.79 ± 0.32 2.18 ± 0.40 < 0.001* Ejection fraction (%) 54.2 ± 6.6 53.6 ± 4.6 0.634 Fasting glucose (mg/dL) 131.2 ± 29.9 135.4 ± 25.9 0.485 Creatinine (mg/dL) 6.6 ± 2.1 6.9 ± 1.7 0.385 Sodium (mmol/L) 136.9 ± 2.4 137.6 ± 2.4 0.194 Potassium (mmol/L) 4.9 ± 0.87 4.9 ± 0.57 0.965 Calcium (mg/dL) 8.4 ± 0.6 8.2 ± 0.3 0.079 Albumin (g/dL) 4.20 ± 0.44 4.12 ± 0.57 0.613 Hemoglobin (g/dL) 11.4 ± 1.05 11.2 ± 1.57 0.634 MCV (fL) 83.9 ± 8.8 89.1 ± 6.7 0.002* Neutrophils (×10³/µL) 6.1 ± 1.2 5.5 ± 0.9 < 0.001* Lymphocytes (×10³/µL) 2.05 ± 0.60 2.19 ± 0.57 0.253 Platelets (×10³/µL) 213.7 ± 16.8 232.5 ± 15.7 0.161 Triglycerides (mg/dL) 172.1 ± 38.4 159.5 ± 25.8 0.072 LDL-C (mg/dL) 126.7 ± 22.3 126.5 ± 17.7 0.970 HDL-C (mg/dL) 38.1 ± 6.4 40.1 ± 5.7 0.189 Follow-up time (months) 49.8 ± 10.7 77.3 ± 15.8 < 0.001* SDNN 114.36 ± 14.13 119.39 ± 12.35 0.075 SDANN 101.49 ± 11.03 105.80 ± 8.79 0.042* SDNN index 51.23 ± 3.06 51.82 ± 3.80 0.428 Triangular index 34.97 ± 3.26 35.12 ± 2.45 0.813 Turbulence onset −1.92 ± 0.42 −2.40 ± 0.43 < 0.001* Turbulence slope 7.49 ± 1.06 7.78 ± 1.12 0.210 *p < 0.05 statistically significant Abbreviations: n, number; BMI, body mass index; CAD, coronary artery disease; TIA, transient ischemic attack; HD, hemodialysis; MCV, mean corpuscular volume; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; SDNN, standard deviation of all normal-to-normal intervals; SDANN, standard deviation of the averages of normal-to-normal intervals in all 5-min segments Correlation analysis was performed between mortality and clinical parameters. Mortality showed a moderate positive correlation with TO (ρ = 0.489, p < 0.001) and significant inverse correlations with SDANN and ultrafiltration volume. The correlation analysis results are presented in Table 4 . Table 4 Correlation analysis of death and other variables Death - Rho value P-value HD volume recording −.243* 0.021 Stroke .251* 0.017 Pre-HD diastolic pressure −.237* 0.024 Post-HD diastolic pressure −.207* 0.050 Creatinine −.248* 0.018 SDNN −.188 0.075 SDANN −.215* 0.042 TI −.025 0.813 TO .489** < 0.001 TS −.133 0.210 P < 0.05 statistically significant. Abbreviations: HD, hemodialysis; SDNN, standard deviation of all normal-to-normal intervals; SDANN, standard deviation of the averages of normal-to-normal intervals in all 5-min segments; TI, triangular index; TO, turbulence onset; TS, turbulence slope; Rho, correlation coefficient Table 5 Regression analysis for predicting mortality Variables B P-value Exp (B) 95% CI Lower Upper Age .044 0.263 1.045 .968 1.129 Sex −.248 0.750 .780 .170 3.585 BMI −.072 0.598 .931 .713 1.215 HD duration −.110 0.318 .896 .722 1.112 DM −.905 0.182 .405 .107 1.526 HD received volume −.344 0.686 .709 .133 3.766 EF −.120 0.062 .887 .782 1.006 Pre-HD S BP −.024 0.522 .976 .907 1.051 Pre-HD D BP .047 0.626 1.048 .867 1.267 Post-HD S BP −.017 0.421 .983 .942 1.025 Post-HD D BP −.034 0.689 .966 .818 1.142 Non-dipping pattern −1.016 0.154 .362 .089 1.465 Creatinine −.123 0.504 .885 .618 1.267 SDNN .026 0.602 1.026 .930 1.132 SDANN .098 0.181 1.103 .956 1.272 SDANN index −.169 0.990 .845 .691 1.032 RMSSD −.161 0.222 .851 .657 1.103 pNN50 .304 0.110 1.355 .933 1.969 TI .077 0.503 1.080 .862 1.353 TO 4.356 < 0.001* 3.796 1.971 8.719 TS .067 0.860 1.069 .509 2.243 Abbreviations: CI, confidence interval; BMI, body mass index; DM, diabetes mellitus; EF, ejection fraction; S, systolic; BP, blood pressure; D, diastolic; SDNN, standard deviation of all normal-to-normal intervals; SDANN, standard deviation of the averages of normal-to-normal intervals in all 5-min segments; RMSSD, square root of the mean of the sum of the squares of differences between adjacent normal-to-normal intervals; pNN50, number of pairs of adjacent normal-to-normal intervals differing by more than 50 ms divided by the total number of all normal-to-normal intervals; TI, triangular index; TO, turbulence onset; TS, turbulence slope Kaplan–Meier Survival Analysis According to HRV and HRT Parameters Kaplan–Meier survival analyses demonstrated significant differences in all-cause mortality according to the median-split groups of selected HRV and HRT parameters (Fig. 1 ). Patients with lower SDNN (≤ 116.50 ms) exhibited a significantly higher mortality rate than those with higher SDNN (log-rank p < 0.01). Similarly, lower SDANN (≤ 103.50 ms) was strongly associated with increased mortality, with early and sustained separation of survival curves over time (log-rank p 0.05), suggesting a limited prognostic contribution of short-term HRV components. With regard to geometric HRV indices, lower TI (≤ 35.00) was associated with moderately reduced survival probability (log-rank p < 0.05). Among HRT parameters, TO emerged as the most powerful discriminator of mortality risk. Patients with higher (less negative) TO values ( ≥ − 2.12) exhibited markedly poorer survival than those with more preserved baroreflex responses, with pronounced early divergence of survival curves (log-rank p 0.05). In multivariable Cox regression analysis, TO emerged as the only autonomic parameter that was independently associated with mortality (p < 0.001), whereas HRV indices lost statistical significance after adjustment Discussion In this study, we demonstrated that impaired cardiac autonomic function, assessed by HRV and HRT parameters, is strongly associated with increased all-cause mortality among hypotensive HD patients during the interdialytic period. Specifically, reduced SDNN and SDANN values, as well as abnormal TO, were powerful predictors of adverse outcomes. Previous studies investigating HRV in HD patients have yielded inconsistent results. Although several studies identified reduced HRV as an independent predictor of mortality, others failed to confirm this association [ 8 , 9 ]. These discrepancies may be explained by differences in patient selection, measurement timing, and inclusion of intradialytic recordings. Our study minimizes hemodynamic confounding related to the dialysis procedure itself by focusing specifically on hypotensive patients and performing autonomic assessments during interdialytic periods. Among HRV parameters, SDANN emerged as one of the strongest predictors of mortality. SDANN reflects long-term components of HRV and circadian autonomic modulation, which are frequently disrupted in ESRD patients with autonomic dysfunction. The nonsignificant findings for the SDNN index and TS likely reflect their greater sensitivity to short-term autonomic fluctuations and patient phenotype. As such, long-term indices and TO better capture chronic autonomic impairment in hypotensive HD patients. The loss of circadian blood pressure rhythm and non-dipping patterns have been associated with adverse cardiovascular outcomes in HD populations, supporting our findings [ 12 ]. Hannane et al. showed that impaired HRT during dialysis, particularly in diabetic HD patients, significantly contributed to predicting long-term mortality risk [ 13 ]. The difference in the present study is that we demonstrate the mortality association in hypotensive patients during the non-HD period, just as we did with advanced heart and liver failure. When the results were evaluated, we determined that other HRV variables, especially TO, could predict mortality. HRV and HRT measurement increases the sensitivity of risk estimation and classification by identifying high-risk patients in a dialysis setting and offers an opportunity for personalized monitoring and prevention. Independent of known risk factors, HRT assessment can determine the cardiovascular mortality risk of HD patients [ 14 ]. The most striking observation in our study was the strong prognostic impact of TO. HRT reflects baroreflex sensitivity and vagal function, and impaired TO has been shown to predict mortality in post-myocardial infarction populations [ 15 ]. Cardiac rhythm analysis can be used to predict long-term cardiovascular outcomes, including those receiving peritoneal dialysis in addition to HD. The main reason for this is that cardiovascular disease is the leading cause of morbidity and mortality in patients with ESRD [ 16 ]. Our findings extend these observations to hypotensive HD patients and suggest that baroreflex failure may play a central role in arrhythmogenesis and sudden cardiac death in this vulnerable group. Furthermore, we demonstrated impaired cardiac autonomic function in the hypotensive and deceased groups. Impaired autonomic function is associated with mortality in patients with ESRD. Noninvasive electrocardiographic Holter monitoring with HRT and HRV analysis can be used to reduce cardiovascular events in these patients and identify at-risk patients. Clinically, these results have important implications. HRV and HRT analyses are noninvasive, widely available, and easily applicable in routine practice. Incorporating autonomic assessment into the evaluation of hypotensive HD patients may allow the early identification of individuals at high risk for adverse outcomes and prompt more intensive cardiologic evaluation and monitoring. Study Limitations This study has several limitations that should be acknowledged. Although the cohort size was larger than that in our previous study, the number of events limits the complexity of multivariable models. In addition, cause-specific mortality could not be reliably determined in all cases. Finally, autonomic parameters were assessed at baseline and may have changed over time. Conclusions Impaired cardiac autonomic function, as reflected by reduced HRV and abnormal HRT parameters, is strongly associated with increased mortality in hypotensive HD patients. These findings support the use of HRV and HRT as practical risk stratification tools and highlight the importance of autonomic dysfunction in the pathophysiology of adverse outcomes in this high-risk population. Statements and Declarations C onflict of Interest The authors declare that they have no conflicts of interest regarding the publication of this paper. Ethics Approval and Consent to Participate The study protocol complied with the ethical principles of the Declaration of Helsinki and received full approval from the institutional review boards of Afyonkarahisar Healty Sciences University Ethics Committee (no. 2019/284). Under this approval, informed consent was waived. Consent for Publication Not applicable Availability of Data and Materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors have no competing interests to declare that are relevant to the content of this article. Funding The authors did not receive support from any organization for the submitted work. Author Contributions Concept and design of the study: ZY. Analysis and interpretation of data and Drafting and critical revision of the article: ZY and SAY. Critical revision of the article: ZY and SAY. All authors read and approved the final manuscript. Acknowledgements None References Lysaght MJ (2002) Maintenance dialysis population dynamics. J Am Soc Nephrol 13:S37-S40. https://doi.org/10.1681/asn.v13suppl_1s37 Green D, Roberts PR, New DI, Kalra PA (2011) Sudden cardiac death in hemodialysis patients: an in-depth review. Am J Kidney Dis 57:921-929. https://doi.org/10.1053/j.ajkd.2011.02.376 Bleyer AJ, Hartman J, Brannon PC, Reeves-Daniel A, Satko SG, Russell G (2006) Characteristics of sudden death in hemodialysis patients. Kidney Int 69:2268-2273. https://doi.org/10.1038/sj.ki.5000446 Shoji T, Tsubakihara Y, Fujii M, Imai E (2004) Hemodialysis-associated hypotension as an independent risk factor for two-year mortality in hemodialysis patients. Kidney Int 66:1212-1220. https://doi.org/10.1111/j.1523-1755.2004.00812.x McIntyre CW, Burton JO, Selby NM, Leccisotti L, Korsheed S, Baker CSR, Camici PG (2008) Hemodialysis-induced cardiac dysfunction is associated with an acute reduction in global and segmental myocardial blood flow. Clin J Am Soc Nephrol 3:19-26. https://doi.org/10.2215/cjn.03170707 Rao PK, Dadmi P, Putturaya S (2026) Correlation of cardiac autonomic neuropathy in predialysis and dialysis chronic kidney disease patients. Int J Multidiscipl Res Dev Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17:354-381. https://doi.org/10.1093/oxfordjournals.eurheartj.a014868 Fukuta H, Hayano J, Ishihara S, Sakata S, Mukai S, Ohte N, Ojika K, Yagi K, Matsumoto H, Sohmiya S, Kimura G (2003) Prognostic value of heart rate variability in patients with end-stage renal disease on chronic haemodialysis. Nephrol Dial Transpl 18:318-325. https://doi.org/10.1093/ndt/18.2.318 Oikawa K, Ishihara R, Maeda T, Yamaguchi K, Koike A, Kawaguchi H, Tabata Y, Murotani N, Itoh H (2009) Prognostic value of heart rate variability in patients with renal failure on hemodialysis. Int J Cardiol 131:370-377. https://doi.org/10.1016/j.ijcard.2007.10.033 Suzuki M, Hiroshi T, Aoyama T, Tanaka M, Ishii H, Kisohara M, Iizuka N, Murohara T, Hayano J (2012) Nonlinear measures of heart rate variability and mortality risk in hemodialysis patients. Clin J Am Soc Nephrol 7:1454-1460. https://doi.org/10.2215/cjn.09430911 Yalım Z, Demir ME, Yalım SA, Alp Ç (2020) Investigation of heart rate variability and heart rate turbulence in chronic hypotensive hemodialysis patients. Int Urol Nephrol 52:775-782. https://doi.org/10.1007/s11255-020-02429-7 Amar J, Vernier I, Rossignol E, Bongard V, Arnaud C, Conte JJ, Salvador M, Chamontin B (2000) Nocturnal blood pressure and 24-hour pulse pressure are potent indicators of mortality in hemodialysis patients. Kidney Int 57:2485-2491. https://doi.org/10.1046/j.1523-1755.2000.00107.x Hannane N, Mayer CC, Matschkal J, Bormann F, Krieter A, Braun JR, Küchle C, Renders L, Günthner R, Schmidt G, Müller A, Wassertheurer S, Heemann U, Haller B, Malik M, Schmaderer C, Braunisch MC (2025) Long-term prediction of mortality by heart rate turbulence in hemodialysis patients and the impact of diabetes mellitus-a longitudinal observational study. J Nephrol 38:2261-2272. https://doi.org/10.1007/s40620-025-02357-8 Braunisch MC, Mayer CC, Bauer A, Lorenz G, Haller B, Rizas KD, Hagmair S, von Stülpnagel L, Hamm W, Günthner R, Angermann S, Matschkal J, Kemmner S, Hasenau A-L, Zöllinger I, Steubl D, Mann JF, Lehnert T, Scherf J, Braun JR, Moog P, Küchle C, Renders L, Malik M, Schmidt G, Wassertheurer S, Heemann U, Schmaderer C (2020) Cardiovascular mortality can be predicted by heart rate turbulence in hemodialysis patients. Front Physiol 11:77. https://doi.org/10.3389/fphys.2020.00077 Schmidt G, Malik M, Barthel P, Schneider R, Ulm K, Rolnitzky L, Camm AJ, Bigger JT, Schömig A (1999) Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet 353:1390-1396. https://doi.org/10.1016/s0140-6736(98)08428-1 Tsai C, Huang J, Lin C, Ma H, Lo M, Liu LD, Lin L, Lin C, Hung C, Peng C, Lin Y (2020) Heart rhythm complexity predicts long‐term cardiovascular outcomes in peritoneal dialysis patients: a prospective cohort study. J Am Heart Assoc 9:e013036. https://doi.org/10.1161/jaha.119.013036 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8743385","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584107690,"identity":"5925200f-dd08-45ae-a3d3-35296571449c","order_by":0,"name":"Zafer Yalım","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYFACxgcgUoaBvYGBmUgtzAYgkoeB5wCSlgNEaZFIIFKLfPthxseVbXY8ujPfGH4uqLBh4G/vTmD+uAe3FsaeZGbDs23JPGa3c4ylZ5xJY5A4c3YDw4FneJzFkH9MsrGNGaTFQJq37TCDgUQuUAsel7HxP2b/2dhWz2N284zxb6K08EgkszE2th3mMbvBY0acLRISj5klG84d5zE7k1ZmzXMmjQfklwNn8GiR709m/NhQVi1ndvzw5ts8FTZy/O29Gx9U4NECBoxsIJIDGkEMhGISDP6ACPYHhBWOglEwCkbBiAQAeE1PTdcENMMAAAAASUVORK5CYII=","orcid":"","institution":"Afyonkarahisar Healty Sciences University","correspondingAuthor":true,"prefix":"","firstName":"Zafer","middleName":"","lastName":"Yalım","suffix":""},{"id":584107691,"identity":"2fb8822b-539f-418b-9a63-43376cd7e0bf","order_by":1,"name":"Sümeyra Alan Yalım","email":"","orcid":"","institution":"Afyonkarahisar Healty Sciences University","correspondingAuthor":false,"prefix":"","firstName":"Sümeyra","middleName":"Alan","lastName":"Yalım","suffix":""}],"badges":[],"createdAt":"2026-01-30 16:38:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8743385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8743385/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101792433,"identity":"1aeb0ed1-020d-4578-9885-8f8b83834bc4","added_by":"auto","created_at":"2026-02-03 16:12:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105872,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic impact of autonomic dysfunction on mortality in hypotensive hemodialysis patients\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8743385/v1/1e5d13223d7c637143144e13.png"},{"id":102115110,"identity":"0806042a-ead3-491e-95ed-8d30145861a0","added_by":"auto","created_at":"2026-02-07 16:39:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1085432,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8743385/v1/e264c224-de7e-41e3-9e11-166ad561d292.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autonomic Dysfunction Predicts Long-Term Mortality in Hypotensive Hemodialysis Patients: Prognostic Value of Heart Rate Variability and Turbulence","fulltext":[{"header":"Background","content":"\u003cp\u003eHemodialysis (HD) is a life-sustaining therapy for patients with end-stage renal disease (ESRD). However, cardiovascular mortality remains disproportionately high in this population. Among HD patients, sudden cardiac death accounts for a substantial proportion of deaths and is predominantly attributed to malignant ventricular arrhythmias rather than progressive heart failure [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite improvements in dialysis techniques and cardiovascular management, identifying patients at the highest risk for fatal cardiac events remains a major clinical challenge. Intradialytic hypotension (a well-known complication of HD) has been associated with myocardial ischemia, repetitive myocardial stunning, and increased mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nevertheless, hypotensive episodes are not limited to dialysis sessions. A subset of HD patients experiences recurrent hypotension during interdialytic periods, frequently accompanied by impaired organ perfusion symptoms. Outside dialysis sessions, the long-term prognostic implications of this chronic hypotensive phenotype remain to be fully elucidated.\u003c/p\u003e \u003cp\u003eIn ESRD, autonomic nervous system dysfunction is highly prevalent and considered as an important pathophysiological mechanism linking hypotension, arrhythmogenesis, and sudden cardiac death [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Heart rate variability (HRV), a noninvasive marker of cardiac autonomic modulation, reflects the balance between sympathetic and parasympathetic activity and has been extensively studied in cardiovascular diseases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Studies have shown that reduced HRV is associated with increased mortality in HD patients. However, the reported results remain inconsistent, partly because of heterogeneity in study populations, HRV assessment timing, and outcome definitions.\u003c/p\u003e \u003cp\u003eHeart rate turbulence (HRT) evaluates baroreflex-mediated sinus rhythm responses following ventricular premature beats, providing complementary information regarding autonomic integrity. Impaired HRT parameters (specifically abnormal turbulence onset [TO] and turbulence slope [TS]) have been shown to be strong predictors of mortality after myocardial infarction and in other high-risk cardiac populations [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite the high burden of ventricular ectopy and autonomic neuropathy in HD patients, there are limited data regarding the prognostic value of HRT in this population. Moreover, its role in patients with chronic hypotension remains largely unexplored.\u003c/p\u003e \u003cp\u003eIn our previous study, we demonstrated that HD patients with frequent hypotensive episodes during interdialytic periods exhibit significantly impaired HRV and HRT parameters compared with normotensive HD patients, indicating advanced autonomic dysfunction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, whether these autonomic alterations translate into adverse long-term outcomes remains unknown. Addressing this gap is clinically important as the early identification of high-risk patients may allow closer cardiologic surveillance and the implementation of preventive strategies.\u003c/p\u003e \u003cp\u003eTherefore, the present study was designed to extend our previous findings by evaluating the prognostic significance of HRV and HRT parameters in a larger cohort of hypotensive HD patients with extended follow-up. We aimed to investigate the association between baseline autonomic markers and all-cause mortality using time-to-event analyses.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis prospective observational cohort study was conducted among patients with ESRD receiving maintenance HD. Patients were recruited from multiple dialysis centers and followed longitudinally for clinical outcomes. Patients were considered eligible if they were undergoing thrice-weekly HD and had a history of recurrent hypotensive episodes during interdialytic periods. Hypotension was defined as systolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;90 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;60 mmHg, accompanied by impaired organ perfusion symptoms. Compared with our previous study, this study was conducted with an increased number of patients (44 hypotensive and 46 normotensive). Patients were followed up according to our previous study\u0026rsquo;s methodology. Each patient was followed up for mortality as the primary endpoint, and the follow-up time between baseline and death was recorded.\u003c/p\u003e \u003cp\u003ePatients reporting at least two symptomatic hypotensive episodes per day during the interdialytic period were considered eligible for further evaluation. Importantly, patient selection relied on the presence of clinically meaningful symptoms following a documented reduction in blood pressure, rather than isolated blood pressure readings alone. All ambulatory blood pressure monitoring (ABPM) and 24-h rhythm Holter recordings were performed exclusively during the interdialytic period to ensure that hypotensive events were not attributable to the dialysis procedure itself. Dialysis schedules were temporarily adjusted to allow uninterrupted ambulatory monitoring. This approach ensured that autonomic and hemodynamic assessments were performed at least 24 h after the most recent dialysis session, thereby minimizing acute dialysis-related confounding.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExclusion Criteria\u003c/h2\u003e \u003cp\u003ePatients with atrial fibrillation; permanent pacemakers; significant valvular heart disease; hormonal disorders; serious infection and neurological diseases; known autonomic neuropathies unrelated to ESRD; active infection; malignancy; or use of medications affecting heart rate, autonomic function, and blood pressure (including β-blockers, antihypertensives, antiarrhythmic agents, and nitrates) were excluded.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAmbulatory Blood Pressure and Rhythm Monitoring\u003c/h3\u003e\n\u003cp\u003eAll patients underwent 24-h ABPM using a validated device (CardioSoft Diagnostic System Ambulatory Blood Pressure, General Electric, Boston, USA). Measurements were obtained at predefined intervals throughout the day to capture circadian blood pressure patterns. Simultaneously, 24-h ambulatory electrocardiographic Holter (Pathfinder Holter Software, version 8.255, Reynolds Medical, England) monitoring was performed to assess cardiac rhythm status, ventricular ectopy burden, and autonomic modulation.\u003c/p\u003e \u003cp\u003eHolter data were analyzed for the presence of atrial and ventricular arrhythmias, and recordings with inadequate signal quality were excluded. In accordance with established guidelines, HRV analysis was performed using time-domain parameters, including standard deviation of all normal-to-normal intervals (SDNN), standard deviation of the averages of normal-to-normal intervals in all 5-min segments (SDANN), and SDNN index. Meanwhile, HRT analysis was conducted in patients with adequate ventricular premature beats suitable for analysis. TO and TS were calculated using validated algorithms. HRV and HRT analyses were evaluated in accordance with the methodology described in our previous study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eLaboratory Measurements\u003c/h3\u003e\n\u003cp\u003eBlood samples were obtained during the interdialytic period on the same day as ABPM and Holter recordings. Routine biochemical and hematological parameters were measured using standardized laboratory techniques. The laboratory results were evaluated in conjunction with HRV and HRT findings.\u003c/p\u003e\n\u003ch3\u003eFollow-Up and Outcome Assessment\u003c/h3\u003e\n\u003cp\u003ePatients were prospectively followed for the occurrence of all-cause mortality, which served as the primary endpoint of the study. The follow-up duration was calculated from the date of baseline autonomic assessment to the date of death or last clinical contact. Patients who were alive at the end of follow-up were censored.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS software (version 23.0, IBM Corp., Armonk, NY, USA). Data distribution was assessed using visual and analytical tests for normality. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed data and median (interquartile range) for non-normally distributed data. Conversely, categorical variables were summarized as frequencies and percentages. Comparisons between categorical variables were performed using the Chi-square test. Continuous variables with normal distributions were compared using the independent samples T-test, whereas those with non-normal distributions were analyzed using the Mann\u0026ndash;Whitney U test. Correlation analyses were performed using Pearson and Spearman correlation. HRV and HRT parameters were dichotomized according to median values. Survival analyses were performed using Kaplan\u0026ndash;Meier curves and compared with log-rank tests. Binary logistic regression analyses were conducted to estimate factors that could predict mortality. Statistical significance was considered at a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eNinety hypotensive HD patients were included in the analysis. The median follow-up duration was 61 months (interquartile range: 49\u0026ndash;83.5 months). During the follow-up period, 39 patients (43%) died. The baseline demographic and clinical characteristics of patients are presented 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 and clinical characteristics of study groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \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\u003eHypotensive HD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormotensive HD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.02\u0026thinsp;\u0026plusmn;\u0026thinsp;7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.06\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.393\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\u003e24.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke or TIA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuropathy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD time (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD volume removed (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.2\u0026thinsp;\u0026plusmn;\u0026thinsp;29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.4\u0026thinsp;\u0026plusmn;\u0026thinsp;25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mmol/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mmol/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonosit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrigliserid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.1\u0026thinsp;\u0026plusmn;\u0026thinsp;38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159.5\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.7\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time (month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (%54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (%32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistically significant. Values are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is significant. Abbreviations: HD, hemodialysis; n, number; BMI, body mass index; CAD, coronary artery disease; TIA, transient ischemic attack; EF, ejection fraction, \u0026plusmn; MCV, mean corpuscular volume; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHypotensive patients had significantly lower daytime, nighttime, pre-dialysis, and post-dialysis blood pressure values and a higher prevalence of a non-dipping pattern, indicating impaired circadian blood pressure regulation. With regard to autonomic function, hypotensive patients exhibited significantly reduced long-term HRV indices (SDNN, SDNN index, and SDANN). However, short-term parasympathetic markers (RMSSD and pNN50) did not differ between groups. HRT parameters showed marked impairment in hypotensive patients, with less negative TO and lower TS, consistent with baroreflex dysfunction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eABPM and HRV\u0026ndash;HRT analyses of the study groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \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\u003eHypotensive HD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormotensive HD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABPM day S (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABPM day D (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABPM night S (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABPM night D (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABPM 24 h S (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABPM 24 h D (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-dipping pattern, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (%72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (%41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of hypotension in HD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 / %82.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 / %64.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-HD S (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-HD D (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-HD S (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-HD S (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHRV and HRV analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean heart rate, beats/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN, ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN index, ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDANN, ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD, ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epNN50, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriangular index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbulence onset, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbulence slope, ms/RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistically significant. Abbreviations: ABPM, ambulatory blood pressure monitoring; HRV, heart rate variability; HRT, heart rate turbulence; HD, hemodialysis; n, number; S, systole; D, diastole; SDNN, standard deviation of all normal-to-normal intervals; SDANN, standard deviation of the averages of normal-to-normal intervals in all 5-min segments; RMSSD, square root of the mean of the sum of the squares of differences between adjacent normal-to-normal intervals; pNN50, number of pairs of adjacent normal-to-normal intervals differing by more than 50 ms divided by the total number of all normal-to-normal intervals\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCompared with survivors, non-survivors had a significantly higher prevalence of coronary artery disease (66.7% vs. 31.4%, p\u0026thinsp;=\u0026thinsp;0.021), lower ultrafiltration volumes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher neutrophil counts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean corpuscular volume was significantly lower among non-survivors than survivors (p\u0026thinsp;=\u0026thinsp;0.002). Moreover, the follow-up duration was substantially shorter among non-survivors than survivors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among HRV indices, SDANN was significantly lower in non-survivors than in survivors (101.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03 ms vs. 105.80\u0026thinsp;\u0026plusmn;\u0026thinsp;8.79 ms, p\u0026thinsp;=\u0026thinsp;0.042), indicating impaired long-term autonomic modulation. SDNN showed a borderline association with mortality (p\u0026thinsp;=\u0026thinsp;0.075), whereas the SDNN and triangular indexes did not significantly differ between groups. In addition, TO demonstrated the strongest association with mortality, with non-survivors exhibiting markedly impaired (less negative) TO values (\u0026minus;\u0026thinsp;1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 vs. \u0026minus;2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). TS did not significantly differ between groups. The baseline demographic and clinical characteristics of survivors and non-survivors are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographic and clinical characteristics, and autonomic function analysis results of survivors and non-survivors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \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\u003eDeath (n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvival (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke/TIA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuropathy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD session duration (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltrafiltration volume (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEjection fraction (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.2\u0026thinsp;\u0026plusmn;\u0026thinsp;29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.4\u0026thinsp;\u0026plusmn;\u0026thinsp;25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.385\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 \u003cp\u003e136.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \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\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (fL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.1\u0026thinsp;\u0026plusmn;\u0026thinsp;38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159.5\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.7\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.36\u0026thinsp;\u0026plusmn;\u0026thinsp;14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.39\u0026thinsp;\u0026plusmn;\u0026thinsp;12.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDANN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.80\u0026thinsp;\u0026plusmn;\u0026thinsp;8.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriangular index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbulence onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbulence slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistically significant Abbreviations: n, number; BMI, body mass index; CAD, coronary artery disease; TIA, transient ischemic attack; HD, hemodialysis; MCV, mean corpuscular volume; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; SDNN, standard deviation of all normal-to-normal intervals; SDANN, standard deviation of the averages of normal-to-normal intervals in all 5-min segments\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis was performed between mortality and clinical parameters. Mortality showed a moderate positive correlation with TO (ρ\u0026thinsp;=\u0026thinsp;0.489, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and significant inverse correlations with SDANN and ultrafiltration volume. The correlation analysis results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of death and other variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath -\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRho value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\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\u003eHD volume recording\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.243*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.251*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-HD diastolic pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.237*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-HD diastolic pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.207*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.248*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDANN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.215*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.489**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistically significant. Abbreviations: HD, hemodialysis; SDNN, standard deviation of all normal-to-normal intervals; SDANN, standard deviation of the averages of normal-to-normal intervals in all 5-min segments; TI, triangular index; TO, turbulence onset; TS, turbulence slope; Rho, correlation coefficient\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=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression analysis for predicting mortality\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExp (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD received volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-HD S BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-HD D BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-HD S BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-HD D BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-dipping pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDANN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDANN index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epNN50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CI, confidence interval; BMI, body mass index; DM, diabetes mellitus; EF, ejection fraction; S, systolic; BP, blood pressure; D, diastolic; SDNN, standard deviation of all normal-to-normal intervals; SDANN, standard deviation of the averages of normal-to-normal intervals in all 5-min segments; RMSSD, square root of the mean of the sum of the squares of differences between adjacent normal-to-normal intervals; pNN50, number of pairs of adjacent normal-to-normal intervals differing by more than 50 ms divided by the total number of all normal-to-normal intervals; TI, triangular index; TO, turbulence onset; TS, turbulence slope\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eKaplan–Meier Survival Analysis According to HRV and HRT Parameters\u003c/h3\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier survival analyses demonstrated significant differences in all-cause mortality according to the median-split groups of selected HRV and HRT parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients with lower SDNN (\u0026le;\u0026thinsp;116.50 ms) exhibited a significantly higher mortality rate than those with higher SDNN (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, lower SDANN (\u0026le;\u0026thinsp;103.50 ms) was strongly associated with increased mortality, with early and sustained separation of survival curves over time (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). By contrast, the SDNN index (median: 51.00 ms) demonstrated no statistically significant difference in survival between groups (log-rank p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting a limited prognostic contribution of short-term HRV components. With regard to geometric HRV indices, lower TI (\u0026le;\u0026thinsp;35.00) was associated with moderately reduced survival probability (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong HRT parameters, TO emerged as the most powerful discriminator of mortality risk. Patients with higher (less negative) TO values (\u0026thinsp;\u0026ge;\u0026thinsp;\u0026minus;\u0026thinsp;2.12) exhibited markedly poorer survival than those with more preserved baroreflex responses, with pronounced early divergence of survival curves (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Meanwhile, TS (median: 7.49) demonstrated a weaker and statistically nonsignificant association with mortality (log-rank p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn multivariable Cox regression analysis, TO emerged as the only autonomic parameter that was independently associated with mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas HRV indices lost statistical significance after adjustment\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrated that impaired cardiac autonomic function, assessed by HRV and HRT parameters, is strongly associated with increased all-cause mortality among hypotensive HD patients during the interdialytic period. Specifically, reduced SDNN and SDANN values, as well as abnormal TO, were powerful predictors of adverse outcomes.\u003c/p\u003e \u003cp\u003ePrevious studies investigating HRV in HD patients have yielded inconsistent results. Although several studies identified reduced HRV as an independent predictor of mortality, others failed to confirm this association [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These discrepancies may be explained by differences in patient selection, measurement timing, and inclusion of intradialytic recordings. Our study minimizes hemodynamic confounding related to the dialysis procedure itself by focusing specifically on hypotensive patients and performing autonomic assessments during interdialytic periods.\u003c/p\u003e \u003cp\u003eAmong HRV parameters, SDANN emerged as one of the strongest predictors of mortality. SDANN reflects long-term components of HRV and circadian autonomic modulation, which are frequently disrupted in ESRD patients with autonomic dysfunction. The nonsignificant findings for the SDNN index and TS likely reflect their greater sensitivity to short-term autonomic fluctuations and patient phenotype. As such, long-term indices and TO better capture chronic autonomic impairment in hypotensive HD patients.\u003c/p\u003e \u003cp\u003eThe loss of circadian blood pressure rhythm and non-dipping patterns have been associated with adverse cardiovascular outcomes in HD populations, supporting our findings [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Hannane et al. showed that impaired HRT during dialysis, particularly in diabetic HD patients, significantly contributed to predicting long-term mortality risk [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The difference in the present study is that we demonstrate the mortality association in hypotensive patients during the non-HD period, just as we did with advanced heart and liver failure. When the results were evaluated, we determined that other HRV variables, especially TO, could predict mortality. HRV and HRT measurement increases the sensitivity of risk estimation and classification by identifying high-risk patients in a dialysis setting and offers an opportunity for personalized monitoring and prevention.\u003c/p\u003e \u003cp\u003eIndependent of known risk factors, HRT assessment can determine the cardiovascular mortality risk of HD patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The most striking observation in our study was the strong prognostic impact of TO. HRT reflects baroreflex sensitivity and vagal function, and impaired TO has been shown to predict mortality in post-myocardial infarction populations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Cardiac rhythm analysis can be used to predict long-term cardiovascular outcomes, including those receiving peritoneal dialysis in addition to HD. The main reason for this is that cardiovascular disease is the leading cause of morbidity and mortality in patients with ESRD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our findings extend these observations to hypotensive HD patients and suggest that baroreflex failure may play a central role in arrhythmogenesis and sudden cardiac death in this vulnerable group. Furthermore, we demonstrated impaired cardiac autonomic function in the hypotensive and deceased groups.\u003c/p\u003e \u003cp\u003eImpaired autonomic function is associated with mortality in patients with ESRD. Noninvasive electrocardiographic Holter monitoring with HRT and HRV analysis can be used to reduce cardiovascular events in these patients and identify at-risk patients. Clinically, these results have important implications. HRV and HRT analyses are noninvasive, widely available, and easily applicable in routine practice. Incorporating autonomic assessment into the evaluation of hypotensive HD patients may allow the early identification of individuals at high risk for adverse outcomes and prompt more intensive cardiologic evaluation and monitoring.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be acknowledged. Although the cohort size was larger than that in our previous study, the number of events limits the complexity of multivariable models. In addition, cause-specific mortality could not be reliably determined in all cases. Finally, autonomic parameters were assessed at baseline and may have changed over time.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eImpaired cardiac autonomic function, as reflected by reduced HRV and abnormal HRT parameters, is strongly associated with increased mortality in hypotensive HD patients. These findings support the use of HRV and HRT as practical risk stratification tools and highlight the importance of autonomic dysfunction in the pathophysiology of adverse outcomes in this high-risk population.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003eonflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol complied with the ethical principles of the Declaration of Helsinki and received full approval from the institutional review boards of Afyonkarahisar Healty Sciences University Ethics Committee (no. 2019/284). Under this approval, informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design of the study: ZY. Analysis and interpretation of data and Drafting and critical revision of the article: ZY and SAY. Critical revision of the article: ZY and SAY. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLysaght MJ (2002) Maintenance dialysis population dynamics. J Am Soc Nephrol 13:S37-S40. https://doi.org/10.1681/asn.v13suppl_1s37\u003c/li\u003e\n \u003cli\u003eGreen D, Roberts PR, New DI, Kalra PA (2011) Sudden cardiac death in hemodialysis patients: an in-depth review. Am J Kidney Dis 57:921-929. https://doi.org/10.1053/j.ajkd.2011.02.376\u003c/li\u003e\n \u003cli\u003eBleyer AJ, Hartman J, Brannon PC, Reeves-Daniel A, Satko SG, Russell G (2006) Characteristics of sudden death in hemodialysis patients. Kidney Int 69:2268-2273. https://doi.org/10.1038/sj.ki.5000446\u003c/li\u003e\n \u003cli\u003eShoji T, Tsubakihara Y, Fujii M, Imai E (2004) Hemodialysis-associated hypotension as an independent risk factor for two-year mortality in hemodialysis patients. Kidney Int 66:1212-1220. https://doi.org/10.1111/j.1523-1755.2004.00812.x\u003c/li\u003e\n \u003cli\u003eMcIntyre CW, Burton JO, Selby NM, Leccisotti L, Korsheed S, Baker CSR, Camici PG (2008) Hemodialysis-induced cardiac dysfunction is associated with an acute reduction in global and segmental myocardial blood flow. Clin J Am Soc Nephrol 3:19-26. https://doi.org/10.2215/cjn.03170707\u003c/li\u003e\n \u003cli\u003eRao PK, Dadmi P, Putturaya S (2026) Correlation of cardiac autonomic neuropathy in predialysis and dialysis chronic kidney disease patients. Int J Multidiscipl Res Dev\u003c/li\u003e\n \u003cli\u003eMalik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17:354-381. https://doi.org/10.1093/oxfordjournals.eurheartj.a014868\u003c/li\u003e\n \u003cli\u003eFukuta H, Hayano J, Ishihara S, Sakata S, Mukai S, Ohte N, Ojika K, Yagi K, Matsumoto H, Sohmiya S, Kimura G (2003) Prognostic value of heart rate variability in patients with end-stage renal disease on chronic haemodialysis. Nephrol Dial Transpl 18:318-325. https://doi.org/10.1093/ndt/18.2.318\u003c/li\u003e\n \u003cli\u003eOikawa K, Ishihara R, Maeda T, Yamaguchi K, Koike A, Kawaguchi H, Tabata Y, Murotani N, Itoh H (2009) Prognostic value of heart rate variability in patients with renal failure on hemodialysis. Int J Cardiol 131:370-377. https://doi.org/10.1016/j.ijcard.2007.10.033\u003c/li\u003e\n \u003cli\u003eSuzuki M, Hiroshi T, Aoyama T, Tanaka M, Ishii H, Kisohara M, Iizuka N, Murohara T, Hayano J (2012) Nonlinear measures of heart rate variability and mortality risk in hemodialysis patients. Clin J Am Soc Nephrol 7:1454-1460. https://doi.org/10.2215/cjn.09430911\u003c/li\u003e\n \u003cli\u003eYalım Z, Demir ME, Yalım SA, Alp \u0026Ccedil; (2020) Investigation of heart rate variability and heart rate turbulence in chronic hypotensive hemodialysis patients. Int Urol Nephrol 52:775-782. https://doi.org/10.1007/s11255-020-02429-7\u003c/li\u003e\n \u003cli\u003eAmar J, Vernier I, Rossignol E, Bongard V, Arnaud C, Conte JJ, Salvador M, Chamontin B (2000) Nocturnal blood pressure and 24-hour pulse pressure are potent indicators of mortality in hemodialysis patients. Kidney Int 57:2485-2491. https://doi.org/10.1046/j.1523-1755.2000.00107.x\u003c/li\u003e\n \u003cli\u003eHannane N, Mayer CC, Matschkal J, Bormann F, Krieter A, Braun JR, K\u0026uuml;chle C, Renders L, G\u0026uuml;nthner R, Schmidt G, M\u0026uuml;ller A, Wassertheurer S, Heemann U, Haller B, Malik M, Schmaderer C, Braunisch MC (2025) Long-term prediction of mortality by heart rate turbulence in hemodialysis patients and the impact of diabetes mellitus-a longitudinal observational study. J Nephrol 38:2261-2272. https://doi.org/10.1007/s40620-025-02357-8\u003c/li\u003e\n \u003cli\u003eBraunisch MC, Mayer CC, Bauer A, Lorenz G, Haller B, Rizas KD, Hagmair S, von St\u0026uuml;lpnagel L, Hamm W, G\u0026uuml;nthner R, Angermann S, Matschkal J, Kemmner S, Hasenau A-L, Z\u0026ouml;llinger I, Steubl D, Mann JF, Lehnert T, Scherf J, Braun JR, Moog P, K\u0026uuml;chle C, Renders L, Malik M, Schmidt G, Wassertheurer S, Heemann U, Schmaderer C (2020) Cardiovascular mortality can be predicted by heart rate turbulence in hemodialysis patients. Front Physiol 11:77. https://doi.org/10.3389/fphys.2020.00077\u003c/li\u003e\n \u003cli\u003eSchmidt G, Malik M, Barthel P, Schneider R, Ulm K, Rolnitzky L, Camm AJ, Bigger JT, Sch\u0026ouml;mig A (1999) Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet 353:1390-1396. https://doi.org/10.1016/s0140-6736(98)08428-1\u003c/li\u003e\n \u003cli\u003eTsai C, Huang J, Lin C, Ma H, Lo M, Liu LD, Lin L, Lin C, Hung C, Peng C, Lin Y (2020) Heart rhythm complexity predicts long‐term cardiovascular outcomes in peritoneal dialysis patients: a prospective cohort study. J Am Heart Assoc 9:e013036. https://doi.org/10.1161/jaha.119.013036\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"arrhythmia, heart rate turbulence, heart rate variability, hemodialysis, hypotension","lastPublishedDoi":"10.21203/rs.3.rs-8743385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8743385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eAutonomic dysfunction has been implicated in adverse outcomes in end-stage renal disease. However, the prognostic significance of heart rate variability (HRV) and turbulence (HRT) in chronically hypotensive hemodialysis (HD) patients remains unclear. Therefore, the present study aimed to investigate the association between baseline autonomic markers and all-cause mortality using time-to-event analyses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 44 hypotensive and 46 normotensive HD patients who underwent 24-h ambulatory blood pressure monitoring and Holter recording during the interdialytic period were included in this prospective observational cohort study. Moreover, their HRV and HRT parameters were analyzed. Patients were monitored for all-cause mortality outcomes, and survival analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring follow-up, 39 patients (43.3%) died. Non-survivors had significantly lower standard deviation of the averages of normal-to-normal intervals in all 5-min segments (SDANN) (101.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03 ms vs. 105.80\u0026thinsp;\u0026plusmn;\u0026thinsp;8.79 ms; p\u0026thinsp;=\u0026thinsp;0.042) and markedly impaired turbulence onset (TO) (\u0026minus;\u0026thinsp;1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 vs. \u0026minus;2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with survivors. Kaplan\u0026ndash;Meier analysis demonstrated significantly reduced survival among patients with abnormal autonomic parameters (standard deviation of all normal-to-normal intervals, SDANN, TO; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multivariable Cox regression analysis, TO emerged as the strongest independent predictor of mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eImpaired TO is a powerful and independent predictor of long-term mortality among hypotensive HD patients. These findings indicate that TO is a promising noninvasive marker for risk stratification and that autonomic failure plays a central mechanistic role in adverse outcomes in this high-risk population.\u003c/p\u003e","manuscriptTitle":"Autonomic Dysfunction Predicts Long-Term Mortality in Hypotensive Hemodialysis Patients: Prognostic Value of Heart Rate Variability and Turbulence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:08:37","doi":"10.21203/rs.3.rs-8743385/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78a29f01-992b-46a5-b584-3b82c4102db6","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T07:10:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:08:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8743385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8743385","identity":"rs-8743385","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}