Mapping the mortality burden in hemodialysis patients. A multicenter observational study

Mapping the mortality burden in hemodialysis patients. 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A multicenter observational study Gabriela Tamayo, • Jorge Quinchuela, Natalia Benavides, • Franklin Mora-Bravo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7515828/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 Introduction: In patients with chronic kidney disease (CKD), cardiovascular disease is considered the leading cause of mortality. This study aims to analyze mortality and its associated factors in patients undergoing hemodialysis (HD) and hemodiafiltration (HDF) treatments at 14 private centers in Ecuador. Methods: This observational research was conducted from 2018 to 2022. Patients who received conventional three-weekly therapy were included. Those who died by the end of the observation period (Group 1-G1) were compared with those who were alive (Group 2-G2). The variables assessed included demographic data, comorbidities, clinical indicators, laboratory results, and impedance descriptions. Logistic regression was performed to obtain the odds ratio (OR). Results: A total of 821 patients in G1 and 3,586 in G2 were analyzed, yielding a mortality rate of 22.89% over 42 months (6.54% per year). There were 182 deaths attributed to cardiovascular causes (22.17%), 162 to infections (19.73%), and 477 from other causes (58.09%). Patients on HDF in G1 accounted for 167 cases (20.3%), while in G2, there were 1,078 cases (30.5%) (P1.49), and type 2 diabetes mellitus (OR>1.33). Protective factors identified were albumin concentration (OR: 0.61), hemoglobin level (OR: 0.83), and lean tissue index (OR: 0.95). Conclusion: The present study demonstrates that the primary causes of death were non-cardiovascular, cardiovascular, and infections. Higher albumin concentration, elevated hemoglobin levels, increased lean tissue index, and longer effective weekly treatment duration were identified as protective factors against mortality. mortality hemodiafiltration hemodialysis risk factors chronic kidney failure Figures Figure 1 Figure 2 INTRODUCTION Since 2009, the prevalence of stage 5 chronic kidney disease (CKD) in Ecuador has increased from 3,524 to 21,394 cases by 2022, resulting in a prevalence rate of 1,183 patients per million inhabitants [ 1 , 2 ]. The reported survival rate for patients in hemodialysis programs in Ecuador is 3.8 years [ 2 ]. Among patients with advanced CKD, cardiovascular disease is the leading cause of mortality, exacerbated by risk factors such as hypertension, diabetes, dyslipidemia, smoking, and advanced age. These traditional cardiovascular risk factors are highly prevalent in the CKD population and are linked to the severity of kidney dysfunction, which increases the risk of mortality in these patients [ 3 , 4 ]. The contributing factors include underdialysis, uncontrolled anemia, and alterations in bone and mineral metabolism resulting from secondary hyperparathyroidism. Furthermore, many dialysis patients experience proinflammatory states and malnutrition. The combination of these uncontrolled factors heightens the risk of cardiovascular events and, consequently, all-cause mortality. High-volume online hemodiafiltration (OL-HDF) has been developed as an alternative to improve the treatment of patients with CKD, allowing for more significant removal of medium- and high-molecular-weight molecules through a convective process. This treatment has been associated with a reduction in the accumulation of uremic toxins and benefits the hemodynamic stability of patients, which could decrease the incidence of hypotension and other adverse effects common in conventional hemodialysis [ 5 – 8 ]. Several studies have suggested that OL-HDF may positively impact survival compared with high-flux hemodialysis (HD). Detailed analysis revealed that OL-HDF patients experience lower all-cause mortality, reduced cardiovascular mortality, and better anemia management. Furthermore, studies have reported decreased use of erythropoiesis-stimulating agents (ESAs), improved phosphorus levels, and a lower incidence of beta-2 microglobulin-related amyloidosis [ 9 , 10 ]. OL-HDF has also been associated with improved nutritional parameters and reduced morbidity and hospitalizations, leading to additional benefits for the quality of life of dialysis patients. Since mortality rates in dialysis patients remain high, between 15% and 20% annually, and increased urea removal has not shown significant direct effects on survival, clinical interest has shifted toward convective therapies such as OL-HDF. In recent years, randomized controlled trials comparing conventional hemodialysis with online postdilution hemodiafiltration have reported mixed, although generally positive, results regarding the benefits of OL-HDF on mortality and morbidity. Hemodiafiltration is not widely accepted in medical practice, so reports on its use are limited in Latin America. This study assessed mortality and associated factors in patients receiving hemodialysis and hemodiafiltration treatments at multiple private centers in Ecuador. METHODS Study design This study is observational. The source is prospective. Scenery The study was conducted in 14 hemodialysis clinics in Ecuador belonging to the Davita-Ecuador group. The participating units were as follows: 1. Manadiálisis Manta, 2. Sermens Quito, 3. Dialcentro, 4. Cener SA, 5. Manadiálisis Portoviejo, 6. Sermens Guayaquil, 7. Medicopharma Machala, 8. Dialibarra, 9. Manadiálisis Calle Quito, 10. Manadiálisis Jipijapa, 11. Manadiálisis Chone, 12. Famardial Guayaquil, 13. Farmadial Daule, 14. Nefrosalud, 15. Manadiálisis Bahía. The study period was from September 3, 2018, to March 30, 2022. Participants Patients who had undergone conventional hemodialysis for more than three months and attended three weekly sessions of four hours each were included in the study. The exclusion criteria included patients who died from a diagnosis of COVID-19, those who did not adhere to the established treatment frequency (4 hours of treatment, 3 times per week), and those whose causes of death were unrelated to chronic kidney disease (e.g., cancer or trauma). The sample was divided into two groups: patients who died by the end of the observation period (Group 1) and patients who were alive at the end of the study (Group 2). Variables The independent variables included age, sex, dry weight, body mass index, Charlson comorbidity index, and probability of survival (AACCIS + albumin). The presence of comorbidities included coronary artery disease, heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic lung disease, connective tissue disorders, gastrointestinal bleeding, liver disease, and neurological disease. The etiology of chronic kidney disease features glomerulonephritis, diabetic nephropathy, polycystic kidney disease, interstitial nephritis, vascular disease with arterial hypertension, and diuresis at 100 ml/24 hours. Hemodialysis modalities included OL-HDF and HD, along with Qb (blood flow), Qd (dialysate flow), effective weekly treatment duration, Kt/V, convective volume, and ultrafiltration. Pre- and postdialysis blood pressure were measured. The laboratory parameters included potassium, hemoglobin, ferritin, C-reactive protein, albumin, nCRP (normalized protein catabolism rate), phosphorus, calcium, and PTH (parathyroid hormone). The medications administered included erythropoietin and calcitriol. Predialysis relative overhydration was assessed via bioimpedance, as were predialysis lean tissue and the fat tissue index. The dependent variable was mortality. Data sources/measurements The source was direct. Data were collected via the EuCliD computer system following patient privacy and consent protocols. The collected data are presented as individual averages. Treatments were performed via Fresenius Medical Care supplies; the hemodiafiltration machines included 83 Fresenius Medical Care 5008/S volumetric units and 528 Fresenius Medical Care 4008/S hemodialysis machines. FX 60, 80, and 100 Classix dialyzer filters were used for hemodialysis, and CorDiax along with CorAL 600, 800, and 1000 filters were employed for HDF. Assignment to hemodiafiltration The allocation policy for Ecuador's Renal Units to include patients in the hemodiafiltration program is based on the presence of cardiovascular complications such as congestive heart failure, recurrent intradialytic hypotension, difficult-to-control hyperphosphatemia, challenging arterial hypertension, and borderline low-flow access. Indications are reviewed at each center, and each patient is proposed for admission to the program. The most critically ill patients are generally admitted to the hemodiafiltration program. Biases Observation and selection bias were avoided by applying the participant selection criteria. A medical representative for each coordinating center was assigned to compile the data, which were completed on a single online form. The principal investigator always maintained the data via a guide and records approved in the research protocol to prevent potential interviewer, information, and recall bias. When there was any doubt about the standard deviation of the data, curations were conducted through onsite reviews of anomalous data. Two researchers independently analyzed each record in duplicate, and the variables were entered into the database after verifying their concordance. Study size The sample was probabilistic. Ecuador has a population of 17,980,083 (2023), with a CKD incidence rate of 21,394 cases by 2022. EPI info TM (Stat Calc, Epi Info, CDC, Atlanta. Version 7.2.6 [October 2023]), with an expected mortality frequency of 15.7%, a confidence limit of 5%, and a confidence level of 99.99%, the sample size was 773 cases for deceased patients. The controls were at a ratio of 4 to 1. Quantitative variables The results are presented as frequencies and percentages. A scale variable was converted into a categorical variable. A new variable, "KT/V*Convective volume *QB," was created to standardize HDF and HD treatments across varying degrees of extracorporeal flow prescriptions; the units of convective volume were liters per session, and Qb was measured in milliliters per minute. The variables were categorized into Category 1: 0 to 5.9 L*L/min* kt/V; Category 2: 6–9.9 L*L/min* kt/V; Category 3: 10–13.9 L*L/min* kt/V; Category 4: 14–17.9 L*L/min* kt/V; and Category 5: 18 or more L*L/min* kt/V. Statistical analysis Qualitative variables were analyzed as frequencies and percentages. Proportions were compared via the chi-square test, and means were compared via Student’s t test. Logistic regression was performed to obtain the odds ratio. As a secondary objective, survival was analyzed in specific patient groups, including diabetic patients and those who developed cerebrovascular events. The statistical package used was IBM Corp. (released from 2018). IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp. RESULTS Participants A total of 821 patients who died and 3,586 who survived were analyzed. This overall mortality rate represents 22.89% of the observed population over 42 months (95% confidence interval: 21.53–24.31%). The annual mortality rate was 6.54%. There were 182 deaths from cardiovascular causes (22.17%), 162 deaths (19.73%) associated with infections, and 477 deaths (58.09%) attributed to other causes. Main characteristics of the study groups In the deceased patient group, 693 patients (84.4%) had one or more comorbidities than did those in the alive group, which consisted of 2,580 patients (71.9%) (P < 0.001). The prevalence of patients in the hemodiafiltration programs in group 1 was 167 cases (20.3%), whereas in group 2, it was 1,078 cases (30.5%) (P < 0.0001). The average age was greater in group 1 (64.4 years) than in group 2 (61.1 years) (P < 0.001). There were no significant differences between the means of weight, body mass index, Charlson comorbidity index, or probability of survival (aCIS + Albumin) at the first year of observation. A difference was observed in the probability of survival “aaCCIs + Albumin” at the second year of observation, with 63.6 points in group 1 versus 71.2 points in group 2 (P < 0.001). There were 128 patients without comorbidities in group 1 (15.59%), while in group 2, there were 1,006 patients (28.05%) (P < 0.001). The variables in scale are presented in Table 1 . Etiology of kidney disease and comorbidities Diabetic nephropathy was prevalent in 63.2% of deceased patients compared with 47% of living patients (P < 0.001). Arterial hypertension was less common in the deceased group (20.3% versus 33.8%, P < 0.001). Other etiologies were not significantly different between the two groups (Table 2 ). Among the comorbidities, the absence of comorbidities was less common in group 1, with 15.6% compared with 28% in the living group (P < 0.001). Cerebrovascular disease and dementia were more common in the deceased group, at 13.6%, than in the living group, at 7.4% (P < 0.001). (Table 2 ). Convection-adjusted survival, kt/V, and extracorporeal flow in diabetic patients The Kt/V*Convective Volume* Qb index was lower in group 1 (5.05 ± 7.3) than in group 2 (7.22 ± 9.19 L*L/min* Kt/V), P < 0.001. This index had a greater impact on the group of patients diagnosed with diabetes mellitus, who showed greater survival with increased convective volume, higher extracorporeal flow, and overall greater Kt/V in Category 5 (Table 3 ) (Fig. 1 ). The Cox proportional hazard model for diabetic patients is presented in Table 4 . Convection-adjusted survival, kt/V, and extracorporeal flow in patients with cerebrovascular events The index significantly impacts the group of patients diagnosed with a cerebrovascular event, where greater survival is observed with increased convective volume, enhanced extracorporeal flow, and higher Kt/V overall in Categories 4 and 5 (Table 5 ) (Fig. 2 ). The Cox proportional hazard model for diabetic patients is presented in Table 5 . Factors associated with mortality The logistic regression model for predicting mortality in patients is presented in Table 6 , highlighting the statistically significant factors. Risk factors include the development of cerebrovascular disease, vascular disease with hypertension, and type 2 diabetes mellitus. In contrast, protective factors, listed in order of importance, are the albumin concentration, serum hemoglobin, lean tissue index, postdialysis systolic blood pressure, and effective weekly treatment duration. Table 1 Scale variables of the study groups Group 1: Deceased N = 821 Group 2: Alive N = 3586 P Survival time (months) 42.1 ±35.6 45.6 ±30.1 0.996 Age (years) 64.4 ±12.1 61.1 ±13.9 < 0.001* Dry weight (kg) 62.8 ±31.1 64.0 ±21.1 0.848 Body mass index (kg/m 2 ) 25.3 ±4.9 25.6 ±4.8 0.082 Charlson Comorbidity Index Value 5.7 ±5.4 7.5 ±8.2 1 Age-adjusted Charlson (aCCI) 6.0 ±1.8 5.23 ±2.0 0.404 Survival probability: Charlson + Albumin: 1 year 75.6 ±16.9 75.5 ±23.5 0.807 Charlson + Albumin: 2 years 63.6 ±13.7 71.2 ±13.6 < 0.001* Number of comorbidities 1.6 ±0.7 1.5 ±0.7 0.996 Hemodialysis treatment data Hemodialysis treatments/month 10.7 ±3.2 11.3 ±3.2 1 Qb (ml/min) 348 ±37 365 ±34 1 Qd (ml/min) 466 ±61 490 ±34 1 Effective weekly treatment duration (min) 619 ±133 680 ±100 < 0.001* Effective Infusion Volume (Liters) (HDF Patients) 21.4 ±4.0 (n=167[20.3%]) 22.1 ±4.9 (n=1078[30%]) 0.983/X 2 = P < 0.001 K/TV* Convective Volume* Qb (L*L/min* Kt/V). 5.05 ±7.3 7.22 ±9.19 < 0.001* Effective convective volume (Liters) 6.4 ±8.7 8.89 ±10.6 1 Ultrafiltration (ml) 2186 ±672 2282 ±647 1 Predialysis systolic blood pressure (mmHg) 145 ±16 146 ±15 1 Predialysis diastolic blood pressure (mmHg) 73 ±8 74 ±8 1 Postdialysis systolic blood pressure (mmHg) 141 ±14 142 ±14 1 Postdialysis diastolic blood pressure (mmHg) 73 ±6 74 ±6 1 Laboratories sp Kt/V 1.82 ±0.46 1.95 ±0.37 1 Predialysis potassium * (meq/L) 5.0 ±0.62 5.05 ±0.56 0.988 Hemoglobin* (g/dl) 10.5 ±1.4 11.01 ±1.2 1 Ferritin*(ng/ml) 811 ±437 808 ±423 1 C-reactive protein 40.4 ±66.3 22.5 ±38.7 < 0.001* Albumin (g/dL) 3.77 ±0.49 4.02 ±0.38 < 0.001* PCRn gr/kg/d 0.97 ±0.21 1 ±0.2 1 Phosphorus (mg/dl) 4.24 ±1.42 4.21 ±1.44 1 Corrected Calcium (mg/dl) 8.74 ±0.59 8.74 ±0.51 1 iPTH (pg/ml) 264 ±262 335 ±349 1 Medications Erythropoietin (Units/kg/week) 92.1 ±54.2 (n=760) 83.81 ±45.9 (n=3417) 1 Iron (mg/month) 219 ±64 (n=720) 212 ±63 (n=3316) 0.997 Calcium carbonate (mg/day) 886 ±1087 (n=533) 819,716 ± (n=2851 ) 1 Oral aluminum (grams/week) 3.37 ±73 (n=202) 5.08 ±6.2 (n=596) 1 Oral Calcitriol (µg/week) 0.50 ±0.83 (n=556) 0.35 ±1.20 (n=2303) 1 Bioimpedance Relative overhydration predialysis (%) 15.2 ±9.3 12.3 ±7.9 < 0.001* Lean tissue index 11.0 ±2.6 11.94 ±2.9 1 Fat tissue index 14.0 ±5.9 13.7 ±6.7 1 Table 2 Etiology and comorbidities. Group 1: Deceased N = 821 Group 2: Alive N = 3586 P Etiology Diabetic nephropathy 519 (63.2%) 1685 (47.0%) < 0.001* Vascular disease/Hypertension 167 (20.3%) 1212 (33.8%) < 0.001* Interstitial nephritis 28 (3.4%) 166 (4.6%) 0.125 Cystic kidney disease 17 (2.1%) 76 (2.1%) 0.931 Glomerulonephritis 14 (1.7%) 104 (2.9%) 0.056 Comorbidities Congestive heart failure 136 (16.6%) 612 (17.1%) 0.732 Coronary artery disease 130 (15.8%) 475 (13.2%) 0.052 No comorbidities 128 (15.6%) 1006 (28.0%) < 0.001* Cerebrovascular disease 114 (13.9%) 264 (7.4%) < 0.001* Peripheral vascular disease 74 (9.0%) 346 (9.6%) 0.578 Chronic lung disease 47 (5.5%) 190 (5.3%) 0.624 Digestive bleeding 41 (5.0%) 154 (4.3%) 0.378 Liver disease 29 (3.5%) 95 (2.6%) 0.167 Dementia or other psychiatric illness 21 (2.6%) 46 (1.3%) 0.007* Connective tissue disorder 10 (1.2%) 70 (2.0%) 0.156 HIV 2 (0.3%) 26 (1.0%) 0.091 Other features Residual diuresis > 100 ml/day 8 (1.4%) 116 (4.0%) 0.002* Table 3 Survival in diabetic patients by categories of convective volume, Kt/v and Qb. Mean estimate (months) Median estimate Median lower 95% CI Upper median 95% CI Category 1 41.32 39 (32–43) 32 43 Category 2 55.46 55 (48–59) 48 59 Category 3 64.58 67 (48–80) 48 80 Category 4 55.46 56 (47–63) 47 63 Category 5 65.12 62 (56–69) 56 69 Category 1: 0 to 5.9 L*L/min* Kt/V; Category 2: 6-9.9 L*L/min* Kt/V; Category 3: 10-13.9 L*L/min* Kt/V; Category 4: 14-17.9 L*L/min* Kt/V; Category 5: greater than or equal to 18 L/L/min* Kt/V. Log rank x 2 = 35.1 P < 0.001 Table 4 Cox proportional hazard model for diabetic patients with convective volume, Kt/v and Qb. Coefficients Lower 95% CI Upper 95% CI Std. Error z p Exp (B) Lower 95% CI Upper 95% CI Category 4 -0.61 -0.93 -0.3 0.16 3.8 < 0.001 0.54 0.4 0.74 Category 5 -0.9 -1.13 -0.68 0.12 7.81 < 0.001 0.41 0.32 0.51 Category 3 -0.36 -0.77 0.05 0.21 1.73 0.083 0.7 0.46 1.05 Category 2 -0.11 -0.66 0.44 0.28 0.4 0.693 0.9 0.52 1.55 T2DM 0.62 0.48 0.76 0.07 8.63 < 0.001 1.86 1.62 2.15 Category 1: 0 to 5.9 L*L/min* Kt/V; category 2: 6-9.9 L*L/min* Kt/V; category 3: 10-13.9 L*L/min* Kt/V; category 4: 14-17.9 L*L/min* Kt/V; category 5: greater than or equal to 18 L/L/min* Kt/V. T2DM: Type 2 diabetes mellitus. Table 5 Cox proportional hazard model for patients with cerebrovascular events with different categories of convective volume, Kt/v and Qb. Coefficients Lower 95% CI Upper 95% CI Std. Error z P Exp (B) Lower 95% CI Upper 95% CI Cerebrovascular disease. 0.41 0.21 0.61 0.1 4.04 < .001 1.51 1.24 1.84 Kt/V*Convective volume*QB Category 4 -0.61 -0.93 -0.3 0.16 3.8 < .001 0.54 0.4 0.74 Kt/V*Convective volume*QB Category 5 -0.98 -1.21 -0.76 0.12 8.54 < .001 0.37 0.3 0.47 Kt/V*Convective volume*QB Category 3 -0.37 -0.78 0.04 0.21 1.77 .077 0.69 0.46 1.04 Kt/V*Convective volume*QB Category 2 -0.13 -0.68 0.42 0.28 0.46 .647 0.88 0.51 1.52 Category 1: 0 to 5.9 L*L/min*Kt/V; category 2: 6-9.9 L*L/min*Kt/V; category 3: 10-13.9 L*L/min*Kt/V; category 4: 14-17.9 L*L/min*Kt/V; category 5: greater than or equal to 18 L/L/min*Kt/V. Table 6 Logistic regression of risk and protection factors for death in hemodialysis patients. Coefficient B Standard error Z p Odds Ratio 95% confidence interval Constant 5.82 0.82 7.08 < 0.001 337.47 67.31–1692.06 Cerebrovascular disease 0.59 0.13 4.49 < 0.001 1.81 1.4–2.34 Vascular disease/Hypertension 0.4 0.11 3.53 < 0.001 1.49 1.19–1.86 Type 2 diabetes mellitus 0.29 0.1 2.88 0.004 1.33 1.1–1.62 Age (years) 0.01 0 3.85 < 0.001 1.01 1.01–1.02 Relative overhydration predialysis 0.01 0.01 2.5 0.013 1.01 1–1.03 Ultrafiltration (ml) 0 0 2.8 0.005 1 1–1 Effective weekly treatment duration (min) -0.01 0 9.86 < 0.001 0.99 0.99–1 Postdialysis systolic blood pressure (mmHg) -0.01 0 3.14 0.002 0.99 0.98–1 Lean tissue index -0.05 0.02 2.58 0.01 0.95 0.92–0.99 Hemoglobin (g/dL) -0.18 0.04 4.35 < 0.001 0.83 0.77–0.91 Albumin (g/dL) -0.49 0.13 3.87 < 0.001 0.61 0.48–0.78 DISCUSSION Main findings A total of 821 patients who died and 3,586 who survived were analyzed. This overall mortality rate represents 22.89% of the observed population, with cardiovascular problems (22.17%) and infections (19.73%) as notable contributing factors. However, the majority of deaths are attributed to various causes, which together account for more than half of the total deaths (57.13%). Because more patients with one or more comorbidities were found, these data strongly suggest that the presence of comorbidities is associated with a greater risk of death in this group. Age was 3.3 years older in the deceased patient group. The simple Charlson comorbidity index and the age- and albumin-adjusted indices at 1 year were not different between deceased and surviving patients; however, the albumin-adjusted Charlson odds ratio at 2 years was 7.6 points greater in surviving patients. With respect to the hemodialysis treatment data, there were no differences in the number of treatments per month, Qb, Qd, convective volume, ultrafiltration, or pre- and postdialysis blood pressure. The effective weekly treatment duration was 61 minutes longer in group 2 patients. The number of patients who received hemodiafiltration was 10% greater in the surviving group. The Kt/V * Convective Volume * Qb ratio was greater in the surviving group at 2.17 L * kt/v * L/min. With respect to laboratory tests, there were no differences in Kt/V, predialysis potassium, hemoglobin, ferritin, phosphorus, calcium, or PTH. Albumin levels were higher in living patients (4.02 g/dL) than in deceased patients (3.77 g/dL) (P<0.001). The C-reactive protein level was 17.9 mg/L higher in deceased patients. There were no differences in the use of medications such as erythropoietin, iron, calcium carbonate, oral aluminum, or oral calcitriol. In bioimpedance assessments, relative overhydration was 2.9% greater in deceased patients. Diabetic nephropathy was 16.2% more prevalent among deceased patients, with a number needed to harm of 6.17. Conversely, hypertension was more prevalent in living patients (13.5% higher) than in deceased patients (P < 0.001). Cerebrovascular disease was present in 6.5% of the deceased patients. Dementia or other psychiatric illnesses were 1.3% more common in deceased patients (P = 0.007). A residual urine output greater than 100 ml per day was recorded in 4% of living patients and 1.4% of deceased patients (P 18 L * L/min * Kt/V), with survival proportionally and progressively decreasing in each lower convective therapy category. The worst survival was observed in categories 1 (0 to 5.9 L * L/min * Kt/V), 2 (6 to 9.9 L * L/min * Kt/V), and 3 (10 to 13.9 L * L/min * Kt/V). In logistic regression, the major risk factors for death included cerebrovascular disease (OR: 1.81), vascular disease/hypertension (OR: 1.49), and type 2 diabetes mellitus (OR: 1.33). Protective factors included higher albumin levels, hemoglobin, lean tissue index, predialysis systolic and diastolic blood pressures (negative), and effective weekly treatment duration. Interpretations The current findings support the established notion that diabetic patients with hypertension experience a high mortality rate during hemodialysis programs. However, this situation could be alleviated by achieving optimal nutritional status, indicated by albumin levels greater than 4.02 g/dL, along with a significant improvement in the dialysis dose administered in each session. The relevant factors include the effective treatment time, convective volume, Kt/V, and extracorporeal flow (Qb), as well as controlling overhydration during each treatment. Cerebrovascular disease is particularly devastating for this patient group, hastening the onset of death. Nonmodifiable factors such as age and the loss of residual urine output also contribute to an increased mortality risk. Practical application Practical applications in hemodialysis programs focus on optimizing treatment and managing risk factors to increase patient survival, particularly for those with diabetes and hypertension. Prioritizing nutritional status: Ensuring optimal nutritional status in patients is crucial and involves monitoring and maintaining albumin levels close to 5 g/dL through nutritional interventions, such as a high-protein diet and strengthening exercises to improve muscle mass in the extremities. Optimizing the dialysis dose: Programs should work to increase the dialysis dose administered during each treatment. This entails considering and adjusting factors such as an effective treatment time of at least 680 minutes per week, convective volume, Kt/V, and an extracorporeal flow (Qb) of more than 18 liters per treatment. Strict control of overhydration: In the coming years, it will be essential to implement strategies for monitoring and managing overhydration during each hemodialysis session via bioimpedance due to its detrimental effects on survival. Surveillance and management of cerebrovascular disease: Given the high prevalence and devastating impact of cerebrovascular disease in these patients, it is vital to adopt early surveillance strategies and aggressively manage associated risk factors. These methods may include the detection of atrial fibrillation, atrial dilation via echocardiographic and carotid ultrasound monitoring, or simple cranial computed tomography at the time of patient admission to hemodialysis programs. Personalization of convective therapy: The results of the subanalysis indicate that a higher dose of convective therapy (>18 L*K/min*Kt/V) is linked to improved survival in diabetic patients. Hemodialysis programs should consider implementing hemodiafiltration strategies to achieve these doses in appropriate patients who need them as candidates. Related studies The optimal accepted convective volume is 23 liters [11]. However, some studies report no mortality differences with these volumes. In the authors' opinion, these differences might be attributed to varying extracorporeal volumes, interdialysis, and interpatient studies. Therefore, we propose standardizing the convective volume by multiplying it by Kt/V and the extracorporeal flow in liters. This study supports the notion that controlling hypervolemia in hemodialysis patients is crucial for patient survival [7,12] and that nutrition and increased muscle mass contribute to improved survival [13]. Limitations Owing to the observational nature of the study, the ability to establish causal relationships is limited. Additional unmeasured or confounding factors may explain these associations. In some instances, reverse causality may be present. Specifically, overhydration could be a consequence of declining nutritional status, muscle mass, and overall health in patients who are nearing death rather than a direct cause of mortality. Lines of research Future studies should explore the relationships among hypervolemia, arterial hypertension, muscle mass loss, and mortality in patients undergoing hemodialysis hemodiafiltration. Generalisability The study includes a diverse ethnic group of Ecuadorian adult patients: 60% mestizos and 40% indigenous people from the Ecuadorian highlands, Afro-Ecuadorians, and Montubios. It encompasses patients with diabetes mellitus and hypertension, which are prevalent causes of kidney failure globally. Patients with disabilities and lower extremity amputations are also included. CONCLUSIONS The overall mortality rate among the study population of hemodialysis patients was 22.89% over a 42-month observation period. The leading causes of death included noncardiovascular causes (58.09%), cardiovascular issues (22.17%), and infections (19.73%). Patients who died had significantly more comorbidities and were older than those who survived. Compared with survivors, deceased patients also presented significantly lower albumin levels and higher C-reactive protein levels. Relative overhydration was notably greater among deceased patients. High-convection hemodiafiltration led to improved survival rates in the diabetic patient group. Patients who experienced cerebrovascular events had a very low survival rate. Higher albumin concentrations, elevated hemoglobin levels, a higher lean tissue index, adequate postdialysis systolic blood pressure, and a longer effective weekly treatment duration are recognized as protective factors against mortality. Abbreviations HDF-OL: High-volume online hemodiafiltration. HD: hemodialysis. CKD: chronic kidney disease. Qd: dialysate flow Qb: blood flow Declarations ADDITIONAL INFORMATION The database is attached as a related file: Database_mortality_burden.xlsx. ACKNOWLEDGMENTS We thank the staff and patients of the Davita-Ecuador clinics where the study was conducted. AUTHORS' CONTRIBUTIONS Gabriela Tamayo¹, conceptualization, research, writing-original draft, resources, software, supervision. Jorge Quinchuela¹, Methodology, Data curation, Formal analysis, Funding acquisition, Project management, Validation, Visualization, Writing – review and editing. Natalia Benavides¹, conceptualization, research, writing-original draft, resources, software, supervision. Franklin Mora-Bravo² : Methodology, Data curation, Formal analysis, Validation, Visualization, Writing – review and editing. All the authors read and approved the final version of the manuscript. FUNDING The authors funded the costs of this research. Health insurance entities covered treatments, procedures, and laboratory studies. AVAILABILITY OF DATA AND MATERIALS The datasets used and analyzed during the present study are available from the corresponding author upon reasonable request. Ethics committee approval and consent to participate The Bioethics Committee of the Ecuadorian Society of Nephrology, Quito, Ecuador approved the study. The study was conducted in accordance with the Declaration of Helsinki. CONSENT TO PUBLICATION This information was not needed because the present study did not publish images, radiographs or specific studies of patients. CONFLICTS OF INTEREST The research has no financial interests or conflicts of interest. AUTHORS' INFORMATION Gabriela Tamayo, Medical degree in Medicine from the Pontifical Catholic University of Ecuador, Specialist in Internal Medicine from the Central University of Ecuador, and Specialist in Nephrology from the Pontifical Catholic University of Santamaría de los Buenos Aires (Argentina). Medical Director of the Sermens Specialized Renal Health Center at Davita Ecuador. Email: [email protected] ; https://orcid.org/0009-0007-6681-0718. Jorge Quinchuela, Doctor of Medicine from the Central University of Ecuador, Specialist in Nephrology from the Pontifical Catholic University of Santamaría de los Buenos Aires (Argentina).Master's degree in Diabetes Mellitus, Arterial Hypertension, and Vascular Mechanics from the Universidad Austral (Argentina). Master's degree in Osteology and Bone-Mineral Metabolism from the Universidad del Salvador (Argentina). Medical Director of the "Dialcentro" Specialized Renal Health Center at Davita Ecuador. Email: [email protected] ; https://orcid.org/0000-0002-1294-6405. Natalia Benavides, Doctor of Medicine from the Central University of Ecuador, Specialist in Nephrology from the Central University of Ecuador.Graduate Health Promotion and Prevention Diploma from the Regional Autonomous University of the Andes (Ecuador). Coordinator of the Nephrology Service at San Vicente de Paúl Hospital, Ibarra (Ecuador). Medical Director of the Dialibarra Specialized Renal Health Center, Davita Ecuador. Email: [email protected] ; https://orcid.org/0009-0008-2175-7121. Franklin Mora-Bravo :He holds a degree in Medicine and Surgery from the University of Cuenca (Ecuador). He is a specialist in internal medicine from the National University of Loja (Loja, Ecuador). He is also a specialist in Nephrology from the Ignacio Chávez National Institute of Cardiology and the National Autonomous University of Mexico (Mexico).Master's degree in Health Research from the University of Cuenca (Ecuador). Medical Director of Pafram, Kidney Clinic in Morona Santiago, Ecuador. Email: [email protected] ; ORCID: https://orcid.org/0000-0002-5978-3420 References Abril J, Sanchez J. Characteristics of chronic kidney disease in Ecuador from 2009 to 2012. [Thesis] University of Cuenca, Ecuador: 2014. dspace Gahona J, Meza K. Update, characterization, and survival analysis of patients on renal replacement therapy in Ecuador, according to the national dialysis and transplant registry. Periodic report of the Vice Ministry of Comprehensive Care, Undersecretary of Mobile Hospital Health Care and Specialized Centers, National Directorate of Specialized Centers. Ministry of Public Health of Ecuador. November 2022. URL Salud / Nov_2022 Grooteman MP, van den Dorpel MA, Bots ML, Penne EL, van der Weerd NC, Mazairac AH, den Hoedt CH, van der Tweel I, Lévesque R, Nubé MJ, ter Wee PM, Blankestijn PJ; CONTRAST Investigators . Effect of online hemodiafiltration on all-cause mortality and cardiovascular outcomes. J Am Soc Nephrol. 2012 Jun;23(6):1087-96. doi: 10.1681/ASN.2011121140. Epub 2012 Apr 26. PMID: 22539829; PMCID: PMC3358764. Ok E, Asci G, Toz H, Ok ES, Kircelli F, Yilmaz M, Hur E, Demirci MS, Demirci C, Duman S, Basci A, Adam SM, Isik IO, Zengin M, Suleymanlar G, Yilmaz ME, Ozkahya M; Turkish Online Hemodiafiltration Study. Mortality and cardiovascular events in online hemodiafiltration (OL-HDF) compared with high-flux dialysis: results from the Turkish OL-HDF Study. Nephrol Dial Transplant. 2013 Jan;28(1):192-202. doi: 10.1093/ndt/gfs407. Epub 2012 Dec 9. PMID: 23229932. Siriopol D, Canaud B, Stuard S, Mircescu G, Nistor I, Covic A. New insights into the effect of hemodiafiltration on mortality: the Romanian experience. Nephrol Dial Transplant. 2015 Feb;30(2):294-301. doi: 10.1093/ndt/gfu347. Epub 2014 Nov 13. PMID: 25395391. Mora-Bravo FG, De-La-Cruz G, Rivera S, Ramírez AM, Raimann JG, Pérez- Grovas H. Association of intradialytic hypotension and convective volume in hemodiafiltration: results from a retrospective cohort study. BMC Nephrol. 2012 Sep 10;13:106. doi:10.1186/1471-2369-13-106. PMID: 22963170; PMCID: PMC3575237. Mora-Bravo FG, Torres PTM, Campoverde NR, Carcelen GLB, Mancheno JCS, Tipanta ÁCS, Perez-Grovas H, Abarca WPR. Blood pressure control with active ultrafiltration measures and without antihypertensives is essential for survival in hemodiafiltration and hemodialysis programs for patients with CKD: a prospective observational study. BMC Nephrol. 2025 Jan 17;26(1):30. doi:10.1186/s12882-025-03948-0. PMID: 39825259; PMCID: PMC11742504. Mora-Bravo FG, Mariscal A, Herrera-Felix JP, Magaña S, De-La-Cruz G, Flores N, Rosales L, Franco M, Pérez- Grovas H. Arterial line pressure control enhanced extracorporeal blood flow prescription in hemodialysis patients. BMC Nephrol. 2008 Nov 24;9:15. doi:10.1186/1471-2369-9-15. PMID: 19025625; PMCID: PMC2613872. Peters SA, Bots ML, Canaud B, Davenport A, Grooteman MP, Kircelli F, Locatelli F, Maduell F, Morena M, Nubé MJ, Ok E, Torres F, Woodward M, Blankestijn PJ; HDF Pooling Project Investigators. Hemodiafiltration and mortality in end-stage kidney disease patients: a pooled individual participant data analysis from four randomized controlled trials. Nephrol Dial Transplant. 2016 Jun;31(6):978-84. doi: 10.1093/ndt/gfv349. Epub 2015 Oct 22. PMID: 26492924. Zoccali C, Moissl U, Chazot C, Mallamaci F, Tripepi G, Arkossy O, Wabel P, Stuard S. Chronic Fluid Overload and Mortality in ESRD. J Am Soc Nephrol. 2017 Aug;28(8):2491-2497. doi:10.1681/ASN.2016121341. Epub 2017 May 4. PMID: 28473637; PMCID: PMC5533242. Shin SK, Jo YI. Why should we focus on high-volume hemodiafiltration? Kidney Res Clin Pract . 2022 Nov;41(6):670-681. doi:10.23876/j.krcp.21.268. Epub 2022 Feb 22. PMID: 35286790; PMCID: PMC9731779. Rivera-González SC, Pérez- Grovas H, Madero M, Saavedra N, López-Rodriguez J, Lerma C. Identification of impeding factors for dry weight achievement in end-stage renal disease after appropriate kidney graft function. Artif Organs. 2014 Feb;38(2):113-20. doi: 10.1111/aor.12133. Epub 2013 Jul 25. PMID: 23889479. Shu X, Lin T, Wang H, Zhao Y, Jiang T, Peng X, Yue J. Diagnosis, prevalence, and mortality of sarcopenia in dialysis patients: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2022 Feb;13(1):145-158. doi:10.1002/jcsm.12890. Epub 2022 Jan 5. PMID: 34989172; PMCID: PMC8818609. Additional Declarations The authors declare no competing interests. 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. 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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-7515828","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508987761,"identity":"ac0bfc3b-8d13-4f4b-88bb-880152805537","order_by":0,"name":"Gabriela Tamayo","email":"","orcid":"https://orcid.org/0009-0007-6681-0718","institution":"Davita Ecuador, Unidad Sermens, Quito, Ecuador.","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"","lastName":"Tamayo","suffix":""},{"id":508987762,"identity":"48ea6382-8240-4be6-bc46-aa98961b1723","order_by":1,"name":"•\tJorge Quinchuela","email":"","orcid":"https://orcid.org/0000-0002-1294-6405","institution":"Davita Ecuador, Unidad Dialcentro, Quito, Ecuador.","correspondingAuthor":false,"prefix":"","firstName":"•\tJorge","middleName":"","lastName":"Quinchuela","suffix":""},{"id":508987763,"identity":"dc451a18-78f6-4bb8-bc97-fd79f820adfc","order_by":2,"name":"Natalia Benavides","email":"","orcid":"https://orcid.org/0009-0008-2175-7121","institution":"Davita Ecuador, Unidad Dialibarra, Ibarra, Ecuador.","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Benavides","suffix":""},{"id":508987764,"identity":"17d8473c-c436-432c-94c3-7131c5745c85","order_by":3,"name":"•\tFranklin Mora-Bravo","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-5978-3420","institution":"Pafram Hemodiafiltration Unit, Sucúa, Ecuador.","correspondingAuthor":true,"prefix":"","firstName":"•\tFranklin","middleName":"","lastName":"Mora-Bravo","suffix":""}],"badges":[],"createdAt":"2025-09-02 09:18:33","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7515828/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7515828/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90544661,"identity":"05006bd5-f723-4509-8f70-e2f587a677da","added_by":"auto","created_at":"2025-09-04 00:20:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier survival function plot for patients with type 2 diabetes mellitus with different convective volume doses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCategory 1: 0--5.9 L*L/min* Kt/V; Category 2: 6--9.9 L*L/min* Kt/V; Category 3: 10--13.9 L*L/min* Kt/V; Category 4: 14--17. 9 L *L/min* Kt/V; Category 5: greater than or equal to 18 L/L/min* Kt/V.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7515828/v1/b4190297ac6416f4ac18fe44.png"},{"id":90544676,"identity":"07b888c1-8097-4c80-b94e-17ea31c405a3","added_by":"auto","created_at":"2025-09-04 00:20:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier survival function graph for patients with cerebrovascular events at different convective volume doses.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7515828/v1/663ad6f54ad3ef06971143f1.png"},{"id":90545516,"identity":"17b6fa0b-5ea8-4063-b244-cf83ea3f40d0","added_by":"auto","created_at":"2025-09-04 00:44:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1267533,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7515828/v1/f662d82d-321f-41a2-8658-2a1b65e3b6cc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMapping the mortality burden in hemodialysis patients. A multicenter observational study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSince 2009, the prevalence of stage 5 chronic kidney disease (CKD) in Ecuador has increased from 3,524 to 21,394 cases by 2022, resulting in a prevalence rate of 1,183 patients per million inhabitants [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The reported survival rate for patients in hemodialysis programs in Ecuador is 3.8 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong patients with advanced CKD, cardiovascular disease is the leading cause of mortality, exacerbated by risk factors such as hypertension, diabetes, dyslipidemia, smoking, and advanced age. These traditional cardiovascular risk factors are highly prevalent in the CKD population and are linked to the severity of kidney dysfunction, which increases the risk of mortality in these patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The contributing factors include underdialysis, uncontrolled anemia, and alterations in bone and mineral metabolism resulting from secondary hyperparathyroidism. Furthermore, many dialysis patients experience proinflammatory states and malnutrition. The combination of these uncontrolled factors heightens the risk of cardiovascular events and, consequently, all-cause mortality. High-volume online hemodiafiltration (OL-HDF) has been developed as an alternative to improve the treatment of patients with CKD, allowing for more significant removal of medium- and high-molecular-weight molecules through a convective process. This treatment has been associated with a reduction in the accumulation of uremic toxins and benefits the hemodynamic stability of patients, which could decrease the incidence of hypotension and other adverse effects common in conventional hemodialysis [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have suggested that OL-HDF may positively impact survival compared with high-flux hemodialysis (HD). Detailed analysis revealed that OL-HDF patients experience lower all-cause mortality, reduced cardiovascular mortality, and better anemia management. Furthermore, studies have reported decreased use of erythropoiesis-stimulating agents (ESAs), improved phosphorus levels, and a lower incidence of beta-2 microglobulin-related amyloidosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. OL-HDF has also been associated with improved nutritional parameters and reduced morbidity and hospitalizations, leading to additional benefits for the quality of life of dialysis patients. Since mortality rates in dialysis patients remain high, between 15% and 20% annually, and increased urea removal has not shown significant direct effects on survival, clinical interest has shifted toward convective therapies such as OL-HDF. In recent years, randomized controlled trials comparing conventional hemodialysis with online postdilution hemodiafiltration have reported mixed, although generally positive, results regarding the benefits of OL-HDF on mortality and morbidity.\u003c/p\u003e\u003cp\u003eHemodiafiltration is not widely accepted in medical practice, so reports on its use are limited in Latin America. This study assessed mortality and associated factors in patients receiving hemodialysis and hemodiafiltration treatments at multiple private centers in Ecuador.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eStudy design\u003c/p\u003e\u003cp\u003eThis study is observational. The source is prospective.\u003c/p\u003e\u003cp\u003eScenery\u003c/p\u003e\u003cp\u003eThe study was conducted in 14 hemodialysis clinics in Ecuador belonging to the Davita-Ecuador group. The participating units were as follows: 1. Manadi\u0026aacute;lisis Manta, 2. Sermens Quito, 3. Dialcentro, 4. Cener SA, 5. Manadi\u0026aacute;lisis Portoviejo, 6. Sermens Guayaquil, 7. Medicopharma Machala, 8. Dialibarra, 9. Manadi\u0026aacute;lisis Calle Quito, 10. Manadi\u0026aacute;lisis Jipijapa, 11. Manadi\u0026aacute;lisis Chone, 12. Famardial Guayaquil, 13. Farmadial Daule, 14. Nefrosalud, 15. Manadi\u0026aacute;lisis Bah\u0026iacute;a. The study period was from September 3, 2018, to March 30, 2022.\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003ePatients who had undergone conventional hemodialysis for more than three months and attended three weekly sessions of four hours each were included in the study. The exclusion criteria included patients who died from a diagnosis of COVID-19, those who did not adhere to the established treatment frequency (4 hours of treatment, 3 times per week), and those whose causes of death were unrelated to chronic kidney disease (e.g., cancer or trauma). The sample was divided into two groups: patients who died by the end of the observation period (Group 1) and patients who were alive at the end of the study (Group 2).\u003c/p\u003e\u003cp\u003eVariables\u003c/p\u003e\u003cp\u003eThe independent variables included age, sex, dry weight, body mass index, Charlson comorbidity index, and probability of survival (AACCIS\u0026thinsp;+\u0026thinsp;albumin). The presence of comorbidities included coronary artery disease, heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic lung disease, connective tissue disorders, gastrointestinal bleeding, liver disease, and neurological disease. The etiology of chronic kidney disease features glomerulonephritis, diabetic nephropathy, polycystic kidney disease, interstitial nephritis, vascular disease with arterial hypertension, and diuresis at 100 ml/24 hours. Hemodialysis modalities included OL-HDF and HD, along with Qb (blood flow), Qd (dialysate flow), effective weekly treatment duration, Kt/V, convective volume, and ultrafiltration. Pre- and postdialysis blood pressure were measured. The laboratory parameters included potassium, hemoglobin, ferritin, C-reactive protein, albumin, nCRP (normalized protein catabolism rate), phosphorus, calcium, and PTH (parathyroid hormone). The medications administered included erythropoietin and calcitriol. Predialysis relative overhydration was assessed via bioimpedance, as were predialysis lean tissue and the fat tissue index. The dependent variable was mortality.\u003c/p\u003e\u003cp\u003eData sources/measurements\u003c/p\u003e\u003cp\u003eThe source was direct. Data were collected via the EuCliD computer system following patient privacy and consent protocols. The collected data are presented as individual averages. Treatments were performed via Fresenius Medical Care supplies; the hemodiafiltration machines included 83 Fresenius Medical Care 5008/S volumetric units and 528 Fresenius Medical Care 4008/S hemodialysis machines. FX 60, 80, and 100 Classix dialyzer filters were used for hemodialysis, and CorDiax along with CorAL 600, 800, and 1000 filters were employed for HDF.\u003c/p\u003e\u003cp\u003eAssignment to hemodiafiltration\u003c/p\u003e\u003cp\u003eThe allocation policy for Ecuador's Renal Units to include patients in the hemodiafiltration program is based on the presence of cardiovascular complications such as congestive heart failure, recurrent intradialytic hypotension, difficult-to-control hyperphosphatemia, challenging arterial hypertension, and borderline low-flow access. Indications are reviewed at each center, and each patient is proposed for admission to the program. The most critically ill patients are generally admitted to the hemodiafiltration program.\u003c/p\u003e\u003cp\u003eBiases\u003c/p\u003e\u003cp\u003eObservation and selection bias were avoided by applying the participant selection criteria. A medical representative for each coordinating center was assigned to compile the data, which were completed on a single online form. The principal investigator always maintained the data via a guide and records approved in the research protocol to prevent potential interviewer, information, and recall bias. When there was any doubt about the standard deviation of the data, curations were conducted through onsite reviews of anomalous data. Two researchers independently analyzed each record in duplicate, and the variables were entered into the database after verifying their concordance.\u003c/p\u003e\u003cp\u003eStudy size\u003c/p\u003e\u003cp\u003eThe sample was probabilistic. Ecuador has a population of 17,980,083 (2023), with a CKD incidence rate of 21,394 cases by 2022. EPI info \u003csup\u003eTM\u003c/sup\u003e (Stat Calc, Epi Info, CDC, Atlanta. Version 7.2.6 [October 2023]), with an expected mortality frequency of 15.7%, a confidence limit of 5%, and a confidence level of 99.99%, the sample size was 773 cases for deceased patients. The controls were at a ratio of 4 to 1.\u003c/p\u003e\u003cp\u003eQuantitative variables\u003c/p\u003e\u003cp\u003eThe results are presented as frequencies and percentages. A scale variable was converted into a categorical variable. A new variable, \"KT/V*Convective volume *QB,\" was created to standardize HDF and HD treatments across varying degrees of extracorporeal flow prescriptions; the units of convective volume were liters per session, and Qb was measured in milliliters per minute. The variables were categorized into Category 1: 0 to 5.9 L*L/min* kt/V; Category 2: 6\u0026ndash;9.9 L*L/min* kt/V; Category 3: 10\u0026ndash;13.9 L*L/min* kt/V; Category 4: 14\u0026ndash;17.9 L*L/min* kt/V; and Category 5: 18 or more L*L/min* kt/V.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eQualitative variables were analyzed as frequencies and percentages. Proportions were compared via the chi-square test, and means were compared via Student\u0026rsquo;s t test. Logistic regression was performed to obtain the odds ratio. As a secondary objective, survival was analyzed in specific patient groups, including diabetic patients and those who developed cerebrovascular events. The statistical package used was IBM Corp. (released from 2018). IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eA total of 821 patients who died and 3,586 who survived were analyzed. This overall mortality rate represents 22.89% of the observed population over 42 months (95% confidence interval: 21.53\u0026ndash;24.31%). The annual mortality rate was 6.54%. There were 182 deaths from cardiovascular causes (22.17%), 162 deaths (19.73%) associated with infections, and 477 deaths (58.09%) attributed to other causes.\u003c/p\u003e\u003cp\u003eMain characteristics of the study groups\u003c/p\u003e\u003cp\u003eIn the deceased patient group, 693 patients (84.4%) had one or more comorbidities than did those in the alive group, which consisted of 2,580 patients (71.9%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prevalence of patients in the hemodiafiltration programs in group 1 was 167 cases (20.3%), whereas in group 2, it was 1,078 cases (30.5%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The average age was greater in group 1 (64.4 years) than in group 2 (61.1 years) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no significant differences between the means of weight, body mass index, Charlson comorbidity index, or probability of survival (aCIS\u0026thinsp;+\u0026thinsp;Albumin) at the first year of observation. A difference was observed in the probability of survival \u0026ldquo;aaCCIs\u0026thinsp;+\u0026thinsp;Albumin\u0026rdquo; at the second year of observation, with 63.6 points in group 1 versus 71.2 points in group 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were 128 patients without comorbidities in group 1 (15.59%), while in group 2, there were 1,006 patients (28.05%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The variables in scale are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eEtiology of kidney disease and comorbidities\u003c/p\u003e\u003cp\u003eDiabetic nephropathy was prevalent in 63.2% of deceased patients compared with 47% of living patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Arterial hypertension was less common in the deceased group (20.3% versus 33.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other etiologies were not significantly different between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the comorbidities, the absence of comorbidities was less common in group 1, with 15.6% compared with 28% in the living group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Cerebrovascular disease and dementia were more common in the deceased group, at 13.6%, than in the living group, at 7.4% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConvection-adjusted survival, kt/V, and extracorporeal flow in diabetic patients\u003c/p\u003e\u003cp\u003eThe Kt/V*Convective Volume* Qb index was lower in group 1 (5.05\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3) than in group 2 (7.22\u0026thinsp;\u0026plusmn;\u0026thinsp;9.19 L*L/min* Kt/V), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This index had a greater impact on the group of patients diagnosed with diabetes mellitus, who showed greater survival with increased convective volume, higher extracorporeal flow, and overall greater Kt/V in Category 5 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Cox proportional hazard model for diabetic patients is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConvection-adjusted survival, kt/V, and extracorporeal flow in patients with cerebrovascular events\u003c/p\u003e\u003cp\u003eThe index significantly impacts the group of patients diagnosed with a cerebrovascular event, where greater survival is observed with increased convective volume, enhanced extracorporeal flow, and higher Kt/V overall in Categories 4 and 5 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Cox proportional hazard model for diabetic patients is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFactors associated with mortality\u003c/p\u003e\u003cp\u003eThe logistic regression model for predicting mortality in patients is presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, highlighting the statistically significant factors. Risk factors include the development of cerebrovascular disease, vascular disease with hypertension, and type 2 diabetes mellitus. In contrast, protective factors, listed in order of importance, are the albumin concentration, serum hemoglobin, lean tissue index, postdialysis systolic blood pressure, and effective weekly treatment duration.\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\u003eScale variables 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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup 1: Deceased\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;821\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup 2:\u003c/p\u003e\u003cp\u003eAlive\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;3586\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvival time (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.1 \u0026plusmn;35.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.6 \u0026plusmn;30.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.4 \u0026plusmn;12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.1 \u0026plusmn;13.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.8 \u0026plusmn;31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.0 \u0026plusmn;21.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index (kg/m \u003csup\u003e2\u003c/sup\u003e )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.3 \u0026plusmn;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.6 \u0026plusmn;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson Comorbidity Index Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.7 \u0026plusmn;5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5 \u0026plusmn;8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge-adjusted Charlson (aCCI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.0 \u0026plusmn;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.23 \u0026plusmn;2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvival probability: Charlson\u0026thinsp;+\u0026thinsp;Albumin: 1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.6 \u0026plusmn;16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.5 \u0026plusmn;23.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson\u0026thinsp;+\u0026thinsp;Albumin: 2 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.6 \u0026plusmn;13.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.2 \u0026plusmn;13.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of comorbidities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.6 \u0026plusmn;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5 \u0026plusmn;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHemodialysis treatment data\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemodialysis treatments/month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.7 \u0026plusmn;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.3 \u0026plusmn;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQb (ml/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e348 \u0026plusmn;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e365 \u0026plusmn;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQd (ml/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e466 \u0026plusmn;61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e490 \u0026plusmn;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffective weekly treatment duration (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e619 \u0026plusmn;133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e680 \u0026plusmn;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffective Infusion Volume (Liters) (HDF Patients)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.4 \u0026plusmn;4.0 \u003csup\u003e(n=167[20.3%])\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.1 \u0026plusmn;4.9 \u003csup\u003e(n=1078[30%])\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.983/X \u003csup\u003e2\u003c/sup\u003e = \u003cb\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK/TV* Convective Volume* Qb (L*L/min* Kt/V).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.05 \u0026plusmn;7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.22 \u0026plusmn;9.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffective convective volume (Liters)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.4 \u0026plusmn;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.89 \u0026plusmn;10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUltrafiltration (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2186 \u0026plusmn;672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2282 \u0026plusmn;647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredialysis systolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145 \u0026plusmn;16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 \u0026plusmn;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredialysis diastolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 \u0026plusmn;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 \u0026plusmn;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostdialysis systolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 \u0026plusmn;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142 \u0026plusmn;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostdialysis diastolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 \u0026plusmn;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 \u0026plusmn;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratories\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esp Kt/V\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.82 \u0026plusmn;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.95 \u0026plusmn;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredialysis potassium * (meq/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.0 \u0026plusmn;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.05 \u0026plusmn;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.988\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\u003e10.5 \u0026plusmn;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.01 \u0026plusmn;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFerritin*(ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e811 \u0026plusmn;437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e808 \u0026plusmn;423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.4 \u0026plusmn;66.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.5 \u0026plusmn;38.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.77 \u0026plusmn;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.02 \u0026plusmn;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCRn gr/kg/d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97 \u0026plusmn;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 \u0026plusmn;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphorus (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.24 \u0026plusmn;1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.21 \u0026plusmn;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrected Calcium (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.74 \u0026plusmn;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.74 \u0026plusmn;0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eiPTH (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e264 \u0026plusmn;262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e335 \u0026plusmn;349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErythropoietin (Units/kg/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.1 \u0026plusmn;54.2 \u003csup\u003e(n=760)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.81 \u0026plusmn;45.9 \u003csup\u003e(n=3417)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron (mg/month)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e219 \u0026plusmn;64 \u003csup\u003e(n=720)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e212 \u0026plusmn;63 \u003csup\u003e(n=3316)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium carbonate (mg/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e886 \u0026plusmn;1087 \u003csup\u003e(n=533)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e819,716 \u0026plusmn;\u003csup\u003e(n=2851\u003c/sup\u003e )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOral aluminum (grams/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.37 \u0026plusmn;73 \u003csup\u003e(n=202)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.08 \u0026plusmn;6.2 \u003csup\u003e(n=596)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOral Calcitriol (\u0026micro;g/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.50 \u0026plusmn;0.83 \u003csup\u003e(n=556)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35 \u0026plusmn;1.20 \u003csup\u003e(n=2303)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBioimpedance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative overhydration predialysis (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.2 \u0026plusmn;9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.3 \u0026plusmn;7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLean tissue index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.0 \u0026plusmn;2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.94 \u0026plusmn;2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFat tissue index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.0 \u0026plusmn;5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.7 \u0026plusmn;6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\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\u003eEtiology and comorbidities.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup 1: Deceased\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;821\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup 2:\u003c/p\u003e\u003cp\u003eAlive\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;3586\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEtiology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetic nephropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e519 (63.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1685 (47.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular disease/Hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e167 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1212 (33.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterstitial nephritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e166 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCystic kidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlomerulonephritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCongestive heart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e136 (16.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e612 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary artery disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e130 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e475 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo comorbidities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e128 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1006 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114 (13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e264 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral vascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e346 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic lung disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e190 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigestive bleeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e154 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDementia or other psychiatric illness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.007*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConnective tissue disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther features\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual diuresis\u0026thinsp;\u0026gt;\u0026thinsp;100 ml/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\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\u003eSurvival in diabetic patients by categories of convective volume, Kt/v and Qb.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean estimate (months)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian lower 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper median 95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (32\u0026ndash;43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (48\u0026ndash;59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (48\u0026ndash;80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (47\u0026ndash;63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (56\u0026ndash;69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eCategory 1: 0 to 5.9 L*L/min* Kt/V; Category 2: 6-9.9 L*L/min* Kt/V; Category 3: 10-13.9 L*L/min* Kt/V; Category 4: 14-17.9 L*L/min* Kt/V; Category 5: greater than or equal to 18 L/L/min* Kt/V.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eLog rank x \u003csup\u003e2\u003c/sup\u003e = 35.1 P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCox proportional hazard model for diabetic patients with convective volume, Kt/v and Qb.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUpper 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eExp (B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLower 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUpper 95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2DM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eCategory 1: 0 to 5.9 L*L/min* Kt/V; category 2: 6-9.9 L*L/min* Kt/V; category 3: 10-13.9 L*L/min* Kt/V; category 4: 14-17.9 L*L/min* Kt/V; category 5: greater than or equal to 18 L/L/min* Kt/V. T2DM: Type 2 diabetes mellitus.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\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\u003eCox proportional hazard model for patients with cerebrovascular events with different categories of convective volume, Kt/v and Qb.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUpper 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eExp (B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLower 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUpper 95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular disease.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKt/V*Convective volume*QB Category 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKt/V*Convective volume*QB Category 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKt/V*Convective volume*QB Category 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKt/V*Convective volume*QB Category 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eCategory 1: 0 to 5.9 L*L/min*Kt/V; category 2: 6-9.9 L*L/min*Kt/V; category 3: 10-13.9 L*L/min*Kt/V; category 4: 14-17.9 L*L/min*Kt/V; category 5: greater than or equal to 18 L/L/min*Kt/V.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression of risk and protection factors for death in hemodialysis patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% confidence interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e337.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67.31\u0026ndash;1692.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.4\u0026ndash;2.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascular disease/Hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.19\u0026ndash;1.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.1\u0026ndash;1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.01\u0026ndash;1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative overhydration predialysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u0026ndash;1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUltrafiltration (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffective weekly treatment duration (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.99\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostdialysis systolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.98\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLean tissue index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.92\u0026ndash;0.99\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\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.77\u0026ndash;0.91\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\u003e-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.48\u0026ndash;0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003eMain findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 821 patients who died and 3,586 who survived were analyzed. This overall mortality rate represents 22.89% of the observed population, with cardiovascular problems (22.17%) and infections (19.73%) as notable contributing factors. However, the majority of deaths are attributed to various causes, which together account for more than half of the total deaths (57.13%).\u003c/p\u003e\n\u003cp\u003eBecause more patients with one or more comorbidities were found, these data strongly suggest that the presence of comorbidities is associated with a greater risk of death in this group.\u003c/p\u003e\n\u003cp\u003eAge was 3.3 years older in the deceased patient group. The simple Charlson comorbidity index and the age- and albumin-adjusted indices at 1 year were not different between deceased and surviving patients; however, the albumin-adjusted Charlson odds ratio at 2 years was 7.6 points greater in surviving patients. With respect to the hemodialysis treatment data, there were no differences in the number of treatments per month, Qb, Qd, convective volume, ultrafiltration, or pre- and postdialysis blood pressure. The effective weekly treatment duration was 61 minutes longer in group 2 patients. The number of patients who received hemodiafiltration was 10% greater in the surviving group. The Kt/V * Convective Volume * Qb ratio was greater in the surviving group at 2.17 L * kt/v * L/min. With respect to laboratory tests, there were no differences in Kt/V, predialysis potassium, hemoglobin, ferritin, phosphorus, calcium, or PTH. Albumin levels were higher in living patients (4.02 g/dL) than in deceased patients (3.77 g/dL) (P\u0026lt;0.001). The C-reactive protein level was 17.9 mg/L higher in deceased patients. There were no differences in the use of medications such as erythropoietin, iron, calcium carbonate, oral aluminum, or oral calcitriol. In bioimpedance assessments, relative overhydration was 2.9% greater in deceased patients.\u003c/p\u003e\n\u003cp\u003eDiabetic nephropathy was 16.2% more prevalent among deceased patients, with a number needed to harm of 6.17. Conversely, hypertension was more prevalent in living patients (13.5% higher) than in deceased patients (P \u0026lt; 0.001). Cerebrovascular disease was present in 6.5% of the deceased patients. Dementia or other psychiatric illnesses were 1.3% more common in deceased patients (P = 0.007). A residual urine output greater than 100 ml per day was recorded in 4% of living patients and 1.4% of deceased patients (P \u0026lt; 0.002).\u003c/p\u003e\n\u003cp\u003eIn the subanalysis, the survival of diabetic patients was highest in the category 5 convective therapy group (\u0026gt;18 L * L/min * Kt/V), with survival proportionally and progressively decreasing in each lower convective therapy category. The worst survival was observed in categories 1 (0 to 5.9 L * L/min * Kt/V), 2 (6 to 9.9 L * L/min * Kt/V), and 3 (10 to 13.9 L * L/min * Kt/V).\u003c/p\u003e\n\u003cp\u003eIn logistic regression, the major risk factors for death included cerebrovascular disease (OR: 1.81), vascular disease/hypertension (OR: 1.49), and type 2 diabetes mellitus (OR: 1.33). Protective factors included higher albumin levels, hemoglobin, lean tissue index, predialysis systolic and diastolic blood pressures (negative), and effective weekly treatment duration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current findings support the established notion that diabetic patients with hypertension experience a high mortality rate during hemodialysis programs. However, this situation could be alleviated by achieving optimal nutritional status, indicated by albumin levels greater than 4.02 g/dL, along with a significant improvement in the dialysis dose administered in each session. The relevant factors include the effective treatment time, convective volume, Kt/V, and extracorporeal flow (Qb), as well as controlling overhydration during each treatment. Cerebrovascular disease is particularly devastating for this patient group, hastening the onset of death. Nonmodifiable factors such as age and the loss of residual urine output also contribute to an increased mortality risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical application\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePractical applications in hemodialysis programs focus on optimizing treatment and managing risk factors to increase patient survival, particularly for those with diabetes and hypertension.\u003c/p\u003e\n\u003cp\u003ePrioritizing nutritional status: Ensuring optimal nutritional status in patients is crucial and involves monitoring and maintaining albumin levels close to 5 g/dL through nutritional interventions, such as a high-protein diet and strengthening exercises to improve muscle mass in the extremities.\u003c/p\u003e\n\u003cp\u003eOptimizing the dialysis dose: Programs should work to increase the dialysis dose administered during each treatment. This entails considering and adjusting factors such as an effective treatment time of at least 680 minutes per week, convective volume, Kt/V, and an extracorporeal flow (Qb) of more than 18 liters per treatment.\u003c/p\u003e\n\u003cp\u003eStrict control of overhydration: In the coming years, it will be essential to implement strategies for monitoring and managing overhydration during each hemodialysis session via bioimpedance due to its detrimental effects on survival.\u003c/p\u003e\n\u003cp\u003eSurveillance and management of cerebrovascular disease: Given the high prevalence and devastating impact of cerebrovascular disease in these patients, it is vital to adopt early surveillance strategies and aggressively manage associated risk factors. These methods may include the detection of atrial fibrillation, atrial dilation via echocardiographic and carotid ultrasound monitoring, or simple cranial computed tomography at the time of patient admission to hemodialysis programs.\u003c/p\u003e\n\u003cp\u003ePersonalization of convective therapy: The results of the subanalysis indicate that a higher dose of convective therapy (\u0026gt;18 L*K/min*Kt/V) is linked to improved survival in diabetic patients. Hemodialysis programs should consider implementing hemodiafiltration strategies to achieve these doses in appropriate patients who need them as candidates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelated studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe optimal accepted convective volume is 23 liters [11]. However, some studies report no mortality differences with these volumes. In the authors' opinion, these differences might be attributed to varying extracorporeal volumes, interdialysis, and interpatient studies. Therefore, we propose standardizing the convective volume by multiplying it by Kt/V and the extracorporeal flow in liters. This study supports the notion that controlling hypervolemia in hemodialysis patients is crucial for patient survival [7,12] and that nutrition and increased muscle mass contribute to improved survival [13].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOwing to the observational nature of the study, the ability to establish causal relationships is limited. Additional unmeasured or confounding factors may explain these associations. In some instances, reverse causality may be present. Specifically, overhydration could be a consequence of declining nutritional status, muscle mass, and overall health in patients who are nearing death rather than a direct cause of mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLines of research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuture studies should explore the relationships among hypervolemia, arterial hypertension, muscle mass loss, and mortality in patients undergoing hemodialysis hemodiafiltration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneralisability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study includes a diverse ethnic group of Ecuadorian adult patients: 60% mestizos and 40% indigenous people from the Ecuadorian highlands, Afro-Ecuadorians, and Montubios. It encompasses patients with diabetes mellitus and hypertension, which are prevalent causes of kidney failure globally. Patients with disabilities and lower extremity amputations are also included.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe overall mortality rate among the study population of hemodialysis patients was 22.89% over a 42-month observation period. The leading causes of death included noncardiovascular causes (58.09%), cardiovascular issues (22.17%), and infections (19.73%). Patients who died had significantly more comorbidities and were older than those who survived. Compared with survivors, deceased patients also presented significantly lower albumin levels and higher C-reactive protein levels. Relative overhydration was notably greater among deceased patients. High-convection hemodiafiltration led to improved survival rates in the diabetic patient group. Patients who experienced cerebrovascular events had a very low survival rate. Higher albumin concentrations, elevated hemoglobin levels, a higher lean tissue index, adequate postdialysis systolic blood pressure, and a longer effective weekly treatment duration are recognized as protective factors against mortality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHDF-OL: High-volume online hemodiafiltration.\u003c/p\u003e\n\u003cp\u003eHD: hemodialysis.\u003c/p\u003e\n\u003cp\u003eCKD: chronic kidney disease.\u003c/p\u003e\n\u003cp\u003eQd: dialysate flow\u003c/p\u003e\n\u003cp\u003eQb: blood flow\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eADDITIONAL INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe database is attached as a related file: Database_mortality_burden.xlsx.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staff and patients of the Davita-Ecuador clinics where the study was conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026apos; CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGabriela Tamayo\u0026sup1;,\u0026nbsp;\u003c/strong\u003econceptualization, research, writing-original draft, resources, software, supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJorge Quinchuela\u0026sup1;,\u0026nbsp;\u003c/strong\u003eMethodology, Data curation, Formal analysis, Funding acquisition, Project management, Validation, Visualization, Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNatalia Benavides\u0026sup1;,\u0026nbsp;\u003c/strong\u003econceptualization, research, writing-original draft, resources, software, supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFranklin Mora-Bravo\u0026sup2;\u003c/strong\u003e: Methodology, Data curation, Formal analysis, Validation, Visualization, Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003eAll the authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors funded the costs of this research. Health insurance entities covered treatments, procedures, and laboratory studies.\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 present study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics committee approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Bioethics Committee of the Ecuadorian Society of Nephrology, Quito, Ecuador approved the study. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT TO PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis information was not needed because the present study did not publish images, radiographs or specific studies of patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research has no financial interests or conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026apos; INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGabriela Tamayo,\u0026nbsp;\u003c/strong\u003eMedical degree in Medicine from the Pontifical Catholic University of Ecuador, Specialist in Internal Medicine from the Central University of Ecuador, and Specialist in Nephrology from the Pontifical Catholic University of Santamar\u0026iacute;a de los Buenos Aires (Argentina). Medical Director of the Sermens Specialized Renal Health Center at Davita Ecuador.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected];\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"37\" height=\"11\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp; https://orcid.org/0009-0007-6681-0718.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJorge Quinchuela,\u0026nbsp;\u003c/strong\u003eDoctor of Medicine from the Central University of Ecuador, Specialist in Nephrology from the Pontifical Catholic University of Santamar\u0026iacute;a de los Buenos Aires (Argentina).Master\u0026apos;s degree in Diabetes Mellitus, Arterial Hypertension, and Vascular Mechanics from the Universidad Austral (Argentina). Master\u0026apos;s degree in Osteology and Bone-Mineral Metabolism from the Universidad del Salvador (Argentina). Medical Director of the \u0026quot;Dialcentro\u0026quot; Specialized Renal Health Center at Davita Ecuador.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected];\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"37\" height=\"11\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;https://orcid.org/0000-0002-1294-6405.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNatalia Benavides,\u0026nbsp;\u003c/strong\u003eDoctor of Medicine from the Central University of Ecuador, Specialist in Nephrology from the Central University of Ecuador.Graduate Health Promotion and Prevention Diploma from the Regional Autonomous University of the Andes (Ecuador). Coordinator of the Nephrology Service at San Vicente de Pa\u0026uacute;l Hospital, Ibarra (Ecuador). Medical Director of the Dialibarra Specialized Renal Health Center, Davita Ecuador.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003ca href=\"mailto:[email protected]\" target=\"_blank\"\u003e;\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"37\" height=\"11\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;https://orcid.org/0009-0008-2175-7121.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFranklin Mora-Bravo\u003c/strong\u003e:He holds a degree in Medicine and Surgery from the University of Cuenca (Ecuador). He is a specialist in internal medicine from the National University of Loja (Loja, Ecuador). He is also a specialist in Nephrology from the Ignacio Ch\u0026aacute;vez National Institute of Cardiology and the National Autonomous University of Mexico (Mexico).Master\u0026apos;s degree in Health Research from the University of Cuenca (Ecuador). Medical Director of Pafram, Kidney Clinic in Morona Santiago, Ecuador.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected];\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg width=\"37\" height=\"11\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/strong\u003e ORCID: https://orcid.org/0000-0002-5978-3420\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbril J, Sanchez J. Characteristics of chronic kidney disease in Ecuador from 2009 to 2012. [Thesis] University of Cuenca, Ecuador: 2014. dspace\u003c/li\u003e\n\u003cli\u003eGahona J, Meza K. Update, characterization, and survival analysis of patients on renal replacement therapy in Ecuador, according to the national dialysis and transplant registry. Periodic report of the Vice Ministry of Comprehensive Care, Undersecretary of Mobile Hospital Health Care and Specialized Centers, National Directorate of Specialized Centers. Ministry of Public Health of Ecuador. November 2022. URL Salud / Nov_2022\u003c/li\u003e\n\u003cli\u003eGrooteman MP, van den Dorpel MA, Bots ML, Penne EL, van der Weerd NC, Mazairac AH, den Hoedt CH, van der Tweel I, L\u0026eacute;vesque R, Nub\u0026eacute; MJ, ter Wee PM, Blankestijn PJ; CONTRAST Investigators . Effect of online hemodiafiltration on all-cause mortality and cardiovascular outcomes. J Am Soc Nephrol. 2012 Jun;23(6):1087-96. doi: 10.1681/ASN.2011121140. Epub 2012 Apr 26. PMID: 22539829; PMCID: PMC3358764.\u003c/li\u003e\n\u003cli\u003eOk E, Asci G, Toz H, Ok ES, Kircelli F, Yilmaz M, Hur E, Demirci MS, Demirci C, Duman S, Basci A, Adam SM, Isik IO, Zengin M, Suleymanlar G, Yilmaz ME, Ozkahya M; Turkish Online Hemodiafiltration Study. Mortality and cardiovascular events in online hemodiafiltration (OL-HDF) compared with high-flux dialysis: results from the Turkish OL-HDF Study. Nephrol Dial Transplant. 2013 Jan;28(1):192-202. doi: 10.1093/ndt/gfs407. Epub 2012 Dec 9. PMID: 23229932.\u003c/li\u003e\n\u003cli\u003eSiriopol D, Canaud B, Stuard S, Mircescu G, Nistor I, Covic A. New insights into the effect of hemodiafiltration on mortality: the Romanian experience. Nephrol Dial Transplant. 2015 Feb;30(2):294-301. doi: 10.1093/ndt/gfu347. Epub 2014 Nov 13. PMID: 25395391.\u003c/li\u003e\n\u003cli\u003eMora-Bravo FG, De-La-Cruz G, Rivera S, Ram\u0026iacute;rez AM, Raimann JG, P\u0026eacute;rez- Grovas H. Association of intradialytic hypotension and convective volume in hemodiafiltration: results from a retrospective cohort study. BMC Nephrol. 2012 Sep 10;13:106. doi:10.1186/1471-2369-13-106. PMID: 22963170; PMCID: PMC3575237.\u003c/li\u003e\n\u003cli\u003eMora-Bravo FG, Torres PTM, Campoverde NR, Carcelen GLB, Mancheno JCS, Tipanta \u0026Aacute;CS, Perez-Grovas H, Abarca WPR. Blood pressure control with active ultrafiltration measures and without antihypertensives is essential for survival in hemodiafiltration and hemodialysis programs for patients with CKD: a prospective observational study. BMC Nephrol. 2025 Jan 17;26(1):30. doi:10.1186/s12882-025-03948-0. PMID: 39825259; PMCID: PMC11742504.\u003c/li\u003e\n\u003cli\u003eMora-Bravo FG, Mariscal A, Herrera-Felix JP, Maga\u0026ntilde;a S, De-La-Cruz G, Flores N, Rosales L, Franco M, P\u0026eacute;rez- Grovas H. Arterial line pressure control enhanced extracorporeal blood flow prescription in hemodialysis patients. BMC Nephrol. 2008 Nov 24;9:15. doi:10.1186/1471-2369-9-15. PMID: 19025625; PMCID: PMC2613872.\u003c/li\u003e\n\u003cli\u003ePeters SA, Bots ML, Canaud B, Davenport A, Grooteman MP, Kircelli F, Locatelli F, Maduell F, Morena M, Nub\u0026eacute; MJ, Ok E, Torres F, Woodward M, Blankestijn PJ; HDF Pooling Project Investigators. Hemodiafiltration and mortality in end-stage kidney disease patients: a pooled individual participant data analysis from four randomized controlled trials. Nephrol Dial Transplant. 2016 Jun;31(6):978-84. doi: 10.1093/ndt/gfv349. Epub 2015 Oct 22. PMID: 26492924.\u003c/li\u003e\n\u003cli\u003eZoccali C, Moissl U, Chazot C, Mallamaci F, Tripepi G, Arkossy O, Wabel P, Stuard S. Chronic Fluid Overload and Mortality in ESRD. J Am Soc Nephrol. 2017 Aug;28(8):2491-2497. doi:10.1681/ASN.2016121341. Epub 2017 May 4. PMID: 28473637; PMCID: PMC5533242.\u003c/li\u003e\n\u003cli\u003eShin SK, Jo YI. Why should we focus on high-volume hemodiafiltration? Kidney Res Clin Pract . 2022 Nov;41(6):670-681. doi:10.23876/j.krcp.21.268. Epub 2022 Feb 22. PMID: 35286790; PMCID: PMC9731779.\u003c/li\u003e\n\u003cli\u003eRivera-Gonz\u0026aacute;lez SC, P\u0026eacute;rez- Grovas H, Madero M, Saavedra N, L\u0026oacute;pez-Rodriguez J, Lerma C. Identification of impeding factors for dry weight achievement in end-stage renal disease after appropriate kidney graft function. Artif Organs. 2014 Feb;38(2):113-20. doi: 10.1111/aor.12133. Epub 2013 Jul 25. PMID: 23889479.\u003c/li\u003e\n\u003cli\u003eShu X, Lin T, Wang H, Zhao Y, Jiang T, Peng X, Yue J. Diagnosis, prevalence, and mortality of sarcopenia in dialysis patients: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2022 Feb;13(1):145-158. doi:10.1002/jcsm.12890. Epub 2022 Jan 5. PMID: 34989172; PMCID: PMC8818609.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Davita ","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":"mortality, hemodiafiltration, hemodialysis, risk factors, chronic kidney failure","lastPublishedDoi":"10.21203/rs.3.rs-7515828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7515828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e In patients with chronic kidney disease (CKD), cardiovascular disease is considered the leading cause of mortality. This study aims to analyze mortality and its associated factors in patients undergoing hemodialysis (HD) and hemodiafiltration (HDF) treatments at 14 private centers in Ecuador.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This observational research was conducted from 2018 to 2022. Patients who received conventional three-weekly therapy were included. Those who died by the end of the observation period (Group 1-G1) were compared with those who were alive (Group 2-G2). The variables assessed included demographic data, comorbidities, clinical indicators, laboratory results, and impedance descriptions. Logistic regression was performed to obtain the odds ratio (OR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 821 patients in G1 and 3,586 in G2 were analyzed, yielding a mortality rate of 22.89% over 42 months (6.54% per year). There were 182 deaths attributed to cardiovascular causes (22.17%), 162 to infections (19.73%), and 477 from other causes (58.09%). Patients on HDF in G1 accounted for 167 cases (20.3%), while in G2, there were 1,078 cases (30.5%) (P\u0026lt;0.0001). Risk factors for mortality included the development of cerebrovascular disease (OR: 1.81), vascular disease with hypertension (OR\u0026gt;1.49), and type 2 diabetes mellitus (OR\u0026gt;1.33). Protective factors identified were albumin concentration (OR: 0.61), hemoglobin level (OR: 0.83), and lean tissue index (OR: 0.95).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The present study demonstrates that the primary causes of death were non-cardiovascular, cardiovascular, and infections. Higher albumin concentration, elevated hemoglobin levels, increased lean tissue index, and longer effective weekly treatment duration were identified as protective factors against mortality.\u003c/p\u003e","manuscriptTitle":"Mapping the mortality burden in hemodialysis patients. A multicenter observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 00:20:09","doi":"10.21203/rs.3.rs-7515828/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":"9fa58d35-557a-4dff-9d28-b9949269b3d0","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-04T00:20:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 00:20:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7515828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7515828","identity":"rs-7515828","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}