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BACKGROUND: Hypertension is a leading cause of kidney failure, affects most dialysis patients and associates with adverse outcomes. Hypertension can be difficult to control with dialysis modalities having differential effects on sodium and water removal. There are two main types of peritoneal dialysis (PD), automated peritoneal dialysis (APD) and continuous ambulatory peritoneal dialysis (CAPD). It is unknown whether one is superior to the other in controlling blood pressure (BP). Therefore, the aim of our study was to analyse the impact of switching between these two PD modalities on BP levels in a nationally representative cohort. METHODS: This was a cohort study of patients on PD from 122 dialysis centres in Brazil (BRAZPD II study). Clinical and laboratory data were collected monthly throughout the study duration. We selected all patients who remained on PD at least 6 months and 3 months on each modality at minimum. We compared the changes in mean systolic/diastolic blood pressures (SBP/DBP) before and after modality transition using a multilevel mixed-model where patients were at first level and their clinics at the second level. RESULTS: We analysed data of 848 patients (814 starting on CAPD and 34 starting on APD). The SBP decreased by 4 (SD 22) mmHg when transitioning from CAPD to APD (p < 0.001) and increased by 4 (SD 21) mmHg when transitioning from APD to CAPD (p = 0.38); consistent findings were seen for DBP. There was no significant change in the number of antihypertensive drugs prescribed before and after transition. CONCLUSIONS: Transition between PD modalities seems to directly impact on BP levels. Further studies are needed to confirm if switching to APD could be an effective treatment for uncontrolled hypertension among CAPD patients.

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BACKGROUND: We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas. METHODS: We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0-14, 15-30, > 30 days after COVID-19 presentation and identified the importance of predictors. XGBoost models were built in parallel using the same programming with a 60%:20%:20% random split for training, validation, & testing data for the datasets from LatAm (Argentina, Columbia, Ecuador) and North America (United States) countries. RESULTS: Among HD patients with COVID-19, 28.8% (1,001/3,473) died in LatAm and 20.5% (4,426/21,624) died in North America. Mortality occurred earlier in LatAm versus North America; 15.0% and 7.3% of patients died within 0-14 days, 7.9% and 4.6% of patients died within 15-30 days, and 5.9% and 8.6% of patients died > 30 days after COVID-19 presentation, respectively. Area under curve ranged from 0.73 to 0.83 across prediction models in both regions. Top predictors of death after COVID-19 consistently included older age, longer vintage, markers of poor nutrition and more inflammation in both regions at all timepoints. Unique patient attributes (higher BMI, male sex) were top predictors of mortality during 0-14 and 15-30 days after COVID-19, yet not mortality > 30 days after presentation. CONCLUSIONS: Findings showed distinct profiles of mortality in COVID-19 in LatAm and North America throughout 2020. Mortality rate was higher within 0-14 and 15-30 days after COVID-19 in LatAm, while mortality rate was higher in North America > 30 days after presentation. Nonetheless, a remarkable proportion of HD patients died > 30 days after COVID-19 presentation in both regions. We were able to develop a series of suitable prognostic prediction models and establish the top predictors of death in COVID-19 during shorter-, intermediate-, and longer-term follow up periods.

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BACKGROUND: We tested if fatigue in incident Peritoneal Dialysis associated with an increased risk for mortality, independently from main confounders. METHODS: We conducted a side-by-side study from two of incident PD patients in Brazil and the United States. We used the same code to independently analyze data in both countries during 2004 to 2011. We included data from adults who completed KDQOL-SF vitality subscale within 90 days after starting PD. Vitality score was categorized in four groups: >50 (high vitality), ≥40 to ≤50 (moderate vitality), >35 to <40 (moderate fatigue), ≤35 (high fatigue; reference group). In each country's cohort, we built four distinct models to estimate the associations between vitality (exposure) and all-cause mortality (outcome): (i) Cox regression model; (ii) competing risk model accounting for technique failure events; (iii) multilevel survival model of clinic-level clusters; (iv) multivariate regression model with smoothing splines treating vitality as a continuous measure. Analyses were adjusted for age, comorbidities, PD modality, hemoglobin, and albumin. A mixed-effects meta-analysis was used to pool hazard ratios (HRs) from both cohorts to model mortality risk for each 10-unit increase in vitality. RESULTS: We used data from 4,285 PD patients (Brazil n = 1,388 and United States n = 2,897). Model estimates showed lower vitality levels within 90 days of starting PD were associated with a higher risk of mortality, which was consistent in Brazil and the United States cohorts. In the multivariate survival model, each 10-unit increase in vitality score was associated with lower risk of all-cause mortality in both cohorts (Brazil HR = 0.79 [95%CI 0.70 to 0.90] and United States HR = 0.90 [95%CI 0.88 to 0.93], pooled HR = 0.86 [95%CI 0.75 to 0.98]). Results for all models provided consistent effect estimates. CONCLUSIONS: Among patients in Brazil and the United States, lower vitality score in the initial months of PD was independently associated with all-cause mortality.

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Peritoneal dialysis (PD) modalities affect solute removal differently. However, the impacts of switching PD modalities on serum levels of biomarkers of different sizes are not known. Our objective was to analyze whether a change in the PD modality associates with the levels of two routine biochemical laboratories. In this multicentric prospective cohort study. we selected all patients who remained on a PD modality for at least 6 months and switched PD modality. Patients were also required to be treated with the same PD modality for at least 3 months before and after the modality change. The primary outcome was change in potassium and phosphate serum levels. We identified 737 eligible patients who switched their PD modality during the study. We found mean serum phosphate levels increased during the 3 months after switching from CAPD to APD and conversely decreased after switching to from APD to CAPD. In contrast, for potassium the difference in the mean serum levels was comparable between groups switching from CAPD to APD, and vice versa. In conclusion, CAPD seems to be as efficient as APD for the control of potassium serum levels, but more effective for the control of phosphate serum levels. The effect of a higher removal of middle size molecules as result of PD modalities in terms of clinical and patient-reported outcomes should be further explored.

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BACKGROUND AND OBJECTIVES: High-volume online hemodiafiltration (OL-HDF) associates with improved outcomes compared to hemodialysis (HD), provided adequate dosing is achieved as estimated from convective volume (CV). Achievement of high CV and its impact on biochemical indicators following a standardized protocol converting HD patients to OL-HDF has not been systematically reported. We assessed the success of implementation of OL-HDF in clinics naïve to the modality. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: We analyzed the results of the implementation of postdilution OL-HDF in patients randomized to the HDF arm of a clinical trial (impact of hemoDiaFIlTration on physical activity and self-reported outcomes: a randomized controlled trial (HDFit) trial [ClinicalTrials.gov:NCT02787161]). The day before randomization of the first patient to OL-HDF at each clinic staff started a 3-day in-person training module on operation of Fresenius 5008 CorDiax machine in HDF mode. Patients were converted from high-flux HD to OL-HDF under oversight of trainers. OL-HDF was performed over a 6-months follow-up with a CV target of 22 L/treatment. We characterized median achieved CV >22 L/treatment record and analyzed the impact of HDF on biochemical variables. RESULTS: Ninety-seven patients (mean age 53 ± 16 years, 29% with diabetes, and 11% had a catheter) from 13 clinics randomized to the OL-HDF arm of the trial were converted from HD to HDF. Median CV > 22 L/treatment was achieved in 99% (94/95) of OL-HDF patients throughout follow-up. Monthly mean CV ranged from 27.1 L to 27.5 L. OL-HDF provided an increased single pool Kt/V at 3-months (0.2 [95% CI: 0.1-0.3]) and 6-months (0.2 [95% CI: 0.1-0.4]) compared to baseline, and reduced phosphate at 3-months (-0.4 mg/dL [95% CI: -0.8 to -0.12]) of follow-up. CONCLUSIONS: High-volume online hemodiafiltration was successfully implemented with 99% of patients achieving protocol defined CV target. Monthly mean CV was consistently >22 L/treatment during follow-up. Kt/V increased, and phosphate decreased with OL-HDF. Findings resulting from a short training period in several dialysis facilities appear to suggest HDF is an easily implementable technique.

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BACKGROUND: Dialysis patients are typically inactive and their physical activity (PA) decreases over time. Uremic toxicity has been suggested as a potential causal factor of low PA in dialysis patients. Post-dilution high-volume online hemodiafiltration (HDF) provides greater higher molecular weight removal and studies suggest better clinical/patient-reported outcomes compared with hemodialysis (HD). METHODS: HDFIT was a randomized controlled trial at 13 clinics in Brazil that aimed to investigate the effects of HDF on measured PA (step counts) as a primary outcome. Stable HD patients (vintage 3-24 months) were randomized to receive HDF or high-flux HD. Treatment effect of HDF on the primary outcome from baseline to 3 and 6 months was estimated using a linear mixed-effects model. RESULTS: We randomized 195 patients (HDF 97; HD 98) between August 2016 and October 2017. Despite the achievement of a high convective volume in the majority of sessions and a positive impact on solute removal, the treatment effect HDF on the primary outcome was +538 [95% confidence interval (CI) -330 to 1407] steps/24 h after dialysis compared with HD, and was not statistically significant. Despite a lack of statistical significance, the observed size of the treatment effect was modest and driven by steps taken between 1.5 and 24.0 h after dialysis, in particular between 20 and 24 h (+197 steps; 95% CI -95 to 488). CONCLUSIONS: HDF did not have a statistically significant treatment effect on PA 24 h following dialysis, albeit effect sizes may be clinically meaningful and deserve further investigation.

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BACKGROUND: Dialysis recovery time (DRT) surveys capture the perceived time after HD to return to performing regular activities. Prior studies suggest the majority of HD patients report a DRT > 2 h. However, the profiles of and modifiable dialysis practices associated with changes in DRT relative to the start of dialysis are unknown. We hypothesized hemodialysis (HD) dose and rates of intradialytic hypotension (IDH) would associate with changes in DRT in the first years after initiating dialysis. METHODS: We analyzed data from adult HD patients who responded to a DRT survey ≤180 days from first date of dialysis (FDD) during 2014 to 2017. DRT survey was administered with annual KDQOL survey. DRT survey asks: "How long does it take you to be able to return to your normal activities after your dialysis treatment?" Answers are: < 0.5, 0.5-to-1, 1-to-2, 2-to-4, or > 4 h. An adjusted logistic regression model computed odds ratio for a change to a longer DRT (increase above DRT > 2 h) in reference to a change to a shorter DRT (decrease below DRT < 2 h, or from DRT > 4 h). Changes in DRT were calculated from incident (≤180 days FDD) to first prevalent (> 365-to- ≤ 545 days FDD) and second prevalent (> 730-to- ≤ 910 days FDD) years. RESULTS: Among 98,616 incident HD patients (age 62.6 ± 14.4 years, 57.8% male) who responded to DRT survey, a higher spKt/V in the incident period was associated with 13.5% (OR = 0.865; 95%CI 0.801-to-0.935) lower risk of a change to a longer DRT in the first-prevalent year. A higher number of HD treatments with IDH episodes per month in the incident period was associated with a 0.8% (OR = 1.008; 95%CI 1.001-to-1.015) and 1.6% (OR = 1.016; 95%CI 1.006-to-1.027) higher probability of a change to a longer DRT in the first- and second-prevalent years, respectively. Consistently, an increased in incidence of IDH episodes/months was associated to a change to a longer DRT over time. CONCLUSIONS: Incident patients who had higher spKt/V and less sessions with IDH episodes had a lower likelihood of changing to a longer DRT in first year of HD. Dose optimization strategies with cardiac stability in fluid removal should be tested.

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AIM: Fatigue in haemodialysis (HD) patients can be captured in quality of life questionnaires and by the dialysis recovery time (DRT) question. The associations between fatigue and measured physical activity has not been explored until the present. We tested our hypothesis that the patient perception of chronic and post dialysis fatigue would be associated with lower physical activity. METHODS: This study was a cross sectional evaluation of baseline data from HD patients recruited in the HDFIT trial. Vitality scores from the Kidney Disease Quality of Life (KDQOL-36) and the dialysis recovery time (DRT) question were used as indicators of chronic and post dialysis fatigue, respectively. Granular physical activity was measured by accelerometers as part of the study protocol. RESULTS: Among 176 patients, Vitality score was 63 ± 21 and the DRT was ≤30 minutes in 57% of patients. The mean number of steps was 5288 ± 3540 in 24 hours after HD and 953 ± 617 in the 2-hour post-HD period. The multivariable analysis confirmed Vitality scores were associated with physical activity in the 24-hour post-HD period. In contrast, DRT was not associated with physical activity captured by the accelerometer in the period immediately (2 hours) after the HD session. CONCLUSION: Chronic fatigue was negatively associated with step counts, while patient perception of post-dialysis fatigue was not associated with physical activity. These patterns indicate limitations in interpretation of DRT. Since physical activity is an important component of a healthy life, our results may partially explain the associations between fatigue and poor outcomes in HD patients.

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BACKGROUND: Physical activity (PA) is typically lower on hemodialysis (HD) days. Albeit intradialytic inactivity is expected, it is unknown whether recovery after HD contributes to low PA. We investigated the impact of HD and post-HD period on granular PA relative to HD timing. METHODS: We used baseline data from the HDFIT trial conducted from August 2016 to October 2017. Accelerometry measured PA over 1 week in patients who received thrice-weekly high-flux HD (vintage 3 to 24 months), were clinically stable, and had no ambulatory limitations. PA was assessed on HD days (0 to ≤24 h after start HD), first non-HD days (> 24 to ≤48 h after start HD) and second non-HD day (> 48 to ≤72 h after start HD). PA was recorded in blocks/slices: 4 h during HD, 0 to ≤2 h post-HD (30 min slices), and > 2 to ≤20 h post-HD (4.5 h slices). Blocks/slices of PA were captured at concurrent/parallel times on first/second non-HD days compared to HD days. RESULTS: Among 195 patients (mean age 53 ± 15 years, 71% male), step counts per 24-h were 3919 ± 2899 on HD days, 5308 ± 3131 on first non-HD days (p < 0.001), and 4926 ± 3413 on second non-HD days (p = 0.032). During concurrent/parallel times to HD on first and second non-HD days, patients took 1308 and 1128 more steps (both p < 0.001). Patients took 276 more steps and had highest rates of steps/hour 2-h post-HD versus same times on first non-HD days (all p < 0.05). Consistent findings were observed on second non-HD days. CONCLUSIONS: PA was higher within 2-h of HD versus same times on non-HD days. Lower PA on HD days was attributable to intradialytic inactivity. The established PA profiles are of importance to the design and development of exercise programs that aim to increase activity during and between HD treatments. TRIAL REGISTRATION: HDFIT was prospectively registered 20 April 2016 on ClinicalTrials.gov (NCT02787161).

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BACKGROUND: End stage renal disease (ESRD) patients require a renal replacement therapy (RRT) to filter accumulated toxins and remove excess water, which are associated with impaired physical function. Hemodialysis (HD) removes middle-molecular weight (MMW) toxins less efficiently compared to hemodiafiltration (HDF); we hypothesized HDF may improve physical function. We detailed the design and methodology of the HDFIT protocol that is testing whether changing from HD to HDF effects physical activity levels and various outcomes. METHODS: HDFIT is a prospective, multi-center, unblinded, randomized control trial (RCT) investigating the impact of dialysis modality (HDF verses HD) on objectively measured physical activity levels, self-reported quality of life, and clinical/non-clinical outcomes. Clinically stable patients with HD vintage of 3 to 24 months without any severe limitation ambulation were recruited from sites throughout southern Brazil. Eligible patients were randomized in a 1:1 ratio to either: 1) be treated with high volume online HDF for 6 months, or 2) continue being treated with high-flux HD. This study includes run-in and randomization visits (baseline), 3- and 6-month study visits during the interventional period, and a 12-month observational follow up. The primary outcome is the difference in the change in steps per 24 h on dialysis days from baseline to the 6-month follow up in patients treated with HDF versus HD. Physical activity is being measured over one week at study visits with the ActiGraph ( www.actigraphcorp.com ). For assessment of peridialytic differences during the dialysis recovery period, we will analyze granular physical activity levels based on the initiation time of HD on dialysis days, or concurrent times on non-dialysis days and the long interdialytic day. DISCUSSION: In this manuscript, we provide detailed information about the HDFIT study design and methodology. This trial will provide novel insights into peridialytic profiles of physical activity and various self-reported, clinical and laboratory outcomes in ESRD patients treated by high volume online HDF versus high-flux HD. Ultimately, this investigation will elucidate whether HDF is associated with patients having better vitality and quality of life, and less negative outcomes as compared to HD. TRIAL REGISTRATION: Registered on ClinicalTrials.gov on 20 April 2016 ( NCT02787161 ).

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