The Feasibility of Semi-Continuous and Multi-Frequency Thoracic Bioimpedance Measurements by a Wearable Device during Fluid Changes in Hemodialysis Patients.

Repeated single-point measurements of thoracic bioimpedance at a single (low) frequency are strongly related to fluid changes during hemodialysis. Extension to semi-continuous measurements may provide longitudinal details in the time pattern of the bioimpedance signal, and multi-frequency measurements may add in-depth information on the distribution between intra- and extracellular fluid. This study aimed to investigate the feasibility of semi-continuous multi-frequency thoracic bioimpedance measurements by a wearable device in hemodialysis patients. Therefore, thoracic bioimpedance was recorded semi-continuously (i.e., every ten minutes) at nine frequencies (8-160 kHz) in 68 patients during two consecutive hemodialysis sessions, complemented by a single-point measurement at home in-between both sessions. On average, the resistance signals increased during both hemodialysis sessions and decreased during the interdialytic interval. The increase during dialysis was larger at 8 kHz (∆ 32.6 Ω during session 1 and ∆ 10 Ω during session 2), compared to 160 kHz (∆ 29.5 Ω during session 1 and ∆ 5.1 Ω during session 2). Whereas the resistance at 8 kHz showed a linear time pattern, the evolution of the resistance at 160 kHz was significantly different (p < 0.0001). Measuring bioimpedance semi-continuously and with a multi-frequency current is a major step forward in the understanding of fluid dynamics in hemodialysis patients. This study paves the road towards remote fluid monitoring.

Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure.

BACKGROUND: In maintenance hemodialysis patients, intradialytic hypotension (IDH) is a frequent complication that has been associated with poor clinical outcomes. Prediction of IDH may facilitate timely interventions and eventually reduce IDH rates. METHODS: We developed a machine learning model to predict IDH in in-center hemodialysis patients 15-75 min in advance. IDH was defined as systolic blood pressure (SBP) <90 mmHg. Demographic, clinical, treatment-related and laboratory data were retrieved from electronic health records and merged with intradialytic machine data that were sent in real-time to the cloud. For model development, dialysis sessions were randomly split into training (80%) and testing (20%) sets. The area under the receiver operating characteristic curve (AUROC) was used as a measure of the model's predictive performance. RESULTS: We utilized data from 693 patients who contributed 42 656 hemodialysis sessions and 355 693 intradialytic SBP measurements. IDH occurred in 16.2% of hemodialysis treatments. Our model predicted IDH 15-75 min in advance with an AUROC of 0.89. Top IDH predictors were the most recent intradialytic SBP and IDH rate, as well as mean nadir SBP of the previous 10 dialysis sessions. CONCLUSIONS: Real-time prediction of IDH during an ongoing hemodialysis session is feasible and has a clinically actionable predictive performance. If and to what degree this predictive information facilitates the timely deployment of preventive interventions and translates into lower IDH rates and improved patient outcomes warrants prospective studies.

Intradialytic Protein Ingestion and Exercise do Not Compromise Uremic Toxin Removal Throughout Hemodialysis.

OBJECTIVE: Dietary protein and physical activity interventions are increasingly implemented during hemodialysis to support muscle maintenance in patients with end-stage renal disease (ESRD). Although muscle maintenance is important, adequate removal of uremic toxins throughout hemodialysis is the primary concern for patients. It remains to be established whether intradialytic protein ingestion and/or exercise modulate uremic toxin removal during hemodialysis. METHODS: We recruited 10 patients with ESRD (age: 65 ± 16 y, BMI: 24.2 ± 4.8 kg/m2) on chronic hemodialysis treatment to participate in this randomized cross-over trial. During hemodialysis, patients were assigned to ingest 40 g protein or a nonprotein placebo both at rest (protein [PRO] and placebo [PLA], respectively) and following 30 min of exercise (PRO + exercise [EX] and PLA + EX, respectively). Blood and spent dialysate samples were collected throughout hemodialysis to assess reduction ratios and removal of urea, creatinine, phosphate, cystatin C, and indoxyl sulfate. RESULTS: The reduction ratios of urea and indoxyl sulfate were higher during PLA (76 ± 6% and 46 ± 9%, respectively) and PLA + EX interventions (77 ± 5% and 45 ± 10%, respectively) when compared to PRO (72 ± 4% and 40 ± 8%, respectively) and PRO + EX interventions (73 ± 4% and 43 ± 7%, respectively; protein effect: P = .001 and P = .023, respectively; exercise effect: P = .25 and P = .52, respectively). Nonetheless, protein ingestion resulted in greater urea removal (P = .046) during hemodialysis. Reduction ratios and removal of creatinine, phosphate, and cystatin C during hemodialysis did not differ following intradialytic protein ingestion or exercise (protein effect: P > .05; exercise effect: P>.05). Urea, creatinine, and phosphate removal were greater throughout the period with intradialytic exercise during PLA + EX and PRO + EX interventions when compared to the same period during PLA and PRO interventions (exercise effect: P = .034, P = .039, and P = .022, respectively). CONCLUSION: The removal of uremic toxins is not compromised by protein feeding and/or exercise implementation during hemodialysis in patients with ESRD.

Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?

Telemedicine and digitalised healthcare have recently seen exponential growth, led, in part, by increasing efforts to improve patient flexibility and autonomy, as well as drivers from financial austerity and concerns over climate change. Nephrology is no exception, and daily innovations are underway to provide digitalised alternatives to current models of healthcare provision. Wearable technology already exists commercially, and advances in nanotechnology and miniaturisation mean interest is also garnering clinically. Here, we outline the current existing wearable technology pertaining to the diagnosis and monitoring of patients with a spectrum of kidney disease, give an overview of wearable dialysis technology, and explore wearables that do not yet exist but would be of great interest. Finally, we discuss challenges and potential pitfalls with utilising wearable technology and the factors associated with successful implementation.

The oxygen cascade in patients treated with hemodialysis and native high-altitude dwellers: lessons from extreme physiology to benefit patients with end-stage renal disease.

Patients treated with hemodialysis (HD) repeatedly undergo intradialytic low arterial oxygen saturation and low central venous oxygen saturation, reflecting an imbalance between upper body systemic oxygen supply and demand, which are associated with increased mortality. Abnormalities along the entire oxygen cascade, with impaired diffusive and convective oxygen transport, contribute to the reduced tissue oxygen supply. HD treatment impairs pulmonary gas exchange and reduces ventilatory drive, whereas ultrafiltration can reduce tissue perfusion due to a decline in cardiac output. In addition to these factors, capillary rarefaction and reduced mitochondrial efficacy can further affect the balance between cellular oxygen supply and demand. Whereas it has been convincingly demonstrated that a reduced perfusion of heart and brain during HD contributes to organ damage, the significance of systemic hypoxia remains uncertain, although it may contribute to oxidative stress, systemic inflammation, and accelerated senescence. These abnormalities along the oxygen cascade of patients treated with HD appear to be diametrically opposite to the situation in Tibetan highlanders and Sherpa, whose physiology adapted to the inescapable hypobaric hypoxia of their living environment over many generations. Their adaptation includes pulmonary, vascular, and metabolic alterations with enhanced capillary density, nitric oxide production, and mitochondrial efficacy without oxidative stress. Improving the tissue oxygen supply in patients treated with HD depends primarily on preventing hemodynamic instability by increasing dialysis time/frequency or prescribing cool dialysis. Whether dietary or pharmacological interventions, such as the administration of L-arginine, fermented food, nitrate, nuclear factor erythroid 2-related factor 2 agonists, or prolyl hydroxylase 2 inhibitors, improve clinical outcome in patients treated with HD warrants future research.

Dietary protein interventions to improve nutritional status in end-stage renal disease patients undergoing hemodialysis.

PURPOSE OF REVIEW: Poor nutritional status is prevalent among end-stage renal disease patients undergoing hemodialysis. Chronic hemodialysis patients show an accelerated decline in skeletal muscle mass and strength, which is associated with higher mortality rates and a reduced quality of life. The current review aims to summarize recent advances regarding underlying causes of muscle loss and interventions that support muscle mass maintenance in patients with chronic hemodialysis. RECENT FINDINGS: Muscle maintenance in chronic hemodialysis patients is compromised by low dietary protein intake levels, anabolic resistance of skeletal muscle tissue, sedentary behavior, and amino acid removal during hemodialysis. Studies assessing the effect of increased protein intake on nutritional status generally show beneficial results, especially in hypoalbuminemic chronic hemodialysis patients. The muscle protein synthetic response following protein ingestion in chronic hemodialysis patients may be enhanced through incorporation of structured physical activity and/or concurrent ketoacid ingestion. SUMMARY: A coordinated program that combines nutritional and physical activity interventions is likely required to attenuate the decline in muscle mass and strength of chronic hemodialysis patients. Nephrologists, dieticians, and exercise specialists should collaborate closely to establish guidelines regarding the appropriate quantity and timing of protein ingestion. In addition, they should provide tailored nutritional and physical activity interventions for chronic hemodialysis patients (see video, Supplemental Digital Content 1, Video abstract, http://links.lww.com/COCN/A14).

Artificial intelligence enabled applications in kidney disease.

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.

COVID-19 in ESRD and Acute Kidney Injury.

The COVID-19 pandemic has greatly affected nephrology. Firstly, dialysis patients appear to be at increased risk for infection due to viral transmission next to an enhanced risk for mortality as compared to the general population, even in the face of an often apparently mild clinical presentation. Derangements in the innate and adaptive immune systems may be responsible for a reduced antiviral response, whereas chronic activation of the innate immune system and endothelial dysfunction provide a background for a more severe course. The presence of severe comorbidity, older age, and a reduction of organ reserve may lead to a rapid deterioration of the clinical situation of the patients in case of severe infection. Secondly, patients with COVID-19 are at increased risk of acute kidney injury (AKI), which is related to the severity of the clinical disease. The presence of AKI, and especially the need for renal replacement therapy (RRT), is associated with an increased risk of mortality. AKI in COVID-19 has a multifactorial origin, in which direct viral invasion of kidney cells, activation of the renin-angiotensin aldosterone system, a hyperinflammatory response, hypercoagulability, and nonspecific factors such as hypotension and hypoxemia may be involved. Apart from logistic challenges and the need for strict hygiene within units, treatment of patients with ESRD and COVID-19 is not different from that of the general population. Extracorporeal treatment of patients with AKI with RRT can be complicated by frequent filter clotting due to the hypercoagulable state, for which regional citrate coagulation provides a reasonable solution. Also, acute peritoneal dialysis may be a reasonable option in these patients. Whether adjuncts to extracorporeal therapies, such as hemoadsorption, provide additional benefits in the case of severely ill COVID-19 patients needs to be addressed in controlled studies.

Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure.

BACKGROUND: Inadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ultrafiltration rate which in turn has a positive effect on intradialytic symptoms. It has been clinically challenging to identify changes RBV in real time to proactively intervene and reduce potential negative consequences of volume depletion. Leveraging advanced technologies to process large volumes of dialysis and machine data in real time and developing prediction models using machine learning (ML) is critical in identifying these signals. METHOD: We conducted a proof-of-concept analysis to retrospectively assess near real-time dialysis treatment data from in-center patients in six clinics using Optical Sensing Device (OSD), during December 2018 to August 2019. The goal of this analysis was to use real-time OSD data to predict if a patient's relative blood volume (RBV) decreases at a rate of at least - 6.5 % per hour within the next 15 min during a dialysis treatment, based on 10-second windows of data in the previous 15 min. A dashboard application was constructed to demonstrate how reporting structures may be developed to alert clinicians in real time of at-risk cases. Data was derived from three sources: (1) OSDs, (2) hemodialysis machines, and (3) patient electronic health records. RESULTS: Treatment data from 616 in-center dialysis patients in the six clinics was curated into a big data store and fed into a Machine Learning (ML) model developed and deployed within the cloud. The threshold for classifying observations as positive or negative was set at 0.08. Precision for the model at this threshold was 0.33 and recall was 0.94. The area under the receiver operating curve (AUROC) for the ML model was 0.89 using test data. CONCLUSIONS: The findings from our proof-of concept analysis demonstrate the design of a cloud-based framework that can be used for making real-time predictions of events during dialysis treatments. Making real-time predictions has the potential to assist clinicians at the point of care during hemodialysis.

End-Stage Renal Disease Patients Lose a Substantial Amount of Amino Acids during Hemodialysis.

BACKGROUND: Poor nutritional status is frequently observed in end-stage renal disease patients and associated with adverse clinical outcomes and increased mortality. Loss of amino acids (AAs) during hemodialysis (HD) may contribute to protein malnutrition in these patients. OBJECTIVE: We aimed to assess the extent of AA loss during HD in end-stage renal disease patients consuming their habitual diet. METHODS: Ten anuric chronic HD patients (mean ± SD age: 67.9 ± 19.3 y, BMI: 23.2 ± 3.5 kg/m2), undergoing HD 3 times per week, were selected to participate in this study. Spent dialysate was collected continuously and plasma samples were obtained directly before and after a single HD session in each participant. AA profiles in spent dialysate and in pre-HD and post-HD plasma were measured through ultra-performance liquid chromatography to determine AA concentrations and, as such, net loss of AAs. In addition, dietary intake before and throughout HD was assessed using a 24-h food recall questionnaire during HD. Paired-sample t tests were conducted to compare pre-HD and post-HD plasma AA concentrations. RESULTS: During an HD session, 11.95 ± 0.69 g AAs were lost via the dialysate, of which 8.26 ± 0.46 g were nonessential AAs, 3.69 ± 0.31 g were essential AAs, and 1.64 ± 0.17 g were branched-chain AAs. As a consequence, plasma total and essential AA concentrations declined significantly from 2.88 ± 0.15 and 0.80 ± 0.05 mmol/L to 2.27 ± 0.11 and 0.66 ± 0.05 mmol/L, respectively (P < 0.05). AA profiles of pre-HD plasma and spent dialysate were similar. Moreover, AA concentrations in pre-HD plasma and spent dialysate were strongly correlated (Spearman's ρ = 0.92, P < 0.001). CONCLUSIONS: During a single HD session, ∼12 g AAs are lost into the dialysate, causing a significant decline in plasma AA concentrations. AA loss during HD can contribute substantially to protein malnutrition in end-stage renal disease patients. This study was registered at the Netherlands Trial Registry (NTR7101).

Associations of 24-Hour Urinary Sodium and Potassium Excretion with Cardiac Biomarkers: The Maastricht Study.

BACKGROUND: It is a matter of debate whether sodium and potassium intake are associated with heart disease. Further, the mechanisms underlying associations of sodium and potassium intake with cardiac events, if any, are not fully understood. OBJECTIVES: We examined cross-sectional associations of 24-h urinary sodium excretion (UNaE) and potassium excretion (UKE), as estimates of their intakes, with high-sensitivity cardiac troponins T (hs-cTnT) and I (hs-cTnI), and N-terminal pro-B-type natriuretic peptide (NT-proBNP), which are markers of cardiomyocyte injury and cardiac dysfunction. METHODS: We included 2961 participants from the population-based Maastricht Study (mean ± SD age 59.8 ± 8.2 y, 51.9% men), who completed the baseline survey between November 2010 and September 2013. Associations were examined with restricted cubic spline linear regression analyses and ordinary linear regression analyses, adjusted for demographics, lifestyle, and cardiovascular disease (CVD) risk factors. RESULTS: Median [IQR] 24-h UNaE and UKE were 3.7 [2.8-4.7] g/24 h and 3.0 [2.4-3.6] g/24 h, respectively. After adjustment for potential confounders, 24-h UNaE was not associated with hs-cTnT, hs-cTnI, and NT-proBNP concentrations. In contrast, after adjustment for potential confounders, lower 24-h UKE was nonlinearly associated with higher hs-cTnT and NT-proBNP. For example, as compared with the third/median quintile of 24-h UKE (range: 2.8-3.2 g/24 h), participants in the first quintile (range: 0.5-2.3 g/24 h) had 1.05 (95% CI: 0.99, 1.11) times higher hs-cTnT and 1.14 (95% CI: 1.03, 1.26) times higher NT-proBNP. Associations were similar after further adjustment for estimated glomerular filtration rate, albuminuria, blood pressure, and serum potassium. CONCLUSIONS: Twenty-four-hour UNaE was not associated with the studied cardiac biomarkers. In contrast, lower 24-h UKE was nonlinearly associated with higher hs-cTnT and NT-proBNP. This finding supports recommendations to increase potassium intake in the general population. In addition, it suggests that cardiac dysfunction and/or cardiomyocyte injury may underlie previously reported associations of lower potassium intake with CVD mortality.

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care. This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated 'digital clinics'. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased 'digital literacy' for all those implicated in their care.

Association of all-cause mortality with pre-dialysis systolic blood pressure and its peridialytic change in chronic hemodialysis patients.

BACKGROUND: Pre-dialysis systolic blood pressure (pre-HD SBP) and peridialytic SBP change have been associated with morbidity and mortality among hemodialysis (HD) patients in previous studies, but the nature of their interaction is not well understood. METHODS: We analyzed pre-HD SBP and peridialytic SBP change (calculated as post-HD SBP minus pre-HD SBP) between January 2001 and December 2012 in HD patients treated in US Fresenius Medical Care facilities. The baseline period was defined as Months 4-6 after HD initiation, and all-cause mortality was noted during follow-up. Only patients who survived baseline and had no missing covariates were included. Censoring events were renal transplantation, modality change or study end. We fitted a Cox proportional hazard model with a bivariate spline functions for the primary predictors (pre-HD SBP and peridialytic SBP change) with adjustment for age, gender, race, diabetes, access-type, relative interdialytic weight gain, body mass index, albumin, equilibrated normalized protein catabolic rate and ultrafiltration rate. RESULTS: A total of 172 199 patients were included. Mean age was 62.1 years, 61.6% were white and 55% were male. During a median follow-up of 25.0 months, 73 529 patients (42.7%) died. We found that a peridialytic SBP rise combined with high pre-HD SBP was associated with higher mortality. In contrast, when concurrent with low pre-HD SBP, a peridialytic SBP rise was associated with better survival. CONCLUSION: The association of pre-HD and peridialytic SBP change with mortality is complex. Our findings call for a joint, not isolated, interpretation of pre-HD SBP and peridialytic SBP change.

Intradialytic Hypotension: Mechanisms and Outcome.

Intradialytic hypotension (IDH) occurs in approximately 10-12% of treatments. Whereas several definitions for IDH are available, a nadir systolic blood pressure carries the strongest relation with outcome. Whereas the relation between IDH may partly be based on patient characteristics, it is likely that also impaired organ perfusion leading to permanent damage, plays a role in this relationship. The pathogenesis of IDH is multifactorial and is based on a combination of a decline in blood volume (BV) and impaired vascular resistance at a background of a reduced cardiovascular reserve. Measurements of absolute BV based on an on-line dilution method appear more promising than relative BV measurements in the prediction of IDH. Also, feedback treatments in which ultrafiltration rate is automatically adjusted based on changes in relative BV have not yet resulted in improvement. Frequent assessment of dry weight, attempting to reduce interdialytic weight gain and prescribing more frequent or longer dialysis treatments may aid in preventing IDH. The impaired vascular response can be improved using isothermic or cool dialysis treatment which has also been associated with a reduction in end organ damage, although their effect on mortality has not yet been assessed. For the future, identification of vulnerable patients based on artificial intelligence and on-line assessment of markers of organ perfusion may aid in individualizing treatment prescription, which will always remain dependent on the clinical context of the patient.

Using Bioimpedance Spectroscopy to Assess Volume Status in Dialysis Patients.

The aim of the paper is to reflect on the current status of bioimpedance spectroscopy (BIS) in fluid management in dialysis patients. BIS identifies fluid overload (FO) as a virtual (overhydration) compartment, which is calculated from the difference between the measured extracellular volume and the predicted values based on a fixed hydration of lean and adipose tissue mass. FO is highly prevalent in both hemodialysis (HD) and peritoneal dialysis (PD) patients, while levels of FO are at a population level comparable between PD patients and HD patients when measured before the dialysis treatment. Even mild levels of FO are independently related to outcome in patients on HD, PD as well as in nondialysis patients with advanced chronic kidney disease. FO is not only related to left ventricular hypertrophy (LVH) but also forms part of a multidimensional spectrum with noncardiovascular risk factors such as malnutrition and inflammation. Even after multiple adjustments, FO remains an independent predictor of mortality. BIS-assisted adjustment of dry weight in HD patients has been shown to improve hypertension control and LVH and has resulted in a decline in intradialytic symptomatology. On the other hand, with increased fluid removal, target weight may not always be reached due to an increase in intradialytic symptomatology, and care should be applied in target weight adjustment in fluid overloaded patients with severe malnutrition and/or inflammation. Although a reduction in hospitalization rate was suggested, the effect of BIS-guided dry weight adjustment on mortality has not yet been shown, however, although available studies are underpowered. In PD patients, results have been more equivocal, which may be partly related to differences in treatment protocols or study populations. Future large-scale studies are needed to assess the full potential of BIS.

Increased Mortality Associated with Higher Pre-Dialysis Serum Sodium Variability: Results of the International MONitoring Dialysis Outcome Initiative.

BACKGROUND: Low serum sodium (SNa) is associated with an increased mortality in chronic hemodialysis (HD) patients. Dialysis patients are thought to have an individual pre-dialysis SNa set-point, yet there is evidence for variability of pre-dialysis SNa in individual patient. In this study, we explored the association of several SNa variability metrics with all-cause mortality in a large patient population from the international MONitoring Dialysis Outcomes (MONDO) Initiative. METHODS: All adult incident patients from the MONDO database with more than 5 SNa measurements during the first year on HD were included. All patients were required to survive the first year on HD (defined as the baseline). During the subsequent 2 years of follow-up, all-cause mortality was recorded. The following variability indicators were calculated during baseline: mean SNa and its SD; average real variability (ARV, average the absolute distance of the 2 consecutive SNa measurements), and average directional range (DR, the difference between minimum and maximum values). We used Cox Proportional hazard model with bivariate spline terms to analyze the joint association of SNa and SD, ARV and DR, respectively, with all-cause mortality. While conducting the multivariate Cox regression analyses, patients were stratified into 3 groups of DR (Negative DR: -20≤ DR ≤ -6, Null DR: -6< DR < 6 and Positive DR: 6≤ DR ≤20) with the Null DR as the reference group. RESULTS: We included 20,216 patients in the study. A SNa ≤135 mEq/L was observed to be the strongest predictor of evaluated mortality risk. Higher SNa variability (quantified as SD, ARV, and DR) was also associated with an increased mortality irrespective of SNa levels. When compared with higher SD or ARV, greater DR showed a stronger association with an elevated risk of death. Controlling the Cox Proportional hazard models for additional parameters showed consistent results. CONCLUSION: Higher SNa variability associated with increased all-cause mortality at all levels of SNa. DR of SNa showed the strongest association with mortality and may constitute a Simple and novel prognostic indicator, easily applicable at the bedside.

All-cause mortality in relation to changes in relative blood volume during hemodialysis.

BACKGROUND: Relative blood volume (RBV) monitoring is widely used in hemodialysis (HD) patients, yet the association between intradialytic RBV and mortality is unknown. METHODS: Intradialytic RBV was recorded once/min during a 6-month baseline period; all-cause mortality was noted during follow-up. RBV at 1, 2 and 3 h into HD served as a predictor of all-cause mortality during follow-up. We employed Kaplan-Meier analysis, univariate and adjusted Cox proportional hazards models for survival analysis. RESULTS: We studied 842 patients. During follow-up (median 30.8 months), 249 patients (29.6%) died. The following hourly RBV ranges were associated with improved survival: first hour, 93-96% [hazard ratio (HR) 0.58 (95% confidence interval (CI) 0.42-0.79)]; second hour, 89-94% [HR 0.54 (95% CI 0.39-0.75)]; third hour, 86-92% [HR 0.46 (95% CI 0.33-0.65)]. In about one-third of patients the RBV was within these ranges and in two-thirds it was above. Subgroup analysis by median age (≤/> 61 years), sex, race (white/nonwhite), predialysis systolic blood pressure (SBP; ≤/> 130 mmHg) and median interdialytic weight gain (≤/> 2.3 kg) showed comparable favorable RBV ranges. Patients with a 3-h RBV between 86 and 92% were younger, had higher ultrafiltration volumes and rates, similar intradialytic average and nadir SBPs and hypotension rates, lower postdialysis SBP and a lower prevalence of congestive heart failure when compared with patients with an RBV >92%. In the multivariate Cox analysis, RBV ranges remained independent and significant outcome predictors. CONCLUSION: Specific hourly intradialytic RBV ranges are associated with lower all-cause mortality in chronic HD patients.

Body Fluids in End-Stage Renal Disease: Statics and Dynamics.

BACKGROUND: Abnormalities in fluid status in hemodialysis (HD) patients are highly prevalent and are related to adverse outcomes. SUMMARY: The inherent discontinuity of the HD procedure in combination with an often compromised cardiovascular response is a major contributor to this phenomenon. In addition, systemic inflammation and endothelial dysfunction are related to extracellular fluid overload (FO). Underlying this relation may be factors such as hypoalbuminemia and an increased capillary permeability, leading to an altered fluid distribution between the blood volume (BV) and the interstitial fluid compartments, compromising fluid removal during dialysis. Indeed, whereas estimates of extracellular volume by bioimpedance spectroscopy are highly predictive of mortality, absolute BV assessed by the saline dilution technique was predictive of intra-dialytic morbidity. Changes in relative BV during HD are positively related to ultrafiltration rate (UFR) and, at least in some studies, negatively to FO. High UFR is also related to changes in central venous oxygen saturation (ScvO2), a marker for tissue perfusion. On the one hand, high UFR and more pronounced declines in ScvO2, but on the other hand, flat relative BV curves are also predictive of mortality; the relation between outcome which statics and dynamics of fluid status appears to be complex. Key Message: While technological developments enable the clinician to monitor statics and dynamics of fluid status and hemodynamics during HD in an accessible way, the role of technology-based interventions needs further study.

Dialysate Calcium Levels: Do They Matter?

BACKGROUND: Calcium (Ca) is an essential element that plays a critical role in many biological processes. In dialysis patients, the regulation of Ca balance is highly complex, given the absence of kidney function, endocrine disturbances and the use of drugs such as phosphate binders, vitamin D analogues, and calcimimetics. Also, the use of different dialysate Ca (DCa) baths has profound effect on Ca balance, which depends both on the difference between the Ca concentration in the bath and the serum of the patients, as on the ultrafiltration volume. SUMMARY: The choice of DCa may have important short- and long-term consequences. While lower DCa (especially < 2.5 mEq/L) concentrations have been associated with an increased risk of sudden cardiac death in observational studies, DCa in the higher ranges (3.0 mEq/L and above) may contribute to vascular pathology. Intra-dialytic hemodynamics may also be affected by the choice of DCa. In general, lower DCa concentrations are associated with an increase, and higher DCa concentrations with a decrease in parathyroid hormone (PTH) levels. Preliminary data has suggested that a DCa of 2.75 mEq/L may help in obtaining a net zero intradialytic Ca balance in individual patients, but clinical experience is still limited. Key Message: The optimal Ca balance depends on multiple parameters including blood Ca levels, PTH and the use of phosphate binders and vitamin D analogues, as well as on the risk of hemodynamic stability and cardiac arrhythmias. Therefore, DCa prescription should be individualised. A DCa of 2.75 mEq/L may be useful adjunct for dialysis providers.

Cycles, Arrows and Turbulence: Time Patterns in Renal Disease, a Path from Epidemiology to Personalized Medicine?

Patients with end-stage renal disease (ESRD) experience unique patterns in their lifetime, such as the start of dialysis and renal transplantation. In addition, there is also an intricate link between ESRD and biological time patterns. In terms of cyclic patterns, the circadian blood pressure (BP) rhythm can be flattened, contributing to allostatic load, whereas the circadian temperature rhythm is related to the decline in BP during hemodialysis (HD). Seasonal variations in BP and interdialytic-weight gain have been observed in ESRD patients in addition to a profound relative increase in mortality during the winter period. Moreover, nonphysiological treatment patters are imposed in HD patients, leading to an excess mortality at the end of the long interdialytic interval. Recently, new evidence has emerged on the prognostic impact of trajectories of common clinical and laboratory parameters such as BP, body temperature, and serum albumin, in addition to single point in time measurements. Backward analysis of changes in cardiovascular, nutritional, and inflammatory parameters before the occurrence as hospitalization or death has shown that changes may already occur within months to even 1-2 years before the event, possibly providing a window of opportunity for earlier interventions. Disturbances in physiological variability, such as in heart rate, characterized by a loss of fractal patterns, are associated with increased mortality. In addition, an increase in random variability in different parameters such as BP and sodium is also associated with adverse outcomes. Novel techniques, based on time-dependent analysis of variability and trends and interactions of multiple physiological and laboratory parameters, for which machine-learning -approaches may be necessary, are likely of help to the clinician in the future. However, upcoming research should also evaluate whether dynamic patterns observed in large epidemiological studies have relevance for the individual risk profile of the patient.