European Nephrologists' Attitudes toward the Application of Artificial Intelligence in Clinical Practice: A Comprehensive Survey.

INTRODUCTION: The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyze large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients' treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, reducing healthcare costs, and consequently creating values for all the involved stakeholders. OBJECTIVES: We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care NephroCare) toward the application of artificial intelligence in clinical practice. METHODS: An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey. RESULTS: The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes. CONCLUSION: While artificial intelligence's potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice.

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.

Cephalic arch restenosis rates in hemodialysis patients with brachiocephalic fistulae: a retrospective multicenter analysis of 3301 patients.

BACKGROUND: We evaluated restenosis rates at the cephalic arch after percutaneous angioplasty and stenting procedures in patients with brachial artery to cephalic vein arteriovenous fistula (BCAVF) hemodialysis access. METHODS: We used data from adult hemodialysis patients treated at a national network of 44 outpatient interventional facilities during Oct 2011-2015. We included data from patients with BCAVF who received an exclusive angioplasty, or stent with angioplasty, for treatment of cephalic arch stenosis and had ≥1 subsequent evaluation of the cephalic arch. Median percent restenosis per month at cephalic arch and days between encounters was calculated from the 1st index to 2nd procedure, and for up to 4 subsequent encounters. Analyses were stratified by intervention and device types. RESULTS: We identified a cohort of 3301 patients (mean age 62.2 ± 13.9 years, 58.5% male, 33.2% white race) with a BCAVF who had an angioplasty, or stent, at the cephalic arch for an index and ≥ 1 follow-up procedure. Between the 1st index to 2nd procedure, patients who received an angioplasty (n = 2663) or stent (n = 933) showed a median decrease of 18.9 and 16.5% in luminal diameter per month and a median time of 93 and 91 days between encounters, respectively. Restenosis and day rates were similar for standard versus high-pressure angioplasties. Bare metal stents showed 10.1 percentage point higher restenosis rate compared to stent grafts. Restenosis rates and time to restenosis were relatively consistent across subsequent encounters. CONCLUSIONS: Findings suggest hemodialysis patients with a BCAVF who require an angioplasty or stent to treat a stenosis at the cephalic arch will have stenosis reformed at a rate of 18.9 and 16.5% per month after the first intervention, respectively. Findings suggest patients are at risk of having significant lesions at the cephalic arch within 3 months after the previous intervention.

Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries.

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.

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.

Transmission of SARS-CoV-2 considering shared chairs in outpatient dialysis: a real-world case-control study.

BACKGROUND: SARS-CoV-2 can remain transiently viable on surfaces. We examined if use of shared chairs in outpatient hemodialysis associates with a risk for indirect patient-to-patient transmission of SARS-CoV-2. METHODS: We used data from adults treated at 2,600 hemodialysis facilities in United States between February 1st and June 8th, 2020. We performed a retrospective case-control study matching each SARS-CoV-2 positive patient (case) to a non-SARS-CoV-2 patient (control) treated in the same dialysis shift. Cases and controls were matched on age, sex, race, facility, shift date, and treatment count. For each case-control pair, we traced backward 14 days to assess possible prior exposure from a 'shedding' SARS-CoV-2 positive patient who sat in the same chair immediately before the case or control. Conditional logistic regression models tested whether chair exposure after a shedding SARS-CoV-2 positive patient conferred a higher risk of SARS-CoV-2 infection to the immediate subsequent patient. RESULTS: Among 170,234 hemodialysis patients, 4,782 (2.8 %) tested positive for SARS-CoV-2 (mean age 64 years, 44 % female). Most facilities (68.5 %) had 0 to 1 positive SARS-CoV-2 patient. We matched 2,379 SARS-CoV-2 positive cases to 2,379 non-SARS-CoV-2 controls; 1.30 % (95 %CI 0.90 %, 1.87 %) of cases and 1.39 % (95 %CI 0.97 %, 1.97 %) of controls were exposed to a chair previously sat in by a shedding SARS-CoV-2 patient. Transmission risk among cases was not significantly different from controls (OR = 0.94; 95 %CI 0.57 to 1.54; p = 0.80). Results remained consistent in adjusted and sensitivity analyses. CONCLUSIONS: The risk of indirect patient-to-patient transmission of SARS-CoV-2 infection from dialysis chairs appears to be low.

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.

Clinical and predictive value of simplified creatinine index used as muscle mass surrogate in end-stage kidney disease haemodialysis patients-results from the international MONitoring Dialysis Outcome initiative.

BACKGROUND: Protein-energy wasting, muscle mass (MM) loss and sarcopenia are highly prevalent and associated with poor outcome in haemodialysis (HD) patients. Monitoring of MM and/or muscle metabolism in HD patients is of paramount importance for timely detection of muscle loss and to intervene adequately. In this study we assessed the reliability and reproducibility of a simplified creatinine index (SCI) as a surrogate marker of MM and explored its predictive value on outcome. METHOD: We included all in-centre HD patients from 16 European countries with at least one SCI. The baseline period was defined as 30 days before and after the first multifrequency bioimpedance spectroscopy measurement; the subsequent 7 years constituted the follow-up. SCI was calculated by the Canaud equation. Multivariate Cox proportional hazards models were applied to assess the association of SCI with all-cause mortality. Using backward analysis, we explored the trends of SCI before death. Bland-Altman analysis was performed to analyse the agreement between estimated and measured MM. RESULTS: We included 23 495 HD patients; 3662 were incident. Females and older patients have lower baseline SCI. Higher SCI was associated with a lower risk of mortality [hazard ratio 0.81 (95% confidence interval 0.79-0.82)]. SCI decline accelerated ∼5-7 months before death. Lean tissue index (LTI) estimated by SCI was correlated with measured LTI in both sexes (males: R2 = 0.94; females: R2 = 0.92; both P < 0.001). Bland-Altman analysis showed that measured LTI was 4.71 kg/m2 (±2 SD: -12.54-3.12) lower than estimated LTI. CONCLUSION: SCI is a simple, easily obtainable and clinically relevant surrogate marker of MM in HD patients.

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.

Impacts of dialysis adequacy and intradialytic hypotension on changes in dialysis recovery time.

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.

Impact of hemodialysis and post-dialysis period on granular activity levels.

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).

Prediction of Mortality and Hospitalization Risk Using Nutritional Indicators and Their Changes Over Time in a Large Prevalent Hemodialysis Cohort.

OBJECTIVE(S): Malnutrition and protein-energy wasting are associated with morbidity and mortality in hemodialysis patients. Existing nutritional scores rely primarily on cross-sectional data. Using readily available nutritional indicators, we developed models to predict the risk of mortality and hospitalization in prevalent hemodialysis patients. DESIGN AND METHODS: In this retrospective study, we constructed prediction models of 1-year mortality and hospitalization using generalized linear models, generalized additive models (GAM), classification tree, and random forest models. The models were compared using area under the receiver-operating characteristics curve (AUC) and calibration curves. Model predictors included nutritional and inflammation indicators, demographics, comorbidities, and slopes of all continuous variables over 6 months. Patients were randomly split in the ratio 2:1:1 into training, testing, and validation cohorts, respectively. We included patients with hemodialysis vintage ≥1 year from Fresenius Medical Care North America clinics from July 2011 to December 2012 (N = 21,802 in mortality analysis; N = 13,892 in hospitalization analysis).The outcome variables were 1-year mortality and hospitalization. RESULTS: For mortality prediction, GAM was the best model (AUC = 0.85, 95% confidence interval = 0.83-0.86), comprised of neutrophil-to-lymphocyte ratio slope, serum bicarbonate slope, and vintage as nonlinear predictors, and age, serum albumin, and creatinine as linear predictors. For hospitalization prediction, GAM was also the best model (AUC = 0.70, 95% confidence interval = 0.62-0.79) and included neutrophil-to-lymphocyte ratio slope, bicarbonate slope, volume of urea distribution, vintage, and phosphate slope as nonlinear predictors, in addition to albumin, congestive heart failure, age, phosphate, equilibrated normalized protein catabolic rate, and creatinine as linear predictors. Both models demonstrated good calibration, with mild overestimation of hospitalization risk at the highest risk interval. CONCLUSIONS: The GAM model can accurately predict the risk of mortality and hospitalization. Application of these prediction models could inform allocation of nutritional interventions to patients at highest nutritional risk.

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.

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.

Longitudinal patterns of health-related quality of life and dialysis modality: a national cohort study.

BACKGROUND: Health-related quality of life (HrQoL) varies among dialysis patients. However, little is known about the association of dialysis modality with HrQoL over time. We describe longitudinal patterns of HrQoL among chronic dialysis patients by treatment modality. METHODS: National retrospective cohort study of adult patients who initiated in-center dialysis or a home modality (peritoneal or home hemodialysis) between 1/2013 and 6/2015. Patients remained on the same modality for the first 120 days of the first two years. HrQoL was assessed by the Kidney Disease and Quality of Life-36 (KDQOL) survey in the first 120 days of the first two years after dialysis initiation. Home modality patients were matched to in-center patients in a 1:5 fashion. RESULTS: In-center (n=4234) and home modality (n=880) patients had similar demographic and clinical characteristics. In-center dialysis patients had lower mean KDQOL scores across several domains compared to home modality patients. For patients who remained on the same modality, there was no change in HrQoL. However, there were trends towards clinically meaningful changes in several aspects of HrQoL for patients who switched modalities. Specifically, physical functioning decreased for patients who switched from home to in-center dialysis (p< 0.05). CONCLUSIONS: Among a national cohort of chronic dialysis patients, there was a trend towards different patterns of HrQoL life that were only observed among patients who changed modality. Patients who switched from home to in-center modalities had significant lower physical functioning over time. Providers and patients should be mindful of HrQoL changes that may occur with dialysis modality change.

Lipid levels are inversely associated with infectious and all-cause mortality: international MONDO study results.

Cardiovascular (CV) events are increased 36-fold in patients with end-stage renal disease. However, randomized controlled trials to lower LDL cholesterol (LDL-C) and serum total cholesterol (TC) have not shown significant mortality improvements. An inverse association of TC and LDL-C with all-cause and CV mortality has been observed in patients on chronic dialysis. Lipoproteins also may protect against infectious diseases. We used data from 37,250 patients in the international Monitoring Dialysis Outcomes (MONDO) database to evaluate the association between lipids and infection-related or CV mortality. The study began on the first day of lipid measurement and continued for up to 4 years. We applied Cox proportional models with time-varying covariates to study associations of LDL-C, HDL cholesterol (HDL-C), and triglycerides (TGs) with all-cause, CV, infectious, and other causes of death. Overall, 6,147 patients died (19.2% from CV, 13.2% from infection, and 67.6% from other causes). After multivariable adjustment, higher LDL-C, HDL-C, and TGs were independently associated with lower all-cause death risk. Neither LDL-C nor TGs were associated with CV death, and HDL-C was associated with lower CV risk. Higher LDL-C and HDL-C were associated with a lower risk of death from infection or other non-CV causes. LDL-C was associated with reduced all-cause and infectious, but not CV mortality, which resulted in the inverse association with all-cause mortality.

A Cross-Sectional Study of Growth and Metabolic Bone Disease in a Pediatric Global Cohort Undergoing Chronic Hemodialysis.

OBJECTIVE: We sought to assess worldwide differences among pediatric patients undergoing hemodialysis. Because practices differ widely regarding nutritional resources, treatment practice, and access to renal replacement therapy, investigators from the Pediatric Investigation and Close Collaboration to examine Ongoing Life Outcomes, the pediatric subset of the MONitoring Dialysis Outcomes Cohort (PICCOLO MONDO) performed this cross-sectional study. We hypothesized that growth would be better in developed countries, possibly at the expense of bone mineral disease. STUDY DESIGN: In this cross-sectional study, we analyzed growth by height z score and recommended age-specific bone mineral metabolism markers from 225 patients <18 years of age maintained on hemodialysis, between the years of 2000 to 2012 from 21 countries in different regions. RESULTS: The patients' median age was 16 (IQR 14-17) years, and 45% were females. A height z score less than the third percentile was noted in 34% of the cohort, whereas >66% of patients reported normal heights, with patients from North America having the greatest proportion (>80%). More than 70% of the entire cohort had greater than the age-recommended levels of phosphorus, particularly in the Asia-Pacific and North America, where we also observed the greatest body mass index z score (0.99 ± 1.6) and parathyroid hormone levels (557.1 [268.4-740.5]). Below-recommended parathyroid hormone levels were noted in 26% and elevated levels in 61% of the entire sample, particularly in the Asia Pacific region. Lower-than-recommended calcium levels were noted in 36% of the entire cohort, particularly in Latin America. CONCLUSIONS: We found regional differences in growth- and age-adjusted bone mineral metabolism markers. Children from North America had the best growth, received the most dialysis, but also had the worst phosphate control and body mass index z scores.

Pre-dialysis fluid status, pre-dialysis systolic blood pressure and outcome in prevalent haemodialysis patients: results of an international cohort study on behalf of the MONDO initiative.

Background: Pre-dialysis fluid overload (FO) associates with mortality and causes elevated pre-dialysis systolic blood pressure (pre-SBP). However, low pre-SBP is associated with increased mortality in haemodialysis patients. The objective of this study was to investigate the interaction between pre-dialysis fluid status (FS) and pre-SBP in association with mortality. Methods: We included all patients from the international Monitoring Dialysis Outcome Initiative (MONDO) database with a pre-dialysis multifrequency bioimpedance spectroscopy measurement in the year 2011. We used all parameters available during a 90-day baseline period. All-cause mortality was recorded during 1-year follow-up. Associations with outcome were assessed with Cox models and a smoothing spline Cox analysis. Results: We included 8883 patients. In patients with pre-dialysis FO (>+1.1 to +2.5 L), pre-SBP <110 mmHg was associated with an increased risk of death {hazard ratio (HR) 1.52 [95% confidence interval (CI) 1.06-2.17]}. An increased risk of death was also associated with pre-dialysis fluid depletion (FD; <-1.1 L) combined with a pre-SBP <140 mmHg. In normovolemic (NV) patients, low pre-SBP <110 mmHg was associated with better survival [HR 0.46 (95% CI 0.23-0.91)]. Also, post-dialysis FD associated with a survival benefit. Results were similar when inflammation was present. Only high ultrafiltration rate could not explain the higher mortality rates observed. Conclusion: The relation between pre-SBP and outcome is dependent on pre-dialysis FS. Low pre-SBP appears to be disadvantageous in patients with FO or FD, but not in NV patients. Post-dialysis FD was found to associate with improved survival. Therefore, we suggest interpreting pre-SBP levels in the context of FS and not as an isolated marker.

The Association of Weekly pre-Hemodialysis Systolic Blood Pressure and Following Week Mortality.

BACKGROUND/AIMS: Few studies examine the impact of systolic blood pressure (SBP) on mortality in the incident hemodialysis (HD) period, and throughout the first HD year. This large retrospective observational study analyzes the impact of "current" and cumulative low preSBP <110 mmHg (L), and variations in preSBP on short-term (1 week) mortality over the first HD year. METHODS: Weekly mean preSBP for HD weeks 1 to 51 was categorized into L or high preSBP>=110 mmHg (H) for each patient. A generalized linear model (GLM) was used to compute the probability of death in the following week. The model includes age, gender, race and three preSBP-related parameters: (a) percent of prior weeks with L preSBP; (b) percent of prior weeks with switching between L to H; (c) "current" week's preSBP as a binary variable. Separate models were constructed that include demographics and BP-related parameters (a), (b), and (c) separately. RESULTS: In a model combining (a), (b), and (c) above, "current" week L preSBP is associated with increased odds ratio for following week mortality throughout the first HD year. The percent of prior week's L and more switching between L and H are less significantly associated with short-term mortality. In models including (a), (b), and (c) separately, "current" L preSBP is associated with higher mortality. CONCLUSION: This study confirms an association of L preSBP with increased short-term mortality which is maintained over the first HD year. Percent of L preSBP in prior weeks, switching between L and H, and "current" week L are all associated with short-term mortality risk, but "current" week L preSBP is most significant.