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.

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.

Mineral bone disease in autosomal dominant polycystic kidney disease.

Mice with disruption of Pkd1 in osteoblasts demonstrate reduced bone mineral density, trabecular bone volume and cortical thickness. To date, the bone phenotype in adult patients with autosomal dominant polycystic kidney disease (ADPKD) with stage I and II chronic kidney disease has not been investigated. To examine this, we characterized biochemical markers of mineral metabolism, examined bone turnover and biology, and estimated risk of fracture in patients with ADPKD. Markers of mineral metabolism were measured in 944 patients with ADPKD and other causes of kidney disease. Histomorphometry and immunohistochemistry were compared on bone biopsies from 20 patients with ADPKD with a mean eGFR of 97 ml/min/1.73m2 and 17 healthy individuals. Furthermore, adults with end stage kidney disease (ESKD) initiating hemodialysis between 2002-2013 and estimated the risk of bone fracture associated with ADPKD as compared to other etiologies of kidney disease were examined. Intact fibroblast growth factor 23 was higher and total alkaline phosphatase lower in patients with compared to patients without ADPKD with chronic kidney disease. Compared to healthy individuals, patients with ADPKD demonstrated significantly lower osteoid volume/bone volume (0.61 vs. 1.21%) and bone formation rate/bone surface (0.012 vs. 0.026 µm3/µm2/day). ESKD due to ADPKD was not associated with a higher risk of fracture as compared to ESKD due to diabetes (age adjusted incidence rate ratio: 0.53 (95% confidence interval 0.31, 0.74) or compared to other etiologies of kidney disease. Thus, individuals with ADPKD have lower alkaline phosphatase, higher circulating intact fibroblast growth factor 23 and decreased bone formation rate. However, ADPKD is not associated with higher rates of bone fracture in ESKD.

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.

Hepatitis B Vaccination Response in Hemodialysis Patients: The Impact of Dialysis Shift.

BACKGROUND/AIMS: Hepatitis B (HB) vaccination in hemodialysis patients is important as they are at a higher risk of contracting HB. However, hemodialysis patients have a lower HB seroconversion rate than their healthy counterparts. As better sleep has been associated with better seroconversion in healthy populations and early hemodialysis start has been linked to significant sleep-wake disturbances in hemodialysis patients, we examined if hemodialysis treatment start time is associated with HB vaccination response. METHODS: Demographics, standard-of-care clinical, laboratory, and treatment parameters, dialysis shift data, HB antigen status, HB vaccination status, and HB titers were collected from hemodialysis patients in Fresenius clinics from January 2010 to December 2015. Patients in our analysis received 90% of dialysis treatments either before or after 8:30 a.m., were negative for HB antigen, and received a complete series of HB vaccination (Engerix B® or Recombivax HB™). Univariate and multivariate regression models examined whether dialysis start time is a predictor of HB vaccination response. RESULTS: Patients were 65 years old, 57% male, and had a HD vintage of 10 months. Patients whose dialysis treatments started before 8:30 a.m. were more likely to be younger, male, and have a greater dialysis vintage. Patients receiving Engerix B® and starting dialysis before 8:30 a.m. had a significantly higher seroconversion rate compared to patients who started dialysis after 8:30 a.m. Early dialysis start was a significant predictor of seroconversion in univariate and multivariate regression including male gender, but not in multivariate regression including age, neutrophil-to-lymphocyte ratio, and vintage. CONCLUSION: While better sleep following vaccination is associated with seroconversion in the general population, this is not the case in hemodialysis patients after multivariate adjustment. In the context of end-stage kidney disease, early dialysis start is not a significant predictor of HB vaccination response. The association between objectively measured postvaccination sleep duration and seroconversion rate should be investigated.

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.

Implications of the Advancing American Kidney Health Initiative for kidney transplant centers.

The announcement of the Advancing American Kidney Health (AAKH) Initiative on July 10, 2019 was met with a mix of excitement and trepidation, befitting a proposed radical reconfiguration of the delivery of kidney disease care. Aspiring to reduce the incidence of end-stage renal disease, increase the prevalence of home dialysis, and double the number of organs available for transplant, the AAKH payment models primarily focus on incenting behaviors of general nephrologists, though actualizing positive incentives will require the active cooperation of dialysis providers and transplant centers. Here, we review the AAKH initiatives' potential impact on all stakeholders and opine on financial and regulatory pressures on kidney transplant programs, outlining areas of uncertainty and concern, and suggest key points of reflection for clinical and administrative leaders of kidney transplant centers weighing participation in any of the voluntary payment models.

Mortality and Hospitalizations among Sickle Cell Disease Patients with End-Stage Kidney Disease Initiating Dialysis.

BACKGROUND: Sickle cell disease (SCD) is the most common inherited hematological disorder and a well-described risk factor for end-stage kidney disease (ESKD). Mortality and hospitalizations among patients with SCD who develop ESKD remain understudied. Furthermore, prior studies focused only on SCD patients where ESKD was caused by SCD. We aimed to describe mortality and hospitalization risk in all SCD patients initiating dialysis and explore risk factors for mortality and hospitalization. METHODS: We performed a national observational cohort study of African American ESKD patients initiating dialysis (2000-2014) in facilities affiliated with a large dialysis provider. SCD was identified by diagnosis codes and matched to a reference population (non-SCD) by age, sex, dialysis initiation year, and geographic region of care. Sensitivity analyses were conducted by restricting to patients where SCD was recorded as the cause of ESKD. RESULTS: We identified 504 SCD patients (mean age: 47 ± 14 years; 48% females) and 1,425 reference patients (mean age: 46 ± 14 years; 49% females). The median follow-up was 2.4 (IQR 1.0-4.5) years. Compared to the reference, SCD was associated with higher mortality risk (hazard ratio 1.66; 95% confidence interval [CI]: 1.36-2.03) and higher hospitalization rates (incidence rate ratio 2.12; 95% CI: 1.88-2.38) in multivariable analyses. Exploratory multivariable mortality risk models showed the largest mortality risk attenuation with the addition of time-varying hemoglobin and high-dose erythropoietin, but the association of SCD with mortality remained significant. Sensitivity analyses (restricted to ESKD caused by SCD) also showed significant associations between SCD and mortality and hospitalizations, but with larger effect estimates. High-dose erythropoietin was associated with the highest risk for mortality and hospitalization in SCD. CONCLUSIONS: Among ESKD patients, SCD is associated with a higher risk for mortality and hospitalization, particularly in patients where SCD is identified as the cause of ESKD.

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.

The authority of courage and compassion: Healthcare policy leadership in addressing the kidney disease public health epidemic.

Recent developments in US kidney-related healthcare policy have made chronic kidney disease (CKD) a societal focus in the United States. In the biggest policy change since the 1972 Social Security Amendments that extended Medicare coverage to patients with kidney failure regardless of age, a 2019 presidential executive order pledged to reduce end-stage kidney disease, slow CKD progression, increase kidney transplants, and focus on home dialysis care. This manuscript seeks to outline key factors that can enable this milestone moment to evolve a policy framework that improves the health of society while being economically sustainable. Understanding the sociohistorical context of healthcare policy and the related lessons learned demonstrates that policy must take a broader view of the societal and system wide factors that affect chronic illness. Addressing the full breadth of the CKD epidemic requires looking at factors from both inside and outside traditional medical-pathophysiological environments, including social determinants of health. This more fulsome insight will enable policy to better align the broad range of people and organizations who are working to combat the disease. By creating patient-centered policy that both evolves with the speed of innovation and addresses root causes of CKD instead of narrowly focusing on symptoms or comorbidities alone, leaders in the public square have an historic opportunity to thoughtfully create the common ground of a lasting policy legacy that improves society's health today and for generations to come.

Phosphates in medications: Impact on dialysis patients
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Maintaining phosphorus balance in in-center hemodialysis (ICHD) patients is problematic despite recommended dietary restriction, dialysis, and phosphate binder use. Rarely is P content in prescribed medications considered, but this source should raise concern. Data was obtained from the Fresenius Kidney Care (FKC) electronic data warehouse Knowledge Center and MedReview-eRx accessed Surescripts, housing > 80% of US-filled prescriptions. Adult FKC ICHD patients prescribed ≥ 1 medication in the MedReview-eRx database were analyzed (695,759 prescriptions). Information collected included medication dose, dose unit, dose timing, strength, start and stop dates, refills, demographic information, admission history, and modality type. Numbers of patients, prescriptions by individual medication, and drug class were then analyzed. Medications prescribed > 100 times were reported. Median doses/day (number of tablets) were calculated for each medication (open order on randomly selected day). Phosphate content of medications taken in FKC clinics was assessed using routinely used pharmacology references, and potential resulting phosphate and pill burden were also calculated. The top five prescribed drug classes in FKC dialysis patients were calcium-channel blockers (22%), proton pump inhibitors (PPIs; 18%), acetaminophen-opioid (AO; 13%), angiotensin-converting enzyme inhibitors (ACEi; 10%), and α2-agonists (9%). The maximum phosphate added for different medications varied by manufacturer. For instance, at median daily doses, phosphate contributions from the top five medications prescribed were 112 mg for amlodipine, 116.2 mg from lisinopril, 6.7 mg from clonidine, 0 mg from acetaminophen, and 200 mg for omeprazole. Prescribing these together could increase the daily phosphate load by 428 mg, forcing the patient to exceed the recommended daily intake (RDI) with food and drink. Phosphate content in medications prescribed to HD patients can substantially contribute to the daily phosphate load and, in combination, may even exceed the daily recommended dietary phosphate intake. Healthcare providers should monitor all medications containing phosphate prescribed in order to minimize risk of uncontrolled hyperphosphatemia and poor adherence.
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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.

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.

The TiME Trial: A Fully Embedded, Cluster-Randomized, Pragmatic Trial of Hemodialysis Session Duration.

BACKGROUND: Data from clinical trials to inform practice in maintenance hemodialysis are limited. Incorporating randomized trials into dialysis clinical care delivery should help generate practice-guiding evidence, but the feasibility of this approach has not been established. METHODS: To develop approaches for embedding trials into routine delivery of maintenance hemodialysis, we performed a cluster-randomized, pragmatic trial demonstration project, the Time to Reduce Mortality in ESRD (TiME) trial, evaluating effects of session duration on mortality (primary outcome) and hospitalization rate. Dialysis facilities randomized to the intervention adopted a default session duration ≥4.25 hours (255 minutes) for incident patients; those randomized to usual care had no trial-driven approach to session duration. Implementation was highly centralized, with no on-site research personnel and complete reliance on clinically acquired data. We used multiple strategies to engage facility personnel and participating patients. RESULTS: The trial enrolled 7035 incident patients from 266 dialysis units. We discontinued the trial at a median follow-up of 1.1 years because of an inadequate between-group difference in session duration. For the primary analysis population (participants with estimated body water ≤42.5 L), mean session duration was 216 minutes for the intervention group and 207 minutes for the usual care group. We found no reduction in mortality or hospitalization rate for the intervention versus usual care. CONCLUSIONS: Although a highly pragmatic design allowed efficient enrollment, data acquisition, and monitoring, intervention uptake was insufficient to determine whether longer hemodialysis sessions improve outcomes. More effective strategies for engaging clinical personnel and patients are likely required to evaluate clinical trial interventions that are fully embedded in care delivery.

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.

Integrating kidney transplantation into value-based care for people with renal failure.

Healthcare reimbursement is increasingly tied to value instead of volume, with special attention paid to resource-intensive populations such as patients with renal disease. To this end, Medicare has sponsored pilot projects to encourage providers to develop care coordination and population health management strategies to provide quality care while reducing resource utilization. In this Personal Viewpoint essay, we argue in favor of expanding one such pilot project-the Comprehensive ESRD Care (CEC) initiative-to include patients with advanced chronic kidney disease and kidney transplant recipients. The implementation of the Medicare Access and CHIP Reauthorization Act (MACRA) offers a time-sensitive incentive for transplant centers in particular to align with extant CECs. An "expanded" CEC model proffers opportunity for robust cooperation between general nephrology practices, dialysis providers, and transplant centers to develop care coordination strategies for all patients with renal disease, realign incentives for all clinical stakeholders to increase kidney transplantation rates, and reduce total costs of care.

Association of Smoking Status With Mortality and Hospitalization in Hemodialysis Patients.

RATIONALE & OBJECTIVE: The relationship between tobacco smoking and comorbid condition outcomes in hemodialysis (HD) patients is not well understood. This study examined the association of tobacco smoking status with hospitalization and mortality in HD patients. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: Adult HD patients at 2,223 US dialysis centers with HD vintage of 30 days or less who completed a tobacco smoking status survey as part of standard care between April 2013 and June 2015. PREDICTOR: Tobacco smoking category: never smoked, currently living with smoker, former smoker, moderate smoker (<1 pack per day), or heavy smoker (≥1 pack per day). OUTCOMES: Death and hospital admissions within 2 years of the tobacco smoking survey. ANALYTICAL APPROACH: Kaplan-Meier analysis and Cox proportional hazards regression for time to death; cumulative incidence function and Cox proportional hazards regression for time to first hospitalization; negative-binomial regression for number of hospitalizations. RESULTS: Of 22,230 patients studied, 13% were active smokers. Mortality probabilities increased with greater exposure to smoking (17%, 22%, 23%, and 27% for never, moderate, former, and heavy smokers, respectively; P<0.001), as did incidence rates for first hospitalization (23%, 27%, 27%, and 30%, respectively; P<0.001). Compared to never smoked, heavy smokers had the highest mortality rate (HR for heavy smokers, 1.41 [95% CI, 1.18-1.69]; HR for moderate smokers, 1.39 [95% CI, 1.24-1.55]; HR for former smokers, 1.19 [95% CI, 1.11-1.28]). Living with a smoker was not associated with mortality (HR, 0.93; 95% CI, 0.72-1.22). HRs for first hospitalization followed similar patterns. The incidence rate of mortality for active smokers with diabetes was 173.7/1,000 patient-years and 103.5/1,000 patient-years for those who never smoked (incidence rate ratio, 1.68; P<0.001). LIMITATIONS: Self-reported survey without detailed history of smoking/cessation. CONCLUSIONS: Risks for death and hospitalization are elevated among HD patients who smoke, being highest among younger individuals and those with diabetes. Second-hand smoke was not associated with poor clinical outcomes.

Switching from Epoetin Alfa (Epogen®) to Epoetin Alfa-Epbx (RetacritTM) Using a Specified Dosing Algorithm: A Randomized, Non-Inferiority Study in Adults on Hemodialysis.

BACKGROUND: For patients with anemia undergoing hemodialysis, erythropoiesis-stimulating agents (ESAs) are typically dosed via precise algorithms. Using one such algorithm, we assessed the maintenance of hemoglobin levels in patients switched from epoetin alfa reference product (Epogen®) to epoetin alfa-epbx (RetacritTM; a biosimilar to US-licensed Epogen®/Procrit®). METHODS: This randomized, open-label, non-inferiority study was conducted at Fresenius Medical Care North America (FMCNA) hemodialysis centers. Patients with anemia and chronic kidney disease undergoing maintenance hemodialysis and receiving routine intravenous (IV) Epogen® were randomized 1: 1 to switch to IV RetacritTM or continue standard-of-care (Epogen®) for 24 weeks, using analogous versions of the FMCNA ESA-dosing algorithm. The primary endpoint was the proportion of time patients' hemoglobin was 9-11 g/dL during weeks 17-24. RESULTS: Of 432 randomized patients, 418 received treatment (RetacritTM, n = 212; standard-of-care, n = 206) and comprised the full analysis set. A similar proportion of patients discontinued from each arm. The proportion of time patients' hemoglobin was within the target range was 61.9% (95% CI 57.5-66.2) in the RetacritTM arm and 63.3% (95% CI 58.7-67.7) in the standard-of-care arm. The difference in proportions between treatment arms was -1.4% (95% CI -7.6 to 4.9), and the lower bound of the confidence interval was within the pre-specified non-inferiority margin of -12.5%. There was no statistically significant difference between arms in the mean change from baseline in the weekly mean ESA dose during weeks 17-24, and no clinically relevant differences in safety outcomes. CONCLUSIONS: Switching to RetacritTM was non-inferior to continuing -Epogen® in maintaining hemoglobin levels in patients receiving hemodialysis, when both ESAs were dosed using a specified algorithm (ClinicalTrials.gov, NCT02504294).