A phase 3b, multicenter, open-label, single-arm study of roxadustat within a US dialysis organization: The DENALI study.

INTRODUCTION: Roxadustat is an oral hypoxia-inducible factor prolyl hydroxylase inhibitor approved in several regions for the treatment of anemia of chronic kidney disease (CKD). DENALI, a phase 3b study, evaluated the efficacy, safety, and feasibility of roxadustat in patients with anemia of CKD receiving in-center or home dialysis. METHODS: Eligible patients received open-label roxadustat, dosed three times weekly for 24 weeks, with an optional extension of ≤1 year. Initial dosing depended on erythropoiesis-stimulating agent (ESA) dose at screening for patients receiving ESAs (≥6 weeks) and weight-based for those not (total <6 weeks). Primary efficacy endpoints were proportion of patients with mean hemoglobin (Hb) ≥10.0 g/dL averaged over Weeks 16-24, and mean Hb change from baseline to the average during Weeks 16-24. Treatment-emergent adverse events (TEAEs) and treatment-emergent serious adverse events (TESAEs) were assessed. FINDINGS: Of 281 patients screened, 203 were treated and 201 included in the full analysis set. Overall, 166 patients completed the 24-week treatment period and 126 continued into the extension period. Mean baseline Hb was 10.4 g/dL and 82.6% received in-center hemodialysis. Overall, 84.6% of patients achieved a mean Hb ≥ 10.0 g/dL averaged Weeks 16-24. Mean (standard deviation) Hb change from baseline averaged Weeks 16-24 was 0.5 (1.0) g/dL. Prespecified subgroup analyses were consistent with primary analyses. Dosing adherence was 94%. Overall, 3.0% of patients received a red blood cell transfusion at up to Week 24. TEAEs and TESAEs were reported by 71.4% and 25.6% of patients, respectively. The most frequently reported TESAEs were COVID-19 (n = 5; 2.5%), and acute myocardial infarction, pneumonia, and sepsis (each n = 4; 2.0%). DISCUSSION: Roxadustat effectively achieved and/or maintained mean Hb levels ≥10.0 g/dL in patients receiving dialysis. The feasibility of incorporating oral roxadustat into dialysis organizations was successfully demonstrated with high dosing adherence. No new safety signals were identified.

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.

Medication Safety in Dialysis Care.

The outpatient dialysis setting presents unique challenges in the medication process. Dialysis staff conduct all steps in the medication process, including transcribing and verifying orders, preparing and administering medications, and monitoring for therapeutic and adverse effects. When addressing best medication practices, consideration should be given to education and resources provided to staff. This article explores the multiple strategies taken by a national dialysis network to support clinical staff and improve patient safety.

War in Ukraine and dialysis treatment: human suffering and organizational challenges.

In January 2021, there were 9648 patients in Ukraine on kidney replacement therapy, including 8717 on extracorporeal therapies and 931 on peritoneal dialysis. On 24 February 2022, foreign troops entered the territory of Ukraine. Before the war, the Fresenius Medical Care dialysis network in Ukraine operated three medical centres. These medical centres provided haemodialysis therapy to 349 end-stage kidney disease patients. In addition, Fresenius Medical Care Ukraine delivered medical supplies to almost all regions of Ukraine. Even though Fresenius Medical Care's share of end-stage kidney disease patients on dialysis is small, a brief narrative account of the managerial challenges that Fresenius Medical Care Ukraine and the clinical directors of the Fresenius Medical Care centres had to face, as well as the suffering of the dialysis population, is a useful testimony of the burden imposed by war on these frail, high-risk patients dependent on a complex technology such as dialysis. The war in Ukraine is causing immense suffering for the dialysis population of this country and has called for heroic efforts from dialysis personnel. The experience of a small dialysis network treating a minority of dialysis patients in Ukraine is described. Guaranteeing dialysis treatment has been and remains an enormous challenge in Ukraine and we are confident that the generosity and the courage of Ukrainian dialysis staff and international aid will help to mitigate this tragic suffering.

High Protein Whole Food Snack and Albumin Outcomes in Patients With Dialysis-Dependent Chronic Kidney Disease.

OBJECTIVE: Protein-energy wasting is common among patients on hemodialysis (HD). This study sought to define effects that a novel, post-HD, high-calorie, high-protein whole food snack had on patients' serum albumin (serum alb), serum phosphorus and equilibrated normalized protein catabolic rate (enPCR). METHODS: A 12-month (6 months intervention, 6 months pre/post data collection), single-center, unblinded study was conducted. Participants (n = 67) consumed, ad libitum, a whole food snack post-HD for 6 treatments each month. Upon analysis, regression models identified relationships between serum alb and whole food snack consumption across follow up. Predefined effect size anticipated was + 0.2 g/dL. Patients were stratified by high (≥4 g/dL) or low (<4 g/dL) mean serum alb during a 3-month baseline period. Paired t-tests compared mean per patient difference in serum alb, enPCR and serum phosphorus from baseline to each month of follow up, stratified by high (≥640 g) or low (<640 g) consumption of the whole food snack (a priori caloric estimation). RESULTS: Linear regression models showed positive associations between higher serum alb and enPCR with higher whole food snack consumption across follow up (all P < .05). Assessments from baseline to each follow-up month show some increases in serum alb, yet t test comparisons were not significant. No significant changes were seen in serum phosphorus levels during follow-up. CONCLUSION: Albeit the catabolic effects of HD are well-known, effective nutritional interventions are scarce. Results showed that providing a whole food snack post-HD to individuals with serum alb <4.0 g/dL may be beneficial but further studies are recommended.

Effectiveness of cold HD for the prevention of HD hypotension and mortality in the general HD population.

BACKGROUND: Cold hemodialysis (HD) prevented intradialysis hypotension (IDH) in small, short-term, randomized trials in selected patients with IDH. Whether this treatments prevents IDH and mortality in the HD population at large is unknown. METHODS: We investigated the relationship between dialysate temperature and the risk of IDH, i.e. nadir blood pressure <90 mmHg (generalized estimating equation model) and all-cause mortality (Cox's regression) in an incident cohort of HD patients (n = 8071). To control for confounding by bias by indication and other factors we applied instrumental variables adjusting for case mix at facility level. RESULTS: Twenty-seven percent of patients in the study cohort were systematically treated with a dialysate temperature ≤35.5°C. Over a median follow-up of 13.6 months (interquartile range 5.2-26.1 months), a 0.5°C reduction of the dialysate temperature was associated with a small (-2.4%) reduction of the risk of IDH [odds ratio (OR) 0.976, 95% confidence interval (CI) 0.957-0.995, P = .013]. In case-mix, facility-level adjusted analysis, the association became much stronger (OR 0.67, 95% CI 0.63-0.72, risk reduction = 33%, P < .001). In contrast, colder dialysate temperature had no effect on mortality both in the unadjusted [hazard ratio (HR) (0.5°C decrease) 1.074, 95% CI 0.972-1.187, P = .16] and case-mix-adjusted analysis at facility level (HR 1.01, 95% CI 0.88-1.16, P = .84). Similar results were registered in additional analyses by instrumental variables applying the median dialysate temperature or the facility percentage of patients prescribed a dialysate temperature <36°C. Further analyses restricted to patients with recurrent IDH fully confirmed these findings. CONCLUSIONS: Cold HD was associated with IDH in the HD population but had no association with all-cause mortality.

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.

Virtual Screening for the Discovery of Microbiome ß-Glucuronidase Inhibitors to Alleviate Cancer Drug Toxicity.

Despite the potency of most first-line anti-cancer drugs, nonadherence to these drug regimens remains high and is attributable to the prevalence of "off-target" drug effects that result in serious adverse events (SAEs) like hair loss, nausea, vomiting, and diarrhea. Some anti-cancer drugs are converted by liver uridine 5'-diphospho-glucuronosyltransferases through homeostatic host metabolism to form drug-glucuronide conjugates. These sugar-conjugated metabolites are generally inactive and can be safely excreted via the biliary system into the gastrointestinal tract. However, ß-glucuronidase (ßGUS) enzymes expressed by commensal gut bacteria can remove the glucuronic acid moiety, producing the reactivated drug and triggering dose-limiting side effects. Small-molecule ßGUS inhibitors may reduce this drug-induced gut toxicity, allowing patients to complete their full course of treatment. Herein, we report the discovery of novel chemical series of ßGUS inhibitors by structure-based virtual high-throughput screening (vHTS). We developed homology models for ßGUS and applied them to large-scale vHTS against nearly 400,000 compounds within the chemical libraries of the National Center for Advancing Translational Sciences at the National Institutes of Health. From the vHTS results, we cherry-picked 291 compounds via a multifactor prioritization procedure, providing 69 diverse compounds that exhibited positive inhibitory activity in a follow-up ßGUS biochemical assay in vitro. Our findings correspond to a hit rate of 24% and could inform the successful downstream development of a therapeutic adjunct that targets the human microbiome to prevent SAEs associated with first-line, standard-of-care anti-cancer drugs.

The CALCIPHYX study: a randomized, double-blind, placebo-controlled, Phase 3 clinical trial of SNF472 for the treatment of calciphylaxis.

BACKGROUND: Calcific uraemic arteriolopathy (CUA; calciphylaxis) is a rare disease seen predominantly in patients receiving dialysis. Calciphylaxis is characterized by poorly healing or non-healing wounds, and is associated with mortality, substantial morbidity related to infection and typically severe pain. In an open-label Phase 2 clinical trial, SNF472, a selective inhibitor of vascular calcification, was well-tolerated and associated with improvement in wound healing, reduction of wound-related pain and improvement in wound-related quality of life (QoL). Those results informed the design of the CALCIPHYX trial, an ongoing, randomized, placebo-controlled, Phase 3 trial of SNF472 for treatment of calciphylaxis. METHODS: In CALCIPHYX, 66 patients receiving haemodialysis who have an ulcerated calciphylaxis lesion will be randomized 1:1 to double-blind SNF472 (7 mg/kg intravenously) or placebo three times weekly for 12 weeks (Part 1), then receive open-label SNF472 for 12 weeks (Part 2). All patients will receive stable background care, which may include pain medications and sodium thiosulphate, in accordance with the clinical practices of each site. A statistically significant difference between the SNF472 and placebo groups for improvement of either primary endpoint at Week 12 will demonstrate efficacy of SNF472: change in Bates-Jensen Wound Assessment Tool-CUA (a quantitative wound assessment tool for evaluating calciphylaxis lesions) or change in pain visual analogue scale score. Additional endpoints will address wound-related QoL, qualitative changes in wounds, wound size, analgesic use and safety. CONCLUSIONS: This randomized, placebo-controlled Phase 3 clinical trial will examine the efficacy and safety of SNF472 in patients who have ulcerated calciphylaxis lesions. Patient recruitment is ongoing.

Trajectories of clinical and laboratory characteristics associated with COVID-19 in hemodialysis patients by survival.

INTRODUCTION: The clinical impact of COVID-19 has not been established in the dialysis population. We evaluated the trajectories of clinical and laboratory parameters in hemodialysis (HD) patients. METHODS: We used data from adult HD patients treated at an integrated kidney disease company who received a reverse transcription polymerase chain reaction (RT-PCR) test to investigate suspicion of a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection between May 1 and September 1, 2020. Nonparametric smoothing splines were used to fit data for individual trajectories and estimate the mean change over time in patients testing positive or negative for SARS-CoV-2 and those who survived or died within 30 days of first suspicion or positive test date. For each clinical parameter of interest, the difference in average daily changes between COVID-19 positive versus negative group and COVID-19 survivor versus nonsurvivor group was estimated by fitting a linear mixed effects model based on measurements in the 14 days before (i.e., Day -14 to Day 0) Day 0. RESULTS: There were 12,836 HD patients with a suspicion of COVID-19 who received RT-PCR testing (8895 SARS-CoV-2 positive). We observed significantly different trends (p < 0.05) in pre-HD systolic blood pressure (SBP), pre-HD pulse rate, body temperature, ferritin, neutrophils, lymphocytes, albumin, and interdialytic weight gain (IDWG) between COVID-19 positive and negative patients. For COVID-19 positive group, we observed significantly different clinical trends (p < 0.05) in pre-HD pulse rate, lymphocytes, neutrophils, and albumin between survivors and nonsurvivors. We also observed that, in the group of survivors, most clinical parameters returned to pre-COVID-19 levels within 60-90 days. CONCLUSION: We observed unique temporal trends in various clinical and laboratory parameters among HD patients who tested positive versus negative for SARS-CoV-2 infection and those who survived the infection versus those who died. These trends can help to define the physiological disturbances that characterize the onset and course of COVID-19 in HD 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.