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Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen-Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk.

BACKGROUND: In the United States, there are over seven million stroke survivors, with many facing gait impairments due to foot drop. This restricts their community ambulation and hinders functional independence, leading to several long-term health complications. Despite the best available physical therapy, gait function is incompletely recovered, and this occurs mainly during the acute phase post-stroke. Therapeutic options are limited currently. Novel therapies based on neurobiological principles have the potential to lead to long-term functional improvements. The Brain-Computer Interface (BCI) controlled Functional Electrical Stimulation (FES) system is one such strategy. It is based on Hebbian principles and has shown promise in early feasibility studies. The current study describes the BCI-FES clinical trial, which examines the safety and efficacy of this system, compared to conventional physical therapy (PT), to improve gait velocity for those with chronic gait impairment post-stroke. The trial also aims to find other secondary factors that may impact or accompany these improvements and establish the potential of Hebbian-based rehabilitation therapies. METHODS: This Phase II clinical trial is a two-arm, randomized, controlled, longitudinal study with 66 stroke participants in the chronic (> 6 months) stage of gait impairment. The participants undergo either BCI-FES paired with PT or dose-matched PT sessions (three times weekly for four weeks). The primary outcome is gait velocity (10-meter walk test), and secondary outcomes include gait endurance, range of motion, strength, sensation, quality of life, and neurophysiological biomarkers. These measures are acquired longitudinally. DISCUSSION: BCI-FES holds promise for gait velocity improvements in stroke patients. This clinical trial will evaluate the safety and efficacy of BCI-FES therapy when compared to dose-matched conventional therapy. The success of this trial will inform the potential utility of a Phase III efficacy trial. TRIAL REGISTRATION: The trial was registered as BCI-FES Therapy for Stroke Rehabilitation on February 19, 2020, at clinicaltrials.gov with the identifier NCT04279067.

As of 2020, 807,920 individuals in the U.S. had end-stage kidney disease (ESKD) with about 70% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience high mortality rates, where frequent hospitalizations are a major contributor to morbidity and mortality. There is growing interest in identifying the risk factors for the correlated outcomes of hospitalization and mortality among dialysis patients across the U.S. Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate varying coefficient spatiotemporal model to study the time-dynamic effects of risk factors (e.g., urbanicity and area deprivation index) on the multivariate outcome of hospitalization and mortality rates, as a function of time on dialysis. While capturing time-varying effects of risk factors on the mean, the proposed model also incorporates spatiotemporal patterns of the residuals for efficient inference. Estimation is based on the fusion of functional principal component analysis and Markov Chain Monte Carlo techniques, following basis expansions of the varying coefficient functions and multivariate Karhunen–Loéve expansion of region-specific random deviations. The finite sample performance of the proposed method is studied through extensive simulations. Novel applications to the USRDS data highlight significant risk factors of hospitalizations and mortality as well as characterizing time periods on dialysis and spatial locations across U.S. with elevated hospitalization and mortality risks.

OBJECTIVE: To examine the relationship between thyroid status and incident kidney dysfunction/chronic kidney disease (CKD) progression. PATIENTS AND METHODS: We examined incident thyroid status, ascertained by serum thyrotropin (TSH) levels measured from January 1, 2007, through December 31, 2018, among 4,152,830 patients from the Optum Labs Data Warehouse, containing deidentified retrospective administrative claims data from a large national health insurance plan and electronic health record data from a nationwide network of provider groups. Associations of thyroid status, categorized as hypothyroidism, euthyroidism, or hyperthyroidism (TSH levels >5.0, 0.5-5.0, and 5.0-10.0, >10.0-20.0, and >20.0 mIU/L, respectively) were associated with increasingly higher risk of the composite end point (reference: TSH level, 0.5 to

Using non-equilibrium molecular simulations, we systematically elaborate the relationships among synthesis (membrane's thickness and morphology), atomic-scale transport mechanism, and separation performance (permeability and selectivity) for 3D-printed polyamide (PA) membranes. Results indicated that water diffusion, swelling ratio, water flux, and water permeance proportionally decrease with increasing membrane thickness (4.0–32.5 nm). PA membranes with different thicknesses can achieve almost 100% salt rejection over the simulation time. Importantly, water permeability exponentially decreases with increasing thickness, and 15 nm is identified as the critical membrane thickness for efficient water transport. The discontinuous water-available space spreads all over PA membranes with thicknesses greater than 5 nm, allowing water molecules to jump by way of the temporary open-and-close pores. However, the connected water-useable space exists in PA membranes with thicknesses below 5 nm, offering the continuous channels to dominate water transport. More significantly, pore distribution is more homogeneous as the thickness increases. The applied high pressures can lead to membrane compaction during reverse osmosis and the thicker membranes show a lower compression ratio. In short, these investigations provide molecular insights for effectively designing and manufacturing PA membranes for water desalination and treatment at the molecular level.

Profiling analysis aims to evaluate health care providers, including hospitals, nursing homes, or dialysis facilities among others with respect to a patient outcome, such as 30-day unplanned hospital readmission or mortality. Fixed effects (FE) profiling models have been developed over the last decade, motivated by the overall need to (a) improve accurate identification or flagging of under-performing providers, (b) relax assumptions inherent in random effects (RE) profiling models, and (c) take into consideration the unique disease characteristics and care/treatment processes of end-stage kidney disease (ESKD) patients on dialysis. In this paper, we review the current state of FE methodologies and their rationale in the ESKD population and illustrate applications in four key areas: profiling dialysis facilities for (1) patient hospitalizations over time (longitudinally) using standardized dynamic readmission ratio (SDRR), (2) identification of dialysis facility characteristics (e.g., staffing level) that contribute to hospital readmission, and (3) adverse recurrent events using standardized event ratio (SER). Also, we examine the operating characteristics with a focus on FE profiling models. Throughout these areas of applications to the ESKD population, we identify challenges for future research in both methodology and clinical studies.

The field of cell biology has seen major advances in both cellular imaging modalities and the development of automated image analysis platforms that increase rigor, reproducibility, and throughput for large imaging data sets. However, there remains a need for tools that provide accurate morphometric analysis of single cells with complex, dynamic cytoarchitecture in a high-throughput and unbiased manner. We developed a fully automated image-analysis algorithm to rapidly detect and quantify changes in cellular morphology using microglia cells, an innate immune cell within the central nervous system, as representative of cells that exhibit dynamic and complex cytoarchitectural changes. We used two preclinical animal models that exhibit robust changes in microglia morphology: (1) a rat model of acute organophosphate intoxication, which was used to generate fluorescently labeled images for algorithm development; and (2) a rat model of traumatic brain injury, which was used to validate the algorithm using cells labeled using chromogenic detection methods. All ex vivo brain sections were immunolabeled for IBA-1 using fluorescence or diaminobenzidine (DAB) labeling, images were acquired using a high content imaging system and analyzed using a custom-built algorithm. The exploratory data set revealed eight statistically significant and quantitative morphometric parameters that distinguished between phenotypically distinct groups of microglia. Manual validation of single-cell morphology was strongly correlated with the automated analysis and was further supported by a comparison with traditional stereology methods. Existing image analysis pipelines rely on high-resolution images of individual cells, which limits sample size and is subject to selection bias. However, our fully automated method integrates quantification of morphology and fluorescent/chromogenic signals in images from multiple brain regions acquired using high-content imaging. In summary, our free, customizable image analysis tool provides a high-throughput, unbiased method for accurately detecting and quantifying morphological changes in cells with complex morphologies.

Although Realistic Mathematics Education (RME) has become familiar to many mathematics teachers, we still have little understanding of the extent to which mathematics teachers are willing to employ RME rather than traditional teaching approaches. Based on the theory of planned behavior, in conjunction with some other factors, including facilitating conditions and perceived autonomy, this study investigated a model explaining the continued intention of mathematics teachers to use Realistic Mathematics Education. A structural equation model was used to access data from an online survey involving 500 secondary school mathematics teachers in Vietnam. The results revealed that while attitude, perceived behavioral control and perceived autonomy have positive significant impacts on intention to use RME, it appears that subjective norms and facilitating conditions do not. These findings are of significance to stakeholders, including policymakers, school managers, and mathematics teachers.

Over 782 000 individuals in the United States have end-stage kidney disease with about 72% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience high mortality and frequent hospitalizations, at about twice per year. These poor outcomes are exacerbated at key time periods, such as the fragile period after transition to dialysis. In order to study the time-varying effects of modifiable patient and dialysis facility risk factors on hospitalization and mortality, we propose a novel Bayesian multilevel time-varying joint model. Efficient estimation and inference is achieved within the Bayesian framework using Markov chain Monte Carlo, where multilevel (patient- and dialysis facility-level) varying coefficient functions are targeted via Bayesian P-splines. Applications to the United States Renal Data System, a national database which contains data on nearly all patients on dialysis in the United States, highlight significant time-varying effects of patient- and facility-level risk factors on hospitalization risk and mortality. Finite sample performance of the proposed methodology is studied through simulations.

BackgroundIn trials conducted in India, recombinant granulocyte colony stimulating factor (GCSF) improved survival in alcohol-associated hepatitis (AH). The aim of this trial was to determine the safety and efficacy of pegfilgrastim, a long-acting recombinant GCSF, in patients with AH in the United States.MethodsThis prospective, randomized, open label trial conducted between March 2017 and March 2020 randomized patients with a clinical diagnosis of AH and a Maddrey discriminant function score ≥32 to standard of care (SOC) or SOC+pegfilgrastim (0.6 mg subcutaneously) on Day 1 and Day 8 (clinicaltrials.gov NCT02776059). SOC was 28 days of either pentoxifylline or prednisolone, as determined by the patient's primary physician. The second injection of pegfilgrastim was not administered if the white blood cell count exceeded 30,000/mm3 on Day 8. Primary outcome was survival at Day 90. Secondary outcomes included the incidence of acute kidney injury (AKI), hepatorenal syndrome (HRS), hepatic encephalopathy, or infections.FindingsThe study was terminated early due to COVID19 pandemic. Eighteen patients were randomized to SOC and 16 to SOC+pegfilgrastim. All patients received prednisolone as SOC. Nine patients failed to receive a second dose of pegfilgrastin due to WBC > 30,000/mm3 on Day 8. Survival at 90 days was similar in both groups (SOC: 0.83 [95% confidence interval [CI]: 0.57-0.94] vs. pegfilgrastim: 0.73 [95% CI: 0.44-0.89]; p > 0.05; CI for difference: -0.18-0.38). The incidences of AKI, HRS, hepatic encephalopathy, and infections were similar in both treatment arms and there were no serious adverse events attributed to pegfilgrastim.InterpretationThis phase II trial found no survival benefit at 90 days among subjects with AH who received pegfilgrastim+prednisolone compared with subjects receiving prednisolone alone.Fundingwas provided by the United States National Institutes of Health and National Institute on Alcohol Abuse and Alcoholism U01-AA021886 and U01-AA021884.

Over 785,000 individuals in the U.S. have end-stage renal disease (ESRD) with about 70% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience frequent hospitalizations. In order to identify risk factors of hospitalizations, we utilize data from the large national database, United States Renal Data System (USRDS). To account for the hierarchical structure of the data, with longitudinal hospitalization rates nested in dialysis facilities and dialysis facilities nested in geographic regions across the U.S., we propose a multilevel varying coefficient spatiotemporal model (M-VCSM) where region- and facility-specific random deviations are modeled through a multilevel Karhunen-Loéve (KL) expansion. The proposed M-VCSM includes time-varying effects of multilevel risk factors at the region- (e.g., urbanicity and area deprivation index) and facility-levels (e.g., patient demographic makeup) and incorporates spatial correlations across regions via a conditional autoregressive (CAR) structure. Efficient estimation and inference is achieved through the fusion of functional principal component analysis (FPCA) and Markov Chain Monte Carlo (MCMC). Applications to the USRDS data highlight significant region- and facility-level risk factors of hospitalizations and characterize time periods and spatial locations with elevated hospitalization risk. Finite sample performance of the proposed methodology is studied through simulations.

BackgroundEmerging data suggest that sodium disarrays including hyponatremia are potential risk factors for infection ensuing from impairments in host immunity, which may be exacerbated by coexisting conditions (i.e. mucosal membrane and cellular edema leading to breakdown of microbial barrier function). While dysnatremia and infection-related mortality are common in dialysis patients, little is known about the association between serum sodium levels and the risk of bloodstream infection in this population.MethodsAmong 823 dialysis patients from the national Biospecimen Registry Grant Program who underwent serum sodium testing over the period January 2008-December 2014, we examined the relationship between baseline serum sodium levels and subsequent rate of bloodstream infection. Bloodstream infection events were directly ascertained using laboratory blood culture data. Associations between serum sodium level and the incidence of bloodstream infection were estimated using expanded case mix-adjusted Poisson regression models.ResultsIn the overall cohort, ∼10% of all patients experienced one or more bloodstream infection events during the follow-up period. Patients with both lower sodium levels

BACKGROUND: In healthy adults, higher dietary potassium intake is recommended given that potassium-rich foods are major sources of micronutrients, antioxidants, and fiber. Yet among patients with advanced kidney dysfunction, guidelines recommend dietary potassium restriction given concerns about hyperkalemia leading to malignant arrhythmias and mortality. OBJECTIVES: Given sparse data informing these recommendations, we examined associations of dietary potassium intake with mortality in a nationally representative cohort of adults from the NHANES. METHODS: We examined associations between daily dietary potassium intake scaled to energy intake (mg/1000 kcal), ascertained by 24-h dietary recall, and all-cause mortality among 37,893 continuous NHANES (1999-2014) participants stratified according to impaired and normal kidney function (estimated glomerular filtration rates

Background: Digital transformation refers to applying digital technology in various fields of society. In the last 5 years, digital transformation has spread to most areas of social life, including education. However, research on digital transformation in education is still fragmented.Objectives: The aim of the study was to present a comprehensive review of studies on digital transformation in education using bibliometric analysis.Methods: We searched the Scopus database from inception to 1 January 2023 using the search terms ‘digital transformation’ AND ‘education’ within abstracts, keywords, or titles of journal articles or conference papers written in English. The retrieved articles were analysed using VOSviewer and Biblioshiny tools.Results: A total of 1329 relevant studies were retrieved. Although the first paper in this field was published in 1999, the number of publications has increased rapidly only in the past 4 years. The most influential countries in this field are the developed countries (Russian Federation, Germany, and the United States), but scholars from the developing countries (Indonesia and Thailand) are among the most productive. Papers on digital transformation are frequently published in journals with lower rankings within the Scopus database. Using VOSviewer for keyword co-occurrence analysis, we classified the research topics related to digital transformation in educa-tion into four main groups: digital transformation in higher education under the impact of the coronavirus disease pandemic 2019 pandemic, applying the technolo-gies of the Fourth Industrial Revolution to education, digitization and digital compe-tence in education in the context of digital transformation, and learning forms using technology (for example, e-learning, m-learning, and blended learning) in higher-education institutions.Conclusions: Four research trends related to digital transformation in education were identified. These trends may also change as digital transformation continues to develop.

BackgroundDialysis patients experience a high symptom burden, which may adversely impact their quality of life. Whereas other specialties emphasize routine symptom assessment, symptom burden is not well-characterized in dialysis patients. We sought to examine the prevalence and severity of unpleasant symptoms in a prospective hemodialysis cohort.MethodsAmong 122 hemodialysis patients from the prospective Malnutrition, Diet, and Racial Disparities in Chronic Kidney Disease (CKD) study, CKD-associated symptoms were ascertained by the Dialysis Symptom Index, a validated survey assessing symptom burden/severity (with higher scores indicating greater symptom severity), over 6/2020-10/2020. We examined the presence of (1) individual symptoms and symptom severity scores, and (2) symptom clusters (defined as ≥ 2 related concurrent symptoms), as well as correlations with clinical characteristics.ResultsSymptom severity scores were higher among non-Hispanic White and Hispanic patients, whereas scores were lower in Black and Asian/Pacific Islander patients. In the overall cohort, the most common individual symptoms included feeling tired/lack of energy (71.3%), dry skin (61.5%), trouble falling asleep (44.3%), muscle cramps (42.6%), and itching (42.6%), with similar patterns observed across racial/ethnic groups. The most prevalent symptom clusters included feeling tired/lack of energy + trouble falling asleep (37.7%); trouble falling asleep + trouble staying asleep (34.4%); and feeling tired/lack of energy + trouble staying asleep (32.0%). Lower hemoglobin, iron stores, and dialysis adequacy correlated with higher individual and overall symptom severity scores.ConclusionWe observed a high prevalence of unpleasant symptoms and symptom clusters in a diverse hemodialysis cohort. Further studies are needed to identify targeted therapies that ameliorate symptom burden in CKD.

Abstract: BACKGROUND AND AIMS: Within the Veterans Health Administration, the largest integrated healthcare system in the USA, 1.1 million Veterans (16.4%) have been identified as having chronic kidney disease (CKD). Annual spending on US Veterans with non-dialysis dependent CKD is estimated at $19 billion/year, and each year ∼10% of US Veterans with advanced CKD progress to end-stage renal disease (ESRD) requiring renal replacement therapy in the form of dialysis or kidney transplantation. While dialysis has been the dominant treatment paradigm in this population, with ∼22 000 enrolled Veterans receiving dialysis from VA-based dialysis facilities or from VA-contracted community providers, this treatment approach may not offer survival benefit nor improved quality of life in certain subgroups (elderly, multi-morbid). We sought to examine the clinical characteristics of a contemporary cohort of US Veterans with advanced CKD treated with conservative non-dialytic management versus dialysis. METHOD: In a national cohort of US Veterans receiving care from the VA healthcare system, we identified patients with advanced CKD, identified as those with ≥ 2 eGFR measurements

Abstract: BACKGROUND AND AIMS: Low physical activity is common in hemodialysis (HD) patients and is associated with adverse outcomes in this population (poor health-related quality of life, cardiovascular [CV] disease, death). Prior studies show that hypothyroidism is highly prevalent in HD patients, and is associated with worse self-reported physical function. METHOD: In a substudy of the ongoing multi-center NIH THYROID-HD Trial, we examined baseline physical activity scores determined by the Human Activity Profile (HAP), a validated 94-item instrument assessing daily activities across a wide range of energy expenditures, in HD patients with TSH levels in the high-normal (TSH >3–5 mIU/L) and subclinical hypothyroid range (TSH >5–10 mIU/L). The HAP was used to derive the Maximum Activity Score (MAS) and Adjusted Activity Score (AAS), representing greatest and mean estimated energy expenditures, respectively (range 0–94, segmented to low [74] scores). RESULTS: Among 100 HD patients who underwent baseline HAP assessment, the mean ± SD MAS and AAS scores were 48 ± 22 and 23 ± 27,  respectively; median (IQR) MAS and AAS scores were 49 (31–64) and 9 (0–39), respectively (Figure 1). In the overall cohort, 83% had low, 10% moderate and 7% high AAS scores. MAS and AAS scores were significantly lower in patients who had underlying diabetes versus those without diabetes (Table 1). There was also a trend towards lower (worse) MAS and AAS scores among older versus younger patients (≥65 versus >65 years, respectively) and patients with underlying coronary artery disease (CAD) versus those without CAD. In logistic regression analyses, older age (+∆ 1-year increments) was associated with higher likelihood of low MAS scores, and diabetes trended towards a significant association (defined as

Abstract: BACKGROUND AND AIMS: While dialysis has been the prevailing treatment paradigm in advanced CKD patients progressing to ESRD, this treatment approach may lead to a decline in physical function, loss of independence, and greater healthcare utilization among certain subgroups. We sought to compare the impact of dialysis versus conservative dialysis-free management on hospitalization lengths of stay (LOS) in advanced CKD patients. METHOD: We examined a national cohort of advanced CKD patients (≥2 eGFRs 20-years old had longer average LOS versus those treated with CM, with differences increasing with older age (∆ of + 1.25-days for hospitalizations for patients 70-years old; Fig. 1B, upper panel). In secondary analyses, differences in average LOS were even greater for those treated with early dialysis versus CM (∆ of + 1.87-days for patients 70-years old, Fig. 1B, lower panel). CONCLUSION: In a nationally representative CKD cohort, compared with dialysis, those treated with CM as an alternative patient-centered treatment strategy had shorter LOS across varying time points and age groups. Further studies are needed to examine the comparative effectiveness of CM versus dialysis transition on CKD outcomes.

The latest data from the United States Renal Data Systems show over 134,000 individuals with end-stage kidney disease (ESKD) starting dialysis in the year 2019. ESKD patients on dialysis, the default treatment strategy, have high mortality and hospitalization, especially in the first year of dialysis. An alternative treatment strategy is (non-dialysis) conservative management (CM). The relative effectiveness of CM with respect to various patient outcomes, including survival, hospitalization, and health-related quality of life among others, especially in elderly ESKD or advanced chronic kidney disease patients with serious comorbidities, is an active area of research. A technical challenge inherent in comparing patient outcomes between CM and dialysis patient groups is that the start of follow-up time is "not defined" for patients on CM because they do not initiate dialysis. One solution is the use of putative dialysis initiation (PDI) time. In this work, we examine the validity of the use of PDI time to determine the start of follow-up for longitudinal retrospective and prospective cohort studies involving CM. We propose and assess the efficacy of estimating PDI time using linear mixed effects model of kidney function decline over time via simulation studies. We also illustrate how the estimated PDI time can be used to effectively estimate the survival distribution.

IntroductionThyroid dysfunction is a highly prevalent yet under-recognized complication in hemodialysis patients. In the general population, hypothyroidism has been associated with endothelial dysfunction due to impaired vasodilator synthesis and activity. Little is known about the association of serum thyrotropin (TSH), the most sensitive and specific single biochemical metric of thyroid function, with endothelial function in hemodialysis patients.MethodsIn a secondary analysis of 99 patients from the Anti-inflammatory and anti-oxidative nutrition in hypoalbuminemic dialysis patients (AIONID) trial, we examined measurements of serum TSH and endothelial function ascertained by fingertip digital thermal monitoring (DTM), a novel method used to measure micro-vascular reactivity, collected within a 90-day period. DTM was used to measure changes in fingertip temperature during and after an ischemic stimulus (blood pressure cuff occlusion) as an indicator of changes in blood flow, and two DTM indices were assessed, namely adjusted (a) Temperature Rebound (TR), defined as the maximum temperature rebound post-cuff deflation, and adjusted (b) Area Under the Temperature Curve (TMP-AUC), defined as area under the curve between the maximum and minimum temperatures. We examined the relationship between serum TSH with impaired TR (separately) and TMP-AUC (both defined as less than the median level of observed values) using multivariable logistic regression.FindingsIn unadjusted and case-mix analyses, higher serum TSH levels (defined as the three highest quartiles) were associated with lower (worse) TR (ref: lowest TSH quartile): ORs (95% CI) 2.64 (1.01-6.88) and 2.85 (1.08-7.57), respectively. In unadjusted and case-mix analyses, higher TSH levels were associated with lower (worse) TMP-AUC: ORs (95% CI) 2.64 (1.01-6.88) and 2.79 (1.06-7.38), respectively.DiscussionIn HD patients, higher serum TSH levels were associated with worse micro-vascular reactivity measured by DTM. Further studies are needed to determine if thyroid hormone supplementation improves endothelial function in hemodialysis patients with lower levels of thyroid function.

(1) Background: Current dietary recommendations for dialysis patients suggest that high phosphorus diets may be associated with adverse outcomes such as hyperphosphatemia and death. However, there has been concern that excess dietary phosphorus restriction may occur at the expense of adequate dietary protein intake in this population. We hypothesized that higher dietary phosphorus intake is associated with higher mortality risk among a diverse cohort of hemodialysis patients. (2) Methods: Among 415 patients from the multi-center prospective Malnutrition, Diet, and Racial Disparities in Kidney Disease Study, we examined the associations of absolute dietary phosphorus intake (mg/day), ascertained by food frequency questionnaires, with all-cause mortality using multivariable Cox models. In the secondary analyses, we also examined the relationship between dietary phosphorus scaled to 1000 kcal of energy intake (mg/kcal) and dietary phosphorus-to-protein ratio (mg/g) with survival. (3) Results: In expanded case-mix + laboratory + nutrition adjusted analyses, the lowest tertile of dietary phosphorus intake was associated with higher mortality risk (ref: highest tertile): adjusted HR (aHR) (95% CI) 3.33 (1.75-6.33). In the analyses of dietary phosphorus scaled to 1000 kcal of energy intake, the lowest tertile of intake was associated with higher mortality risk compared to the highest tertile: aHR (95% CI) 1.74 (1.08, 2.80). Similarly, in analyses examining the association between dietary phosphorus-to-protein ratio, the lowest tertile of intake was associated with higher mortality risk compared to the highest tertile: aHR (95% CI) 1.67 (1.02-2.74). (4) Conclusions: A lower intake of dietary phosphorus was associated with higher mortality risk in a prospective hemodialysis cohort. Further studies are needed to clarify the relationship between specific sources of dietary phosphorus intake and mortality in this population.

IntroductionHypothyroidism is highly prevalent in end-stage kidney disease patients, and emerging data show that lower circulating thyroid hormone levels lead to downregulation of vascular calcification inhibitors and coronary artery calcification (CAC) in this population. To date, no studies have examined the association of serum thyrotropin (TSH), the most sensitive and specific single biochemical metric of thyroid function, with CAC risk in hemodialysis patients.MethodsIn secondary analyses of patients from the Anti-Inflammatory and Anti-Oxidative Nutrition in Hypoalbuminemic Dialysis Patients trial, we examined serum TSH levels and CAC risk assessed by cardiac computed tomography scans collected within a 90-day period. We evaluated the relationship between serum TSH with CAC Volume (VS) and Agatston score (AS) (defined as >100 mm3 and >100 Houndsfield Units, respectively) using multivariable logistic regression.ResultsAmong 104 patients who met eligibility criteria, higher TSH levels in the highest tertile were associated with moderately elevated CAC VS and AS in case-mix-adjusted analyses (ref: lowest tertile): adjusted ORs (95% CIs) 4.26 (1.18, 15.40) and 5.53 (1.44, 21.30), respectively. TSH levels >3.0 mIU/L (ref: ≤3.0 mIU/L) were also associated with moderately elevated CAC VS and AS. In secondary analyses, point estimates of incrementally lower direct free thyroxine levels trended toward elevated CAC VS and AS, although associations did not achieve statistical significance.ConclusionsIn hemodialysis patients, higher serum TSH was associated with elevated CAC VS and AS. Further studies are needed to determine if thyroid hormone supplementation can attenuate CAC burden in this population.

More than 720,000 patients with end-stage renal disease in the United States require life-sustaining dialysis treatment. In this population of typically older patients with a high morbidity burden, hospitalization is frequent at a rate of about twice per patient-year. Aside from frequent hospitalizations, which is a major source of death risk, overall mortality in dialysis patients is higher than other comparable populations, including Medicare patients with cancer. Thus, understanding patient- and facility-level risk factors that jointly contribute to longitudinal hospitalizations and mortality is of interest. Towards this objective, we propose a novel methodology to jointly model hospitalization, a binary longitudinal outcome, and survival, based on multilevel data from the United States Renal Data System (USRDS), with repeated observations over time nested in patients and patients nested in dialysis facilities. In our approach, the outcomes are modeled through a common set of multilevel random effects. In order to accommodate the USRDS data structure, we depart from the literature on joint modeling of longitudinal and survival data by including multilevel random effects and multilevel covariates, at both the patient and facility levels. An approximate Expectation-Maximization algorithm is developed for estimation and inference where fully exponential Laplace approximations are utilized to address computational challenges.