Kidney Failure Attributed to Focal Segmental Glomerulosclerosis: A USRDS Retrospective Cohort Study of Epidemiology, Treatment Modalities, and Economic Burden.

Rationale & Objective: This study describes the epidemiology, characteristics, and clinical outcomes of patients with focal segmental glomerulosclerosis (FSGS)-attributed kidney failure in the US Renal Data System (USRDS) during 2008-2018, and health care resource utilization and costs among those with Medicare-linked data. Study Design: This was a retrospective cohort study. Setting & Population: Patients with FSGS-attributed kidney failure in the USRDS were enrolled in the study. Outcomes: The outcomes were as follows: Prevalence and incidence, clinical and demographic characteristics, time to kidney transplant or death, health care resource utilization, and direct health care costs. Analytical Approach: Patients with FSGS as the primary cause of kidney failure were followed from USRDS registration (index date) until death or data end. Prevalence and incidence were calculated per 1,000,000 US persons. Patient characteristics at index and treatment modalities during follow-up were described. Time to kidney transplant or death was assessed with Kaplan-Meier and competing risk analyses. Health care resource utilization and costs were reported among patients with 1 year Medicare Part A+B coverage postindex, including (Medicare Coverage subgroup) or excluding (1-year Medicare Coverage subgroup) those who died. Results: The FSGS cohort and Medicare Coverage and 1-year Medicare Coverage subgroups included 25,699, 6,340, and 5,575 patients, respectively. Mean annual period prevalence and incidence rates of FSGS-attributed kidney failure were 87.6 and 7.5 per 1,000,000 US persons, respectively. Initial treatment for most patients was in-center hemodialysis (72.1%), whereas 7.3% received kidney transplant. Accounting for competing risk of death, year 1 and 5 kidney transplant rates were 15% and 34%, respectively. In the Medicare Coverage and 1-year Medicare Coverage subgroups, 76.6% and 74.2% required inpatient admission, 69.9% and 67.3% visited the emergency room, and mean monthly health care costs were $6,752 and $5,575 in the year postindex, respectively. Limitations: Drug costs may be underestimated because Medicare Part D coverage was not required; kidney acquisition costs were not available. Conclusions: FSGS-attributed kidney failure is associated with substantial clinical and economic burden, prompting the need for novel therapies for FSGS to delay kidney failure.

This study of patients in the US Renal Data System observed increasing prevalence and fluctuating incidence of focal segmental glomerulosclerosis (FSGS)-attributed kidney failure from 2008 to 2018. Patients experienced a high clinical burden, including more than 3 years of treatment with dialysis, one-third receiving a kidney transplant, and one-third dying during follow-up. In the first year after US Renal Data System registration, three-quarters of patients with Medicare coverage required hospitalization, and more than two-thirds visited the emergency room. The total annual health care costs were >$68,000 per patient with FSGS-attributed kidney failure, underscoring the high economic burden of this disorder and the treatments required to sustain life. Novel therapies for FSGS are needed to delay or ideally prevent the need dialysis and transplantation after kidney failure.

Kidney Failure Attributed to Immunoglobulin A Nephropathy: A USRDS Retrospective Cohort Study of Epidemiology, Treatment Modalities, and Economic Burden.

Rationale & Objective: This study describes the epidemiology, characteristics, and outcomes of patients with immunoglobulin A nephropathy (IgAN)-attributed kidney failure in the US Renal Data System (USRDS) from 2008 to 2018, including health care resource utilization and costs among patients with Medicare-linked data. Study Design: Retrospective cohort study. Setting & Population: Patients with IgAN-attributed kidney failure in the USRDS. Outcomes: Prevalence/incidence, clinical/demographic characteristics, time to kidney transplant, and health care resource utilization and costs. Analytical Approach: Patients with IgAN as primary cause of kidney failure (IgAN cohort) were followed from USRDS registration (index date) until data end/death. Prevalence/incidence were calculated per 1,000,000 US persons. Demographic and clinical characteristics at index and treatment modality during follow-up were summarized. Time from index to kidney transplant was assessed using Kaplan-Meier and competing risk analyses. Health care resource utilization and health care costs were reported among patients with 1 year Medicare Part A+B coverage postindex, including or excluding those who died (Medicare Coverage and 1-year Medicare Coverage subgroups, respectively). Results: The IgAN cohort, Medicare Coverage, and 1-year Medicare Coverage subgroups included 10,101, 1,696, and 1,510 patients, respectively. Mean annual period prevalence and incidence of IgAN-attributed kidney failure were 39.3 and 2.9 per 1,000,000 US persons, respectively. Initial treatment was in-center hemodialysis (63.1%) or kidney transplant (15.1%). Year 1 and 5 kidney transplant rates were 5% and 17%, respectively, accounting for competing risk of death. In the Medicare Coverage and 1-year Medicare Coverage subgroups, 74.4% and 72.3%, respectively, required inpatient admission, 67.3% and 64.4%, respectively, visited the emergency room, and mean total health care costs were $6,293 (SD: $6,934) and $5,284 ($3,455), respectively, per-patient-per-month in the year postindex. Limitations: Drug costs may be underestimated as Medicare Part D coverage was not required; kidney acquisition costs were unavailable. Conclusions: IgAN-attributed kidney failure is associated with substantial clinical and economic burdens. Novel therapies for IgAN that delay kidney failure are needed.

This study of patients in the United States Renal Data System (USRDS) observed fluctuating incidence and increasing prevalence of immunoglobulin A nephropathy (IgAN)-attributed kidney failure from 2008 to 2018. Patients experienced a high clinical burden, with 63% receiving in-center dialysis and over 15% receiving transplantation as initial therapy. In the first year after USRDS registration, nearly three-quarters of patients with Medicare coverage required hospitalization, and around two-thirds visited the emergency room. The total annual health care costs were >$63,000 per patient with IgAN-attributed kidney failure, underscoring the high economic burden of this disorder and currently available treatments. Novel therapies for IgAN are needed to delay or prevent the need for costly dialysis and transplantation after kidney failure.

Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population.

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.

Parathyroidectomy and Cinacalcet Use in Medicare-Insured Kidney Transplant Recipients.

RATIONALE & OBJECTIVE: Posttransplant hyperparathyroidism is common, and treatment practices are poorly characterized. The goal of this study was to examine the incidence, associations, and outcomes of posttransplant parathyroidectomy and calcimimetic use in a cohort of Medicare-insured US kidney transplant recipients. STUDY DESIGN: Retrospective observational cohort study. SETTING & PARTICIPANTS: We used the US Renal Data System to extract demographic, clinical, and prescription data from Medicare Parts A, B, and D-insured patients who received their first kidney transplant in 2007-2013. We excluded patients with pretransplant parathyroidectomy. PREDICTORS: Calendar year of transplantation and pretransplant patient characteristics. OUTCOME: (1) Incidence of and secular trends in parathyroidectomy and cinacalcet use in the 3 years after transplant; (2) 90-day outcomes after posttransplant parathyroidectomy and cinacalcet initiation. ANALYTICAL APPROACH: Temporal trends and pretransplant correlates of parathyroidectomy and cinacalcet use were assessed using proportional hazards models and multivariable Poisson regression, respectively. RESULTS: The inclusion criteria were met by 30,127 patients, of whom 10,707 used cinacalcet before transplant, 551 underwent posttransplant parathyroidectomy, and 5,413 filled≥1 prescription for cinacalcet. The rate of posttransplant parathyroidectomy was stable over time. By contrast, cinacalcet use increased during the period studied. Long dialysis vintage and pretransplant cinacalcet use were strongly associated with posttransplant parathyroidectomy and cinacalcet use. Roughly 1 in 4 patients were hospitalized within 90 days of posttransplant parathyroidectomy, with hypocalcemia-related diagnoses being the most common complication. Parathyroidectomy (vs cinacalcet initiation) was not associated with an increase in acute kidney injury. LIMITATIONS: We lacked access to laboratory data to help assess the severity of secondary/tertiary hyperparathyroidism. The cohort was limited to Medicare beneficiaries. CONCLUSIONS: Almost one-fifth of our study cohort was treated with parathyroidectomy and/or cinacalcet. Further studies are needed to establish the optimal treatment for posttransplant hyperparathyroidism.

Risk Factors and Outcomes of Invasive Aspergillosis in Kidney Transplant Recipients: A Case-Control Study of United States Renal Data System Data.

BACKGROUND: Kidney transplant recipients are at increased risk for invasive aspergillosis (IA), a disease with poor outcomes and substantial economic burden. We aimed to determine risk factors for posttransplant IA by using a national database and to assess the association of IA with mortality and allograft failure. METHODS: Using the United States Renal Data System database, we performed a retrospective case-control study of patients who underwent kidney transplant from 1998 through 2017. To evaluate risk factors for IA, we performed conditional logistic regression analysis by comparing characteristics between IA-infected patients and their matched uninfected controls. We performed Cox regression analysis to evaluate the effects of IA on mortality and death-censored allograft failure. RESULTS: We matched 359 patients with IA to 1436 uninfected controls (1:4). IA was diagnosed at a median of 22.5 months (interquartile range, 5.4-85.2 months) after kidney transplant. Risk factors for IA were Black/African American race, duration of pretransplant hemodialysis, higher Elixhauser Comorbidity Index score, weight loss, chronic pulmonary disease, need for early posttransplant hemodialysis, and a history of cytomegalovirus infection. Receiving an allograft from a living donor was protective against IA. IA was a strong independent predictor of 1-year mortality (adjusted hazard ratio [aHR], 5.02 [95% confidence interval {CI}, 3.58-7.04], P < .001). Additionally, IA was associated with 1-year allograft failure (aHR, 3.37 [95% CI, 1.96-5.77], P < .001). CONCLUSIONS: Our findings emphasize the importance of timely transplant to mitigate the risk of posttransplant IA. An individualized approach to disease prevention is essential to decrease mortality and allograft failure.

High-Dimensional Fixed Effects Profiling Models and Applications in End-Stage Kidney Disease Patients: Current State and Future Directions.

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.

Associations of COVID-19 Outcomes with Dialysis Modalities and Settings.

How maintenance dialysis modality, dialysis setting, and residence in a nursing facility have jointly associated with coronavirus disease 2019 (COVID-19)-related outcomes in the United States is relevant to future viral outbreaks. Using Medicare claims, we determined the incidence of COVID-19-related infection, hospitalization, and death between March 15, 2020 and June 5, 2021. The exposure was one of five combinations of dialysis modality and care setting: in-facility hemodialysis without a recent history of skilled nursing facility care, in-facility hemodialysis with a recent history of skilled nursing facility care, hemodialysis in a skilled nursing facility, home hemodialysis, and (home) peritoneal dialysis. Patient-weeks were pooled to estimate the adjusted associations of event incidence with each dialysis modality/setting during four intervals in 2020-2021. Relative to in-facility hemodialysis without a recent history of skilled nursing facility care, home dialysis was associated with 36%-60% lower odds of all events during weeks 12-23 of 2020; 24%-37% lower odds of all events during weeks 24-37 of 2020; 20%-33% lower odds of infection and hospitalization during the winter of 2020-2021; and similar odds of all events thereafter. In contrast, exposure to skilled nursing facilities was associated with 570%-1140% higher odds of all events during spring of 2020, although excess risk attenuated as the pandemic transpired, especially among patients who received hemodialysis in skilled nursing facilities. In conclusion, home dialysis was associated with lower risks of COVID-19 diagnosis, hospitalization, and death until vaccines were available, whereas care in skilled nursing facilities was associated with higher risks.

A Bayesian multilevel time-varying framework for joint modeling of hospitalization and survival in patients on dialysis.

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.

Development of an Administrative Data-Based Frailty Index for Older Adults Receiving Dialysis.

Background: Frailty is present in ≥50% of older adults receiving dialysis. Our objective was to a develop an administrative data-based frailty index and assess the frailty index's predictive validity for mortality and future hospitalizations. Methods: We used United States Renal Data System data to establish two cohorts of adults aged ≥65 years, initiating dialysis in 2013 and in 2017. Using the 2013 cohort (development dataset), we applied the deficit accumulation index approach to develop a frailty index. Adjusting for age and sex, we assessed the extent to which the frailty index predicts the hazard of time until death and time until first hospitalization over 12 months. We assessed the Harrell's C-statistic of the frailty index, a comorbidity index, and jointly. The 2017 cohort was used as a validation dataset. Results: Using the 2013 cohort (n=20,974), we identified 53 deficits for the frailty index across seven domains: disabilities, diseases, equipment, procedures, signs, tests, and unclassified. Among those with ≥1 deficit, the mean (SD) frailty index was 0.30 (0.13), range 0.02-0.72. Over 12 months, 18% (n=3842) died, and 55% (n=11,493) experienced a hospitalization. Adjusted hazard ratios for each 0.1-point increase in frailty index in models of time to death and time to first hospitalization were 1.41 (95% confidence interval, 1.37 to 1.44) and 1.33 (95% confidence interval, 1.31 to 1.35), respectively. For mortality, C-statistics for frailty index, comorbidity index, and both indices were 0.65, 0.65, and 0.66, respectively. For hospitalization, C-statistics for frailty index, comorbidity index, and both indices were 0.61, 0.60, and 0.61, respectively. Data from the 2017 cohort were similar. Conclusions: We developed a novel frailty index for older adults receiving dialysis. Further studies are needed to improve on this frailty index and validate its use for clinical and research applications.

A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation.

Background: The first 90 days after dialysis initiation are associated with high morbidity and mortality in end-stage kidney disease (ESKD) patients. A machine learning-based tool for predicting mortality could inform patient-clinician shared decision making on whether to initiate dialysis or pursue medical management. We used the eXtreme Gradient Boosting (XGBoost) algorithm to predict mortality in the first 90 days after dialysis initiation in a nationally representative population from the United States Renal Data System. Methods: A cohort of adults initiating dialysis between 2008-2017 were studied for outcome of death within 90 days of dialysis initiation. The study dataset included 188 candidate predictors prognostic of early mortality that were known on or before the first day of dialysis and was partitioned into training (70%) and testing (30%) subsets. XGBoost modeling used a complete-case set and a dataset obtained from multiple imputation. Model performance was evaluated by c-statistics overall and stratified by subgroups of age, sex, race, and dialysis modality. Results: The analysis included 1,150,195 patients with ESKD, of whom 86,083 (8%) died in the first 90 days after dialysis initiation. The XGBoost models discriminated mortality risk in the nonimputed (c=0.826, 95% CI, 0.823 to 0.828) and imputed (c=0.827, 95% CI, 0.823 to 0.827) models and performed well across nearly every subgroup (race, age, sex, and dialysis modality) evaluated (c>0.75). Across predicted risk thresholds of 10%-50%, higher risk thresholds showed declining sensitivity (0.69-0.04) with improving specificity (0.79-0.99); similarly, positive likelihood ratio was highest at the 40% threshold, whereas the negative likelihood ratio was lowest at the 10% threshold. After calibration using isotonic regression, the model accurately estimated the probability of mortality across all ranges of predicted risk. Conclusions: The XGBoost-based model developed in this study discriminated risk of early mortality after dialysis initiation with excellent calibration and performed well across key subgroups.

Hidden Costs Associated with Conversion from Peritoneal Dialysis to Hemodialysis.

Background: Increasing use of peritoneal dialysis (PD) will likely lead to increasing numbers of patients transitioning from PD to hemodialysis (HD). We describe the characteristics of patients who discontinued PD and converted to HD, trajectories of acute-care encounter rates and the total cost of care both before and after PD discontinuation, and the incidence of modality-related outcomes after PD discontinuation. Methods: We analyzed data in the United States Renal Data System to identify patients aged ≥12 years who were newly diagnosed with ESKD in 2001-2017, initiated PD during the first year of ESKD, and discontinued PD in 2009-2018. We estimated monthly rates of hospital admissions, observation stays, emergency department encounters, and Medicare Parts A and B costs during the 12 months before and after conversion from PD to HD, and the incidence of home HD initiation, death, and kidney transplantation after conversion to in-facility HD. Results: Among 232,699 patients who initiated PD, there were 124,213 patients who discontinued PD. Among them, 68,743 (55%) converted to HD. In this subgroup, monthly rates of acute-care encounters and total costs of care to Medicare sharply increased during the 6 months preceding PD discontinuation, peaking at 96.2 acute-care encounters per 100 patient-months and $20,701 per patient in the last month of PD. After conversion, rates decreased, but remained higher than before conversion. Among patients who converted to in-facility HD, the cumulative incidence of home HD initiation, death, and kidney transplantation at 24 months was 3%, 25%, and 7%, respectively. Conclusions: The transition from PD to HD is characterized by high rates of acute-care encounters and health-care expenditures. Quality improvement efforts should be aimed at improving transitions and encouraging both home HD and kidney transplantation after PD discontinuation.

Multilevel Varying Coefficient Spatiotemporal Model.

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.

Health Disparities in Autosomal Dominant Polycystic Kidney Disease (ADPKD) in the United States.

BACKGROUND AND OBJECTIVES: Autosomal dominant polycystic kidney disease (ADPKD) occurs at conception and is often diagnosed decades prior to kidney failure. Nephrology care and transplantation access should be independent of race and ethnicity. However, institutional racism and barriers to health care may affect patient outcomes in ADPKD. We sought to ascertain the effect of health disparities on outcomes in ADPKD by examining age at onset of kidney failure and access to preemptive transplantation and transplantation after dialysis initiation. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Retrospective cohort analyses of adults with ADPKD in the United States Renal Data System from January 2000 to June 2018 were merged to US Census income data and evaluated by self-reported race and ethnicity. Age at kidney failure was analyzed in a linear model, and transplant rates before and after dialysis initiation were analyzed in logistic and proportional hazards models in Black and Hispanic patients with ADPKD compared with White patients with ADPKD. RESULTS: A total of 41,485 patients with ADPKD were followed for a median of 25 (interquartile range, 5-54) months. Mean age was 56±12 years; 46% were women, 13% were Black, and 10% were Hispanic. Mean ages at kidney failure were 55±13, 53±12, and 57±12 years for Black patients, Hispanic patients, and White patients, respectively. Odds ratios for preemptive transplant were 0.33 (95% confidence interval, 0.29 to 0.38) for Black patients and 0.50 (95% confidence interval, 0.44 to 0.56) for Hispanic patients compared with White patients. Transplant after dialysis initiation was 0.61 (95% confidence interval, 0.58 to 0.64) for Black patients and 0.78 (95% confidence interval, 0.74 to 0.83) for Hispanic patients. CONCLUSIONS: Black and Hispanic patients with ADPKD reach kidney failure earlier and are less likely to receive a kidney transplant preemptively and after initiating dialysis compared with White patients with ADPKD.

Catheter-Associated Bloodstream Infections among Patients on Hemodialysis: Progress before and during the COVID-19 Pandemic.

Measures implemented to prevent transmission of severe acute respiratory syndrome coronavirus 2 in outpatient dialysis facilities may also help to prevent catheter-associated bloodstream infections in patients receiving hemodialysis. We used United States Renal Data System data to examine rates of antibiotic administration within dialysis facilities and rates of hospital admission for catheter-associated bloodstream infection from March 2018 through November 2020, and rates of hospitalization for sepsis, to address overall changes in hospitalization during the coronavirus disease 2019 (COVID-19) pandemic. Using logistic regression, we estimated year-over-year adjusted odds ratios of these events in 3-month intervals. During the first 6 months of the pandemic, rates of antibiotic administration were between 20% and 21% lower, and rates of hospitalization for catheter-associated bloodstream infection were between 17% and 24% lower than during corresponding periods in 2019, without significant changes in rates of hospitalization for sepsis. However, rates of catheter-associated events also decreased between 2018 and 2019, driven by reductions in facilities operated by a large dialysis provider. These data suggest that significant reductions in catheter-associated infections occurred during the pandemic, superimposed on nonpandemic-related reductions in some facilities before the pandemic. Even after the pandemic, it may be prudent to continue some COVID-19 mitigation measures to prevent catheter-associated bloodstream infections.

Use and Outcomes of Induction Therapy in Well-Matched Kidney Transplant Recipients.

BACKGROUND AND OBJECTIVES: The optimal induction treatment in low-immune risk kidney transplant recipients is uncertain. We therefore investigated the use and outcomes of induction immunosuppression in a low-risk cohort of patients who were well matched with their donor at HLA-A, -B, -DR, -DQB1 on the basis of serologic typing. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Our study was an observational study of first adult kidney-only transplant recipients in the United States recorded by the Organ Procurement and Transplant Network. RESULTS: Among 2976 recipients, 57% were treated with T cell-depleting antibodies, 28% were treated with an IL-2 receptor antagonist, and 15% were treated without induction. There was no difference in allograft survival, death-censored graft survival, or death with function between patients treated with an IL-2 receptor antagonist and no induction therapy. In multivariable models, patients treated with T cell-depleting therapy had a similar risk of graft loss from any cause, including death (hazard ratio, 1.19; 95% confidence interval, 0.98 to 1.45), compared with patients treated with an IL-2 receptor antagonist or no induction. The findings were consistent in subgroup analyses of Black recipients, patients grouped by calculated panel reactive antibody, and donor source. The incidence of acute rejection at 1 year was low (≤5%) and did not vary between treatment groups. CONCLUSIONS: Use of induction therapy with T cell-depleting therapy or IL-2 receptor antagonists in first kidney transplant recipients who are well matched with their donor at the HLA-A, -B, -DR, -DQB1 gene loci is not associated with improved post-transplant outcomes.

Causes of Death in End-Stage Kidney Disease: Comparison between the United States Renal Data System and a Large Integrated Health Care System.

INTRODUCTION: Using a large diverse population of incident end-stage kidney disease (ESKD) patients from an integrated health system, we sought to evaluate the concordance of causes of death (CODs) between the underlying COD from the United States Renal Data System (USRDS) registry and CODs obtained from Kaiser Permanente Southern California (KPSC). METHODS: A retrospective cohort study was performed among incident ESKD patients who had mortality records and CODs reported in both KPSC and USRDS databases between January 1, 2007, and December 31, 2016. Underlying CODs reported by the KPSC were compared to the CODs reported by USRDS. Overall and subcategory-specific COD agreements were assessed using Cohen's weighted kappa statistic (95% CI). Proportions of positive and negative agreement were also determined. RESULTS: Among 4,188 ESKD patient deaths, 4,118 patients had CODs recorded in both KPSC and USRDS. The most common KPSC CODs were circulatory system diseases (35.7%), endocrine/nutritional/metabolic diseases (24.2%), genitourinary diseases (12.9%), and neoplasms (9.6%). Most common USRDS CODs were cardiac disease (46.9%), withdrawal from dialysis (12.6%), and infection (10.1%). Of 2,593 records with causes listed NOT as "Other," 453 (17.4%) had no agreement in CODs between the USRDS and the underlying, secondary, tertiary, or quaternary causes recorded by KPSC. In comparing CODs recorded within KPSC to the USRDS, Cohen's weighted kappa (95% CI) was 0.20 (0.18-0.22) with overall agreement of 36.4%. CONCLUSION: Among an incident ESKD population with mortality records, we found that there was only fair or slight agreement between CODs reported between the USRDS registry and KPSC, a large integrated health care system.

Medication Burden and Prescribing Patterns in Patients on Hemodialysis in the USA, 2013-2017.

INTRODUCTION: The medication burden of patients with end-stage renal disease (ESRD) on hemodialysis, a patient population with a high comorbidity burden and complex care requirements, is among the highest of any of the chronic diseases. The goal of this study was to describe the medication burden and prescribing patterns in a contemporary cohort of patients with ESRD on hemodialysis in the USA. METHODS: We used the United States Renal Data System database from January 1, 2013, and December 31, 2017, to quantify the medication burden of patients with ESRD on hemodialysis aged ≥18 years. We calculated the average number of prescription medications per patient during each respective year (January-December), number of medications within classes, including potentially harmful medications, and trends in the number of medications and classes over the 5-year study period. RESULTS: We included a total of 163,228 to 176,133 patients from 2013 to 2017. The overall medication burden decreased slightly, from a mean of 7.4 (SD 3.8) medications in 2013 to 6.8 (SD 3.6) medications in 2017. Prescribing of potentially harmful medications decreased over time (74.0% with at least one harmful medication class in 2013-68.5% in 2017). In particular, the prescribing of non-benzodiazepine hypnotics, benzodiazepines, and opioids decreased from 2013 to 2017 (12.2%-6.3%, 23.4%-19.3%, and 60.0%-53.4%, respectively). This trend was consistent across subgroups of age, sex, race, and low-income subsidy status. CONCLUSIONS: Patients with ESRD on hemodialysis continued to have a high overall medication burden, with a slight reduction over time accompanied by a decrease in prescribing of several classes of harmful medications. Continued emphasis on assessment of appropriateness of high medication burden in patients with ESRD is needed to avoid exposure to potentially harmful or futile medications in this patient population.

Patient Health Outcomes following Dialysis Facility Closures in the United States.

BACKGROUND: Ongoing changes to reimbursement of United States dialysis care may increase the risk of dialysis facility closures. Closures may be particularly detrimental to the health of patients receiving dialysis, who are medically complex and clinically tenuous. METHODS: We used two separate analytic strategies-one using facility-based matching and the other using propensity score matching-to compare health outcomes of patients receiving in-center hemodialysis at United States facilities that closed with outcomes of similar patients who were unaffected. We used negative binomial and Cox regression models to estimate associations of facility closure with hospitalization and mortality in the subsequent 180 days. RESULTS: We identified 8386 patients affected by 521 facility closures from January 2001 through April 2014. In the facility-matched model, closures were associated with 9% higher rates of hospitalization (relative rate ratio [RR], 1.09; 95% confidence interval [95% CI], 1.03 to 1.16), yielding an absolute annual rate difference of 1.69 hospital days per patient-year (95% CI, 0.45 to 2.93). Similarly, in a propensity-matched model, closures were associated with 7% higher rates of hospitalization (RR, 1.07; 95% CI, 1.00 to 1.13; P=0.04), yielding an absolute rate difference of 1.08 hospital days per year (95% CI, 0.04 to 2.12). Closures were associated with nonsignificant increases in mortality (hazard ratio [HR], 1.08; 95% CI, 1.00 to 1.18; P=0.05 for the facility-matched comparison; HR, 1.08; 95% CI, 0.99 to 1.17; P=0.08 for the propensity-matched comparison). CONCLUSIONS: Patients affected by dialysis facility closures experienced increased rates of hospitalization in the subsequent 180 days and may be at increased risk of death. This highlights the need for effective policies that continue to mitigate risk of facility closures.

Dialysis Facility Profit Status and Early Steps in Kidney Transplantation in the Southeastern United States.

BACKGROUND AND OBJECTIVES: Dialysis facilities in the United States play a key role in access to kidney transplantation. Previous studies reported that patients treated at for-profit facilities are less likely to be waitlisted and receive a transplant, but their effect on early steps in the transplant process is unknown. The study's objective was to determine the association between dialysis facility profit status and critical steps in the transplantation process in Georgia, North Carolina, and South Carolina. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: In this retrospective cohort study, we linked referral and evaluation data from all nine transplant centers in the Southeast with United States Renal Data System surveillance data. The cohort study included 33,651 patients with kidney failure initiating dialysis from January 1, 2012 to August 31, 2016. Patients were censored for event (date of referral, evaluation, or waitlisting), death, or end of study (August 31, 2017 for referral and March 1, 2018 for evaluation and waitlisting). The primary exposure was dialysis facility profit status: for profit versus nonprofit. The primary outcome was referral for evaluation at a transplant center after dialysis initiation. Secondary outcomes were start of evaluation at a transplant center after referral and waitlisting. RESULTS: Of the 33,651 patients with incident kidney failure, most received dialysis treatment at a for-profit facility (85%). For-profit (versus nonprofit) facilities had a lower cumulative incidence difference for referral within 1 year of dialysis (-4.5%; 95% confidence interval, -6.0% to -3.2%). In adjusted analyses, for-profit versus nonprofit facilities had lower referral (hazard ratio, 0.84; 95% confidence interval, 0.80 to 0.88). Start of evaluation within 6 months of referral (-1.0%; 95% confidence interval, -3.1% to 1.3%) and waitlisting within 6 months of evaluation (1.0%; 95% confidence interval, -1.2 to 3.3) did not meaningfully differ between groups. CONCLUSIONS: Findings suggest lower access to referral among patients dialyzing in for-profit facilities in the Southeast United States, but no difference in starting the evaluation and waitlisting by facility profit status.

Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population.

End-stage renal disease patients on dialysis experience frequent hospitalizations. In addition to known temporal patterns of hospitalizations over the life span on dialysis, where poor outcomes are typically exacerbated during the first year on dialysis, variations in hospitalizations among dialysis facilities across the US contribute to spatial variation. Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multilevel spatiotemporal functional model to study spatiotemporal patterns of hospitalization rates among dialysis facilities. Hospitalization rates of dialysis facilities are considered as spatially nested functional data (FD) with longitudinal hospitalizations nested in dialysis facilities and dialysis facilities nested in geographic regions. A multilevel Karhunen-Loéve expansion is utilized to model the two-level (facility and region) FD, where spatial correlations are induced among region-specific principal component scores accounting for regional variation. A new efficient algorithm based on functional principal component analysis and Markov Chain Monte Carlo is proposed for estimation and inference. We report a novel application using USRDS data to characterize spatiotemporal patterns of hospitalization rates for over 400 health service areas across the US and over the posttransition time on dialysis. Finite sample performance of the proposed method is studied through simulations.