Patient-Centered Quality Measures for Dialysis Care: A Report of a Kidney Disease Outcomes Quality Initiative (KDOQI) Scientific Workshop Sponsored by the National Kidney Foundation.

Providing high-quality patient-centered care is the central mission of dialysis facilities. Assessing quality and patient-centeredness of dialysis care is necessary for continuous dialysis facility improvement. Based predominantly on readily measured items, current quality measures in dialysis care emphasize biochemical and utilization outcomes, with very few patient-reported items. Additionally, current metrics often do not account for patient preferences and may compromise patient-centered care by limiting the ability of providers to individualize care targets, such as dialysis adequacy, based on patient priorities rather than a fixed numerical target. Developing, implementing, and maintaining a quality program using readily quantifiable data while also allowing for individualization of care targets that emphasize the goals of patients and their care partners provided the motivation for a September 2022 Kidney Disease Outcomes Quality Initiative (KDOQI) Workshop on Patient-Centered Quality Measures for Dialysis Care. Workshop participants focused on 4 questions: (1) What are the outcomes that are most important to patients and their care partners? (2) How can social determinants of health be accounted for in quality measures? (3) How can individualized care be effectively addressed in population-level quality programs? (4) What are the optimal means for collecting valid and robust patient-reported outcome data? Workshop participants identified numerous gaps within the current quality system and favored a conceptually broader, but not larger, quality system that stresses highly meaningful and adaptive measures that incorporate patient-centered principles, individual life goals, and social risk factors. Workshop participants also identified a need for new, low-burden tools to assess patient goals and priorities.

Understanding the Tradeoffs Between Travel Burden and Quality of Care for In-center Hemodialysis Patients.

BACKGROUND: Renal dialysis is a lifesaving but demanding therapy, requiring 3 weekly treatments of multiple-hour durations. Though travel times and quality of care vary across facilities, the extent to which patients are willing and able to engage in weighing tradeoffs is not known. Since 2015, Medicare has summarized and reported quality data for dialysis facilities using a star rating system. We estimate choice models to assess the relative roles of travel distance and quality of care in explaining patient choice of facility. RESEARCH DESIGN: Using national data on 2 million patient-years from 7198 dialysis facilities and 4-star rating releases, we estimated travel distance to patients' closest facilities, incremental travel distance to the next closest facility with a higher star rating, and the difference in ratings between these 2 facilities. We fit mixed effects logistic regression models predicting whether patients dialyzed at their closest facilities. RESULTS: Median travel distance was 4 times that in rural (10.9 miles) versus urban areas (2.6 miles). Higher differences in rating [odds ratios (OR): 0.56; 95% confidence interval (CI): 0.50-0.62] and greater area deprivation (OR: 0.50; 95% CI: 0.48-0.53) were associated with lower odds of attending one's closest facility. Stratified models were also fit based on urbanicity. For rural patients, excess travel was associated with higher odds of attending the closer facility (per 10 miles; OR: 1.05; 95% CI: 1.04-1.06). Star rating differences were associated with lower odds of receiving care from the closest facility among urban (OR: 0.57; 95% CI: 0.51-0.63) and rural patients (OR: 0.18; 95% CI: 0.08-0.44). CONCLUSIONS: Most dialysis patients have higher rated facilities located not much further than their closest facility, suggesting many patients could evaluate tradeoffs between distance and quality of care in where they receive dialysis. Our results show that such tradeoffs likely occur. Therefore, quality ratings such as the Dialysis Facility Compare (DFC) Star Rating may provide actionable information to patients and caregivers. However, we were not able to assess whether these associations reflect a causal effect of the Star Ratings on patient choice, as the Star Ratings served only as a marker of quality of care.

COVID-19 Risk Factors and Mortality Outcomes Among Medicare Patients Receiving Long-term Dialysis.

Importance: There is a need for studies to evaluate the risk factors for COVID-19 and mortality among the entire Medicare long-term dialysis population using Medicare claims data. Objective: To identify risk factors associated with COVID-19 and mortality in Medicare patients undergoing long-term dialysis. Design, Setting, and Participants: This retrospective, claims-based cohort study compared mortality trends of patients receiving long-term dialysis in 2020 with previous years (2013-2019) and fit Cox regression models to identify risk factors for contracting COVID-19 and postdiagnosis mortality. The cohort included the national population of Medicare patients receiving long-term dialysis in 2020, derived from clinical and administrative databases. COVID-19 was identified through Medicare claims sources. Data were analyzed on May 17, 2021. Main Outcomes and Measures: The 2 main outcomes were COVID-19 and all-cause mortality. Associations of claims-based risk factors with COVID-19 and mortality were investigated prediagnosis and postdiagnosis. Results: Among a total of 498 169 Medicare patients undergoing dialysis (median [IQR] age, 66 [56-74] years; 215 935 [43.1%] women and 283 227 [56.9%] men), 60 090 (12.1%) had COVID-19, among whom 15 612 patients (26.0%) died. COVID-19 rates were significantly higher among Black (21 787 of 165 830 patients [13.1%]) and Hispanic (13 530 of 86 871 patients [15.6%]) patients compared with non-Black patients (38 303 of 332 339 [11.5%]), as well as patients with short (ie, 1-89 days; 7738 of 55 184 patients [14.0%]) and extended (ie, ≥90 days; 10 737 of 30 196 patients [35.6%]) nursing home stays in the prior year. Adjusting for all other risk factors, residing in a nursing home 1 to 89 days in the prior year was associated with a higher hazard for COVID-19 (hazard ratio [HR] vs 0 days, 1.60; 95% CI 1.56-1.65) and for postdiagnosis mortality (HR, 1.31; 95% CI, 1.25-1.37), as was residing in a nursing home for an extended stay (COVID-19: HR, 4.48; 95% CI, 4.37-4.59; mortality: HR, 1.12; 95% CI, 1.07-1.16). Black race (HR vs non-Black: HR, 1.25; 95% CI, 1.23-1.28) and Hispanic ethnicity (HR vs non-Hispanic: HR, 1.68; 95% CI, 1.64-1.72) were associated with significantly higher hazards of COVID-19. Although home dialysis was associated with lower COVID-19 rates (HR, 0.77; 95% CI, 0.75-0.80), it was associated with higher mortality (HR, 1.18; 95% CI, 1.11-1.25). Conclusions and Relevance: These results shed light on COVID-19 risk factors and outcomes among Medicare patients receiving long-term chronic dialysis and could inform policy decisions to mitigate the significant extra burden of COVID-19 and death in this population.

Excess Death Estimates in Patients with End-Stage Renal Disease - United States, February-August 2020.

End-stage renal disease (ESRD) is a condition in which kidney function has permanently declined such that renal replacement therapy* is required to sustain life (1). The mortality rate for patients with ESRD in the United States has been declining since 2001 (2). However, during the COVID-19 pandemic, ESRD patients are at high risk for COVID-19-associated morbidity and mortality, which is due, in part, to weakened immune systems and presence of multiple comorbidities (3-5). The ESRD National Coordinating Center (ESRD NCC) supports the Centers for Medicare & Medicaid Services (CMS) and the ESRD Networks†,§ through analysis of data, dissemination of best practices, and creation of educational materials. ESRD NCC analyzed deaths reported to the Consolidated Renal Operations in a Web-Enabled Network (CROWNWeb), a system that facilitates the collection of data and maintenance of information about ESRD patients on chronic dialysis or receiving a kidney transplant who are treated in Medicare-certified dialysis facilities and kidney transplant centers in the United States. Excess death estimates were obtained by comparing observed and predicted monthly numbers of deaths during February 1-August 31, 2020; predicted deaths were modeled based on data from January 1, 2016, through December 31, 2019. The analysis estimated 8.7-12.9 excess deaths per 1,000 ESRD patients, or a total of 6,953-10,316 excess deaths in a population of 798,611 ESRD patients during February 1-August 31, 2020. These findings suggest that deaths among ESRD patients during the early phase of the pandemic exceeded those that would have been expected based on previous years' data. Geographic and temporal patterns of excess mortality, including those among persons with ESRD, should be considered during planning and implementation of interventions, such as COVID-19 vaccination, infection control guidance, and patient education. These findings underscore the importance of data-driven technical assistance and further analyses of the causes and patterns of excess deaths in ESRD patients.

Evaluating national trends in outcomes after implementation of a star rating system: Results from dialysis facility compare.

OBJECTIVE: To examine which factors are driving improvement in the Dialysis Facility Compare (DFC) star ratings and to test whether nonclinical facility characteristics are associated with observed longitudinal changes in the star ratings. DATA SOURCES: Data were collected from eligible patients in over 6,000 Medicare-certified dialysis facilities from three annual star rating and individual measure updates, publicly released on DFC in October 2015, October 2016, and April 2018. STUDY DESIGN: Changes in the star rating and individual quality measures were investigated across three public data releases. Year-to-year changes in the star ratings were linked to facility characteristics, adjusting for baseline differences in quality measure performance. DATA COLLECTION: Data from publicly reported quality measures, including standardized mortality, hospitalization, and transfusion ratios, dialysis adequacy, type of vascular access for dialysis, and management of mineral and bone disease, were extracted from annual DFC data releases. PRINCIPAL FINDINGS: The proportion of four- and five-star facilities increased from 30.0% to 53.4% between October 2015 and April 2018. Quality improvement was driven by the domain of care containing the dialysis adequacy and hypercalcemia measures. Additionally, independently owned facilities and facilities belonging to smaller dialysis organizations had significantly lower odds of year-to-year improvement than facilities belonging to either of the two large dialysis organizations (Odds Ratio [OR]: 0.736, 95% Confidence Interval [CI]: 0.631-0.856 and OR: 0.797, 95% CI: 0.723-0.879, respectively). CONCLUSIONS: The percentage of four- and five-star facilities has increased markedly over a three-year time period. These changes were driven by improvement in the specific quality measures that may be most directly under the control of the dialysis facility.

Multiwell CO2 injectivity: impact of boundary conditions and brine extraction on geologic CO2 storage efficiency and pressure buildup.

CO2 storage efficiency is a metric that expresses the portion of the pore space of a subsurface geologic formation that is available to store CO2. Estimates of storage efficiency for large-scale geologic CO2 storage depend on a variety of factors including geologic properties and operational design. These factors govern estimates on CO2 storage resources, the longevity of storage sites, and potential pressure buildup in storage reservoirs. This study employs numerical modeling to quantify CO2 injection well numbers, well spacing, and storage efficiency as a function of geologic formation properties, open-versus-closed boundary conditions, and injection with or without brine extraction. The set of modeling runs is important as it allows the comparison of controlling factors on CO2 storage efficiency. Brine extraction in closed domains can result in storage efficiencies that are similar to those of injection in open-boundary domains. Geomechanical constraints on downhole pressure at both injection and extraction wells lower CO2 storage efficiency as compared to the idealized scenario in which the same volumes of CO2 and brine are injected and extracted, respectively. Geomechanical constraints should be taken into account to avoid potential damage to the storage site.

Using race as a case-mix adjustment factor in a renal dialysis payment system: potential and pitfalls.

BACKGROUND: Racial disparities in health care are widespread in the United States. Identifying contributing factors may improve care for underserved minorities. To the extent that differential utilization of services, based on need or biological effect, contributes to outcome disparities, prospective payment systems may require inclusion of race to minimize these adverse effects. This research determines whether costs associated with end-stage renal disease (ESRD) care varied by race and whether this variance affected payments to dialysis facilities. STUDY DESIGN: We compared the classification of race across Medicare databases and investigated differences in cost of care for long-term dialysis patients by race. SETTING & PARTICIPANTS: Medicare ESRD database including 890,776 patient-years in 2004-2006. PREDICTORS: Patient race and ethnicity. OUTCOMES: Costs associated with ESRD care and estimated payments to dialysis facilities under a prospective payment system. RESULTS: There were inconsistencies in race and ethnicity classification; however, there was significant agreement for classification of black and nonblack race across databases. In predictive models evaluating the cost of outpatient dialysis care for Medicare patients, race is a significant predictor of cost, particularly for cost of separately billed injectable medications used in dialysis. Overall, black patients had 9% higher costs than nonblack patients. In a model that did not adjust for race, other patient characteristics accounted for only 31% of this difference. LIMITATIONS: Lack of information about biological causes of the link between race and cost. CONCLUSIONS: There is a significant racial difference in the cost of providing dialysis care that is not accounted for by other factors that may be used to adjust payments. This difference has the potential to affect the delivery of care to certain populations. Of note, inclusion of race into a prospective payment system will require better understanding of biological differences in bone and anemia outcomes, as well as effects of inclusion on self-reported race.

A compartmental-spatial system dynamics approach to ground water modeling.

High-resolution, spatially distributed ground water flow models can prove unsuitable for the rapid, interactive analysis that is increasingly demanded to support a participatory decision environment. To address this shortcoming, we extend the idea of multiple cell (Bear 1979) and compartmental (Campana and Simpson 1984) ground water models developed within the context of spatial system dynamics (Ahmad and Simonovic 2004) for rapid scenario analysis. We term this approach compartmental-spatial system dynamics (CSSD). The goal is to balance spatial aggregation necessary to achieve a real-time integrative and interactive decision environment while maintaining sufficient model complexity to yield a meaningful representation of the regional ground water system. As a test case, a 51-compartment CSSD model was built and calibrated from a 100,0001 cell MODFLOW (McDonald and Harbaugh 1988) model of the Albuquerque Basin in central New Mexico (McAda and Barroll 2002). Seventy-seven percent of historical drawdowns predicted by the MODFLOW model were within 1 m of the corresponding CSSD estimates, and in 80% of the historical model run years the CSSD model estimates of river leakage, reservoir leakage, ground water flow to agricultural drains, and riparian evapotranspiration were within 30% of the corresponding estimates from McAda and Barroll (2002), with improved model agreement during the scenario period. Comparisons of model results demonstrate both advantages and limitations of the CCSD model approach.