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Quality-based ratings in Medicare and trends in kidney transplantation.

ABSTRACT: OBJECTIVE:To assess the relationship between the 2007 Medicare regulation enforcing quality standards for transplant centers and trends in kidney transplantation. DATA SOURCES:Transplant centers' biannual reports and the national registry for kidney transplantation from 2003 to 2010. STUDY DESIGN:Non-compliant (low-performing) centers were compared with centers in compliance with quality standards according to: number of transplants, waiting-list registrations, and rates of graft failures, transfers, and deaths. Multivariate regressions were estimated to evaluate the association between the regulation and transplantation outcomes. DATA EXTRACTION METHODS:Patient characteristics and outcomes were aggregated to six-month periods and linked to centers' reports. PRINCIPAL FINDINGS:Relative to average-performing centers, 12 percent of transplants shifted away from low-performing centers and high-performing centers captured 6 percent of this decline. Low-performing centers experienced a 2-percentage point per period decline in 1-year graft failure rates and a 15-percent decrease in registrations post-regulation, whereas high-performing centers incurred a 5-percent decrease in registrations relative to average-performing centers. CONCLUSIONS:Government oversight in kidney transplantation was associated with a small downward shift in overall kidney transplants. Reductions in graft failure rates at low-performing centers may imply an increase in quality or a decline in transplantation of either marginal organs or riskier patients; whereas reductions in registrations may indicate risk aversion toward high-risk patients. Policy makers should consider making less punitive requirements for programs, which employ new transplantation techniques to expand access.

SUBMITTER: Ouayogode MH

PROVIDER: S-EPMC6338323 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Quality-based ratings in Medicare and trends in kidney transplantation.

Ouayogodé Mariétou H MH

Health services research 20181205 1

<h4>Objective</h4>To assess the relationship between the 2007 Medicare regulation enforcing quality standards for transplant centers and trends in kidney transplantation.<h4>Data sources</h4>Transplant centers' biannual reports and the national registry for kidney transplantation from 2003 to 2010.<h4>Study design</h4>Non-compliant (low-performing) centers were compared with centers in compliance with quality standards according to: number of transplants, waiting-list registrations, and rates of ...[more]

PMID: 30520027

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Project description:ImportanceImproving the quality of dialysis care and access to kidney transplantation for patients with end-stage kidney disease is a national clinical and policy priority. The role of dialysis facility quality in increasing access to kidney transplantation is not known.ObjectiveTo determine whether patient, facility, and kidney transplant waitlisting characteristics are associated with variations in dialysis center quality.Design, setting, and participantsThis population-based cohort study is an analysis of US Renal Data System data and Medicare Dialysis Facility Compare (DFC) data from 2013 to 2018. Participants included all adult (aged ≥18 years) patients in the US Renal Data System beginning long-term dialysis in the US from 2013 to 2017 with follow-up through the end of 2018. Patients with a prior kidney transplant and matched Medicare DFC star ratings to each annual cohort of recipients were excluded. Patients at facilities without a star rating in that year were also excluded. Data analysis was performed from January to April 2021.ExposuresDialysis center quality, as defined by Medicare DFC star ratings.Main outcomes and measuresThe primary outcome was the proportion of patients undergoing incident dialysis who were waitlisted within 1 year of dialysis initiation. Secondary outcomes were patient and facility characteristics.ResultsOf 507 581 patients beginning long-term dialysis in the US from 2013 to 2017, 291 802 (57.4%) were male, 266 517 (52.5%) were White, and the median (interquartile range) age was 65 (55-75) years. Of 5869 dialysis facilities in 2017, 132 (2.2%) were 1-star, 436 (7.4%) were 2-star, 2047 (34.9%) were 3-star, 1660 (28.3%) were 4-star, and 1594 (27.2%) were 5-star. Higher-quality dialysis facilities were associated with 47% higher odds of transplant waitlisting (odds ratio [OR], 1.47; 95% CI, 1.39-1.57 for 5-star facilities vs 1-star facilities; P < .001). Black patients were less likely than White patients to be waitlisted for transplantation (OR, 0.74; 95% CI, 0.72-0.76). In addition, patients at for-profit (OR, 0.78; 95% CI, 0.74-0.81) and rural (OR, 0.63; 95%, CI 0.58-0.68) facilities were less likely to be waitlisted for transplantation compared with those at nonprofit and urban facilities, respectively.Conclusions and relevanceIn this cohort study, patients at higher-quality dialysis facilities had higher odds than patients at lower-quality facilities of being waitlisted for kidney transplantation within 1 year. Waitlisting rates for kidney transplantation should be considered for integration into the current Centers for Medicare & Medicaid Services DFC star ratings to incentivize dialysis facility referral to transplant centers, inform patient choice, and drive quality improvement by increasing transplant waitlisting rates.

Project description:Background and objectivesInfections are the most common noncardiovascular causes of death after kidney transplantation. We analyzed the current infection-related mortality among kidney transplant recipients in a nationwide cohort in Finland.Design, setting, participants, & measurementsAltogether, 3249 adult recipients of a first kidney transplant from 1990 to 2012 were included. Infectious causes of death were analyzed, and the mortality rates for infections were compared between two eras (1990-1999 and 2000-2012). Risk factors for infectious deaths were analyzed with Cox regression and competing risk analyses.ResultsAltogether, 953 patients (29%) died during the follow-up, with 204 infection-related deaths. Mortality rate (per 1000 patient-years) due to infections was lower in the more recent cohort (4.6; 95% confidence interval, 3.5 to 6.1) compared with the older cohort (9.1; 95% confidence interval, 7.6 to 10.7); the incidence rate ratio of infectious mortality was 0.51 (95% confidence interval, 0.30 to 0.68). The main causes of infectious deaths were common bacterial infections: septicemia in 38% and pulmonary infections in 45%. Viral and fungal infections caused only 2% and 3% of infectious deaths, respectively (such as individual patients with Cytomegalovirus pneumonia, Herpes simplex virus meningoencephalitis, Varicella zoster virus encephalitis, and Pneumocystis jirovecii infection). Similarly, opportunistic bacterial infections rarely caused death; only one death was caused by Listeria monocytogenes, and two were caused by Mycobacterium tuberculosis. Only 23 (11%) of infection-related deaths occurred during the first post-transplant year. Older recipient age, higher plasma creatinine concentration at the end of the first post-transplant year, diabetes as a cause of ESKD, longer pretransplant dialysis duration, acute rejection, low albumin level, and earlier era of transplantation were associated with increased risk of infectious death in multivariable analysis.ConclusionsThe risk of death due to infectious causes after kidney transplantation in Finland dropped by one half since the 1990s. Common bacterial infections remained the most frequent cause of infection-related mortality, whereas opportunistic viral, fungal, or unconventional bacterial infections rarely caused deaths after kidney transplantation.

Project description:Background and objectivesLong wait times for deceased donor kidneys and low rates of preemptive wait-listing have limited preemptive transplantation in the United States. We aimed to assess trends in preemptive deceased donor transplantation with the introduction of the new Kidney Allocation System (KAS) in 2014 and identify whether key disparities in preemptive transplantation have changed.Design, setting, participants, & measurementsWe identified adult deceased donor kidney transplant recipients in the United States from 2000 to 2018 using the Scientific Registry of Transplant Recipients. Preemptive transplantation was defined as no dialysis before transplant. Associations between recipient, donor, transplant, and policy era characteristics and preemptive transplantation were calculated using logistic regression. To test for modification by KAS policy era, an interaction term between policy era and each characteristic of interest was introduced in bivariate and adjusted models.ResultsThe proportion of preemptive transplants increased after implementation of KAS from 9.0% to 9.8%, with 1.10 (95% confidence interval [95% CI], 1.06 to 1.14) times higher odds of preemptive transplantation post-KAS compared with pre-KAS. Preemptive recipients were more likely to be white, older, female, more educated, hold private insurance, and have ESKD cause other than diabetes or hypertension. Policy era significantly modified the association between preemptive transplantation and race, age, insurance status, and Human Leukocyte Antigen zero-mismatch (interaction P<0.05). Medicare patients had a significantly lower odds of preemptive transplantation relative to private insurance holders (pre-KAS adjusted OR, [aOR] 0.26; [95% CI, 0.25 to 0.27], to 0.20 [95% CI, 0.18 to 0.22] post-KAS). Black and Hispanic patients experienced a similar phenomenon (aOR 0.48 [95% CI, 0.45 to 0.51] to 0.41 [95% CI, 0.37 to 0.45] and 0.43 [95% CI, 0.40 to 0.47] to 0.40 [95% CI, 0.36 to 0.46] respectively) compared with white patients.ConclusionsAlthough the proportion of deceased donor kidney transplants performed preemptively increased slightly after KAS, disparities in preemptive kidney transplantation persisted after the 2014 KAS policy changes and were exacerbated for racial minorities and Medicare patients.

Project description:ImportanceIntegration of physician practices into health systems composed of hospitals and multispecialty practices is increasing in the era of value-based payment. It is unknown how clinicians who affiliate with such health systems perform under the new mandatory Centers for Medicare & Medicaid Services Merit-based Incentive Payment System (MIPS) relative to their peers.ObjectiveTo assess the relationship between the health system affiliations of clinicians and their performance scores and value-based reimbursement under the 2019 MIPS.Design, setting, and participantsPublicly reported data on 636 552 clinicians working at outpatient clinics across the US were used to assess the association of the affiliation status of clinicians within the 609 health systems with their 2019 final MIPS performance score and value-based reimbursement (both based on clinician performance in 2017), adjusting for clinician, patient, and practice area characteristics.ExposuresHealth system affiliation vs no affiliation.Main outcomes and measuresThe primary outcome was final MIPS performance score (range, 0-100; higher scores intended to represent better performance). The secondary outcome was MIPS payment adjustment, including negative (penalty) payment adjustment, positive payment adjustment, and bonus payment adjustment.ResultsThe final sample included 636 552 clinicians (41% female, 83% physicians, 50% in primary care, 17% in rural areas), including 48.6% who were affiliated with a health system. Compared with unaffiliated clinicians, system-affiliated clinicians were significantly more likely to be female (46% vs 37%), primary care physicians (36% vs 30%), and classified as safety net clinicians (12% vs 10%) and significantly less likely to be specialists (44% vs 55%) (P < .001 for each). The mean final MIPS performance score for system-affiliated clinicians was 79.0 vs 60.3 for unaffiliated clinicians (absolute mean difference, 18.7 [95% CI, 18.5 to 18.8]). The percentage receiving a negative (penalty) payment adjustment was 2.8% for system-affiliated clinicians vs 13.7% for unaffiliated clinicians (absolute difference, -10.9% [95% CI, -11.0% to -10.7%]), 97.1% vs 82.6%, respectively, for those receiving a positive payment adjustment (absolute difference, 14.5% [95% CI, 14.3% to 14.6%]), and 73.9% vs 55.1% for those receiving a bonus payment adjustment (absolute difference, 18.9% [95% CI, 18.6% to 19.1%]).Conclusions and relevanceClinician affiliation with a health system was associated with significantly better 2019 MIPS performance scores. Whether this represents differences in quality of care or other factors requires additional research.

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Quality-based ratings in Medicare and trends in kidney transplantation.

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Quality-based ratings in Medicare and trends in kidney transplantation.

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