Changes in cardiovascular spending, care utilization, and clinical outcomes associated with participation in bundled payments for care improvement - Advanced

BACKGROUND: Bundled Payments for Care Improvement - Advanced (BPCI-A) is a Medicare initiative that aims to incentivize reductions in spending for episodes of care that start with a hospitalization and end 90 days after discharge. Cardiovascular disease, an important driver of Medicare spending, is one of the areas of focus BPCI-A. It is unknown whether BPCI-A is associated with spending reductions or quality improvements for the 3 cardiovascular medical events or 5 cardiovascular procedures in the model. METHODS: In this retrospective cohort study, we conducted difference-in-differences analyses using Medicare claims for patients discharged between January 1, 2017, and September 30, 2019, to assess differences between BPCI-A hospitals and matched nonparticipating control hospitals. Our primary outcomes were the differential changes in spending, before versus after implementation of BPCI-A, for cardiac medical and procedural conditions at BPCI-A hospitals compared with controls. Secondary outcomes included changes in patient complexity, care utilization, healthy days at home, readmissions, and mortality. RESULTS: Baseline spending for cardiac medical episodes at BPCI-A hospitals was $25 606. The differential change in spending for cardiac medical episodes at BPCI-A versus control hospitals was$16 (95% CI, -$228 to$261; CONCLUSIONS: Participation in BPCI-A was not associated with spending reductions, changes in care utilization, or quality improvements for the cardiovascular medical events or procedures offered in the model

Direct regional microvascular monitoring and assessment of blood brain barrier function following cerebral ischemia-reperfusion injury

Evans Blue (EB) is often used to evaluate Blood-Brain Barrier Damage (BBB) in cerebral ischemia, frequently by dye extraction. Herein we present a method that allows assessing regional brain microvasculature, distribution of EB and Fluorescent Isothiocyanate-Labeled Red Blood Cells (FITC-RBCs) in a rat model of acute cerebral Ischemia-Reperfusion (I-R). Wistar rats were subjected to 3 h of middle cerebral artery occlusion and then reperfused. At ~2.5 h of reperfusion, BBB opening was assessed by contrast enhanced magnetic resonance imaging. It was followed by injections of EB and FITC-RBCs that circulated for either 5 or 20 min. Regional microvasculature and tracer distributions were assessed by laser scanning confocal microscopy. Microvascular networks in stroke-affected regions networks were partially damaged with apparent EB extravasation. Brain regions were affected in the following order: preoptic area (PoA)\u3estriatum (Str)\u3ecortex (Ctx). EB leakage increased with circulation time in Str. Cells around the leakage sites sequestered EB. An inverse correlation was observed between low CBF rates recorded during MCA occlusion and post-reperfusion EB extravasation patterns. Accordingly, this approach provided data on brain regional microvascular status, extravascular tracer distribution and its cellular uptake. It may be useful to evaluate model-dependent variations in vascular injury and efficacy of putative vascular protective drugs in stroke

Association of Stratification by Proportion of Patients Dually Enrolled in Medicare and Medicaid With Financial Penalties in the Hospital-Acquired Condition Reduction Program

Importance: The Hospital-Acquired Condition Reduction Program (HACRP) is a value-based payment program focused on safety events. Prior studies have found that the program disproportionately penalizes safety-net hospitals, which may perform more poorly because of unmeasured severity of illness rather than lower quality. A similar program, the Hospital Readmissions Reduction Program, stratifies hospitals into 5 peer groups for evaluation based on the proportion of their patients dually enrolled in Medicare and Medicaid, but the effect of stratification on the HACRP is unknown. Objective: To characterize the hospitals penalized by the HACRP and the distribution of financial penalties before and after stratification. Design, Setting, and Participants: This economic evaluation used publicly available data on HACRP performance and penalties merged with hospital characteristics and cost reports. A total of 3102 hospitals participating in the HACRP in fiscal year 2020 (covering data from July 1, 2016, to December 31, 2018) were studied. Exposures: Hospitals were divided into 5 groups based on the proportion of patients dually enrolled, and penalties were assigned to the lowest-performing quartile of hospitals in each group rather than the lowest-performing quartile overall. Main Outcomes and Measures: Penalties in the prestratification vs poststratification schemes. Results: The study identified 3102 hospitals evaluated by the HACRP. Safety-net hospitals received $111 333 384 in penalties before stratification compared with an estimated$79 087 744 after stratification-a savings of $32 245 640. Hospitals less likely to receive penalties after stratification included safety-net hospitals (33.6% penalized before stratification vs 24.8% after stratification, Δ = -8.8 percentage points [pp], P \u3c .001), public hospitals (34.1% vs 30.5%, Δ = -3.6 pp, P = .003), hospitals in the West (26.8% vs 23.2%, Δ = -3.6 pp, P \u3c .001), hospitals in Medicaid expansion states (27.3% vs 25.6%, Δ = -1.7 pp, P = .003), and hospitals caring for the most patients with disabilities (32.2% vs 28.3%, Δ = -3.9 pp, P \u3c .001) and from racial/ethnic minority backgrounds (35.1% vs 31.5%, Δ = -3.6 pp, P \u3c .001). In multivariate analyses, safety-net status and treating patients with highly medically complex conditions were associated with higher odds of moving from penalized to nonpenalized status. Conclusions and Relevance: This economic evaluation suggests that stratification of hospitals would be associated with a narrowing of disparities in penalties and a marked reduction in penalties for safety-net hospitals. Policy makers should consider adopting stratification for the HACRP