BACKGROUND:The demographics of heart failure are changing. The rate of growth of the "older" heart failure population, specifically those ≥ 75, has outpaced that of any other age group. These older patients were underrepresented in the early beta-blocker trials. There are several reasons, including a decreased potential for mortality benefit and increased risk of side effects, why the risk/benefit tradeoff may be different in this population. OBJECTIVE: We aimed to determine the association between receipt of a beta-blocker after heart failure discharge and early mortality and readmission rates among patients with heart failure and reduced ejection fraction (HFrEF), specifically patients aged 75+. DESIGN AND PARTICIPANTS: We used 100% Medicare Parts A and B and a random 40% sample of Part D to create a cohort of beneficiaries with ≥ 1 hospitalization for HFrEF between 2008 and 2016 to run an instrumental variable analysis. MAIN MEASURE: The primary measure was 90-day, allcause mortality; the secondary measure was 90-day, allcause readmission. KEY RESULTS: Using the two-stage least squared methodology, among all HFrEF patients, receipt of a betablocker within 30-day of discharge was associated with a − 4.35% (95% CI − 6.27 to − 2.42%, p < 0.001) decrease in 90-day mortality and a − 4.66% (95% CI − 7.40 to − 1.91%, p = 0.001) decrease in 90-day readmission rates. Even among patients ≥ 75 years old, receipt of a betablocker at discharge was also associated with a significant decrease in 90-day mortality, − 4.78% (95% CI − 7.19 to − 2.40%, p < 0.001) and 90-day readmissions, − 4.67% (95% CI − 7.89 to − 1.45%, p < 0.001). CONCLUSION: Patients aged ≥ 75 years who receive a beta-blocker after HFrEF hospitalization have significantly lower 90-day mortality and readmission rates. The magnitude of benefit does not appear to wane with age. Absent a strong contraindication, all patients with HFrEF should attempt beta-blocker therapy at/after hospital discharge, regardless of age.

Objective To evaluate long term outcomes (reintervention and late rupture of abdominal aortic aneurysm) of aortic endografts in real world practice using linked registry claims data. Design Observational surveillance study. Setting 282 centers in the Vascular Quality Initiative Registry linked to United States Medicare claims (2003-18). Participants 20 489 patients treated with four device types used for endovascular abdominal aortic aneurysm repair (EVAR): 40.6% (n=8310) received the Excluder (Gore), 32.2% (n=6606) the Endurant (Medtronic), 16.0% (n=3281) the Zenith (Cook Medical), and 11.2% (n=2292) the AFX (Endologix). Given modifications to AFX in late 2014, patients who received the AFX device were categorized into two groups: the early AFX group (n=942) and late AFX group (n=1350) and compared with patients who received the other devices, using propensity matched Cox models. Main outcome measures Reintervention and rupture of abdominal aortic aneurysm post-EVAR; all patients (100%) had complete follow-up via the registry or claims based outcome assessment, or both. Results Median age was 76 years (interquartile range (IQR) 70-82 years), 80.0% (16 386/20 489) of patients were men, and median follow-up was 2.3 years (IQR 0.9-4.1 years). Crude five year reintervention rates were significantly higher for patients who received the early AFX device compared with the other devices: 14.9% (95% confidence interval 13.7% to 16.2%) for Excluder, 19.5% (18.1% to 21.1%) for Endurant, 16.7% (15.0% to 18.6%) for Zenith, and early 27.0% (23.7% to 30.6%) for the early AFX. The risk of reintervention for patients who received the early AFX device was higher compared with the other devices in propensity matched Cox models (hazard ratio 1.61, 95% confidence interval 1.29 to 2.02) and analyses using a surgeon level instrumental variable of >33% AFX grafts used in their practice (1.75, 1.19 to 2.59). The linked registry claims surveillance data identified the increased risk of reintervention with the early AFX device as early as mid-2013, well before the first regulatory warnings were issued in the US in 2017. Conclusions The linked registry claims surveillance data identified a device specific risk in long term reintervention after EVAR of abdominal aortic aneurysm. Device manufacturers and regulators can leverage linked data sources to actively monitor long term outcomes in real world practice after cardiovascular interventions.

Background/Objectives Neurohormonal therapy, which includes beta‐blockers and angiotensin‐converting enzyme inhibitor/angiotensin receptor blockers (ACEi/ARBs), is the cornerstone of heart failure with reduced ejection fraction (HFrEF) treatment. While neurohormonal therapies have demonstrated efficacy in randomized clinical trials, older patients, which now comprise the majority of HFrEF patients, were underrepresented in those original trials. This study aimed to determine the association between short‐ (30 day) and long‐term (1 year) mortality and the use of neurohormonal therapy in HFrEF patients, across the age spectrum. Design/Setting/Participants This is a population‐based, retrospective, cohort study between January 2008 and December 2015. We used 100% Medicare Parts A and B and a random 40% sample of Part D to create a cohort of 295,494 fee‐for‐service beneficiaries with at least one hospitalization for HFrEF between 2008 and 2015. All analyses were performed between May 2019 and July 2020. Exposure We used Part D data to determine exposure to beta‐blocker and ACEi and ARB therapy. Results We found that in 295,494 patients admitted for HFrEF between 2008 and 2015, the average age was 80 years, 54% were female and 17% were non‐white. The baseline mortality rate was higher among those aged ≥85, but the mortality benefits of neurohormonal therapy were preserved across the age spectrum. Among those ≥85 years old, the hazard ratio for death within 30 days was 0.59 (95% confidence interval [CI] 0.56–0.62; p < 0.001) for beta‐blockers and 0.47 (95% CI 0.44–0.49; p < 0.001) for ACEi/ARBs. The hazard ratio for death within 1 year was 0.37–0.56 (95% CI 0.35–0.58; p < 0.001) for beta‐blockers and 0.38–0.53 (95% CI 0.37–0.55; p < 0.001) for ACEi/ARB. Conclusion At a population level, neurohormonal therapy was associated with lower short‐ and long‐term mortality across the age spectrum.

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Background This study illustrates the use of logistic regression and machine learning methods, specifically random forest models, in health services research by analyzing outcomes for a cohort of patients with concomitant peripheral artery disease and diabetes mellitus. Methods Cohort study using fee-for-service Medicare beneficiaries in 2015 who were newly diagnosed with peripheral artery disease and diabetes mellitus. Exposure variables include whether patients received preventive measures in the 6 months following their index date: HbA1c test, foot exam, or vascular imaging study. Outcomes include any reintervention, lower extremity amputation, and death. We fit both logistic regression models as well as random forest models. Results There were 88,898 fee-for-service Medicare beneficiaries diagnosed with peripheral artery disease and diabetes mellitus in our cohort. The rate of preventative treatments in the first six months following diagnosis were 52% (n = 45,971) with foot exams, 43% (n = 38,393) had vascular imaging, and 50% (n = 44,181) had an HbA1c test. The directionality of the influence for all covariates considered matched those results found with the random forest and logistic regression models. The most predictive covariate in each approach differs as determined by the t-statistics from logistic regression and variable importance (VI) in the random forest model. For amputation we see age 85 + (t = 53.17) urban-residing (VI = 83.42), and for death (t = 65.84, VI = 88.76) and reintervention (t = 34.40, VI = 81.22) both models indicate age is most predictive. Conclusions The use of random forest models to analyze data and provide predictions for patients holds great potential in identifying modifiable patient-level and health-system factors and cohorts for increased surveillance and intervention to improve outcomes for patients. Random forests are incredibly high performing models with difficult interpretation most ideally suited for times when accurate prediction is most desirable and can be used in tandem with more common approaches to provide a more thorough analysis of observational data.