Abstract

Age, sex, and race-related disparities in outcomes following abdominal aortic aneurysm (AAA) repair have been described, but the impact of socioeconomic factors on these outcomes is poorly understood. We examined 5-year outcomes after endovascular abdominal aortic aneurysm repair (EVAR) for intact AAA stratified by demographic and socioeconomic factors and explored potential mechanisms underlying outcome disparities. All Medicare fee-for-service beneficiaries ≥66 years of age who underwent EVAR for intact AAA between January 1, 2011, and December 31, 2019, were identified and stratified by the following disparity measures: age category (66-75 years/76-85 years/>85 years), sex, race (White/Black/other), Medicare/Medicaid dual enrollment, distressed communities index (distressed >80th percentile/nondistressed ≤80th percentile), and residential location (urban/rural). The primary outcome was a composite endpoint of late aneurysm rupture, aortic reintervention (graft relining or endograft extension), conversion to open repair, or all-cause mortality. Kaplan-Meier and Cox regression analyses were used to examine outcomes by each disparity measure. A total of 113,261 beneficiaries were identified. Mean age was 77 years, and most beneficiaries were male (79%), White (93%), enrolled in Medicare only (91%), and resided in urban locations (95%; Table). Median follow-up time was 3.2 years. At 5 years, compared with beneficiaries aged 66 to 75, incidence of the composite endpoint was higher in those aged 76 to 85 years (38% vs 25%, unadjusted hazard ratio [uHR], 1.7l 95% confidence interval [CI], 1.6-1.7) and in those aged >85 years (52% vs 25%; uHR, 2.6; 95% CI, 2.5-2.7) (Table). Higher incidence of the composite endpoint was also observed in female vs male beneficiaries (37% vs 32%; uHR, 1.2, 1.2-1.3), Black vs White beneficiaries (38% vs 33%; uHR; 1.2; 95% CI, 1.2-1.3), dual-enrolled vs Medicare-only beneficiaries (42% vs 32%; uHR, 1.4; 95% CI, 1.4-1.4), and in beneficiaries residing in distressed vs non-distressed communities (35% vs 33%; uHR, 1.1; 95% CI, 1.1-1.1). In adjusted analyses, these outcome disparities were partly explained by a higher burden of comorbid conditions in beneficiaries who were older, female, Black, dual-enrolled, or residing in distressed communities. Among Medicare fee-for-service beneficiaries who underwent EVAR for intact AAA, we observed significant 5-year outcome disparities by demographic and socioeconomic measures. These disparities seem to be driven, in part, by a higher burden of comorbidities in beneficiaries who are older, female, Black, or socioeconomically disadvantaged. These findings highlight the need for early identification and management of comorbid conditions to reduce outcome disparities following EVAR. Further work to examine other potential contributing factors, including healthcare utilization and imaging surveillance after EVAR, is ongoing and may highlight additional actionable targets for improvement.TableBaseline rates of disparity measures and associated cumulative incidence of the composite endpoint of late aneurysm rupture, aortic reintervention (graft relining or endograft extension), conversion to open repair, or all-cause mortality in Medicare beneficiaries who underwent EVAR for intact AAADisparity measureNo. (%)5-Year event rate (%)uHR (95% CI)Age category, years 66-7553,009 (47)25Ref 76-8548,084 (42)381.7 (1.6-1.7) >8512,168 (11)522.6 (2.5-2.7)Sex Male89,219 (79)32Ref Female24,042 (21)371.2 (1.2-1.3)Race White105,158 (93)33Ref Black4289 (3.8)381.2 (1.2-1.3) Other3814 (3.4)280.84 (0.79-0.89)Medicare/Medicaid enrollment Medicare only103,095 (91)32Ref Dual10,166 (9.0)421.4 (1.4-1.4)Distressed communities index Nondistressed90,074 (80)33Ref Distressed23,187 (20)351.1 (1.1-1.1)Residential location Urban107,305 (95)33Ref Rural5956 (5.3)320.97 (0.93-1.0)AAA, Abdominal aortic aneurysm; CI, confidence interval; EVAR, endovascular abdominal aortic aneurysm repair; Ref, reference; uHR, unadjusted hazard ratio.aEvent rates (%) at 5 years were estimated using Kaplan-Meier methods, and Cox regression was used to determine the uHRs at each time point. Open table in a new tab

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