Citations

Aim We sought to determine the racial and ethnical disparities in the delivery of TAVR and to evaluate the in-hospital outcomes and utilization of TAVR stratified by patient ethnicity. Method Using a national inpatient sample database between 2011 and 2015, we identified all adult patients who had TAVR. Races were identified and white race was set as control. Multiple logistic regression analysis was performed for the primary outcome of in-hospital mortality. Results Out of 58 174 patients who underwent TAVR, 50 809 (87.3%) were white, 2327 (4.0%) were black, 2311 (4.0%) were Hispanic, 640 (1.1%) Asian, 105 (0.2%) Native American and 1982 (3.4%) of other ethnicities. We found a statistically significant linear uptrend in the utilization of TAVR in patients of all races between the years 2011 and 2015. White, black, Hispanic and Native American patients had a downward linear trend for mortality during the studied years ( P ≤ 0.005 for all). Black patients had lower in-hospital mortality [2.8 vs. 3.6%, odds ratio (OR) = 0.62; 95% confidence interval (CI) 0.44, 0.81 P < 0.001] compared with white patients, whereas Hispanic patients and Native Americans had higher in-hospital mortality compared with white patients (4.5% OR 1.26; 95% CI 1.01, 1.56 P = 0.041), (9.5% OR 4.44; 95% CI 2.25, 8.77 P < 0.001), respectively. Conclusion Overall, TAVR utilization is associated with lower mortality. There is a rising trend in utilization of TAVR in the black population with a significantly favorable mortality trend compared with the white population.

Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real‐time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5‐year follow‐up. The c‐statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74–0.78), and the c‐statistic across 10 cross‐validation data sets was 0.75 (95% CI, 0.73–0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21–0.58]; intermediate‐high: hazard ratio, 2.98 [95% CI, 2.37–3.74]; high: hazard ratio, 8.44 [95% CI, 6.66–10.70], all P <0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and β estimates. Big data infrastructure enabled deployment of the real‐time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real‐time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real‐time risk calculator deployed at the point of care.

The burden and predictors of hospitalization over time in community-based patients with peripheral artery disease (PAD) have not been established. This study evaluates the frequency, reasons and predictors of hospitalization over time in community-based patients with PAD. We assembled an inception cohort of 1798 PAD cases from Olmsted County, MN, USA (mean age 71.2 years, 44% female) from 1 January 1998 through 31 December 2011 who were followed until 2014. Two age- and sex-matched controls ( n = 3596) were identified for each case. ICD-9 codes were used to ascertain the primary reasons for hospitalization. Patients were censored at death or last follow-up. The most frequent reasons for hospitalization were non-cardiovascular: 68% of 8706 hospitalizations in cases and 78% of 8005 hospitalizations in controls. A total of 1533 (85%) cases and 2286 (64%) controls ( p < 0.001) were hospitalized at least once; 1262 (70%) cases and 1588 (44%) controls ( p < 0.001) ≥ two times. In adjusted models, age, prior hospitalization and comorbid conditions were independently associated with increased risk of recurrent hospitalizations in both groups. In cases, severe PAD (ankle–brachial index < 0.5) (HR: 1.25; 95% CI: 1.15, 1.36) and poorly compressible arteries (HR: 1.26; 95% CI: 1.16, 1.38) were each associated with increased risk for recurrent hospitalization. We demonstrate an increased rate of hospitalization in community-based patients with PAD and identify predictors of recurrent hospitalizations. These observations may inform strategies to reduce the burden of hospitalization of PAD patients.