Impact of hospital and sociodemographic factors on utilization of drug-eluting stents in 2011-2012 Medicare cohort

Objective: Insurance status is a predictor of drug-eluting stent (DES) usage. Our study sought to determine the effect of hospital and sociodemographic characteristics on utilization of DES in nationwide inpatient discharges with uniform insurance (Medicare). Methods: We linked data from the 2011 to 2012 Medicare discharges, 2011 Medicare hospital referral region (HRR) report (racial composition of each HRR), American Hospital Association (number of beds, rural/urban location, public/private status, and academic affiliation of hospitals), and American Community Survey 2011 (median income using zip code). We analyzed diagnosis-related group (DRG) codes 249 (bare metal stent without complications), 246, and 247 (DES with and without complications, respectively). Univariate and multivariable logistic regression was conducted to determine odds ratios (OR) for utilization of DES. Results: There were 322,002 discharges with DRG codes 246 (54,279), 247 (209,365), and 249 (58,358) in our database. Higher odds of DES usage was observed in Hispanic dominant HRR(s) (OR: 1.37, 95% confidence interval [CI]: 1.33-1.42, P < 0.001) compared to Caucasian dominant HRR(s). DES utilization was similar in African-American and Caucasian dominant HRR (s). Higher odds of DES use was observed in median household income groups ≥$20,001 (OR: 1.07, 95% CI: 1.01-1.13, P - 0.03). Lower DES usage was observed in hospitals with higher total stent volume (quartile 4 vs. quartile 1: OR: 0.66, 95% CI: 0.63-0.69, P < 0.001) and for-profit hospitals (OR: 0.88, 95% CI: 0.85-0.92, P < 0.001). Conclusions: Our study findings suggest that there are significant differences in DES utilization in a national cohort of individuals with uniform insurance

Electrocardiographic Abnormalities and Reclassification of Cardiovascular Risk: Insights from NHANES-III

We aimed to assess the additive value of electrocardiogram (ECG) findings to risk prediction models for cardiovascular disease. Our dataset consisted of 6025 individuals with ECG data available from the National Health and Nutrition Examination Survey-III. This is a self-weighting sample with a follow-up of 79,046.84 person-years. The primary outcomes were cardiovascular mortality and all-cause mortality. We compared 2 models: Framingham Risk Score (FRS) covariates (Model A) and ECG abnormalities added to Model A (Model B), and calculated the net reclassification improvement index (NRI). Mean age of our study population was 58.7 years; 45.6% were male and 91.7% were white. At baseline, 54.6% of individuals had ECG abnormalities, of which 545 (9%) died secondary to a cardiovascular event, compared with 194 individuals (3.2%) (P <.01) without ECG abnormalities. ECG abnormalities were significant predictors of cardiovascular mortality after adjusting for traditional cardiovascular risk factors (hazard ratio 1.44; 95% confidence interval, 1.13-1.83). Addition of ECG abnormalities led to an overall NRI of 3.6% subjects (P <.001) and 13.24% in the intermediate risk category. The absolute integrated discrimination index was 0.0001 (P <.001). Electrocardiographic abnormalities are independent predictors of cardiovascular mortality, and their addition to the FRS improves model discrimination and calibration. Further studies are needed to assess the prospective application of ECG abnormalities in cardiovascular risk prediction in individual subjects