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Objective: The share of total abdominal aortic aneurysm (AAA) repairs performed by endovascular aneurysm repair (EVAR) increased rapidly from 32% in 2001 to 65% in 2006 with considerable variation between states. We hypothesized that hospitals in competitive markets were early EVAR adopters and had improved AAA repair outcomes. Methods: Nationwide Inpatient Sample and linked Hospital Market Structure (HMS) data was queried for patients who underwent repair for nonruptured AAA in 2003. In HMS, the Herfindahl Hirschman Index (HHI, range 0-1) is a validated and widely accepted economic measure of competition. Hospital markets were defined using a variable geographic radius that encompassed 90% of discharged patients. We conducted bivariate and multivariable linear and logistic regression analyses for the dependent variable of EVAR use. A propensity score-adjusted multivariable logistic regression model was used to control for treatment bias in the assessment of competition on AAA repair outcomes. Results: A weighted total of 21,600 patients was included in our analyses. Patients at more competitive hospitals (lower HHI) were at increased odds of undergoing EVAR vs open repair (odds ratio, 1.127 per 0.1 decrease in HHI; P < .0127) after adjusting for patient demographics, comorbidities, and hospital level factors (bed size, teaching status, AAA repair volume, and ownership). Competition was not associated with differences in in-hospital mortality or vascular, neurologic, or other minor postoperative complications. Conclusions: Greater hospital competition is significantly associated with increased EVAR adoption at a time when diffusion of this technology passed its tipping point. Hospital competition does not influence post-AAA repair outcomes. These results suggest that adoption of novel vascular technology is not solely driven by clinical indications but may also be influenced by market forces. [Copyright &y& Elsevier]

Objective: Major amputation is associated with increased short-term healthcare resource utilization (RU), early mortality, and socioeconomic status (SES) disparities. Our objective is to study patient-specific and SES-related predictors of long-term RU and survival after amputation. Methods: This retrospective analysis identified 364 adult patients who underwent index major amputation for critical limb ischemia from January 1995 through December 2000 at two tertiary centers with outcomes through December 2010. Age, gender, SES (race, income, insurance, and marital status), comorbidities (congestive heart failure [CHF], diabetes, diabetes with complications, and renal failure [RF]), subsequent procedures, cumulative length of stay (cLOS), and mortality were analyzed. Bivariate and multivariate Poisson regression for subsequent procedures and cLOS and Cox proportional hazard modeling for all-cause mortality were undertaken. Results: During a mean follow-up of 3.25 years, amputation patients had mean cLOS of 71.2 days per person-year (median, 17.6), 19.5 readmissions per person-year (median, 2.1), 0.57 amputation-related procedures (median, 0), and 0.31 cardiovascular procedures (median, 0). Below-knee amputation as the index procedure was performed in 70% of patients, and 25% had additional amputation procedures. Of readmissions at ≤30 days, 52% were amputation-related. Overall mortality during follow-up was 86.9%; 37 patients (10.2%) died within 30 days. Among patients surviving >30 days, multivariate Poisson regression demonstrated that younger age (incidence rate ratio [IRR], 0.98), public insurance (IRR, 1.63), CHF (IRR, 1.60), and RF (IRR, 2.12) were associated with increased cLOS. Diabetes with complications (IRR, 1.90) and RF (IRR, 2.47) affected subsequent amputation procedures. CHF (IRR, 1.83) and RF (IRR, 3.67) were associated with a greater number of cardiovascular procedures. Cox proportional hazard modeling indicated older age (hazard ratio [HR], 1.04), CHF (HR, 2.26), and RF (HR, 2.60) were risk factors for decreased survival. Factors associated with SES were not significantly related to the outcomes. Conclusions: This study found that RU is high for amputees, and increased RU persists beyond the perioperative period. Results were similar across SES indices, suggesting higher SES may not be protective against poor outcomes when limb salvage is no longer attainable. These findings support the hypothesis that SES disparities may be more modifiable during earlier stages of care for critical limb ischemia. [Copyright &y& Elsevier]

Objective: Analysis of complex survey databases is an important tool for health services researchers. Missing data elements are challenging because the reasons for “missingness” are multifactorial, especially categorical variables such as race. We simulated missing data for race and analyzed the bias from five methods used in predicting major amputation in patients with critical limb ischemia (CLI). Methods: Patient discharges with fully observed data containing lower extremity revascularization or major amputation and CLI were selected from the 2003 to 2007 Nationwide Inpatient Sample, a complex survey database (weighted n = 684,057). Considering several random missing data schemes, we compared five missing data methods: complete case analysis, replacement with observed frequencies, missing indicator variable, multiple imputation, and reweighted estimating equations. We created 100 simulated data sets, with 5%, 15%, or 30% of subjects' race drawn to be missing from the full data set. Bias was estimated by comparing the estimated regression coefficients averaged over 100 simulated data sets (βmiss) from each method vs estimates from the fully observed data set (βfull), with relative bias calculated as (βfull – βmiss/βfull) × 100%. Results: Our results demonstrate that reweighted estimating equations produce the least biased and the missing indicator variable produces the most biased coefficients. Complete case analysis, replacement with observed frequencies, and multiple imputation resulted in moderate bias. Sensitivity analysis demonstrated the optimal method choice depends on the quantity and type of missing data encountered. Conclusions: Missing data are an important analytic topic in research with large databases. The commonly used missing indicator variable method introduces severe bias and should be used with caution. We present empiric evidence to guide method selection for handling missing data. [Copyright &y& Elsevier]

Introduction: Analysis of large complex survey databases is an important research tool for health services researchers in Surgery. Missing data elements are challenging as reasons they are missing are multi-factorial, especially variables with multiple categories such as race. We simulated missing data for race and analyzed the bias from five missing data methods used in predicting major amputation in patients with critical limb ischemia (CLI). Methods: Discharges with fully observed data containing lower extremity revascularization or major amputation and CLI were selected from the 2003-2007 Nationwide Inpatient Sample, a complex survey database (weighted N=684,057). Considering random and non-random missing data schemes, we simulated missing data for race to compare five missing data methods (complete case analysis (CCA), missing indicator variable (MIV), single imputation with mean frequencies, re-weighted estimating equations (RWEEs), and multiple imputation). Bias was determined by comparing mean regression coefficients estimated from 100 simulated datasets (B_miss) to those estimated from the fully observed dataset (B_full), with relative bias calculated as (B_full–B_miss/B_full)*100%. Results: Our results demonstrate that RWEEs produce the least biased and MIV the most biased coefficients. CCA, single imputation, and multiple imputation resulted in moderate bias. Sensitivity analysis demonstrated that optimal method choice depends on quantity and type of missing data encountered. Conclusions: Missing data is an important analytical topic in research with large databases. RWEEs are flexible and provided the least biased coefficients. The commonly used MIV method introduces the most bias and should be used with caution. We present empirical evidence to guide method selection for handling missing data. [ABSTRACT FROM AUTHOR]

Objective: Disparities in limb salvage procedures may be driven by socioeconomic status (SES) and access to high-volume hospitals. We sought to identify SES factors associated with major amputation in the setting of critical limb ischemia (CLI). Methods: The 2003-2007 Nationwide Inpatient Sample was queried for discharges containing lower extremity revascularization (LER) or major amputation and chronic CLI (N = 958,120). The Elixhauser method was used to adjust for comorbidities. Significant predictors in bivariate logistic regression were entered into a multivariate logistic regression for the dependent variable of amputation vs LER. Results: Overall, 24.2% of CLI patients underwent amputation. Significant differences were seen between both groups in bivariate and multivariate analysis of SES factors, including race, income, and insurance status. Lower-income patients were more likely to be treated at low-LER-volume institutions (odds ratio [OR], 1.74; P < .001). Patients at higher-LER-volume centers (OR, 15.16; P <.001) admitted electively (OR, 2.19; P < .001) and evaluated with diagnostic imaging (OR, 10.63; P < .001) were more likely to receive LER. Conclusions: After controlling for comorbidities, minority patients, those with lower SES, and patients with Medicaid were more likely receive amputation for CLI in low-volume hospitals. Addressing SES and hospital factors may reduce amputation rates for CLI. [ABSTRACT FROM AUTHOR]

Disparities in health care are well documented for several racial, ethnic, and gender groups. In peripheral arterial disease, differences in prevalence, treatment selection, treatment outcomes, and resulting quality of life have negative effects on some minority groups and women. It may be easy to document disparities, but it is harder to understand their underlying causes. Are there biologic differences between members of racial and ethnic groups that influence disease presentation and outcomes? Or is the socioeconomic environment that surrounds them the true driver of observed differences? This article reviews the evidence for racial and gender disparities in vascular surgery and presents some potential mechanisms that may explain the disparities. [Copyright &y& Elsevier]