Readmissions After Revascularization Procedures for Peripheral Arterial Disease

Many observers have advocated reducing readmissions as a way to improve health care quality and decrease costs (1). The Centers for Medicare & Medicaid Services (CMS) Hospital Readmissions Reduction Program (HRRP), passed in March 2010, financially penalizes hospitals on the basis of higher-than-predicted 30-day readmission rates for selected clinical conditions (2). Recent evidence has demonstrated early success of the HRRP at reducing rates of readmission for acute myocardial infarction, congestive heart failure, and pneumonia (3), and the program has since been expanded to cover other conditions, including some that are treated surgically (4). More than 200 million persons worldwide are affected by peripheral arterial disease, with an estimated 27 million residing in North America and Europe and 8.5 million residing in the United States (5). These patients typically are older, have a higher burden of comorbidities, and have lower projected life expectancy (6, 7). There is increasing recognition that more intensive care of patients with peripheral arterial disease may reduce the need for amputation. As a result, the use of peripheral arterial revascularization has increased (8, 9), and this increase has outpaced the reduction in bypass surgery (10). Patients undergoing surgical revascularization have high rates of readmission (11, 12). For example, in a comprehensive analysis of rehospitalizations among Medicare beneficiaries (1), the 30-day readmission rate for patients having surgery for peripheral vascular disease was 24%, the third-highest rate of any diagnosis-related group, behind only congestive heart failure and psychoses. As CMS considers expanding the HRRP to include additional conditions and continues to report all-cause readmission rates at the hospital level, further data are needed to fully understand the national burden of rehospitalization risk among patients undergoing peripheral arterial revascularization, including endovascular treatment. This information can in turn guide interventions aimed at reducing these events. Therefore, we used data from the 2014 Nationwide Readmissions Database (NRD), an all-payer database from 22 U.S. states accounting for 49.3% of all U.S. hospitalizations, to realize 2 objectives. First, we sought to determine rates, causes, and associated costs of nonelective 30-day readmissions among patients who had in-hospital peripheral arterial revascularization (endovascular or surgical). Second, we aimed to evaluate whether heterogeneity of readmission risk exists among U.S. hospitals after standardization for hospital case mix. Methods Data Source We obtained data from the NRD between 1 January and 31 December 2014. The Agency for Healthcare Research and Quality (AHRQ) sponsors the NRD as part of the Healthcare Cost and Utilization Project. The NRD collects discharge data from 22 geographically dispersed U.S. states, accounting for 51.2% of the total U.S. population and 49.3% of all U.S. hospitalizations. The database includes data from all payers as well as uninsured persons and contains more than 100 clinical and nonclinical variables for each hospital stay. These data include diagnosis and procedure codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM); Clinical Classifications Software (CCS) diagnosis and procedure classifications; patient demographic characteristics; expected payment sources; and total charges and hospital costs based on costcharge ratios provided by the NRD. The database also provides weights that allow calculation of national estimates. The Institutional Review Board at Beth Israel Deaconess Medical Center evaluated this study and deemed it not to qualify as human subjects research. Study Population For this study, we identified all adult hospitalizations (aged 18 years) between 1 January and 31 December 2014 that were associated with a procedure code corresponding to peripheral arterial revascularization (endovascular or surgical). Data from December 2014 did not contribute admissions, allowing every patient to have 30 days of follow-up. The list of procedure codes used to ascertain patients who underwent peripheral arterial revascularization is provided in Appendix Table 1. Peripheral arterial revascularization consisted of endovascular interventions, surgical interventions, and hybrid approaches (both interventions performed during the same admission). Unweighted admissions meeting these criteria (n= 94080) were subsequently excluded if they were not associated with a diagnostic code corresponding to peripheral arterial disease (n= 25331) or if the hospitalization occurred in a state other than the patient's primary residence (n= 3215), because any readmission occurring in a different state from that of the index hospitalization would not be captured by the NRD (Appendix Figure). The list of diagnostic codes used to ascertain patients with peripheral arterial disease is provided in Appendix Table 1. Patients were allowed to contribute more than 1 admission as long as it occurred more than 30 days after any previously included admission or readmission. However, a second admission that occurred within 30 days was counted only as a readmission (1980 admissions occurring within 30 days of a prior admission or readmission were excluded) (Appendix Figure). Appendix Table 1. Diagnostic and Procedure Billing Codes Appendix Figure. Flow diagram of admission selection. PAD = peripheral arterial disease. Patient, Hospitalization, and Institutional Characteristics Patient demographic characteristics included age (years), sex, primary insurer, quartile of median household income by ZIP code, and residency categorized by county population. We determined chronic conditions based on the 29 Elixhauser comorbidities provided by AHRQ (13). Diagnosis codes used to identify patients with chronic limb ischemia are listed in Appendix Table 1. Characteristics of the index hospitalization included type of peripheral arterial revascularization (endovascular, surgical, or hybrid); length of stay (days); discharge destination; and cost of the admission using costcharge ratios, which were provided by the NRD. Institutional characteristics included hospital bed volume (small, medium, or large), ownership (government, private nonprofit, or private for-profit), teaching status, and annual volume of peripheral arterial revascularizations. In-hospital adverse events that occurred during the index admission included vascular complications, major bleeding, acute myocardial infarction, cardiogenic shock, cardiac arrest, acute kidney injury, transient ischemic attack or stroke, and sepsis. Diagnostic codes used to identify these events are provided in Appendix Table 1. Study Outcomes For the first objective, the primary outcome was the rate of unplanned all-cause 30-day readmissions, which we defined as hospitalizations for any cause within 30 days of discharge after an index hospitalization. All readmissions that were coded as nonelective in the NRD were considered to be unplanned. We analyzed median and total costs of readmission, in-hospital mortality during readmission, and the need for a repeated procedure (peripheral arterial revascularization or lower extremity amputation) during readmission. Procedure codes used to ascertain patients who had amputation of a lower extremity are listed in Appendix Table 1. We also identified reasons for unplanned 30-day readmissions. These reasons were determined on the basis of the CCS coding system, a diagnosis and procedure categorization scheme that clusters patient diagnoses and procedures into a limited number of meaningful clinical categories based on ICD-9-CM codes (Appendix 1) (14). For the second objective, the primary outcome was hospital-specific, risk-standardized, 30-day all-cause readmission rates. To estimate these rates, we developed a hierarchical logistic regression model accounting for differences in hospital case mix, as described in the next section. Statistical Analysis We report categorical variables as counts and percentages, and continuous variables as medians and ranges. We adapted an established approach (15) to estimate risk-standardized readmission rates (RSRRs) across hospitals. First, we created a logistic regression model predicting 30-day readmission to identify variables for risk adjustment. Candidate variables for the model included patient factors, as reported in Appendix Table 2. We excluded such covariates as in-hospital complications, certain patient demographic characteristics (for example, socioeconomic status and insurance type), and discharge disposition because these may be more related to hospital quality and resource availability. To inform variable selection, we used a modified stepwise logistic regression approach, which involved creating 500 bootstrap samples from the data set. For each sample, we ran a logistic stepwise regression with both backward and forward selection. We set the P value to enter at 0.05 and exit at 0.01. We then evaluated the percentage of time that each candidate variable was significantly associated with readmission in each of the 500 repeated samples and retained all variables above a 70% cutoff. This resulted in a final model with 14 variables. Appendix Table 2. Candidate Variables for 30-Day Readmission Prediction Models Next, we used hierarchical generalized linear models with adjustment for case mix using the 14 selected variables to estimate RSRRs (15). This approach accounts for within-hospital correlation of the observed readmissions. We assumed that heterogeneity of hospital readmission rates that was present after adjustment for patient risk and sampling variability was secondary to hospital quality. A technical description of this approach is provided in Appendix 2. We stratified analyses by type of peripheral arterial revascularization performed during the index hospitalization (endovascular or surgical