Linda R. Valsdottir

Readmissions After Revascularization Procedures for Peripheral Arterial Disease: A Nationwide Cohort Study

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 patients 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

Proprotein Convertase Subtilisin/Kexin Type 9 Inhibitor Therapy: Payer Approvals and Rejections, and Patient Characteristics for Successful Prescribing

Background: Proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) are a novel class of medications for patients with familial hypercholesterolemia or clinical atherosclerotic cardiovascular disease requiring additional lipid lowering beyond dietary measures and statin use. Because of the drugs’ high cost, rates of prescription approval by payers may be low. We aimed to identify payer approval and rejection rates for PCSK9i prescriptions and the potential factors influencing these rates. Methods: This is a retrospective, descriptive cohort study using nationwide pharmacy claims linked to electronic medical records from a nationwide data warehouse. The data set includes >220 million patients from all 50 states and all payer types with 5140 distinct health plans. PCSK9i prescriptions were submitted for 51 466 patients in the pharmacy data set. The main outcome was approval or rejection of PCSK9i prescription claims. Factors associated with approval and rejection of these medications in the United States were assessed. Results: Among patients who were prescribed a PCSK9i, 47.0% were approved for coverage by the payer. Variables that were associated with PCSK9i approval included age >65 years (P<0.01), history of atherosclerotic cardiovascular disease (P<0.01), prescription by a cardiologist or nonprimary care provider (P<0.01), statin intolerance (P=0.03), longer statin duration (P=0.01), and noncommercial payers (P<0.01). Higher low-density lipoprotein cholesterol levels were not associated with higher approval rates. Commercial third-party payers had the lowest approval rates (24.4%) and Medicare had the highest (60.9%). Conclusions: Rates of approval for PCSK9i therapy are low, even for patients who appear to meet labeled indications. Although a combination of clinical characteristics increases the likelihood of approval, payer type is the most significant factor.

Comparison of 30-Day Readmission Rates After Hospitalization for Acute Myocardial Infarction in Men Versus Women

Readmission after hospitalization for acute myocardial infarction (AMI) significantly contributes to preventable morbidity and health-care costs. Outcomes after AMI vary by sex but the relationship of sex to readmissions warrants further exploration. Using the 2013 Nationwide Readmissions Database, we identified patients with a principal discharge diagnosis of AMI and stratified all-cause 30-day readmissions by sex and age. Of 214,824 patients, 44% were 18 to 64 years of age, 56% were ≥65 years, and 28% and 45%, respectively, were female. For patients 18 to 64 years, the readmission rate was 14% for women and 10% for men (p <0.001). For patients ≥65 years, the readmission rate was 18% for women and 16% for men (p <0.001). After adjusting for co-morbidities, women had a significantly higher risk of 30-day readmission compared with men, an effect that was strongest in younger women (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.06 to 1.39, for ages 18 to 44; OR 1.13, 95% CI 1.07 to 1.18, for ages 45 to 64; OR 1.13, 95% CI 1.07 to 1.19, for ages 65 to 74, interaction p <0.001). The procedure rates during the index hospitalization were significantly lower for women. The most common readmission diagnoses were recurrent AMI, ischemic heart disease, and heart failure. Costs associated with readmissions after AMI totaled

Applicability of Publicly Reported Hospital Readmission Measures to Unreported Conditions and Other Patient Populations: A Cross-sectional All-Payer Study

447,506,740, of which

A Survey of Interventional Cardiologists’ Attitudes and Beliefs About Public Reporting of Percutaneous Coronary Intervention

176,743,622 were attributed to readmissions of women. In conclusion, women are at higher risk of short-term readmission after an AMI hospitalization than men, particularly younger women. Sex-specific strategies to reduce these readmissions may be warranted.

Meta-Analysis of Bleeding Risk Prediction Scores in Patients After Percutaneous Coronary Intervention on Dual Antiplatelet Therapy

Hospital readmissions have gained substantial attention from policymakers and health care providers because of their high frequency and tremendous costs (1). In particular, reducing readmission after heart failure (HF), acute myocardial infarction (AMI), and pneumonia has become a leading priority for many health systems since the Centers for Medicare & Medicaid Services (CMS) initiated public reporting in 2009 and financial penalties for readmissions in 2013 (2, 3). Performance on these condition-specific readmission measures determines whether hospitals receive across-the-board payment reductions for all Part A Medicare admissions for these conditions (4). As a result, research has focused largely on hospital readmissions after hospitalizations for HF, AMI, and pneumonia among the Medicare population (516). However, these publicly reported conditions account for only a small fraction of all hospital admissions (1719). Because Medicare readmission rates for specific conditions are used in global hospital quality metrics, such as those of the consumer-oriented Hospital Compare Web site (3), it is important to know whether readmission rates for these conditions reflect broader hospital-wide performance among all conditions and all payers (20, 21). This knowledge, however, currently does not exist. Likewise, whether the relationship between readmission rates for publicly reported and unreported conditions varies according to hospital characteristics has not been investigated. This knowledge might guide future policy regarding public reporting or reimbursement and inform hospital quality improvement strategies. To that end, this study investigated whether 30-day risk-adjusted readmission measures for publicly reported conditions among Medicare patients reflect readmission rates for 2 reference groups: Medicare patients hospitalized for unreported conditions and non-Medicare patients hospitalized with HF, AMI, or pneumonia. Methods Data Source and Study Participants Data from the Healthcare Cost and Utilization Projects Nationwide Readmissions Database (NRD) for 2013 and 2014 were used for this study. The NRD contains verified patient linkage numbers to track patient encounters across hospitals within a state while adhering to strict privacy guidelines and has been used previously for research (19, 2224). This database is a nationwide all-payer hospital inpatient data bank that includes patients of all ages, accounting for 49% of all U.S. hospitalizations each year. The 2013 NRD contains approximately 14 million discharges from 2006 hospitals across 21 states (weighted to estimate around 36 million discharges), and the 2014 database contains approximately 15 million discharges from 2048 hospitals across 22 states (weighted to estimate around 35 million discharges). Although the NRD does not comprise a random sample of states, it is designed to be geographically representative and to generate national estimates of readmissions when sampling weights are used. Hospitals with a minimum of 24 index admissions with a primary discharge diagnosis of a reported condition tied to financial penalties among Medicare patients (at least 8 each for HF, AMI, and pneumonia) were included. Similar to algorithms used by CMS (2527), admissions with an in-hospital death or a discharge against medical advice were excluded. Hospitalizations resulting in transfer to another acute care facility also were excluded, because such episodes of care would be accounted for by including the last hospitalization in the transfer chain. Only the first readmission within 30 days of an index hospitalization was considered. All subsequent admissions after 30 days from discharge were evaluated as another index hospitalization. Readmission hospitalizations within 30 days of a prior discharge were not included as index admissions. Variables For each eligible admission, the discharge diagnosis was ascertained and characterized as reported if it was HF, AMI, or pneumonia and as unreported otherwise. This analysis focused on publicly reported conditions tied to financial penalties during the study period; therefore, admissions with discharge diagnoses of chronic obstructive lung disease, hip or knee arthroplasty, or coronary artery bypass grafting were excluded from the unreported group, because readmissions after these conditions began to be penalized after 2014. The primary insurance payer for each admission was identified and characterized as Medicare or non-Medicare. Hospital characteristics examined included size (defined by number of beds), teaching status (metropolitan teaching, metropolitan nonteaching, or nonmetropolitan), and ownership status (public, private, or nonprofit). Metropolitan categorization included large and small metropolitan areas on the basis of a simplified adaptation of the 2003 version of the Urban Influence Codes. A hospital was classified as teaching if it had an American Medical Associationapproved residency program, was a member of the Council of Teaching Hospitals, or had a ratio of full-time equivalent interns and residents to beds of 0.25 or higher. Ownership status was obtained from the American Hospital Association Annual Survey of Hospitals. Whether a readmission was planned or unplanned was determined by using the algorithm for the 2013 condition-specific readmission measures (28), which has been applied elsewhere in all-payer contexts (29). Statistical Analysis For each hospital, 30-day all-cause risk-standardized unplanned readmission rates (RSRRs) and excess readmission ratios (ERRs) were estimated for 3 groups of patients: Medicare beneficiaries admitted with HF, AMI, or pneumonia (Medicare reported group); Medicare beneficiaries admitted for all other conditions (Medicare unreported group); and non-Medicare beneficiaries admitted for HF, AMI, or pneumonia (non-Medicare group). Patients younger than 65 years were excluded from the Medicare groups because they represented a fundamentally different population that was not included in CMS penalty calculations (2527). Hospital ERRs were estimated as the ratio of the predicted readmission rate for each hospital to the expected rate for a hospital with a similar case mix by using hierarchical models to approximate the risk-adjustment methodology used in the CMS Hospital-Wide Readmission Measure (30). Details on the risk-adjustment methodology and statistical procedures used are provided in Supplement 1. Supplement. Technical Appendix A, B, and C Because CMS uses the point estimate of the ERR as the foundation for determining how much a hospital will be penalized, its precise values are relevant to health policy (Supplement 2). We calculated the range of within-hospital differences in ERRs between groups overall and among subgroups of hospitals. We then assessed the agreement between the ERRs for the Medicare reported group and comparator groups by using BlandAltman plots (Supplement 3) (31). We plotted each hospital according to the average ERR between the Medicare reported group and either the Medicare unreported or the non-Medicare group along the x-axis and the difference in ERR between the Medicare reported group and the comparison group along the y-axis. Limits of agreement in these plots were defined as0.1, which would represent a 10% difference in total dollar amount of a hospitals financial penalty (a financially meaningful difference), assuming that the hospitals penalty does not cross a penalty floor or ceiling. To understand the characteristics of hospitals with the largest differences in ERRs, we examined discordant hospitals, defined as hospitals for which the ERR difference between the Medicare reported and comparator groups was greater than 0.1. In addition, hospital penalty status based on the Medicare reported group and potential penalty status based on non-Medicare and Medicare unreported groups were determined by identifying hospitals with an ERR greater than 1.00, in accordance with the approach used by CMS to calculate penalties (Supplement 2). As a robustness check, all analyses were repeated with the inclusion of hospitals with a minimum of 15 index Medicare admissions for reported conditions (5 for each condition) and 75 such admissions (25 for each condition). We also repeated the analyses, stratifying by year and using a conventional statistical threshold of1.96 SD of the Medicare reported ERR to define limits of agreement in the BlandAltman plots. Medicare groups derived from the NRD differ slightly from those used by CMS in that the NRD includes Medicare managed care; therefore, we also used data from the CMS Medicare Provider and Analysis Review files for the fee-for-service population to calculate raw readmission rates for Medicare groups and compared the results with those from the NRD to validate its use. All analyses were performed by using SAS, version 9.4 (SAS Institute). Role of the Funding Source This study was funded by the Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology at Beth Israel Deaconess Medical Center. No external funding source had any role in the design, conduct, or analysis of the study or in the decision to submit the manuscript for publication. Results Our final sample included 2101 hospitals (1044 hospitals from 2013 and 1057 from 2014). During the study period, Medicare patients had 953086 admissions for reported conditions (HF, AMI, and pneumonia), with a median annual hospital RSRR of 19.8% (interquartile range, 18.4% to 21.4%), compared with 7121223 admissions for unreported conditions, with a median annual hospital RSRR of 17.4% (interquartile range, 16.0% to 18.9%) (Table 1). Non-Medicare patients had 446250 admissions for HF, AMI, or pneumonia, with a median annual hospital RSRR of 14.6% (interquartile range, 13.4% to 16.3%). Table 1. Readmission Rates and Hospital Volume for Publicly Reported Versus Unreported Conditions and Payer Status Hospital Readmission Rates for Reported Versu

Trends in Isolated Surgical Aortic Valve Replacement According to Hospital-Based Transcatheter Aortic Valve Replacement Volumes

Importance Public reporting of procedural outcomes has been associated with lower rates of percutaneous coronary intervention (PCI) and worse outcomes after myocardial infarction. Contemporary data are limited on the influence of public reporting on interventional cardiologists’ clinical decision making. Objective To survey a contemporary cohort of interventional cardiologists in Massachusetts and New York about how public reporting of PCI outcomes influences clinical decision making. Design, Setting, and Participants An online survey was developed with public reporting experts and administered electronically to eligible physicians in Massachusetts and New York who were identified by Doximity (an online physician networking site) and 2014 Medicare fee-for-service claims for PCI procedures. The personal and hospital characteristics of participants were ascertained via a comprehensive database from Doximity and the American Hospital Association annual surveys of US hospitals (2012 and 2013) and linked to survey responses. Associations between survey responses and characteristics of participants were evaluated in univariable and multivariable analyses. Main Outcomes and Measures Reported rate of avoidance of performing PCIs in high-risk patients and of perception of pressure from colleagues to avoid performing PCIs. Results Of the 456 physicians approached, 149 (32.7%) responded, including 67 of 129 (51.9%) in Massachusetts and 82 of 327 (25.1%) in New York. The mean (SD) age was 49 (9.2) years; 141 of 149 participants (94.6%) were men. Most participants reported practicing at medium to large, nonprofit hospitals with high-volume cardiac catheterization laboratories and cardiothoracic surgery capabilities. In 2014, participants had higher annual PCI volumes among Medicare patients than nonparticipants did (median, 31; interquartile range [IQR], 13-47 vs median, 17; IQR, 0-41; P < .001). Among participants, 65% reported avoiding PCIs on at least 2 occasions becase of concern that a bad outcome would negatively impact their publicly reported outcomes; 59% reported sometimes or often being pressured by colleagues to avoid performing PCIs because of a concern about the patient’s risk of death. After multivariable adjustment, more years of experience practicing interventional cardiology was associated with lower odds of PCI avoidance. The state of practice was not associated with survey responses. Conclusions and Relevance Current PCI public reporting programs can foster risk-averse clinical practice patterns, which do not vary significantly between interventional cardiologists in New York and Massachusetts. Coordinated efforts by policy makers, health systems leadership, and the interventional cardiology community are needed to mitigate these unintended consequences.

Impact of a Claims-Based Frailty Indicator on the Prediction of Long-Term Mortality After Transcatheter Aortic Valve Replacement in Medicare Beneficiaries

The duration and type of dual antiplatelet therapy (DAPT) prescribed to patients after percutaneous coronary intervention (PCI) involves carefully balancing reduced ischemia and increased bleeding risk for individual patients. Whereas multiple bleeding risk scores exist, the performance of these models to predict long-term bleeding in the setting of DAPT across different settings and populations is unclear. Therefore, we performed a systematic review and meta-analysis to compare the performance of current bleeding risk prediction scores for predicting major long-term bleeding events in patients on DAPT post-PCI. Based on a search of MEDLINE (January 1, 1946 to March 3, 2017) and EMBASE (January 1, 1974 to March 3, 2017) for studies published in the English language, we identified 10 published studies of 11 risk unique risk prediction models across a wide variety of settings. Area under the receiver operating characteristic curve (AUC) was used to measure discrimination, when available. Our findings reveal that the prediction models created to date demonstrate only modest accuracy, with the reported AUCs ranging from 0.54 to 0.89; aggregated AUC 0.68 (95% confidence intervals 0.65 to 0.72). Although only 5 studies (50%) reported measures of calibration, the reported models were reasonably well calibrated. Only 3 models (33%) were externally validated. Meta-regression demonstrated lack of influence by age (p = 0.99) or length of follow up (p = 0.42). Sensitivity analysis did not significantly change the results. Novel prediction models are warranted to aid in maximizing the benefit of DAPT after PCI while minimizing harm.