vol. 6 no. 9 897-904

DOI: https://doi.org/10.1016/j.jcin.2013.04.016

Published By: JACC: Cardiovascular Interventions

Print ISSN: 1936-8798

Online ISSN: 1876-7605

History:

• Received December 11, 2012.
• Revision received April 18, 2013.
• Accepted April 18, 2013.
• Published online September 1, 2013.

Copyright & Usage: American College of Cardiology Foundation

Author Information

Sunil V. Rao, MD^∗∗ (sunil.rao@duke.edu), Lisa A. McCoy, MS^∗, John A. Spertus, MD, MPH^†, Ronald J. Krone, MD^‡, Mandeep Singh, MD^§, Susan Fitzgerald, MS, RN^‖ and Eric D. Peterson, MD, MPH^∗

• ^∗Duke Clinical Research Institute, Durham, North Carolina
• ^†Saint Luke's Mid America Heart Institute/UMKC, Kansas City, Missouri
• ^‡Washington University, St. Louis, Missouri
• ^§Mayo Clinic, Rochester, Minnesota
• ^‖American College of Cardiology Foundation, Washington, DC

^∗Reprint requests and correspondence:

Dr. Sunil V. Rao, Duke Clinical Research Institute, 508 Fulton Street (111A), Durham, North Carolina 27705.

Objectives This study sought to develop a model that predicts bleeding complications using an expanded bleeding definition among patients undergoing percutaneous coronary intervention (PCI) in contemporary clinical practice.

Background New knowledge about the importance of periprocedural bleeding combined with techniques to mitigate its occurrence and the inclusion of new data in the updated CathPCI Registry data collection forms encouraged us to develop a new bleeding definition and risk model to improve the monitoring and safety of PCI.

Methods Detailed clinical data from 1,043,759 PCI procedures at 1,142 centers from February 2008 through April 2011 participating in the CathPCI Registry were used to identify factors associated with major bleeding complications occurring within 72 h post-PCI. Risk models (full and simplified risk scores) were developed in 80% of the cohort and validated in the remaining 20%. Model discrimination and calibration were assessed in the overall population and among the following pre-specified patient subgroups: females, those older than 70 years of age, those with diabetes mellitus, those with ST-segment elevation myocardial infarction, and those who did not undergo in-hospital coronary artery bypass grafting.

Results Using the updated definition, the rate of bleeding was 5.8%. The full model included 31 variables, and the risk score had 10. The full model had similar discriminatory value across pre-specified subgroups and was well calibrated across the PCI risk spectrum.

Conclusions The updated bleeding definition identifies important post-PCI bleeding events. Risk models that use this expanded definition provide accurate estimates of post-PCI bleeding risk, thereby better informing clinical decision making and facilitating risk-adjusted provider feedback to support quality improvement.

Bleeding complications after percutaneous coronary intervention (PCI) are common and are associated with an increased short- and long-term risk of morbidity and mortality as well as increased costs (1,2). Several bleeding avoidance strategies (BAS), such as bivalirudin, radial approach, and, in some studies, vascular closure devices, have been proposed to reduce periprocedural bleeding among higher-risk patient groups (3–6). Yet previous studies have demonstrated a “risk-treatment” paradox with respect to the use of BAS among patients undergoing PCI: BAS are used the least among patients with the highest bleeding risk (7). Among high-risk patients, such as those with ST-segment elevation myocardial infarction, some of these BAS are associated with reduced mortality (8,9), underscoring the importance of applying BAS in patients most likely to benefit. Moreover, Medicare has begun considering peri-PCI bleeding as a component of its Acute Care Episode Demonstration Project, suggesting the growing importance of bleeding as an indicator of quality.

Previous studies have identified patient factors associated with bleeding in the context of acute coronary syndrome (10,11); however, these studies used a definition of bleeding specific to the dataset in which the models were developed and did not include a broad population of patients undergoing PCI. Given the importance of PCI outcomes as performance measures and the interest in public reporting of PCI-related quality of care (12), pre-procedural identification of patients undergoing PCI who are at higher bleeding risk could support more efficient use of BAS to improve the safety of PCI. Moreover, pre-procedural identification could facilitate better patient informed consent (13) and provide risk-adjusted bleeding outcomes feedback to sites participating in quality improvement registries.

The National Cardiovascular Data Registry (NCDR) CathPCI Registry is an ongoing contemporary quality improvement registry of patients undergoing PCI in the United States. The data elements recorded in the registry undergo periodic review and are updated to support continuous quality improvement. We previously published a model predicting the risk of bleeding for patients undergoing PCI using the data elements captured in the registry (14), but the bleeding definition relied on site identification of hemorrhagic events and was restrictive compared with bleeding definitions used in other studies. For example, bleeding events were not considered complications if they were not associated with a prolonged hospital stay or a hemoglobin decrease of at least 3 g/dl. In 2009, the CathPCI Registry implemented a new data collection form with more detailed data elements associated with bleeding events to capture important complications that were not available in previous versions. Using these data elements, a new CathPCI Registry post-procedure bleeding definition was created, with which we sought to: 1) define contemporary bleeding event rates; 2) define major independent predictors of bleeding; and 3) develop and validate a full pre-procedure risk prediction model as well as a simple bedside additive risk prediction tool.

Study population

The CathPCI Registry is an initiative of the American College of Cardiology and the Society for Cardiovascular Angiography and Interventions and has been previously described (15). This registry records data on patient and hospital characteristics, clinical presentation, hospital length of stay, treatments, and in-hospital outcomes for PCI procedures from >1,000 sites across the United States. The NCDR has a comprehensive data quality program, including both data quality report specifications for data capture and transmission, and an auditing program. Dataset variables are determined and defined by physician work groups; data collection forms and dictionaries can be found on the NCDR website (http://www.ncdr.com).

For this study, we included all PCI procedures performed between February 2008 and April 2011 that had collected data using version 4 of the CathPCI Registry data collection form. Nonindex PCI procedures during the same hospitalization were excluded, as were patients who died the same day as their procedure. In addition, we excluded patients who had missing data on bleeding events and sites that reported no bleeding events (Fig. 1).

Figure 1
Study Sample Selection Flow Diagram

Definitions and outcomes

The primary outcome for this analysis was post-PCI bleeding. Using the updated data collection form and the desire to improve the capture of clinically important bleeding events, a panel of experts amended the definition of bleeding as any of the following occurring within 72 h after PCI or before hospital discharge (whichever occurs first): site-reported arterial access site bleeding, which may be either external or a hematoma >10 cm for femoral access, >5 cm for brachial access, or >2 cm for radial access; retroperitoneal, gastrointestinal, or genitourinary bleeding; intracranial hemorrhage; cardiac tamponade; post-procedure hemoglobin decrease of 3 g/dl in patients with a pre-procedure hemoglobin level ≤16 g/dl; or post-procedure nonbypass surgery–related blood transfusion for patients with a pre-procedure hemoglobin level ≥8 g/dl. This definition includes events such as intracranial hemorrhage, tamponade, hemoglobin decreases that account for potential hemodilution, and transfusions that account for severe anemia that were not included in the previous definition. The definitions of the other data elements are available at http://www.ncdr.com

Statistical analysis

Categorical variables are summarized as frequencies and percentages and compared with Pearson chi-square tests. Continuous variables are summarized as median (interquartile range) and compared using Wilcoxon rank sum tests. Ordinal variables were tested using a chi-square test based on the rank of the group mean score.

The study population was randomly split into a development sample consisting of 80% of admissions and a validation sample consisting of the remaining 20% of admissions. Baseline patient characteristics and variables from diagnostic catheterization were considered candidate variables. Candidate variables had <0.5% missing data except for estimated glomerular filtration rate (7.8%), pre-procedure hemoglobin level (9.5%), and ejection fraction (29.4%). Missing values were imputed to the lower risk group for discrete variables and replaced with sex-specific medians for body mass index (BMI), sex, and renal failure/dialysis–specific medians for estimated glomerular filtration rate, median value for hemoglobin, and congestive heart failure/cardiogenic shock/previous myocardial infarction–specific medians for ejection fraction. We used logistic regression with backward selection to stay criterion of p < 0.05 to develop a model predicting post-PCI bleeding. Variables that showed nonlinear associations with the outcome were transformed using splines.

We developed a full post-PCI bleeding model using all potential predictive variables. We also developed a risk prediction score by taking the regression coefficients from the pre-procedure model and assigning them an integer weighted to the comparative odds ratio associated with the risk factors (16). Covariates selected for the risk score were those with a chi-square >500. An individual patient's bleeding risk score is the sum of their integer weights. Patients were defined as at low, medium, and high risk of bleeding based on the predicted risk of bleeding derived from the prediction score. Patients with a predicted risk of bleeding at or below the 25th percentile probability were considered low risk, patients with a predicted risk of bleeding between the 25th and 75th percentile probability were considered moderate risk, and patients with a predicted risk of bleeding at or above the 75th percentile probability were considered high risk.

The C-statistic was used to compare discrimination between models and in clinical subgroups of interest including patients with ST-segment elevation myocardial infarction, females, those older than 70 years of age, those with diabetes mellitus, and those who did not undergo in-hospital coronary artery bypass grafting. Calibration plots were used to access goodness of fit. A p value <0.05 was considered statistically significant. All statistical tests were 2 sided. All statistical analyses were performed at the Duke Clinical Research Institute using SAS software (version 9.2, SAS Institute, Cary, North Carolina) and Stata version 11 (StataCorp LP, College Station, Texas).

Ethical considerations

The Institutional Review Board of Duke University Medical Center approved this analysis and determined that it met the definition of research not requiring informed consent.

Study sample

Between February 2008 and April 2011, 1,059,474 PCI procedures were performed at 1,232 sites and had data entered into version 4 of the CathPCI Registry data collection form. After applying exclusion criteria, 1,043,759 procedures from 1,142 sites remained (Fig. 1). Table 1 displays the baseline patient, procedure, and hospital characteristics of the development and validation samples. There were 60,194 PCI procedures that had post-procedure bleeding, yielding a post-PCI bleeding event rate of 5.8%. Of these events, 32% were site-reported at a specific anatomic location, whereas 44.6% were detected due to a pre- to post-procedure hemoglobin decrease, 21.8% by a blood transfusion, 1% by cardiac tamponade, and 0.6% were intracranial hemorrhage events.

Risk factors for in-hospital bleeding

Table 2 displays the in-hospital bleeding rates for the overall development and validation samples, as well as the rates for each pre-specified subgroup within the samples. The full model, which includes 33 variables, is displayed in Table 3. The most predictive factors, according to their chi-square, were female sex followed by shock or salvage PCI. In contrast, noninsulin-requiring diabetes mellitus was the least predictive. Several variables required transformation with splines such that the relationship with bleeding changed according to knots at specific values. Pre-procedure hemoglobin value, BMI, and age all had nonlinear associations with bleeding and required transformation. Table 4 shows the bedside NCDR bleeding risk score derived from the pre-procedure model. Using these 10 variables and the scoring system, the risk of post-PCI bleeding can be estimated by summing the point scores between 0 and 210 (Table 5, Fig. 2).

Figure 2
Risk of Post-Percutaneous Coronary Intervention Bleeding Based on the Bedside Bleeding Risk Prediction Score

Model performance

The full bleeding risk model had good discrimination in both the development and validation samples (c-index, development sample 0.78; validation sample 0.77). Table 6 lists the c-indexes of the full model and the risk score in the overall development and validation samples, as well as in pre-specified subgroups. The c-indexes for the subgroups ranged from 0.70 to 0.78. The model calibration plot for the full model is shown in Figure 3. There was high concordance between the risk predicted by the models and the observed bleeding events. Model calibration plots for the pre-specified subgroups are shown in the Online Appendix. There was a high level of concordance among these subgroups as well.

Figure 3
Model Calibration Plot for the Full Model

Bleeding remains one of the most common complications of PCI. Accordingly, as part of its quality improvement efforts, the NCDR seeks to improve its data collection and update its risk models by leveraging new data elements and improving bleeding definitions to capture a range of additional clinically important variables. These new models can be used to improve the safety of PCI by enabling the prospective identification of patients who would benefit most from BAS and by creating the infrastructure to support risk-adjusted provider feedback reports.

Using our updated bleeding definition, ∼1 in 20 patients (5.8%) were observed to have a bleeding event. This rate is higher than previously reported (2.4%) and reflects the inclusion of bleeding complications (such as tamponade and transfusions in clinically appropriate groups) that were not included in the previous definition, but which enabled broader estimates of clinically important bleeding to be generated. The bleeding rate reported in our study is also more consistent with the rate reported in clinical trials, such as the ACUITY (Acute Catheterization and Urgent Intervention Triage Strategy) trial, where the rate of bleeding among patients treated with glycoprotein IIb/IIIa inhibitors was 5.3% to 5.7% (17).

Studies indicate that the reported rate of bleeding is highly dependent on the definition used (18); a standardized bleeding definition, called the Bleeding Academic Research Consortium (BARC) definition, was recently proposed for clinical trials of patients with acute coronary syndrome or those undergoing PCI (19). The BARC definition includes many of the elements used in the current CathPCI Registry bleeding definition, but also relies heavily on adjudication. Although the size and scope of the CathPCI Registry makes adjudication of bleeding events impractical, the new bleeding definition is consistent with the major components of the BARC definition. An ongoing randomized clinical trial, the SAFE-PCI (Study of Access site For Enhancement of PCI for Women [NCT01406236]), is using the CathPCI Registry as a platform for data collection and has BARC type 2 or greater bleeding as the primary endpoint. This study will provide estimates of the correlation between BARC-defined bleeding and the updated CathPCI Registry definition of bleeding.

Importantly, a number of patient characteristics were strongly associated with periprocedural bleeding. Many of the predictive factors that we identified have been shown in other studies to be predictive of bleeding events. For example, female sex is consistently associated with an increased risk of bleeding (20), as are other variables like age, renal function, and BMI (21). In addition to these factors, we also identified unique variables not present in other bleeding risk models, such as pre-procedure hemoglobin level, cardiac arrest, shock, and clinical status (e.g., salvage procedures). For the full model that will be used to support risk-adjusted hospital comparisons, the addition of such variables is a significant advantage over previous models that use clinical trial data where the acuity of clinical presentation is generally not as severe. The inclusion of these variables minimizes the risk that hospitals that disproportionately care for patients with these high-risk characteristics would not be unduly penalized. This model can be used to risk-adjust post-PCI bleeding rates for the centers participating in the CathPCI Registry, identify leaders and laggards, and ultimately improve the safety of PCI by encouraging the adoption of BAS at centers that have higher-than-expected risk-adjusted bleeding rates. For example, previous studies have shown substantially greater absolute risk reductions with BAS use among patients with higher bleeding risks, previously defined as >1% (14). Corresponding thresholds with the new bleeding definition would be a risk of ≤2.0% (integer score ≤25), >2.0%, ≤6.5% (integer bleeding risk score of 25 to 65), and high risk representing risks >6.5% (integer bleeding risk score >65). The use of the CathPCI Registry bleeding risk score may encourage greater adoption of bivalirudin, vascular closure devices, or radial approach among patients in these higher-risk categories. This may be particularly important given the interest in public reporting of PCI-related outcomes (12). The distribution of risk using the new bleeding definition potentially broadens the proportion of patients who might benefit from BAS implementation, but future comparative effectiveness studies are needed to confirm this hypothesis. The bedside risk score that we developed, using 10 key variables, has further utility by facilitating pre-procedure identification of patients at high risk of bleeding, as well as informing the consent process (13).

Study limitations

First, in many states, participation in the CathPCI Registry is voluntary; therefore, this registry may not be completely representative of all PCI procedures performed in the United States. Nevertheless, the CathPCI Registry is the largest ongoing contemporary registry of PCI and there are no a priori reasons to believe that the associations between patient characteristics and periprocedural bleeding would differ among hospitals that do and do not participate in the NCDR. Second, the new definition of bleeding still includes site-identified bleeding complication data, although these data have objective definitions, sites may vary in their threshold for reporting these events. Nevertheless, the definition now also includes blood transfusion, hemoglobin decreases, and intracranial hemorrhage, thereby making it likely to detect the most clinically significant bleeding events. The use of blood transfusion in the registry may not necessarily reflect clinical bleeding, and its use is controversial in patients with coronary artery disease. Although some may argue that other physicians involved in patient care may be ordering “unnecessary” blood transfusions, the limitation of the new definition to only include those transfusions that occur in patients with hemoglobin values >8 mg/dl is congruent with previous data showing harm from transfusions in this population (22,23).

Using data from the NCDR CathPCI Registry, we updated the definition of bleeding to capture hemorrhagic events previously excluded and developed and validated contemporary predictive and risk-adjustment models for post-PCI bleeding. The models had good operating characteristics in the overall dataset of patients undergoing PCI, as well as among high-risk subgroups. This model will serve as the basis for providing risk-adjusted feedback on bleeding rates for sites participating in the CathPCI Registry, and the bedside bleeding risk score can facilitate the use of BAS in patients most likely to benefit.

The authors thank Erin LoFrese, MS, for her editorial contributions to this paper.

This research was supported by the American College of Cardiology Foundation's National Cardiovascular Data Registry (NCDR). The views expressed in this manuscript represent those of the author(s), and do not necessarily represent the official views of the NCDR or its associated professional societies identified at www.ncdr.com. Dr. Rao is a consultant for The Medicines Company and Terumo Medical. Dr. Spertus has received grants from Eli Lilly, Genentech, Bristol-Myers Squibb, and Sanofi-Aventis; a research contract from the American College of Cardiology Foundation; and an equity position in Health Outcomes Sciences. Dr. Peterson has received research funding from Eli Lilly and Janssen. Pharmaceuticals. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Abbreviations and Acronyms

Received December 11, 2012.

Revision received April 18, 2013.

Accepted April 18, 2013.

American College of Cardiology Foundation

1. Doyle B.J., Rihal C.S., Gastineau D.A., Holmes D.R. Jr.. (2009) Bleeding, blood transfusion, and increased mortality after percutaneous coronary intervention: implications for contemporary practice. J Am Coll Cardiol 53:2019–2027.
2. Rao S.V., Kaul P.R., Liao L., et al. (2008) Association between bleeding, blood transfusion, and costs among patients with non-ST-segment elevation acute coronary syndromes. Am Heart J 155:369–374.
3. Sherev D.A., Shaw R.E., Brent B.N. (2005) Angiographic predictors of femoral access site complications: implication for planned percutaneous coronary intervention. Catheter Cardiovasc Interv 65:196–202.
4. Kirtane A.J., Piazza G., Murphy S.A., et al. (2006) Correlates of bleeding events among moderate- to high-risk patients undergoing percutaneous coronary intervention and treated with eptifibatide: observations from the PROTECT-TIMI-30 trial. J Am Coll Cardiol 47:2374–2379.
5. Verheugt F.W., Steinhubl S.R., Hamon M., et al. (2011) Incidence, prognostic impact, and influence of antithrombotic therapy on access and nonaccess site bleeding in percutaneous coronary intervention. J Am Coll Cardiol Intv 4:191–197.
6. Rao S.V., Ou F.S., Wang T.Y., et al. (2008) Trends in the prevalence and outcomes of radial and femoral approaches to percutaneous coronary intervention: a report from the national cardiovascular data registry. J Am Coll Cardiol Intv 1:379–386.
7. Marso S.P., Amin A.P., House J.A., et al. (2010) Association between use of bleeding avoidance strategies and risk of periprocedural bleeding among patients undergoing percutaneous coronary intervention. JAMA 303:2156–2164.
8. Stone G.W., Witzenbichler B., Guagliumi G., et al. (2008) Bivalirudin during primary PCI in acute myocardial infarction. N Engl J Med 358:2218–2230.
9. Jolly S.S., Yusuf S., Cairns J., et al. (2011) Radial versus femoral access for coronary angiography and intervention in patients with acute coronary syndromes (RIVAL): a randomised, parallel group, multicentre trial. Lancet 377:1409–1420.
10. Subherwal S., Bach R.G., Chen A.Y., et al. (2009) Baseline risk of major bleeding in non-ST-segment elevation myocardial infarction: the CRUSADE (Can Rapid risk stratification of Unstable angina patients Suppress Adverse outcomes with Early implementation of the ACC/AHA Guidelines) Bleeding Score. Circulation 119:1873–1882.
11. Mehran R., Pocock S.J., Nikolsky E., et al. (2010) A risk score to predict bleeding in patients with acute coronary syndromes. J Am Coll Cardiol 55:2556–2566.
12. Moscucci M. (2012) Public reporting of PCI outcomes and quality of care: one step forward and new questions raised. JAMA 308:1478–1479.
13. Arnold S.V., Decker C., Ahmad H., et al. (2008) Converting the informed consent from a perfunctory process to an evidence-based foundation for patient decision making. Circ Cardiovasc Qual Outcomes 1:21–28.
14. Mehta S.K., Frutkin A.D., Lindsey J.B., et al., National Cardiovascular Data Registry (2009) Bleeding in patients undergoing percutaneous coronary intervention: the development of a clinical risk algorithm from the National Cardiovascular Data Registry. Circ Cardiovasc Interv 2:222–229.
15. Brindis R.G., Fitzgerald S., Anderson H.V., Shaw R.E., Weintraub W.S., Williams J.F. (2001) The American College of Cardiology-National Cardiovascular Data Registry (ACC-NCDR): building a national clinical data repository. J Am Coll Cardiol 37:2240–2245.
16. Sullivan L., Massaro J., D'Agostino R. (2004) Presentation of multivariate data for clinical use: the Framingham Study risk score functions. StatMed 23:1631–1660.
17. Stone G.W., McLaurin B.T., Cox D.A., et al. (2006) Bivalirudin for patients with acute coronary syndromes. N Engl J Med 355:2203–2216.
18. Rao S.V., O'Grady K., Pieper K.S., et al. (2006) A comparison of the clinical impact of bleeding measured by two different classifications among patients with acute coronary syndromes. J Am Coll Cardiol 47:809–816.
19. Mehran R., Rao S.V., Bhatt D.L., et al. (2011) Standardized bleeding definitions for cardiovascular clinical trials: a consensus report from the Bleeding Academic Research Consortium. Circulation 123:2736–2747.F
20. Alexander K.P., Chen A.Y., Newby L.K., et al. (2006) Sex differences in major bleeding with glycoprotein IIb/IIIa inhibitors: results from the CRUSADE (Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines) initiative. Circulation 114:1380–1387.
21. Rao S.V., Eikelboom J.A., Granger C.B., Harrington R.A., Califf R.M., Bassand J.P. (2007) Bleeding and blood transfusion issues in patients with non-ST-segment elevation acute coronary syndromes. Eur Heart J 28:1193–1204.
22. Rao S.V., Jollis J.G., Harrington R.A., et al. (2004) Relationship of blood transfusion and clinical outcomes in patients with acute coronary syndromes. JAMA 292:1555–1562.
23. Alexander K.P., Chen A.Y., Wang T.Y., et al. (2008) Transfusion practice and outcomes in non-ST-segment elevation acute coronary syndromes. Am Heart J 155:1047–1053.

vol. 9 no. 8 771-779

DOI: https://doi.org/10.1016/j.jcin.2016.01.033

Published By: JACC: Cardiovascular Interventions

Print ISSN: 1936-8798

Online ISSN: 1876-7605

History:

• Received December 22, 2015.
• Accepted January 14, 2016.
• Published online April 25, 2016.

Copyright & Usage: American College of Cardiology Foundation

Author Information

Amit N. Vora, MD, MPH (a.vora@duke.edu), Eric D. Peterson, MD, MPH, Lisa A. McCoy, MS, Kirk N. Garratt, MD, Michael A. Kutcher, MD, Steven P. Marso, MD, Matthew T. Roe, MD, MHS, John C. Messenger, MD and Sunil V. Rao, MD

• ^aDuke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina
• ^bCenter for Heart and Vascular Health, Christiana Care Health System, Wilmington, Delaware
• ^cWake Forest University School of Medicine, Winston-Salem, North Carolina
• ^dUniversity of Texas Southwestern Medical Center, Dallas, Texas
• ^eDepartment of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado

Reprint requests and correspondence:

Dr. Amit N. Vora, Duke Clinical Research Institute, Duke University Medical Center, 2400 Pratt Street, Durham, North Carolina 27705.

Objectives The aim of this study was to explore whether the use of bleeding avoidance strategies (BAS) explains variability in hospital-level bleeding following percutaneous coronary intervention.

Background Prior studies have reported that bleeding rates following percutaneous coronary intervention vary markedly among hospitals, but the extent to which use of BAS explains this variation is unknown.

Methods Using the American College of Cardiology National Cardiovascular Data Registry's CathPCI Registry, estimated hospital-level bleeding rates from 2,459,686 procedures at 1,358 sites were determined. A series of models were fit to estimate random-effect variance, adjusting for patient risk (using the validated CathPCI bleeding risk model, C statistic = 0.77) and various combinations of BAS (transradial access, bivalirudin, vascular closure device use). The rate of any BAS use was also estimated for each hospital, and the association between percentage BAS use and predicted bleeding rates was determined.

Results In total, 125,361 bleeding events (5.1%) were observed; patients experiencing bleeding events had lower rates of radial access (5.0% vs. 11.2%; p < 0.001), bivalirudin therapy (43.8% vs. 59.4%), and vascular closure device use (32.9% vs. 42.4%, p < 0.001) than those without bleeding. There was significant variation in bleeding rates across hospitals (median 5.0%; interquartile range [IQR]: 2.7% to 6.6%), which persisted after incorporating patient-level risk (median 5.1%; IQR: 4.0% to 4.4%). Patient factors accounted for 20% of the overall hospital-level variation, and radial access plus bivalirudin use accounted for an additional 7.8% of the overall hospital-level variation. The median hospital rate of any BAS use was 86.6% (IQR: 72.5% to 94.1%). A significant decrease in observed hospital-level bleeding was seen in hospitals above the median in BAS use (adjusted odds ratio: 0.90; 95% confidence interval: 0.88 to 0.93).

Conclusions A modest proportion of the variation in hospitals' rates of bleeding following percutaneous coronary intervention is attributable to differential use of BAS. Further analyses are required to determine the remaining approximately 70% causes of variation in percutaneous coronary intervention bleeding seen among hospitals.

Periprocedural bleeding is among the most common complications following percutaneous coronary intervention (PCI) and is associated with increased risk for short- and long-term mortality, stroke, increased length of hospital stay, and higher cost (1-9). Multiple bleeding avoidance strategies (BAS) have been developed to reduce the incidence of this common but preventable complication, including transradial access, targeted periprocedural anticoagulation (with bivalirudin), and the use of vascular closure devices. Although these strategies have been shown to reduce bleeding complications post-PCI, previous studies have shown significant variation in BAS use across patient populations (10,11), with lower use among patients at high risk for bleeding complications.

There are limited outcomes-based quality measures following PCI with which to evaluate hospitals. Post-PCI bleeding rates are shared with providers participating in the National Cardiovascular Data Registry's CathPCI Registry, and comparison of post-PCI bleeding rates across facilities has been proposed as a performance measure in the Centers for Medicare and Medicaid Services Acute Care Episode Demonstration Program. Although recent studies have demonstrated wide variation in hospital-level bleeding following PCI (12), data on the use of BAS such as radial access, bivalirudin, and potentially vascular closure devices across hospitals is limited. The available data demonstrate that BAS are used in patients at low risk for bleeding rather than in patients most likely to benefit-a "risk-treatment" paradox (10,13)-however, there may be other unmeasured care processes that affect hospital-level bleeding rates, such as antithrombotic drug dosing, manual compression protocols, and the use of ultrasound-guided femoral access. To date, no large-scale study has compared the association between BAS use and hospital-level bleeding rates and the extent to which individual patient risk and the use of specific BAS may explain the hospital-level variation in observed post-procedural bleeding. Therefore, we used data from the CathPCI Registry to: 1) determine hospital-level variation in observed bleeding rates following PCI; 2) evaluate the extent to which the use of specific BAS may explain the variance in bleeding rates across hospitals; and 3) assess the relationship between hospital-level BAS use and bleeding rates. We hypothesized that there would be significant variation in hospital-level post-PCI bleeding rates, that some of this variation would be explained by patient risk and the use of BAS, and that high use of BAS would be associated with lower rates of bleeding.

Data source and study population

Among the National Cardiovascular Data Registries, the CathPCI Registry is the largest quality improvement program for PCI in the world. Cosponsored by the American College of Cardiology and the Society for Cardiovascular Angiography and Interventions, it captures detailed clinical data on baseline patient and hospital characteristics, clinical presentation, procedural complications, and in-hospital outcomes among patients undergoing PCI at >1,500 sites across the United States. Details of the design and conduct of this registry have been previously described. Data are systematically collected using third-party software platforms certified by the American College of Cardiology or through a secure, Web-based platform and are regularly audited for data completeness and accuracy (14). The Duke University Medical Center Institutional Review Board granted a waiver of informed consent and authorization for this study, as data are collected without individual patient identifiers.

Between July 2009 and June 2013, we identified 2,516,937 patients undergoing PCI at 1,453 hospitals. We excluded patients with missing variables necessary to define bleeding (n = 10,210) and those who underwent coronary artery bypass grafting (n = 30,238). We further excluded patients undergoing PCI at sites that reported no bleeding events (n = 13,752); patients who had contraindications to, were blinded to, or had missing information for bivalirudin administration (n = 2,573); and patients who presented at hospitals performing <50 PCIs annually (n = 478).

Data definitions and outcomes

Patients were treated using any BAS if: 1) they underwent PCI via radial artery access; 2) bivalirudin was used for periprocedural anticoagulation regardless of arterial site of access, or, in case of femoral access; 3) they received vascular closure device to assist with hemostasis at the conclusion of the procedure.

The outcome of interest was the rate of CathPCI bleeding, defined as site-reported arterial access-site bleeding (either external or a hematoma >10 cm for femoral access, >5 cm for brachial access, or >2 cm for radial access); retroperitoneal, gastrointestinal, or genitourinary bleeding; intracranial hemorrhage; cardiac tamponade; post-procedural hemoglobin decrease of ≥3 g/dl in patients with pre-procedural hemoglobin levels ≥16 g/dl; or post-procedural non-bypass surgery-related blood transfusion for patients with pre-procedural hemoglobin levels ≥8 g/dl.

Statistical analysis

We compared baseline demographic, clinical, presentation, and hospital characteristics for patients by tertile of hospital-level use of BAS following PCI. Continuous variables are expressed as median values with interquartile ranges (IQRs), and categorical values are presented as percentages. We used Pearson chi-square tests for categorical variables and Wilcoxon rank sum tests for continuous variables.

The observed rate of bleeding for each hospital was calculated as the observed number of bleeding events divided by the total number of admissions. To estimate adjusted hospital bleeding rates by patient risk, we used logistic regression with random intercepts for hospital. The log odds for random hospital were assumed to be normally distributed, with mean equal to the intercept and variance equal to the random-effect variance or variation in log odds attributable to between-hospital differences. We estimated these parameters and transformed from the log-odds scale to the probability scale. The hospital-specific intercepts were used to estimate hospital-specific bleeding rates.

To assess whether the use of BAS attenuates the variation in adjusted hospital-level bleeding rates, we fit a series of 8 models and estimated random-effect variance. We used the percentage of proportional change in variance (PCV) to assess the incremental effect of adding variables to the model. The PCV is calculated as follows: PCV = (V_a − V_b)/V_a × 100, where V_a is the variance of the initial model and V_b is the variance of the model with more terms. The 8 models were: 1) unadjusted; 2) patient risk adjusted; 3) patient risk adjusted plus radial; 4) patient risk adjusted plus bivalirudin; 5) patient risk adjusted plus vascular closure device; 6) patient risk adjusted plus radial and bivalirudin; 7) patient risk adjusted plus bivalirudin and vascular closure device; and 8) patient risk adjusted plus radial, bivalirudin, and vascular closure device. We calculated the PCV for model 2 versus 1 and for all other models versus model 2. The patient risk-adjusted models included variables from the previously validated CathPCI bleeding model (15). Specifically, included covariates were sex, age, body mass index, cerebrovascular disease, peripheral vascular disease, chronic lung disease, prior PCI, diabetes, left ventricular ejection fraction, chronic kidney disease (no disease, dialysis, mild [glomerular filtration rate 45 to 59 ml/min], and moderate [glomerular filtration rate 30 to 44 ml/min]), cardiogenic shock, PCI status (emergency or salvage vs. urgent), New York Heart Association functional class (I to IV), cardiac arrest, pre-procedural hemoglobin (as a continuous linear spline with 1 knot at 13 g/dl), ST-segment elevation myocardial infarction, pre-procedure TIMI (Thrombolysis In Myocardial Infarction) flow grade, number of diseased vessels, in-stent thrombosis on some lesion previously treated within 1 month, Society for Cardiovascular Angiography and Interventions class, and lesion segment category.

The rate of any BAS use for each hospital was calculated as the number of patients who received some BAS divided by the total number of admissions. To account for the possibility of the risk-treatment paradox for BAS (10), we examined patient-level use of BAS by predicted bleeding risk. To describe whether hospitals with higher use of any BAS had lower adjusted bleeding rates, we used a scatterplot to display the association between hospital adjusted bleeding rates and rates of any BAS use. We also grouped hospitals by deciles of rate of any BAS use and describe mean risk-adjusted hospital bleeding rate. To quantify the association between hospital's rate of any BAS use and patient-level bleeding events, we fit a mixed-effects logistic regression model adjusting for the CathPCI Registry bleeding model covariates and rate of any BAS. We checked for linearity of hospital rate of any BAS using splines.

All analyses were performed at the Duke Clinical Research Institute, which served as the data analytic center, using SAS version 9.4 (SAS Institute, Cary, North Carolina).

Baseline patient and hospital characteristics

After exclusions, our final study sample consisted of 2,459,686 patients at 1,358 sites undergoing PCI, of whom 125,361 (5.1%) experienced bleeding episodes (Figure 1). Compared with patients treated at hospitals in the highest tertile of BAS, those treated in the lowest tertile of hospital BAS use were more likely to be women and more likely to have prior myocardial infarction, heart failure, stroke, peripheral vascular disease, diabetes, and renal failure. Patients treated in the lowest tertile were more likely to present with acute myocardial infarction and to develop cardiogenic shock or cardiac arrest within 24 h of presentation (Table 1). Patients experiencing bleeding events had lower rates of radial access (5.0% vs. 11.2%, p < 0.001), bivalirudin therapy (43.8% vs. 59.4%, p < 0.001), and vascular closure device use (32.9% vs. 42.4%, p < 0.001) than those without bleeding.

Figure 1
Study Population Characteristics

Hospital-level variation in bleeding rates and the influence of BAS use

There was significant variation in unadjusted bleeding rates across hospitals (Figure 2). The median hospital rate of bleeding was 5.0% (IQR: 3.65% to 6.56%), but wide variation, with a hospital bleeding rate of 2.12% in the 5th percentile but 9.84% in the 95th percentile. After risk adjustment that incorporated individual bleeding risk using the CathPCI bleeding risk model (15) and accounting for in-hospital clustering, the median hospital bleeding rate was similar (5.14%; IQR: 4.00% to 6.60%), with a similar range (5th percentile 2.65%, 95th percentile 9.36%).

Figure 2
Distribution of Hospital-Level Post-Percutaneous Coronary Intervention Bleeding Rates Across Hospitals

Distribution of post-percutaneous coronary intervention bleeding, both the (A) observed rates and (B) accounting for patient-level risk adjustment using the CathPCI bleeding risk model.

We created a series of mixed-effects models to estimate the hospital-level variance in bleeding across the spectrum of the different BAS and calculated the PCV compared with the unadjusted model (model 1) (Table 3). After adjusting for patient risk factors using the CathPCI bleeding risk model (model 2), the PCV in bleeding rates across hospitals decreased by 20%. However, the addition of any individual BAS use or a combination of the various BAS options (models 3 to 8) only modestly explained the variation of bleeding rates, and more than 70% of the variation in bleeding remained unexplained, even after adjustment for patient risk factors and BAS use. For example, this analysis suggests that the model incorporating radial use and bivalirudin (model 6) explained an additional 7.8% of the overall variation in hospital-level bleeding rates after adjusting for patient factors. Conversely, the use of vascular closure devices only (model 5) explained only an additional 0.88% of the overall variation after accounting for patient-level factors.

Patient-level BAS use and predicted bleeding

We compared the predicted risk for bleeding among patients receiving BAS with that among those who did not receive BAS. Patients receiving any BAS had lower overall predicted risk for bleeding than patients not receiving any BAS (3.2% vs. 4.5%, p < 0.0001). Patients undergoing either radial access or bivalirudin use during PCI had a lower predicted bleeding risk than those not (3.1% vs. 4.1%, p < 0.0001).

Association between hospital BAS use and patient-level bleeding rates

The median hospital rate of any BAS use was 86.6% (IQR: 72.5% to 94.1%). Increased hospital rate of BAS use was associated with decreased probability of a bleeding event at the patient level (Figure 3). After performing mixed-effects logistic regression modeling adjusting for patient bleeding risk factors and percentage of hospital BAS use, we identified a nonlinear relationship between hospital percentage BAS use and patient-level bleeding. On the basis of the spline plot (Figure 4), we modeled hospital percentage BAS using a continuous linear spline with 1 knot at 85%, the median value of hospital percentage BAS. There was minimal reduction in patient-level bleeding per 5% increase in BAS use for hospitals below the median in BAS use (adjusted odds ratio: 0.99; 95% confidence interval: 0.98 to 1.00) but a significant 10% reduction in the odds of patient-level bleeding among hospitals above the median in BAS use (adjusted odds ratio: 0.90; 95% confidence interval: 0.88 to 0.93).

Figure 3
Observed Use of Bleeding Avoidance Strategies

Figure 4
Adjusted Spline Plot for Percentage of Bleeding Avoidance Strategy Use on Bleeding Rates

In a large, nationally representative analysis of almost 2.5 million PCI procedures across 1,358 sites, we report the following findings: 1) substantial variation in bleeding rates and use of BAS was observed across hospitals, but patient mix explained just one-fifth of the overall hospital level variation in bleeding; 2) the median hospital rate of any BAS use was 86.6%, and higher use of BAS was associated with reduced levels of bleeding at the hospital level; 3) although BAS use was associated with lower risk for bleeding, the use of BAS to reduce PCI bleeding modestly accounted for <10% of the hospital-level variation in bleeding; and 4) hospitals with high levels of BAS show lower rates of post-PCI bleeding, likely by implementing these strategies across the spectrum of bleeding risk. These data have several implications for both clinical care and quality improvement. First, the implementation of BAS routinely may reduce the risk-treatment paradox by reducing bleeding events in the patients at highest risk for this complication. Second, there are likely unmeasured hospital strategies related to BAS that account for the remaining variation in hospital level bleeding rates.

Given the large number of PCIs performed annually and the attendant costs, risk-adjusted, outcomes-based performance measures following PCI are critical in fairly comparing care quality across institutions. Currently used performance measures endorsed by the National Quality Forum include in-hospital mortality following PCI and risk-adjusted 30-day readmission following PCI (16); however, rates of PCI-related in-hospital mortality are low, and 30-day readmission rates following PCI are largely affected by factors not directly related to the index procedure (17); furthermore, it is unclear if either of these metrics can be significantly improved by modifications in care practices (18). As such, PCI-related bleeding appears to be an attractive performance measure. It is the most common noncardiac complication following PCI and is associated with increases in short- and long-term morbidity and mortality, length of stay, and cost. There is significant hospital-level variation in rates of bleeding and strategies exist to reduce bleeding, thus providing a clear opportunity for quality improvement and dissemination of best practices. This is particularly important given that prior studies have shown a risk-treatment paradox for BAS with significantly less application in higher risk patients. However, our analysis highlights a number of limitations with respect to using bleeding rates as a performance measure using current data collection.

In this context, it is important to determine if variation in post-PCI bleeding rates is due simply to differences in patient mix across hospitals, variation in the application of BAS (i.e., the risk-treatment paradox), both, or some unmeasured factor(s). We found that patient-level factors and BAS use explained only 26% of the overall variation in bleeding rates across hospitals. However, patients receiving BAS were at lower predicted risk for bleeding than patients not receiving BAS. This suggests that a significant proportion of hospitals may be using BAS in patients at relatively low risk, despite the likelihood of increased benefit in high-risk patients as described by Marso et al. (10). Among hospitals that used BAS in more than 85% of patients, however, we observed lower rates of overall bleeding, demonstrating that a strategy to broadly use BAS in all patients (i.e., overcoming the risk-treatment paradox) is a reasonable strategy to reduce overall variation in hospital bleeding rates.

As PCI-related bleeding becomes more prominent as a potential hospital quality indicator, it will be important to ensure that hospital bleeding rates are standardized according to patient risk. However, our analysis demonstrates that even after taking patient risk and BAS use into account, more than two-thirds of the overall variation in bleeding rates continues to remain unexplained, highlighting a significant limitation in the use of bleeding rates as a performance measure under the current data collection structure. As such, the stringent use of bleeding rate measures to determine reimbursement rates or to penalize institutions by payers and regulators may be premature at this time, given the significant variability in bleeding that is not well understood at this time offers providers no clear path to improve patient outcomes. Similar to using post-PCI mortality or readmission within 30 days following PCI, there are important limitations with the use of post-PCI as a key performance metric. Therefore, it may be more relevant to use other measures of quality (e.g., appropriate use criteria) instead of these incomplete performance standards measures as a key metric in quality improvement. Indeed, a recent analysis demonstrated a temporal increase in PCI meeting appropriateness criteria, highlighting the utility of such an approach (19). However, none of these metrics should be used for determining reimbursement until they have been appropriately vetted as performance measures. Analyses such as ours may facilitate this determination for candidate performance measures such as appropriate use criteria.

Accordingly, our study additionally highlights the need to collect more granular data on procedural factors and bleeding events to develop more accurate risk adjustment models to account for the variability in bleeding rates across hospitals. Currently, data are not systematically collected with respect to appropriate dosing of anticoagulant and antiplatelet medications, especially in women and older patients, sheath management, interoperator variability in femoral access technique, and other factors that may explain the significant variation in bleeding across sites. It also underscores the need for consistent application of appropriate BAS in all patients, especially those at particularly high risk for bleeding complications following PCI. As hospitals develop quality improvement strategies to reduce rates of post-PCI bleeding, dissemination of best practices from high-performing centers may be critical in reducing the variation in hospital-level bleeding rates and lowering overall bleeding rates. This may include a detailed exploration of antithrombotic dosing protocols, sheath removal strategies, and transfusion practices between sites with high versus low observed bleeding.

Study limitations

First, there may be significant institutional variation in the accuracy of reported bleeding events, including the underreporting of minor and clinically insignificant bleeding episodes. We tried to reduce the effect of underreporting by excluding sites that reported no bleeding events. Additionally, the CathPCI bleeding definition that was used incorporates clinically significant bleeding and is broadly concurrent with previous estimates of significant bleeding following PCI. Next, there may be differential reporting of bleeding events between sites that participate in the CathPCI Registry compared with sites that do not, which may signal a greater commitment to accurate reporting and quality improvement, thus limiting the overall generalizability of our results. Third, the CathPCI Registry captures only in-hospital outcomes and would not capture bleeding events that occurred following discharge. However, the vast majority of bleeding events following PCI occur in the first 24 h after the procedure, and even after excluding patients who were discharged on the day of their procedure, we report no meaningfully significant difference in our primary results. Finally, as with any observational analysis, we are unable to exclude the potential for unmeasured confounding.

In a large, national registry, we found wide variation in rates of hospital-level bleeding following PCI. Patient risk and the use of BAS explain at most 26% of the overall variation in observed hospital-level bleeding rates, and the use of various BAS at most explain an additional 7.8% of the overall variation after accounting for patient-level factors. This analysis highlights the need for more investigation of the causes of site-to-site variability in PCI-related bleeding following PCI.

Perspectives

WHAT IS KNOWN? Previous studies have shown significant variation in bleeding rates following PCI across hospitals, but the extent to which using BAS explains this variation is unknown.

WHAT IS NEW? We demonstrate that about 1 in 20 patients undergoing PCI had bleeding events. Although there was wide hospital-level variation in bleeding, patient factors explained only 20% of this variation, and the use of radial access and bivalirudin only explained another 7.8% of the variation. More than 70% of the variation in bleeding remains unexplained.

WHAT IS NEXT? This study urges caution in the use of post-PCI bleeding as a performance measure, as a significant proportion of the hospital-level variation in bleeding remains unexplained. More granular data collection and further analyses are necessary to explain this variation in bleeding rates to develop best practices to mitigate bleeding following PCI.

This research was supported by the American College of Cardiology Foundation's National Cardiovascular Data Registry. The analytic work for this investigator-initiated study was performed by the Duke Clinical Research Institute, with financial support from the American College of Cardiology. The CathPCI Registry is an initiative of the American College of Cardiology Foundation and the Society for Cardiovascular Angiography and Interventions. The manuscript was reviewed by the National Cardiovascular Data Registry for compliance with registry description and representation, but the sponsor had no role in the design and conduct of the study, analysis and interpretation of the data, preparation of the manuscript, or decision to submit the manuscript for publication. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.

BAS

bleeding avoidance strategy

IQR

interquartile range

PCI

percutaneous coronary intervention

PCV

proportional change in variance

• Received December 22, 2015.
• Accepted January 14, 2016.
• American College of Cardiology Foundation

1. Batchelor WB, Anstrom KJ, Muhlbaier LH, et al. Contemporary outcome trends in the elderly undergoing percutaneous coronary interventions: results in 7,472 octogenarians. National Cardiovascular Network Collaboration. J Am Coll Cardiol 2000;36:723–30.
2. Cohen DJ, Lincoff AM, Lavelle TA, et al. Economic evaluation of bivalirudin with provisional glycoprotein IIB/IIIA inhibition versus heparin with routine glycoprotein IIB/IIIA inhibition for percutaneous coronary intervention: results from the REPLACE-2 trial. J Am Coll Cardiol 2004;44:1792–800.
3. Kinnaird TD, Stabile E, Mintz GS, et al. Incidence, predictors, and prognostic implications of bleeding and blood transfusion following percutaneous coronary interventions. Am J Cardiol 2003;92:930–5.
4. Lopes RD, Alexander KP, Manoukian SV, et al. Advanced age, antithrombotic strategy, and bleeding in non-ST-segment elevation acute coronary syndromes: results from the ACUITY (Acute Catheterization and Urgent Intervention Triage Strategy) trial. J Am Coll Cardiol 2009;53: 1021–30.
5. Manoukian SV, Feit F, Mehran R, et al. Impact of major bleeding on 30-day mortality and clinical outcomes in patients with acute coronary syndromes: an analysis from the ACUITY trial. J Am Coll Cardiol 2007;49:1362–8.
6. Verheugt FW, Steinhubl SR, Hamon M, et al. Incidence, prognostic impact, and influence of antithrombotic therapy on access and nonaccess site bleeding in percutaneous coronary intervention. J Am Coll Cardiol Intv 2011;4:191–7.
7. Eikelboom JW, Mehta SR, Anand SS, Xie C, Fox KA, Yusuf S. Adverse impact of bleeding on prognosis in patients with acute coronary syndromes. Circulation 2006;114:774–82.
8. Rao SV, Kaul PR, Liao L, et al. Association between bleeding, blood transfusion, and costs among patients with non-ST-segment elevation acute coronary syndromes. Am Heart J 2008;155: 369–74.
9. Chhatriwalla AK, Amin AP, Kennedy KF, et al. Association between bleedingevents and in-hospital mortality after percutaneous coronary intervention. JAMA 2013;309:1022–9.
10. Marso SP, Amin AP, House JA, et al. Association between use of bleeding avoidance strategies and risk of periprocedural bleeding among patients undergoing percutaneous coronary intervention. JAMA 2010;303:2156–64.
11. Daugherty SL, Thompson LE, Kim S, et al. Patterns of use and comparative effectiveness of bleeding avoidance strategies in men and women following percutaneous coronary interventions: an observational study from the National Cardiovascular Data Registry. J AmColl Cardiol 2013;61:2070–8.
12. Hess CN, Rao SV, McCoy LA, et al. Identification of hospital outliers in bleeding complications after percutaneous coronary intervention. Circ Cardiovasc Qual Outcomes 2015;8:15–22.
13. Wimmer NJ, Resnic FS, Mauri L, et al. Risktreatment paradox in the selection of transradial access for percutaneous coronary intervention. J Am Heart Assoc 2013;2:e000174.
14. Messenger JC, Ho KK, Young CH, et al. The National Cardiovascular Data Registry (NCDR) data quality brief: the NCDR Data Quality Program in 2012. J Am Coll Cardiol 2012;60:1484–8.
15. Rao SV, McCoy LA, Spertus JA, et al. An updated bleeding model to predict the risk of post-procedure bleeding among patients undergoing percutaneous coronary intervention: a report using an expanded bleeding definition from the National Cardiovascular Data Registry CathPCI Registry. J Am Coll Cardiol Intv 2013;6: 897–904.
16. National Quality Forum. NQF-endorsed measures for cardiovascular conditions. Available at: http://www.qualityforum.org/Publications/2014/11/ NQF-Endorsed_Measures_for_Cardiovascular_Conditions. aspx. Accessed February 4, 2016.
17. Yost GW, Puher SL, Graham J, et al. Readmission in the 30 days after percutaneous coronary intervention. J Am Coll Cardiol Intv 2013;6: 237–44.
18. Thomas JW, Hofer TP. Accuracy of riskadjusted mortality rate as a measure of hospital quality of care. Med Care 1999;37:83–92.
19. Desai NR, Bradley SM, Parzynski CS, et al. Appropriate use criteria for coronary revascularization and trends in utilization, patient selection, and appropriateness of percutaneous coronary intervention. JAMA 2015;314: 2045–53.