Author + information
- Received February 3, 2016
- Revision received February 22, 2016
- Accepted February 29, 2016
- Published online May 17, 2016.
- Usman Baber, MD, MSa,
- Roxana Mehran, MDa,∗ (, )
- Gennaro Giustino, MDa,
- David J. Cohen, MD, MScb,
- Timothy D. Henry, MDc,
- Samantha Sartori, PhDa,
- Cono Ariti, MScd,
- Claire Litherland, MSe,
- George Dangas, MD, PhDa,
- C. Michael Gibson, MDf,
- Mitchell W. Krucoff, MDg,
- David J. Moliterno, MDh,
- Ajay J. Kirtane, MD, SMe,i,
- Gregg W. Stone, MDe,i,
- Antonio Colombo, MDj,
- Alaide Chieffo, MDj,
- Annapoorna S. Kini, MDa,
- Bernhard Witzenbichler, MDk,
- Giora Weisz, MDl,
- Philippe Gabriel Steg, MDm and
- Stuart Pocock, PhDd
- aMount Sinai Heart, Mount Sinai Medical Center, New York, New York
- bSt. Luke’s Mid America Heart Institute, University of Missouri–Kansas City, Kansas City, Missouri
- cMinneapolis Heart Institute Foundation, University of Minnesota, Minneapolis, Minnesota
- dLondon School of Hygiene and Tropical Medicine, London, United Kingdom
- eCardiovascular Research Foundation, New York, New York
- fDivision of Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- gDuke University School of Medicine, Durham, North Carolina
- hUniversity of Kentucky, Lexington, Kentucky
- iDepartment of Cardiology, Columbia University Medical Center, New York, New York
- jCardio-Thoracic Department, San Raffaele Scientific Institute, Milan, Italy
- kHelios Amper-Klinikum, Dachau, Germany
- lShaare Zedek Medical Center, Jerusalem, Israel
- mUniversité Paris-Diderot, Sorbonne Paris-Cité, Paris, France
- ↵∗Reprint requests and correspondence:
Dr. Roxana Mehran, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1030, New York, New York 10029.
Background Dual-antiplatelet therapy with aspirin and clopidogrel after percutaneous coronary intervention reduces the risk for coronary thrombotic events (CTEs) at the expense of increasing risk for major bleeding (MB). Metrics to accurately predict the occurrence of each respective event and inform clinical decision making are lacking.
Objectives The aim of this study was to develop and validate separate models to predict risks for out-of-hospital thrombotic and bleeding events after percutaneous coronary intervention with drug-eluting stents.
Methods Using data from 4,190 patients treated with drug-eluting stents and enrolled in the PARIS (Patterns of Non-Adherence to Anti-Platelet Regimen in Stented Patients) registry, separate risk scores were developed to predict CTE (defined as the composite of stent thrombosis or myocardial infarction) and MB (defined as the occurrence of a Bleeding Academic Research Consortium type 3 or 5 bleed). External validation was performed in the ADAPT-DES (Assessment of Dual Antiplatelet Therapy With Drug-Eluting Stents) registry.
Results Over 2 years, CTEs occurred in 151 patients (3.8%) and MB in 133 (3.3%). Independent predictors of CTEs included acute coronary syndrome, prior revascularization, diabetes mellitus, renal dysfunction, and current smoking. Independent predictors of MB included older age, body mass index, triple therapy at discharge, anemia, current smoking, and renal dysfunction. Each model displayed moderate levels of discrimination and adequate calibration.
Conclusions Simple risk scores of baseline clinical variables may be useful to predict risks for ischemic and bleeding events after PCI with DES, thereby facilitating clinical decisions surrounding the optimal duration of DAPT. (Patterns of Non-Adherence to Anti-Platelet Regimen in Stented Patients [PARIS]; NCT00998127)
Dual-antiplatelet therapy (DAPT) with aspirin and an inhibitor of the platelet P2Y12 receptor is necessary to prevent early and largely stent-related thrombotic complications after percutaneous coronary intervention (PCI) with drug-eluting stents (DES) (1). Although continuation of DAPT confers substantial and durable benefits that extend beyond the local stented segment, bleeding risk also increases with continued exposure to antiplatelet therapy. As a result and in the absence of any mortality advantage with a prolonged DAPT strategy, clinical decision making surrounding the optimal duration of DAPT must be predicated on balancing long-term risks for both coronary thrombosis and major bleeding (MB) (2).
To date, most ischemic and bleeding risk algorithms after PCI have focused on in-hospital events or short-term risk (3–12). Although these scales are useful to inform clinical decisions regarding periprocedural anticoagulation or other bleeding avoidance strategies, their utility with respect to the long-term provision of DAPT is less clear. In contrast, other validated scores have focused on outcomes such as revascularization or rehospitalization that may not be directly influenced by platelet inhibition with clopidogrel. Moreover, the underlying risk factors or their respective weights may vary for early as opposed to later events, reinforcing the need for stratification tools focusing on long-term events. Accordingly, we sought to develop separate models to predict risks for out-of-hospital events that are directly modified by prolonging DAPT, namely, coronary thrombosis and MB.
The PARIS (Patterns of Non-Adherence to Anti-Platelet Regimen in Stented Patients) registry was a prospective, multicenter, observational study of patients undergoing PCI with stent implantation in the United States and Europe between July 2009 and December 2010 designed to examine the different modes of DAPT cessation and to investigate the influence of these modes on subsequent clinical adverse events (13). Adult patients undergoing successful stent implantation in at least 1 native coronary artery and discharged on DAPT were eligible for enrollment. Patients participating in investigational device or drug studies or with evidence of stent thrombosis (ST) at the index procedure were excluded. All patients provided written informed consent.
Coronary thrombotic events (CTE) were defined as the occurrence of a stent-related coronary thrombotic complication (definite or probable ST) or of a non-stent-related coronary thrombotic complication (spontaneous myocardial infarction [MI]). ST was defined according to Academic Research Consortium criteria (14). Spontaneous MI was defined as the presence of clinical or electrocardiographic changes consistent with myocardial ischemia in the setting of increased cardiac biomarkers greater than the upper limit of normal, in accordance with the universal definition (15). MB was defined as the occurrence of Bleeding Academic Research Consortium type 3 or 5 bleed (16). Anemia was classified as a hemoglobin level <12 g/dl in men and <11 g/dl in women (17). Creatinine clearance (CrCl) was calculated using the Cockcroft-Gault formula (18).
Follow-up was done via telephone by trained research coordinators at each participating site at 30 days, 6 months, 12 months, and 24 months. Source documents were obtained for those patients reporting any adverse events (ischemic or bleeding) or any DAPT cessation. An independent clinical events committee adjudicated all adverse events and episodes of DAPT cessation.
We compared baseline clinical and procedural characteristics by the presence or absence of CTE and by the presence or absence of MB using Student t tests and chi-square tests for continuous and categorical variables, respectively. Separate prediction models were generated using Cox proportional hazards regression, with time to first occurrence of CTE or MB serving as the dependent outcome in each respective model. Event-free patients were censored at the time of death, study end, or last contact, whichever came first.
To account for missing values in baseline serum hemoglobin (n = 476 [11.4%]) and serum creatinine (n = 370 [8.8%]), covariates for each model were identified in an iterative process that coupled multiple imputation with automated variable selection, as previously described (19). In the first step, we generated 10 imputations from the original dataset, using multivariate normal regression to substitute missing values for hemoglobin and serum creatinine within each impute. In the second step, separate models, 1 for each dependent outcome, were fit to each replicate using backward selection with an entry criterion of p < 0.05. Candidate covariates for each model included age, sex, triple therapy at discharge, body mass index (<25, 25 to 29.9 [referent], 30 to 34.9, and ≥35 kg/m2), stent length, complex procedure (bifurcation, total occlusion, thrombus, or >2 stents), stent type, anemia, CrCl < 60 ml/min, diabetes mellitus, acute coronary syndrome, smoking status, stent diameter, prior PCI, prior coronary artery bypass grafting, prior stroke, peripheral vascular disease, target vessel, multivessel PCI, and prior MI. In the third step, separate fully fitted models were generated using those covariates selected as independent predictors in at least 1 impute with regression coefficients combined across all imputed datasets as described by Rubin (20). Variables remaining significant at a threshold p value of <0.05 were retained as final predictors. Model performance was assessed using metrics of discrimination (Harrel’s C statistic) and calibration (Hosmer-Lemeshow goodness-of-fit [GOF] statistic) in the overall cohort and after excluding patients with adverse events or lost to follow-up in the first year after PCI. To account for potential heterogeneity arising from the inclusion of centers in the United States and Europe, we tested for interaction by including an exposure-by-region interaction term to each final model. All interaction terms were nonsignificant and were therefore not included in our final models. Results were unchanged when center was included as a random effect.
Using the fully adjusted regression coefficients from each respective model, we generated user-friendly integer risk scores for each outcome, as described by Sullivan et al. (21). Patients were then grouped into levels of low, intermediate, and high risk, with thresholds reflecting clinically meaningful (at least 2-fold) gradients in risk from 1 group to the next. The observed event rate was calculated as a Kaplan-Meier estimate of time to first event. Predicted event rates were estimated from the fully adjusted Cox regression models.
Using the probability-based estimates from each model, we also calculated the absolute risk differences in coronary thrombosis and MB for each individual patient as an indirect marker of a patient’s overall ischemic and bleeding risk. Differences greater than 0 indicate that risks from thrombosis exceed those of bleeding, whereas risk differences less than 0 indicate the opposite.
External validation of each score was performed in the ADAPT-DES (Assessment of Dual Antiplatelet Therapy With Drug-Eluting Stents) registry (22). We chose this cohort for validation given the similar amount of follow-up (2 years), collection of relevant variables to construct each score, and inclusion of a contemporary DES-treated population. For purposes of validation, bleeding requiring hospitalization or transfusion served as the MB endpoint, as previously described (23). Each participant in the validation cohort was assigned a thrombotic and bleeding risk score in a similar manner as in the development population. The distribution of each risk score was visualized graphically. Patients were then grouped into levels of low, intermediate, and high thrombotic and bleeding risk using the same thresholds as in the PARIS population. Rates of adverse events at 2 years were calculated in each risk grouping using the Kaplan-Meier method and compared using the log-rank test. Discrimination was assessed by calculating the area under the receiver-operating characteristic curve and expressed as the C statistic.
Among 5,031 patients enrolled in PARIS, we excluded patients not discharged on DAPT (n = 18), those receiving bare-metal stents (n = 811), and those experiencing in-hospital events (n = 18), for a total study population of 4,190 patients (Figure 1). Over 2 years, 151 patients sustained CTEs, including 45 with ST and 106 with spontaneous MI, while 133 patients experienced MB. Baseline characteristics are illustrated in Tables 1 and 2. Patients with CTE more commonly presented with acute coronary syndrome and had a higher prevalence of diabetes mellitus, renal dysfunction, previous stroke, previous MI, and previous revascularization compared with those without CTEs. Patients with MB were older and more commonly women, with a higher prevalence of diabetes mellitus, anemia, renal dysfunction, and triple therapy use compared with their counterparts without MB.
Predictors of CTE and MB and integer risk scores
Point estimates and corresponding 95% confidence intervals for each covariate in the final prediction models are shown in Table 3. The strongest 3 predictors for CTEs, quantified and ranked using the value of the t statistic, were insulin-dependent diabetes mellitus, CrCl <60 ml/min, and prior PCI. Analogously, the strongest contributors for MB were anemia, CrCl <60 ml/min, and current smoking. For the CTE model, discrimination was moderate, with a C statistic of 0.70 with adequate calibration (GOF chi-square = 9.7, p = 0.37) for the entire cohort. For patients with late events, the C statistic for the CTE model was 0.68 (GOF chi-square = 13.4, p = 0.15). Analogous metrics of model performance for MB in the overall population included a C statistic of 0.72 and GOF chi-square statistic of 10.4 (p = 0.32). After excluding patients with events in the first year, the C statistic for the MB model was 0.75, with a GOF chi-square statistic of 2.1 (p = 0.99). Model performance was similar among patients receiving second-generation DES alone (n = 3,520). Associations between clinical parameters and each respective outcome were largely unchanged in magnitude and direction after the inclusion of DAPT cessation as an additional hierarchical time-dependent covariate in each model (Online Table 1). As in the original PARIS report, this time-dependent covariate allowed a patient to contribute exposure time to the various DAPT cessation modes (discontinuation, interruption, and disruption).
Using the fully adjusted regression coefficients from the final Cox models, we developed integer-based risk scores for both outcomes, shown in Tables 4 and 5, respectively. For CTEs, the scores ranged from 0 to 10, and patients were grouped according to low (0 to 2), intermediate (3 or 4), and high (≥5) thrombotic risk. The overall distribution of thrombotic risk scores and corresponding predicted CTE rates are displayed in Figure 2A. Analogously, the range of integer scores for MB was 0 to 14, with patients categorized at low (0 to 3), intermediate (4 to 7) and high (≥8) bleeding risk. The distribution of bleeding risk scores and predicted estimates for MB are displayed in Figure 2B. As shown in Figure 3, the numbers of patients at low, intermediate, and high bleeding risk were 2,079 (49.6%), 1,713 (40.9%), and 398 (9.5%), respectively, with similar proportions observed for the different thrombotic risk categories. Within each level of bleeding risk, the frequency of low thrombotic risk decreased in a stepwise fashion as bleeding risk increased, whereas the opposite was observed for high thrombotic risk. Among those at highest bleeding risk, approximately one-third were also at highest thrombotic risk.
Observed and predicted CTEs and clinically significant bleeding in the study population
Figures 4A and 4B display the observed and predicted risks for CTEs and MB, respectively, across risk groupings. Observed and predicted risks were comparable, indicating that model calibration was good for both scores.
Hypothetical risk benefit trade-off according to thrombotic and bleeding risk
The Central Illustration illustrates the variable levels of thrombotic and bleeding risk according to each patient’s predicted risk for coronary thrombosis and MB, respectively. Absolute risk differences were positive for most patients at high thrombotic risk, irrespective of underlying bleeding risk. Conversely, risk differences were largely negative for almost all patients at highest bleeding risk, irrespective of thrombotic risk.
Results of the validation analyses are presented in the Online Appendix. Online Figures 1A and 1B display the overall distribution of thrombotic and bleeding risk scores in the ADAPT-DES registry. The numbers of patients at low, intermediate, and high thrombotic risk in the validation cohort were 3,491 (42.9%), 2,806 (34.5%), and 1,833 (22.5%), respectively. Analogously, 4,394 (54.0%), 3,276 (40.3%), and 460 (5.7%) patients were grouped at low, intermediate, and high bleeding risk. Two-year rates of thrombotic events were 1.1%, 3.0%, and 8.5%, respectively, among patients at low, intermediate, and high thrombotic risk (Online Figure 2A). Analogous rates for MB were 2.4%, 4.5%, and 9.4%, respectively (Online Figure 2B). Discrimination was moderate, with C statistics of 0.65 and 0.64 for the thrombotic and bleeding risk scores.
In the present study involving more than 4,000 real-world patients undergoing PCI with DES, we report the development of separate models to predict risks for out-of-hospital coronary thrombotic and bleeding events, respectively, in the first 2 years after DES implantation. Each model displayed at least a moderate level of discrimination concordant with other commonly used scales in the overall population and after excluding patients with early events. Although most predictors were unique to each respective outcome, active smoking and renal dysfunction emerged as common contributors to both events. Gradients in risk for both outcomes according to each score were similar in an external validation cohort. We also illustrate the potential utility of applying both scales in unison to identify patients who may derive the greatest benefit, or alternatively harm, from a prolonged DAPT strategy.
Although multiple tools have been developed to stratify PCI risk, most have included periprocedural events or outcomes that are either not relevant to or necessarily modified by chronic antiplatelet therapy (3–12). In contrast, we modeled only those events occurring after discharge that are most consistently and reproducibly influenced by DAPT. In this regard, we found that clinical risk factors alone, rather than procedural parameters, predicted risks for CTE. This result contrasts with other studies highlighting the importance of lesion complexity, stent size, or stent length on risk for ST (5,6). In part, these differences may be due to the relatively low frequency of ST as a component of CTE in our study or the prevalent use of safe second-generation DES in most PARIS participants. Alternatively, it is plausible that thrombotic risk factors are not static but dynamic over time. Dangas et al. (5), for example, found that a combination of clinical and procedural parameters predicted risk for early ST, whereas clinical risk factors alone were predictors of late ST, consistent with our overall results.
Analogously, we identified older age and renal dysfunction as independent determinants of long-term bleeding, consistent with previously observed associations between these parameters and in-hospital hemorrhage. Nevertheless, female sex was absent from our final bleeding model, a result that differs from earlier studies focusing on shorter term events. As with thrombotic events, it is possible that the underlying risk factors for bleeding, or their relative contributions, are not constant but variable over time, thereby accounting for these discrepancies. This hypothesis is supported by findings recently reported by Genereux et al. (23), in whose study female sex was excluded as a predictor of out-of-hospital 2-year bleeding risk in a DES-treated cohort. Others have reported similar results in stable outpatients (24). Our results, in concert with those of earlier studies, highlight the need for stratification metrics that extend beyond the initial periprocedural period to inform clinical decisions surrounding long-term provision of DAPT.
Although certain independent predictors for thrombosis and bleeding from our study are common to those recently reported by Yeh (25) from the randomized DAPT (Dual Antiplatelet Therapy) trial, other correlates unique to each score reflect differences in study designs, patient population, and methodologic assumptions. The randomized design of the DAPT trial led to the exclusion of certain types of patients that may be commonly encountered in routine clinical practice. The need for long-term oral anticoagulation, for example, precluded patients from randomization at 1 year, an important consideration given that up to 7% of patients undergoing PCI may require such therapy (26). Accordingly, although this variable was not included in the DAPT score, we found that triple therapy was a strong predictor of MB, consistent with earlier studies (23). In addition, etiologic associations between certain risk factors and outcomes may be less apparent in randomized as opposed to observational studies (27). Low and high body mass index, for example, were previously linked to increased risks for in-hospital bleeding by Rao et al. (12), findings that we now extend to longer term bleeding risk after PCI. In contrast, body mass index was not an independent predictor of bleeding in either the DAPT trial or other scores derived from randomized populations (3). In addition, the dependent outcome for the DAPT score was quantified with a metric that combined thrombotic and bleeding risk, an approach that implicitly assumes a comparable weighting to both events on subsequent morbidity and mortality. Whether such an assertion is valid remains unclear, however, as late ST confers a much lower case fatality rate than earlier ST (28). In addition, late bleeding may associate with a much higher risk for mortality compared with late MI (23). These differences notwithstanding, scores derived from registry and randomized trials may serve important and complementary roles, reflecting the underlying strengths and limitations of the study designs from which they are developed. Among patients with atrial fibrillation, for example, ischemic risk is usually calculated from a scale developed in a randomized population (29), whereas commonly used bleeding scales were developed in observational cohorts (30).
The clinical relevance of the present scores is highlighted by the lack of published decision support tools to guide the intensity or duration of out-of-hospital post-PCI pharmacotherapy. Indeed, existing data suggest that potent antiplatelet agents are more commonly prescribed to relatively low-risk patients compared with those receiving clopidogrel (31,32). Such practice patterns might reflect a lack of calibration between perceived and actual risk, thus representing an important opportunity to enhance processes and quality of post-PCI care. As shown by Strauss et al. (33), integrating a quantitative risk scale within an electronic health record may address this gap, thereby leading to more rational use of therapeutic interventions and improving clinical outcomes. Although our study was not specifically designed to address this important issue, we illustrate the utility of applying both scores in unison to stratify patients into various levels of ischemic and bleeding risk, a necessary initial step to inform decisions surrounding the duration or intensity of antiplatelet therapy after PCI. Patients at highest thrombotic risk (scores ≥5), for example, might realize the greatest benefit from a longer duration of DAPT irrespective of underlying bleeding risk, whereas those with thrombotic risk scores of 2 or less are unlikely to benefit from such a strategy. Nevertheless, in the absence of randomization, conclusions surrounding the potential utility of the scores described herein to guide the duration or intensity of antiplatelet therapy are speculative and warrant confirmation with prospective evaluation.
Among the limitations of our study is an observational design that precludes causal inference. Almost all patients in PARIS were treated with clopidogrel, thereby limiting generalizability to more potent P2Y12 inhibitors. Because the duration of DAPT therapy was not randomized in PARIS, and DAPT cessation occurred at the discretion of treating physicians, or because of antecedent events or patient noncompliance, we did not calculate the number needed to treat or to harm. Although we included many clinical and procedural parameters that may influence post-PCI risk, information on certain covariates, such as left ventricular function and platelet reactivity, was not collected in PARIS.
We developed and validated separate scores to predict risks for out-of-hospital coronary thrombotic and bleeding events, respectively, among real-world patients undergoing PCI with DES. Simultaneous application of both scores may be useful in identifying patients with the greatest potential to derive benefit from prolonging DAPT beyond 1 year.
COMPETENCY IN PATIENT CARE AND PROCEDURAL SKILLS: Routine variables collected at the time of PCI that predict risk for coronary thrombosis include acute coronary syndrome, prior revascularization, diabetes mellitus, renal dysfunction, and smoking. Independent predictors of MB include older age, body mass, concurrent therapy with an anticoagulant agent and 2 antiplatelet drugs, anemia, renal dysfunction, and smoking.
TRANSLATIONAL OUTLOOK: Prospective studies are needed to evaluate strategies that integrate periprocedural risk assessment on the basis of registry data into clinical decisions regarding the duration and intensity of DAPT after PCI.
For supplemental figures and a table, please see the online version of this article.
This study was funded by Bristol-Myers Squibb and Sanofi. Dr. Mehran has received institutional grant support from The Medicines Company, Bristol-Myers Squibb/Sanofi, and Eli Lilly & Company/Daiichi-Sankyo; and is a consultant to Janssen Pharmaceuticals and Maya Medical. Dr. Dangas has received consulting fees and honoraria from Johnson & Johnson, Sanofi, Covidien, The Medicines Company, Merck, CSL Behring, AstraZeneca, Medtronic, Abbott, Bayer, Boston Scientific, Osprey Medical, and GE Healthcare; and research grant support from Sanofi, Bristol-Myers Squibb, and Eli Lilly & Company/Daiichi-Sankyo. Dr. Steg has served as an adviser or a consultant for Amarin Corporation, AstraZeneca Pharmaceuticals LP, Bayer HealthCare Pharmaceuticals, Boehringer Ingelheim Pharmaceuticals, Bristol-Myers Squibb, Daiichi-Sankyo, GlaxoSmithKline, Eli Lilly & Company, Medtronic, Otsuka Pharmaceutical, Pfizer, Roche, Sanofi, Servier, Takeda Pharmaceuticals North America, The Medicines Company, and Vivus; and received clinical research grants from Sanofi and Servier. Dr. Cohen has received research grant support from Abbott Vascular, AstraZeneca, Biomet, Boston Scientific, Cardiovascular Systems, Daiichi-Sankyo, Edwards Lifesciences, Eli Lilly & Company, and Medtronic; and is a consultant for Abbott Vascular, Medtronic, and Merck. Dr. Kini serves on the speakers bureau of the American College of Cardiology; and has received consulting fees from WebMD. Dr. Colombo has received consulting fees and honoraria from CID; and other financial benefit from Direct Flow Medical. Dr. Gibson has received research grant support from Angel Medical Corporation, Atrium Medical Systems, Bayer Corporation, Ikaria, Janssen/Johnson & Johnson Corporation, Lantheus Medical Imaging, Merck & Company, Portola Pharmaceuticals, Roche Diagnostics, Sanofi, Stealth Peptides, St. Jude Medical, Volcano Corporation, and Walk Vascular; consulting fees from AstraZeneca, Baxter Healthcare, Bayer Corporation, CRF, Consensus Medical Communications, CSL Behring, Cytori Therapeutics, Eli Lilly & Company/Daiichi-Sankyo, Exeter Group, Genentech, GlaxoSmithKline, Janssen/Johnson & Johnson Corporation, Ortho McNeil, St. Jude Medical, and The Medicines Company; and royalty fees from UpToDate in Cardiovascular Medicine. Dr. Henry has received research grant support from Eli Lilly & Company and Daiichi-Sankyo. Dr. Krucoff has received consulting fees from Abbott Vascular, Abbott, OrbusNeich, Angelmed, Volcano, Biosensors, Svelte, OrbusNeich, Medtronic, and Terumo; and research grant support from Abbott, Terumo, Angelmed, Ikaria, OrbusNeich, Medtronic, CSI, Eli Lilly & Company, and Medtronic. Dr. Kirtane has received institutional research grants to Columbia University from Boston Scientific, Medtronic, Abbott Vascular, Abiomed, St. Jude Medical, Vascular Dynamics, and Eli Lilly & Company. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Dr. Deepak Bhatt served as Guest Editor for this paper.
- Abbreviations and Acronyms
- creatinine clearance
- coronary thrombotic event
- dual-antiplatelet therapy
- drug-eluting stent(s)
- major bleeding
- myocardial infarction
- odds ratio
- percutaneous coronary intervention
- stent thrombosis
- Received February 3, 2016.
- Revision received February 22, 2016.
- Accepted February 29, 2016.
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