Published online before print May 8, 2009, doi: 10.1161/CIRCINTERVENTIONS.108.846741
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Original Articles |
Bleeding in Patients Undergoing Percutaneous Coronary Intervention
The Development of a Clinical Risk Algorithm From the National Cardiovascular Data Registry
From the Division of Cardiovascular Research, Mid America Heart Institute (S.K.M., A.D.F., J.B.L., J.A.H., J.A.S., S.P.M.), Saint Luke’s Hospital, Kansas City, Mo; and Duke Clinical Research Institute (S.V.R., F.-S.O., M.T.R., E.D.P.), Durham, NC.
Correspondence to Steven P. Marso, MD, Mid America Heart Institute, Saint Luke’s Hospital, University of Missouri Kansas City, 4401 Wornall Road, Kansas City, MO 64111. E-mail smarso{at}saint-lukes.org
Received December 22, 2008; accepted April 20, 2009.
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Abstract |
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Background— Bleeding in patients undergoing percutaneous coronary intervention (PCI) is associated with increased morbidity, mortality, length of hospitalization, and cost. We identified baseline clinical characteristics associated with bleeding complications after PCI and developed a simplified, clinically useful algorithm to predict patient risk.
Methods and Results— Data were analyzed from 302 152 PCI procedures performed at 440 US centers participating in the National Cardiovascular Data Registry. As defined by the National Cardiovascular Data Registry, bleeding required transfusion, prolonged hospital stay, and/or a drop in hemoglobin >3.0 g/dL from any location, including percutaneous entry site, retroperitoneal, gastrointestinal, genitourinary, and other/unknown location. Bleeding complications occurred in 2.4% of patients. From the best-fitting model consisting of 15 clinical elements associated with post-PCI bleeding in a random 80% training cohort, we developed a parsimonious risk algorithm. Predictors of bleeding included age, gender, previous heart failure, glomerular filtration rate, peripheral vascular disease, no previous PCI, New York Heart Association/Canadian Cardiovascular Society Functional Classification class IV heart failure, ST-elevation myocardial infarction, non–ST-elevation myocardial infarction, and cardiogenic shock. The parsimonious model was validated in the remaining 20% of the population (c-statistic, 0.72) and in clinically relevant subgroups of patients. This simplified model was used to derive a clinical risk algorithm, with larger numbers corresponding with greater risk. In 3 categories, bleeding rates were greater in patients with higher estimates (
7, 0.7%; 8 to 17, 1.8%; 
18, 5.1%).
Conclusions— This report identifies baseline clinical factors associated with bleeding and proposes a clinically useful algorithm to estimate bleeding risk. This model is potentially actionable in altering therapeutic decision making and improving outcomes in patients undergoing PCI.
Key Words: catheterization • hemorrhage • risk factors
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Introduction |
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Bleeding is the most common noncardiac complication in patients undergoing percutaneous coronary intervention (PCI)1 and is associated with increased risk of adverse outcomes including death, myocardial infarction (MI), and stroke, as well as increased length of hospitalization and cost.2–4 Although a variety of individual risk factors have been linked to bleeding after PCI,5 currently there is no accepted method to categorize patients undergoing PCI by risk of post-PCI bleeding. An algorithm to predict risk of bleeding could be highly actionable, enabling physicians to consider alternative adjunctive PCI care. Using data from the National Cardiovascular Data Registry (NCDR), we identified clinical risk factors for post-PCI bleeding and then developed a clinical algorithm to predict patient risk of bleeding.
Clinical Perspective on p 222
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Methods |
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Study Population
A description of the NCDR has been published.6,7 We used version 3.04 of the CathPCI database, which contains data on PCI procedures performed from January 1, 2004, to March 31, 2006. Initially, data were obtained from 309 351 patients undergoing 317 355 PCI procedures at 484 hospitals. Centers not reporting PCI data (n=14) or with missing bleeding data (n=30) were excluded. Only index PCI procedures performed during the study period were included in this analysis. Patients who died on the day of PCI or with missing bleeding data were excluded from the final analysis. The authors had full access to the data and take responsibility for its integrity. All authors have read and agree to the manuscript as written.
Definitions
Full descriptions of the data element definitions for version 3.04 of the CathPCI registry are available online at https://www.accncdr.com/webncdr/DefaultCathPCI.aspx. Bleeding is defined by the CathPCI registry as (1) occurring at percutaneous entry site, during or after catheterization laboratory visit until discharge, which may be external or a hematoma >10 cm for femoral, >5 cm for brachial, or >2 cm for radial access; (2) retroperitoneal; (3) gastrointestinal; (4) genitourinary; and (5) other/unknown origin during or after catheterization laboratory visit until discharge. All bleeding events required a transfusion, prolonged hospital stay, and/or a drop in hemoglobin >3.0 g/dL. PCI indication consisted of (1) elective; (2) urgent (required during same hospitalization to minimize further clinical deterioration, worsening or sudden chest pain, congestive heart failure, acute MI, anatomy, intra-aortic balloon pump, unstable angina with intravenous nitroglycerin, or angina at rest); (3) emergency (to procedure or in transit to the catheterization laboratory, ongoing ischemia despite maximal medical therapy, acute MI 24 hours before procedure, pulmonary edema requiring intubation, or shock with or without circulatory support); or (4) salvage (undergoing CPR en route to PCI). Indications for acute PCI included (1) primary for ST-elevation MI; (2) rescue (unplanned after failed fibrinolysis for recurrent ischemia); (3) facilitated (planned after reduced-dose fibrinolysis); or (4) for non–ST-elevation MI or unstable angina. Estimated glomerular filtration rate was calculated using admission serum creatinine value and the abbreviated modification of diet in renal disease formula.8 Acute coronary syndromes consisted of ST-elevation MI, non–ST-elevation MI, or unstable angina.
Statistical Analysis
Continuous variables are described as medians (interquartile range) and compared using Wilcoxon rank-sum tests. Categorical variables are described as frequencies and compared using Pearson 
2 tests. Ordinal variables were tested using a 2 test based on the rank of the group mean score. Baseline patient characteristics and variables with clinically significant associations with bleeding were included in a multivariable model. Missing data were <0.5% across covariates except for estimated glomerular filtration rate (
5%). Missing values were imputed to the lower risk group for discrete variables and replaced with gender and renal failure/dialysis-specific medians for estimated glomerular filtration rate. Logistic regression with the generalized estimating equations9 method was used to account for within-hospital clustering. A random sample of 80% of patients formed the training set to develop the predictive model, whereas the remaining 20% of patients were used to validate the model. Using backward selection with a criterion to keep of P<0.05, we developed a best-fitting multivariable model associated with post-PCI bleeding (model 1). From this model, we used stricter criteria of keeping only the 10 most significant variables in backward selection to develop a parsimonious model (model 2), which resulted in the removal of 6 variables: (1) intra-aortic balloon pump, (2) previous valve surgery, (3) cerebrovascular disease, (4) hypertension, (5) weight, and (6) New York Heart Association/Canadian Cardiovascular Society Functional Classification class III heart failure. Based on clinical judgment, the PCI indicator variable was also removed (model 3) because this variable is highly correlated with other clinical variables. A simplified clinical algorithm was then developed using model 3. Variables in the risk score model were assigned an integer weight based on the β coefficient.10 The sum of the integers for each patient is the risk algorithm. C-statistics, Brier score,11 and Quasi-likelihood information criterion12 were used to compare model discrimination between models 2 and 3. The final model’s goodness of fit was determined by calibration plots, and model discrimination was assessed by the c-statistic. After initial development, the model was tested in a variety of clinical scenarios including patients with ST-elevation MI, non–ST-elevation MI or unstable angina, and patients undergoing elective PCI. All comparisons were 2-tailed, with a P<0.05 considered statistically significant. All statistical analyses were performed by the Duke Clinical Research Institute, using SAS software (version 9.0, SAS Institute, Cary, NC).
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Results |
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A total of 302 152 patients undergoing an equal number of PCI procedures at 440 US hospitals met the inclusion criteria for this analysis. Baseline clinical and procedural characteristics stratified by bleeding are shown in Table 1
. Overall, bleeding occurred in 7328 patients (2.4%). The average hospital bleeding rate was 2.7±2.0%. By access site, bleeding occurred at percutaneous entry in 2781 patients (38.0%), retroperitoneal in 941 (12.8%), gastrointestinal in 1369 (18.7%), and genitourinary in 376 (5.1%). There were 399 patients (5.4%) with bleeding at 1 location.
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The pre-PCI clinical variables and metrics of the best fitting (model 1) and parsimonious (model 2) are included in supplemental Table I. The most parsimonious (model 3) is shown in Table 2. Comparisons of model performance are shown in Table 3. Compared with the best fitting, the most parsimonious model had 9 pre-PCI clinical variables and performed similarly as assessed by the c-statistic, quasi-likelihood information criterion, and Brier score.
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To evaluate model 3 further, a calibration plot was calculated applying the predicted risk of bleeding model from the training cohort to the validation cohort and compared with the actual incidence of bleeding in the validation cohort (Figure 1a). Model performance in clinical subsets including ST-elevation MI, non–ST-elevation MI or unstable angina, and nonacute coronary syndromes is shown in Figure 1b through 1d (supplemental Figure I demonstrates model performance in the best-fitting model).
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Using model 3, a risk algorithm for post-PCI bleeding was developed (Table 4). Weighted points were assigned based on the presence or absence of these covariables. Among all patients in the study group, there was an upward trend in observed rates of post-PCI bleeding when patients were categorized by increasing risk score as shown in Figure 2. The risk of bleeding when the clinical risk algorithm was simplified into 3 categories is shown in Figure 3.
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Discussion |
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In a contemporary national registry of patients undergoing PCI, the incidence of post-PCI bleeding was 2.4%. We developed a best-fitting model for bleeding in patients undergoing PCI, using a random sample of 80% of patients in this dataset, and then validated the model in the remaining 20%. From the best-fitting model, a minimal-element model was identified and used to develop a clinically simplified algorithm. The parsimonious model compared favorably with the best-fitting model in tests of discrimination and calibration.
Major bleeding is one of the most common complications after PCI and confers a poor prognosis1; the reported incidence of bleeding ranges from 0.2% to 9.1%.13–17 Numerous studies have described the strong association between bleeding events and increased early18–20 and late mortality.13,15,19–22 The association between bleeding and mortality is evident across the spectrum of indications for PCI, from elective through acute MI.13 In fact, bleeding was as predictive of 1-year mortality as previous MI and urgent repeat revascularization. Bleeding is also associated with an increased risk of recurrent ischemic events,15,22 length of hospital stay,4 and increasing cost—$6300. Given these associations, outcomes, and increased resource utilization, we believe that identification of patients at high risk for bleeding is a clinical imperative. Improved identification of high-risk patients will enable physicians to develop alternative approaches to mitigate the risk of bleeding and potentially improve outcomes among patients undergoing PCI. Nikolsky et al23 have derived a clinical risk model from the Randomized Evaluation in PCI Linking Angiomax to Reduced Clinical Events (REPLACE) trial. We have expanded this approach to a broader population undergoing PCI.
We identified several independent risk factors for bleeding in patients undergoing PCI. Several of these variables, including increased age, female sex, and renal impairment have been well described in previous studies.3,17,23 Patients with advanced disease severity states, such as cardiogenic shock, ST-elevation MI, non–ST-elevation MI, requiring emergency/salvage PCI procedures, and treatment with an intra-aortic balloon pump were also at increased risk. However, knowledge of these individual risk factors alone neither allows physicians to estimate individual patient risk nor enables physicians to alter medical therapy for high-risk patients.
The clinical risk algorithm was simplified based on all multivariable predictors of post-PCI bleeding from the best-fitting model. To maximize clinical utility, we simplified the model following a 2-step approach. First, we eliminated variables believed to add little to model discrimination by keeping only the top 10 significant variables. This resulted in the elimination of hypertension, cerebrovascular disease, New York Heart Association/Canadian Cardiovascular Society Functional Classification class III heart failure, weight, and previous valve surgery. Second, we eliminated the PCI indicator variable because considerable clinical overlap was believed to exist between it and other variables in the model, including ST-elevation and non–ST-elevation MI, cardiogenic shock, and New York Heart Association/Canadian Cardiovascular Society Functional Classification class IV heart failure. More importantly, we believed that the clinical utility of the model would be diminished because these definitions inherent in the PCI indicator variable are not commonly used in the clinical setting, which would unduly burden support staff in the catheterization laboratory. For example, urgent PCI is defined by NCDR as required during the same hospitalization to minimize further clinical deterioration, worsening or sudden chest pain, congestive heart failure, acute MI, anatomy, intra-aortic balloon pump, unstable angina with intravenous nitroglycerin, or angina at rest. Although there was a slight decrease in the c-index after elimination of the PCI indicator variable, the Brier score was similar, thus the magnitude of loss of model discrimination was minimal. The simplified algorithm compared favorably with the best-fitting model. To assess further the predictive accuracy of the model, we generated calibration plots to compare observed and expected number of bleeding events by decile of predicted risk. The identity line is indicative of a perfect model, in which the predicted and actual number of events are equal. It is visually apparent that the parsimonious model performed similarly in tests of calibration and in selected subgroups.
There are several strengths associated with this project. First, we believe this model is highly actionable. We intentionally chose to model only pre-PCI variables, thus allowing physicians the maximal opportunity to consider alternate PCI care for patients at high risk for post-PCI bleeding. There are several strategies to mitigate post-PCI bleeding. The use of smaller sheath sizes (4F to 6F) has been shown to decrease bleeding.24–27 Preferential use of bivalirudin over unfractionated heparin and glycoprotein IIb/IIIa inhibitors is effective in reducing thrombotic complications while minimizing bleeding.14,28–32 Selective use of the radial artery has been shown to decrease access site bleeding complications by 58% compared with femoral access.33 Second, the model can be applied to a broad range of PCI indications23,32,34 because it was developed from a large, contemporary, real-world population of patients undergoing PCI. Last, the model predicts bleeding whether at access or nonaccess site and performs similarly regardless of bleeding location.
Limitations
Bleeding definitions from the NCDR differ from those frequently used in randomized trials.35,36 Thus, we could not assess model performance using alternative bleeding definitions, such as Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded coronary arteries (GUSTO) and Thrombolysis in Myocardial Infarction. Bleeding was not adjudicated in the NCDR, potentially resulting in underreporting. To minimize systematic underreporting, we excluded centers that did not report bleeding to the NCDR, although we recognize institutional variability in systematically reporting post-PCI bleeding. Another anticipated limitation is anticoagulation-associated bleeding. We chose a priori not to model anticoagulants for 2 reasons. First, their use is likely associated with significant selection bias. Operators likely based their anticoagulant choice on many factors, including acute coronary syndromes, acute MI, ST-elevation MI, and risk of bleeding. This decision alone could result in a propensity for bleeding based on anticoagulant choice, which would confound the model. Second, it has been shown that incorrect dosing of many agents, including unfractionated heparin and glycoprotein IIb/IIIa inhibitors, leads to an increased bleeding hazard. In addition, our analysis does not establish a cause-effect relationship between bleeding and long-term morbidity and mortality. Thus, implementation of strategies to reduce bleeding cannot be assumed to improve long-term outcomes. Finally, long-term follow-up is unavailable in the NCDR; thus, we could not examine the influence of various therapeutic strategies on outcomes of patients predicted to be at high bleeding risk.
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Conclusions |
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This report from the NCDR demonstrates a clinical risk algorithm for post-PCI bleeding developed from patient data evaluated before PCI. Further studies should determine whether bleeding reduction strategies (choice of access site, antithrombotic regimens) can be implemented in various patient risk strata in an effort to minimize both immediate and long-term hemorrhagic outcomes.
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Acknowledgments |
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We thank Jose Aceituno and Joseph Murphy for publication assistance.
Disclosures
Dr Marso is a consultant for Volcano Corporation and Novo Nordisk and has received research support from Volcano Corporation, Amylin Pharmaceuticals, The Medicines Company, and Boston Scientific. The other authors report no conflicts of interest.
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References |
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Footnotes |
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The online-only Data Supplement is available at http://circinterventions.ahajournals.org/cgi/content/full/10.1161/CIRCINTERVENTIONS.108.846741/DC1.
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