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Original Contribution |

Association Between Bleeding Events and In-hospital Mortality After Percutaneous Coronary Intervention FREE

Adnan K. Chhatriwalla, MD; Amit P. Amin, MD; Kevin F. Kennedy, MS; John A. House, MS; David J. Cohen, MD, MSc; Sunil V. Rao, MD; John C. Messenger, MD; Steven P. Marso, MD ; for the National Cardiovascular Data Registry
[+] Author Affiliations

AuthorAffiliations: Department of Biostatistics (Mssrs Kennedy and House), Saint Luke's Mid America Heart Institute (Drs Chhatriwalla, Cohen, and Marso), Kansas City, Missouri; Department of Internal Medicine, Division of Cardiology, University of Missouri–Kansas City School of Medicine (Drs Chhatriwalla, Cohen, and Marso); Department of Internal Medicine, Division of Cardiology, Washington University School of Medicine, St Louis, Missouri (Dr Amin); Depart ment of Internal Medicine, Division of Cardiology, Duke Clinical Research Institute, Durham, North Carolina (Dr Rao); and Department of Internal Medicine, Division of Cardiology, University of Colorado Denver and Denver VA Medical Center (Dr Messenger).


JAMA. 2013;309(10):1022-1029. doi:10.1001/jama.2013.1556.
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Published online

Importance Bleeding is the most common complication after percutaneous coronary intervention (PCI) and is associated with increased morbidity and health care costs. The incidence of bleeding-related mortality after PCI has not been described in a nationally representative population. Furthermore, the relationships among bleeding risk, bleeding site, and mortality are unclear.

Objectives To describe the association between bleeding events and in-hospital mortality after PCI and to estimate the adjusted population attributable risk (estimated as the proportion of mortality risk associated with bleeding events), risk difference, and number needed to harm (NNH) for bleeding-related in-hospital mortality after PCI.

Design, Setting, and Patients Data from 3 386 688 procedures in the CathPCI Registry performed in the United States between 2004 and 2011 were analyzed. The population attributable risk was calculated after adjustment for baseline demographic, clinical, and procedural variables. To calculate the NNH for bleeding-related mortality, a propensity-matched analysis was performed.

Main Outcome Measures In-hospital mortality.

Results There were 57 246 bleeding events (1.7%) and 22 165 in-hospital deaths (0.65%) in 3 386 688 PCI procedures. The adjusted population attributable risk for mortality related to major bleeding was 12.1% (95% CI, 11.4%-12.7%) in the entire CathPCI cohort. The propensity-matched population consisted of 56 078 procedures with a major bleeding event and 224 312 controls. In this matched cohort, major bleeding was associated with increased in-hospital mortality (5.26% vs 1.87%; risk difference, 3.39% [95% CI, 3.20%-3.59%]; NNH = 29 [95% CI, 28-31]; P < .001). The association between major bleeding and in-hospital mortality was observed in all strata of preprocedural bleeding risk (low: 1.62% vs 0.17%; risk difference, 1.45% [95% CI, 1.13%-1.77%], NNH = 69 [95% CI, 57-88], P < .001; intermediate: 3.27% vs 0.71%; risk difference, 2.56% [95% CI, 2.33%-2.79%], NNH = 39 [95% CI, 36-43], P < .001; and high: 8.16% vs 3.45%; risk difference, 4.71% [95% CI, 4.35%-5.07%], NNH = 21 [95% CI, 20-23], P < .001). Although both access-site and non–access-site bleeding were associated with increased in-hospital mortality (2.73% vs 1.87%; risk difference, 0.86% [95% CI, 0.66%-1.05%], NNH = 117 [95% CI, 95-151], P < .001; and 8.25% vs 1.87%; risk difference, 6.39% [95% CI, 6.04%-6.73%], NNH = 16 [95% CI, 15-17], P < .001, respectively), the NNH was lower for nonaccess bleeding.

Conclusions and Relevance In a large registry of patients undergoing PCI, postprocedural bleeding events were associated with increased risk of in-hospital mortality, with an estimated 12.1% of deaths related to bleeding complications.

Figures in this Article

Bleeding represents the most common noncardiac complication of percutaneous coronary intervention (PCI). Postprocedural bleeding is associated with short- and long-term death, nonfatal myocardial infarction, stroke, blood transfusion, prolonged hospital stay, rehospitalization, and increased hospital costs.14 Post-PCI bleeding is predictable, using tools such as the bleeding risk algorithm derived from the CathPCI Registry.5 Bleeding risk is modifiable through the use of established bleeding avoidance strategies such as bivalirudin anticoagulation, arterial closure devices, and radial artery access.610

Nevertheless, determining the relationship between bleeding and post-PCI mortality is inherently challenging. Previous studies have focused on patient populations derived from highly selected randomized controlled trials rather than other, more representative populations.1115 It is also difficult to overcome the significant overlap between patient variables that predict bleeding complications and those that predict mortality after PCI. In this large study, we leveraged the CathPCI Registry, which includes data from more than 1500 institutions and approximately 80% of US PCI centers,16 to estimate the adjusted population attributable risk of bleeding-related mortality in the US PCI population. We also estimated the association of bleeding risk and bleeding location with mortality in a selected propensity-matched population.

Data Source and Study Population

The CathPCI Registry, a voluntary US multicenter reporting system for cardiac catheterization procedures, is an initiative of the American College of Cardiology Foundation and the Society for Cardiovascular Angiography and Interventions.17,18 The CathPCI Registry comprises a broad mix of academic, for-profit, not-for-profit, and community hospitals. Demographic, clinical, procedural, and institutional data are collected for all patients undergoing PCI at participating centers and are entered into a secure centralized database (CathPCI versions 3 and 4). Automatic system validation, reporting of data completeness, random auditing of participating centers, and education and training of data site managers are performed to promote quality assurance. Patient information was deidentified, and this research was exempt from institutional review board and ethics committee approval.

Bleeding Risk Assessment

The study population was stratified by individual bleeding risk with the CathPCI Registry bleeding risk model, which uses preprocedural clinical variables. As described previously,5 the bleeding risk score uses 9 variables: acute coronary syndrome type, cardiogenic shock, sex, previous heart failure, previous PCI, New York Heart Association class IV heart failure, peripheral vascular disease, age, and estimated glomerular filtration rate.

Mortality Risk Assessment

Risk of in-hospital mortality was estimated with a validated CathPCI Registry algorithm that uses preprocedural clinical variables and has been described previously.19 The clinical model consists of 8 variables: age, cardiogenic shock, previous congestive heart failure, peripheral vascular disease, chronic lung disease, glomerular filtration rate, New York Heart Association class IV heart failure, and PCI status.

Propensity-Matching Methods

A propensity score matching strategy was used to identify a population from which to estimate the number needed to harm (NNH) for bleeding-related mortality. To minimize confounding, hospital characteristics, bleeding risk, mortality risk, and patient demographics were used to calculate propensity scores for bleeding and mortality, derived with a logistic regression model. These propensity scores were derived with the variables used in the CathPCI Registry bleeding and mortality models5,19 and with the following additional variables: previous myocardial infarction, previous valve surgery, diabetes mellitus, cerebrovascular disease, hypertension, tobacco use, previous coronary artery bypass grafting, body mass index, PCI admission status (elective, urgent, emergency, or salvage), transradial PCI, adjunctive pharmacology at PCI (heparin, bivalirudin, and glycoprotein IIb/IIIa inhibitor), PCI to the left main or proximal left anterior descending artery, number of lesions treated, and suboptimal Thrombolysis in Myocardial Infarction flow grade after PCI.

Propensity matching was then performed according to both bleeding and mortality risk, using the nearest neighbor matching without replacement, with each bleeding patient matched to 4 control patients. A caliper width of 0.2 of the standard deviation of the logit of the propensity score was used for the developed propensity score, and the same caliper width was used for the expected mortality and bleeding probability.20 In addition, patients with ST-segment elevation myocardial infarction (STEMI) were directly matched, and we used a 365-day caliper on procedure date to minimize any time effect. Absolute standardized differences were computed to evaluate matching effectiveness and displayed graphically2123; values less than 10% and closer to zero demonstrate a more balanced cohort.22

Outcomes Definitions

Major bleeding complications were identified with the CathPCI Registry versions 3.0 and 4.0 data collection form definitions, as classified at data collection. In version 3.0, bleeding was defined as suspected bleeding from any location requiring transfusion, prolonged hospital stay, or a decrease in hemoglobin level greater than 3.0 g/dL. In version 4.0, a bleeding event was defined as suspected bleeding with transfusion, a decrease in hemoglobin level of greater than 3.0 g/dL, or a procedural intervention to correct the bleeding event. Access site bleeding was defined as intraprocedural blood loss, retroperitoneal bleeding, external bleeding at the access site, or access site hematoma. In version 3.0, access site hematoma was documented only if the hematoma was greater than 10 cm at the femoral access site, greater than 5 cm at the brachial access site, or greater than 2 cm at the radial access site. In version 4.0, hematoma size was not considered in the definition of access site bleeding. Non–access-site bleeding was defined as gastrointestinal, genitourinary, or other/unknown bleeding meeting the above criteria. In this analysis, cases with both access-site and non–access-site bleeding were included in the non–access-site bleeding cohort so that no cases were counted more than once. Patients with access-site and non–access-site bleeding were compared with a control group of patients without bleeding.

Statistical Analysis

Demographic data were described across treatment groups as mean (standard deviation) for continuous variables and number (%) for categorical variables. The adjusted population attributable risk of mortality related to major bleeding was estimated in the entire cohort after adjustment for baseline mortality risk, bleeding risk, and baseline clinical variables, using the methods described by Spiegelman et al.24 This method first estimates the relative risks for the exposure of interest (bleeding) and for potential confounders and then estimates the number of events expected if the exposure of interest were eliminated, to derive the percentage of outcomes attributable to the exposure of interest. The variables used in the adjusted population attributable risk calculation were the same as those used in the propensity matching analysis outlined above. According to individual risk scores calculated with the National Cardiovascular Data Registry bleeding risk model,5 patients were categorized into 3 groups of risk for post-PCI bleeding events occurring during hospitalization: less than 1% (low), 1% to 3% (intermediate), and greater than 3% (high). Logistic regression was performed, with in-hospital death as the dependent variable. Risk difference and NNH calculations were performed in the matched cohort.25,26 Two-sided statistical significance was defined as P ≤ .05. All statistical analyses were performed by the Saint Luke's Mid America Heart Institute Department of Biostatistics with SAS version 9.2.

Baseline Demographics of Total Analytic Cohort

Data from 3 660 731 procedures in the CathPCI Registry between 2004 and 2011 were available and considered for use in this analysis. Of these, we excluded 7.5% (274 043) due to the following reasons: more than 1 PCI during the index hospitalization (n = 192 654), cardiogenic shock (n = 71 352), missing bleeding information (n = 640), died in the catheterization laboratory (n = 5692), or intra-aortic balloon pump in place at the start of the procedure (n = 3705). The clinical, demographic, and procedural characteristics of the total analytic cohort (N = 3 386 688) are represented in Tables 1 and 2, stratified by the presence or absence of a major bleeding complication. The overall PCI population was predominantly middle-aged (64 years; SD, 12) and men (67%), with a high incidence of previous myocardial infarction (29%) and previous PCI (39%). Patients presented with STEMI in 14% of cases, NSTEMI or unstable angina in 52%, and no acute coronary syndrome in 34%. Heparin anticoagulation was used in 52% of cases, bivalirudin in 48%, and glycoprotein IIb/IIIa inhibitors with heparin or bivalirudin in 34%. PCI was performed via radial artery access in 4.0% of cases.

Table Graphic Jump LocationTable 1. Patient Characteristics, Expected Bleeding, Expected Mortality, and Standardized Differences Before and After Match
Table Graphic Jump LocationTable 2. Patient History and Procedural Characteristics Before and After Match
Unadjusted Bleeding and Mortality Rates

In the total analytic cohort, there were 57 246 major bleeding events (1.69%; 95% CI, 1.68%-1.70%) and 22 165 in-hospital deaths (0.65%; 95% CI, 64%-0.66%).

In-hospital mortality was higher in patients with major bleeding complications compared with patients without bleeding (5.58% vs 0.57%; P < .001). Bleeding risk was classified as low (<1%) in 1 046 665 (31%) procedures, intermediate (1%-3%) in 1 713 785 (51%) procedures, and high (>3%) in 623 678 (18%) procedures. In patients at low bleeding risk, the incidence of major bleeding was 0.60% (95% CI, 0.59%-0.62%) and in-hospital mortality was 0.075% (95% CI, 0.069%-0.080%). In patients at intermediate bleeding risk, the incidence of major bleeding was 1.47% (95% CI, 1.45%-1.49%) and in-hospital mortality was 0.39% (95% CI, 0.38%-0.40%). In patients at high bleeding risk, the incidence of bleeding was 4.12% (95% CI, 4.07%-4.16%) and in-hospital mortality was 2.36% (95% CI, 2.32%-2.40%).

Population Attributable Risk

The adjusted population attributable risk of in-hospital mortality related to major bleeding was 12.1% (95% CI, 11.4%-12.7%). The adjusted population attributable risk was similar across bleeding risk strata: 14.7% (95% CI, 11.0%-18.5%), 11.9% (95% CI, 10.8%-13.0%), and 11.5% (95% CI, 10.8%-12.3%) in the low-, intermediate-, and high-bleeding-risk groups, respectively.

Propensity-matched Analysis

The propensity-matched population consisted of 56 078 procedures with major bleeding and 224 312 matched controls. Baseline demographics for the matched study population are also shown in Tables 1 and 2. The standard difference plot of matched variables before and after match is depicted in the eFigure. After matching, the absolute standardized difference for covariables between procedures with and without major bleeding ranged from 0% to 4.4%. Because control procedures were matched to procedures with bleeding, the propensity matching strategy resulted in a population with both a higher bleeding risk (3.5% vs 2.0%; P < .001) and mortality risk (1.9% vs 0.7%; P < .001) than that observed in the total analytic cohort. Bleeding risk was classified as low in 30 950 (11%) procedures, intermediate in 124 780 (45%) procedures, and high in 124 660 (44%) procedures. Expected bleeding and expected mortality were very well matched between groups both in the overall cohort and within strata of bleeding risk (Table 1 and eFigure).

Bleeding complications are classified by type in Table 3. In-hospital mortality was 2.55% (95% CI, 2.49%-2.61%) in the propensity-matched cohort. The association between major bleeding and in-hospital mortality in the overall cohort is represented in Figure 1. Patients with major bleeding had significantly higher in-hospital mortality relative to patients without bleeding (5.26% vs 1.87%; risk difference 3.39% [95% CI, 3.20%-3.59%]; P < .001). Both access-site and non–access-site bleeding were associated with increased in-hospital mortality (2.73% vs 1.87%, risk difference 0.86% [95% CI, 0.66%-1.05%], P < .001; and 8.25% vs 1.87%, risk difference 6.39% [95% CI, 6.04%-6.73%], P < .001, respectively). The association between major bleeding and in-hospital mortality was observed in all strata of bleeding risk (1.62% vs 0.17%, risk difference 1.45% [95% CI, 1.13%-1.77%], P < .001; 3.27% vs 0.71%, risk difference 2.56% [95% CI, 2.33%-2.79%], P < .001; and 8.16% vs 3.45%, risk difference 4.71% [95% CI, 4.35%-5.07%], P < .001 for the low-, intermediate-, and high-risk groups, respectively). The number of major bleeding events associated with 1 in-hospital mortality (NNH) in selected subgroups is represented in Figure 2. The NNH varied between 16 and 117, depending on bleeding risk and bleeding site, and was lowest in patients at high risk for bleeding (NNH = 21 [95% CI, 20-23]) or with non–access-site bleeding (NNH = 16 [95% CI, 15-17]). NNH values were lowest in the following patient subgroups: age 75 years or older, STEMI, or low glomerular filtration rate. Because bleeding definitions varied slightly between CathPCI Registry versions 3 and 4, the NNH was also calculated for each version separately, and no statistically significant difference was observed.

Place holder to copy figure label and caption
Figure 1. In-hospital Mortality in the Propensity-Matched Cohort
Graphic Jump Location
Place holder to copy figure label and caption
Figure 2. Number Needed to Harm (NNH) for In-hospital Mortality Related to Major Bleeding in the Propensity-Matched Cohort and in Selected Subgroups
Graphic Jump Location

The dashed line at x = 29 represents the NNH for the overall population. STEMI indicates ST-segment elevation myocardial infarction; NSTEMI, non-STEMI; UA, unstable angina; ACS, acute coronary syndrome; Gp, glycoprotein.

Table Graphic Jump LocationTable 3. Bleeding Complications in the Propensity-Matched Cohort, Classified by Type

In this study of more than 3.3 million PCI procedures, we found that major bleeding was associated with significantly increased in-hospital mortality after PCI. The novel findings are that adjusted population attributable risk estimates for an unselected and nationally representative US PCI population suggest that 12.1% of all in-hospital mortality after PCI may be related to bleeding complications and may therefore be modifiable, and that NNH calculations performed in the context of a propensity-matching analysis suggest that the mortality risk associated with bleeding is greatest in patients at highest bleeding risk (NNH = 21) or with non–access-site bleeding (NNH = 16).

The safety of PCI has improved substantially; however, postprocedural bleeding remains a common complication and increases hospital length of stay,27 as well as hospital costs, by $6000 to $8000 per episode.28 Bleeding is also strongly associated with nonfatal myocardial infarction, stroke, and need for blood transfusion.29 Previous work suggests that the association between bleeding and mortality approximates that of a recurrent myocardial infarction.11,29,30 The risk-adjusted hazard ratio for in-hospital mortality after a major bleeding complication was 2.92 (95% CI, 2.78-3.06) in this study, consistent with results of previous analyses of longer-term bleeding-related mortality, which have reported hazard ratios of 2.8 to 6.8, depending on the patient population and length of follow-up.1113,15,31 The adjusted population attributable risk of in-hospital mortality related to major bleeding was 12.1% in this study, similar to that previously reported for 1-year mortality attributable to major bleeding in the Acute Catheterization and Urgent Intervention Triage Strategy (ACUITY) trial (11.7%-12.7% in separate studies).11,13,32

However, there are several differences between the present study and previous analyses. First, the current findings are reported from an unselected population from the CathPCI Registry rather than from highly selected patients enrolled in randomized clinical trials and therefore are more likely to be generalizable. Second, in contrast with previous studies, we evaluated the association between major bleeding and early (in-hospital) rather than longer-term mortality. The risk of mortality is highest early after a bleeding event and attenuated over time.14 Furthermore, we believe that early mortality events are more likely to be related to the index bleeding episode than later events, which may be influenced by unmeasured confounders. Finally, in this analysis, we used multivariable adjustment and propensity-matching techniques, which included validated bleeding and mortality risk estimates, to minimize confounding when calculating the adjusted population attributable risk and NNH for bleeding-related mortality and found that the NNH is lowest in patients at high risk for bleeding and with nonaccess bleeding. Intuitively, the lower the NNH, the greater the association between bleeding and mortality.

The association between major bleeding and mortality may be most substantial in patients at high risk for bleeding complications or mortality. However, this question has not been previously evaluated, in part because, except for a few notable exceptions such as the TIMI risk score and CHADS2 score, which are used to risk-stratify patients with acute coronary syndrome and atrial fibrillation, respectively, physicians have not readily embraced the incorporation of individualized risk estimates into their clinical decision making. In this study, major bleeding was associated with increased in-hospital mortality not only in the overall population but also across all strata of bleeding risk and mortality risk. However, the absolute mortality risk associated with major bleeding was greatest in the subgroups of patients at high bleeding risk, at high mortality risk, or with non–access-site bleeding. Likewise, the NNH was lowest in these subgroups, as well as in patients aged 75 years or older and patients with STEMI or low glomerular filtration rate. According to these findings, patients in these high-risk subgroups may have the greatest potential for mortality reduction through bleeding avoidance. The NNH for bleeding-associated mortality appears to be similar in the subgroups of patients treated with heparin, bivalirudin, and glycoprotein IIb/IIIa inhibitors during PCI. These findings suggest that in patients with major bleeding after PCI, the association between bleeding and mortality is independent of the anticoagulation regimen chosen.

The results from this study suggest that 12.1% of post-PCI deaths may be related to bleeding complications and may therefore be avoidable. Bleeding can be reduced through the use of established bleeding avoidance strategies, including bivalirudin anticoagulation, arterial closure devices, and radial artery access.710 Evidence suggests that the magnitude of the bleeding reduction associated with bleeding avoidance strategies is directly related to patients' underlying bleeding risk; in other words, the number needed to treat to avoid 1 bleeding event is lowest in patients at highest risk for bleeding.6 Intuitively, the lower the number needed to treat, the more effective the treatment. However, data from the CathPCI Registry indicate a risk-treatment paradox, whereby bleeding avoidance strategies are preferentially used in patients at lowest, rather than highest, risk for bleeding complications after PCI.6 The present study suggests that the NNH is also lowest in patients at highest risk for bleeding. These findings are complementary and suggest that bleeding avoidance strategies are of greatest benefit and therefore should be preferentially used in higher-risk patients to reduce the risk of major bleeding complications and bleeding-related mortality after PCI. These data also affirm the importance of using validated risk assessment tools to accurately estimate bleeding risk and guide treatment strategy for all patients undergoing PCI.

Finally, the current study provides additional insight into the differential associations between access-site and non–access-site bleeding and mortality. These findings raise the possibility that interventions that reduce both access-site and non–access-site bleeding, such as bivalirudin,33 may be associated with a greater bleeding-related mortality benefit than would be expected with other bleeding avoidance strategies such as radial artery access and arterial closure devices, which reduce only access-site bleeding. Moreover, it is unknown whether the use of bivalirudin in addition to arterial closure or radial artery access might have an incremental effect on in-hospital mortality after PCI. These questions should be directly evaluated in future studies.

Limitations

The present analysis was performed with data from the multicenter CathPCI Registry and was not randomized; therefore, variables outside of those used in the propensity matching analysis (unmeasured confounders) may affect in-hospital mortality in this analysis. Furthermore, reporting of bleeding complications may vary across institutions and may have affected these results (ascertainment bias). Data for cause-specific death were not available. Therefore, bleeding-related mortality was estimated by calculating the adjusted population attributable risk, and direct causality between bleeding and mortality cannot be established. Data regarding the adjunctive use of cardiovascular medications, such as aspirin, thienopyridines, β-blockers, statins, and angiotensin-converting enzyme inhibitors, may not be reliable. Therefore the effect of these agents on mortality after PCI was not assessed in this study. Finally, long-term mortality data were not available. Therefore, associations between bleeding and long-term mortality after PCI were not assessed.

Conclusions

In the CathPCI Registry, the incidence of major bleeding complications after PCI was only 1.7%. Nonetheless, 12.1% of all in-hospital mortality may be related to bleeding complications. The association between major bleeding and in-hospital mortality was present in all strata of bleeding risk, but the mortality risk appears most substantial in patients at high risk of bleeding or with non–access-site bleeding. The number of major bleeding events associated with 1 in-hospital death was as low as 21 in patients at high risk for bleeding and 16 in patients with non–access-site bleeding.

Corresponding Author: Adnan K. Chhatriwalla, MD, Saint Luke's Mid America Heart Institute, 4300 Wornall Rd, Ste 2000, Kansas City, MO 64111 (achhatriwalla@saint-lukes.org).

Author Contributions: Dr Chhatriwalla and Mr Kennedy had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Chhatriwalla, Amin, House, Marso.

Acquisition of data: Chhatriwalla, House, Marso.

Analysis and interpretation of data: All authors.

Drafting of the manuscript: Chhatriwalla, Kennedy, Marso.

Critical revision of the manuscript for important intellectual content: Chhatriwalla, Amin, House, Cohen, Rao, Messenger, Marso.

Statistical analysis: Chhatriwalla, Kennedy, House, Marso.

Administrative, technical, or material support: Chhatriwalla, Messenger, Marso.

Study supervision: Chhatriwalla.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Chhatriwalla reports that he will be receiving speaking honoraria from Edwards Lifesciences to be paid directly to Saint Luke's Hospital Foundation of Kansas City. Mr House reports receiving consulting honoraria from Vita Solutions and KU Medical Center. Dr Cohen reports medical board membership for Medtronic and Abbott Vascular and reports receiving research support from Medtronic, Edwards Lifesciences, Abbott Vascular, Boston Scientific, Eli Lilly, AstraZeneca, Biomet Inc, and Daiichii Sankyo; consultant honoraria from AstraZeneca, Eli Lilly, Merck, and Janssen Pharmaceuticals; and speaking honoraria from St Jude Medical and Eli Lilly. Dr Rao reports receiving research support and consultant honoraria from The Medicines Company. Dr Marso reports that all compensation for research activities, including research grants and consulting fees from The Medicines Company, Novo Nordisk, Abbott Vascular, Amylin Pharmaceuticals, Boston Scientific, Volcano Corporation, and Terumo Medical, was paid directly to the Saint Luke's Hospital Foundation of Kansas City. No other disclosures were reported.

Funding/Support: This research was supported by the American College of Cardiology Foundation's National Cardiovascular Data Registry (NCDR).

Role of the Sponsor: This was an investigator-initiated study. The analytic work was performed at the Saint Luke's Mid America Heart Institute, an NCDR analytic center. There was no other additional financial or material support and no institutional role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.

Disclaimer:The views expressed in this article represent those of the authors and do not necessarily represent the American College of Cardiology Foundation or Society for Cardiac Angiography and Interventions, which cosponsor the NCDR.

Additional Contributions: We thank Jose Aceituno, MS, for preparation of graphics and Joseph Murphy, MBA, for editorial assistance and publication coordination. Both are employees of Saint Luke's Hospital of Kansas City, Missouri. Neither received any additional compensation beyond usual salary.

This article was corrected for errors on July 1, 2013.

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Weintraub WS, McKay CR, Riner RN,  et al; American College of Cardiology Database Committee.  The American College of Cardiology National Database: progress and challenges.  J Am Coll Cardiol. 1997;29(2):459-465
Link to Article
Peterson ED, Dai D, DeLong ER,  et al.  Contemporary mortality risk prediction for percutaneous coronary intervention: results from 588,398 procedures in the National Cardiovascular Data Registry.  J Am Coll Cardiol. 2010;55(18):1923-1932
Link to Article
Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.  Pharm Stat. 2011;10:150-161
Link to Article
Austin PC. Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score.  Pharmacoepidemiol Drug Saf. 2008;17(12):1202-1217
Link to Article
Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.  Stat Med. 2009;28(25):3083-3107
Link to Article
Austin PC. The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies.  Med Decis Making. 2009;29(6):661-677
Link to Article
Spiegelman D, Hertzmark E, Wand HC. Point and interval estimates of partial population attributable risks in cohort studies: examples and software.  Cancer Causes Control. 2007;18(5):571-579
Link to Article
Bender R, Kuss O, Hildebrandt M, Gehrmann U. Estimating adjusted NNT measures in logistic regression analysis.  Stat Med. 2007;26(30):5586-5595
Link to Article
Bender R, Vervolgyi V. Estimating adjusted NNTs in randomised controlled trials with binary outcomes: a simulation study.  Contemp Clin Trials. 2010;31(5):498-505
Link to Article
Aronow HD, Peyser PA, Eagle KA,  et al.  Predictors of length of stay after coronary stenting.  Am Heart J. 2001;142(5):799-805
Link to Article
Pinto DS, Stone GW, Shi C,  et al.  Economic evaluation of bivalirudin with or without glycoprotein IIb/IIIa inhibition vs heparin with routine glycoprotein IIb/IIIa inhibition for early invasive management of acute coronary syndromes.  J Am Coll Cardiol. 2008;52(22):1758-1768
Link to Article
Ndrepepa G, Berger PB, Mehilli J,  et al.  Periprocedural bleeding and 1-year outcome after percutaneous coronary interventions: appropriateness of including bleeding as a component of a quadruple end point.  J Am Coll Cardiol. 2008;51(7):690-697
Link to Article
Stone GW, Bertrand ME, Moses JW,  et al.  Routine upstream initiation vs deferred selective use of glycoprotein IIb/IIIa inhibitors in acute coronary syndromes: the ACUITY Timing trial.  JAMA. 2007;297(6):591-602
Link to Article
Lindsey JB, Marso SP, Pencina M,  et al.  Prognostic impact of periprocedural bleeding and myocardial infarction after percutaneous coronary intervention in unselected patients: results from the EVENT (Evaluation of Drug-Eluting Stents and Ischemic Events) registry.  J Am Coll Cardiol. 2009;2(11):1074-1082
Link to Article
Mehran R, Stone GW. New paradigms of care for STEMI focusing on mortality and attributable death analysis: what do device and drug trials teach us?  Eur Heart J Suppl. 2009;11:(suppl C)  C4-C8
Link to Article
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. 2011;4(2):191-197
Link to Article

Figures

Place holder to copy figure label and caption
Figure 1. In-hospital Mortality in the Propensity-Matched Cohort
Graphic Jump Location
Place holder to copy figure label and caption
Figure 2. Number Needed to Harm (NNH) for In-hospital Mortality Related to Major Bleeding in the Propensity-Matched Cohort and in Selected Subgroups
Graphic Jump Location

The dashed line at x = 29 represents the NNH for the overall population. STEMI indicates ST-segment elevation myocardial infarction; NSTEMI, non-STEMI; UA, unstable angina; ACS, acute coronary syndrome; Gp, glycoprotein.

Tables

Table Graphic Jump LocationTable 1. Patient Characteristics, Expected Bleeding, Expected Mortality, and Standardized Differences Before and After Match
Table Graphic Jump LocationTable 2. Patient History and Procedural Characteristics Before and After Match
Table Graphic Jump LocationTable 3. Bleeding Complications in the Propensity-Matched Cohort, Classified by Type

References

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(12):1021-1030
Link to Article
Cohen DJ, Lincoff AM, Lavelle TA,  et al.  Economic evaluation of bivalirudin with provisional glycoprotein IIB/IIIA inhibition vs heparin with routine glycoprotein IIB/IIIA inhibition for percutaneous coronary intervention: results from the REPLACE-2 trial.  J Am Coll Cardiol. 2004;44(9):1792-1800
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(12):1362-1368
Link to Article
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(8):930-935
Link to Article
Mehta SK, Frutkin AD, Lindsey JB,  et al.  Bleeding in patients undergoing percutaneous coronary intervention: the development of a clinical risk algorithm from the National Cardiovascular Data Registry.  Circ Cardiovasc Interv. 2009;2(3):222-229
Link to Article
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(21):2156-2164
Link to Article
Brueck M, Bandorski D, Kramer W, Wieczorek M, Holtgen R, Tillmanns H. A randomized comparison of transradial vs transfemoral approach for coronary angiography and angioplasty.  J Am Coll Cardiol. 2009;2(11):1047-1054
Link to Article
Jolly SS, Amlani S, Hamon M, Yusuf S, Mehta SR. Radial vs femoral access for coronary angiography or intervention and the impact on major bleeding and ischemic events: a systematic review and meta-analysis of randomized trials.  Am Heart J. 2009;157(1):132-140
Link to Article
Stone GW, McLaurin BT, Cox DA,  et al.  Bivalirudin for patients with acute coronary syndromes.  N Engl J Med. 2006;355(21):2203-2216
Link to Article
Sanborn TA, Ebrahimi R, Manoukian SV,  et al.  Impact of femoral vascular closure devices and antithrombotic therapy on access site bleeding in acute coronary syndromes: the Acute Catheterization and Urgent Intervention Triage Strategy (ACUITY) trial.  Circ Cardiovasc Interv. 2010;3(1):57-62
Link to Article
Pocock SJ, Mehran R, Clayton TC,  et al.  Prognostic modeling of individual patient risk and mortality impact of ischemic and hemorrhagic complications: assessment from the Acute Catheterization and Urgent Intervention Triage Strategy trial.  Circulation. 2010;121(1):43-51
Link to Article
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(8):774-782
Link to Article
Mehran R, Pocock SJ, Stone GW,  et al.  Associations of major bleeding and myocardial infarction with the incidence and timing of mortality in patients presenting with non-ST-elevation acute coronary syndromes: a risk model from the ACUITY trial.  Eur Heart J. 2009;30(12):1457-1466
Link to Article
Rao SV, O'Grady K, Pieper KS,  et al.  Impact of bleeding severity on clinical outcomes among patients with acute coronary syndromes.  Am J Cardiol. 2005;96(9):1200-1206
Link to Article
Suh JW, Mehran R, Claessen BE,  et al.  Impact of in-hospital major bleeding on late clinical outcomes after primary percutaneous coronary intervention in acute myocardial infarction the HORIZONS-AMI (Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction) trial.  J Am Coll Cardiol. 2011;58(17):1750-1756
Link to Article
 AHA Hospital Statistics: 2011 Edition. Chicago, IL: American Hospital Association; 2010
Brindis RG, Fitzgerald S, Anderson HV, Shaw RE, Weintraub WS, Williams JF. The American College of Cardiology–National Cardiovascular Data Registry (ACC-NCDR): building a national clinical data repository.  J Am Coll Cardiol. 2001;37(8):2240-2245
Link to Article
Weintraub WS, McKay CR, Riner RN,  et al; American College of Cardiology Database Committee.  The American College of Cardiology National Database: progress and challenges.  J Am Coll Cardiol. 1997;29(2):459-465
Link to Article
Peterson ED, Dai D, DeLong ER,  et al.  Contemporary mortality risk prediction for percutaneous coronary intervention: results from 588,398 procedures in the National Cardiovascular Data Registry.  J Am Coll Cardiol. 2010;55(18):1923-1932
Link to Article
Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.  Pharm Stat. 2011;10:150-161
Link to Article
Austin PC. Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score.  Pharmacoepidemiol Drug Saf. 2008;17(12):1202-1217
Link to Article
Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.  Stat Med. 2009;28(25):3083-3107
Link to Article
Austin PC. The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies.  Med Decis Making. 2009;29(6):661-677
Link to Article
Spiegelman D, Hertzmark E, Wand HC. Point and interval estimates of partial population attributable risks in cohort studies: examples and software.  Cancer Causes Control. 2007;18(5):571-579
Link to Article
Bender R, Kuss O, Hildebrandt M, Gehrmann U. Estimating adjusted NNT measures in logistic regression analysis.  Stat Med. 2007;26(30):5586-5595
Link to Article
Bender R, Vervolgyi V. Estimating adjusted NNTs in randomised controlled trials with binary outcomes: a simulation study.  Contemp Clin Trials. 2010;31(5):498-505
Link to Article
Aronow HD, Peyser PA, Eagle KA,  et al.  Predictors of length of stay after coronary stenting.  Am Heart J. 2001;142(5):799-805
Link to Article
Pinto DS, Stone GW, Shi C,  et al.  Economic evaluation of bivalirudin with or without glycoprotein IIb/IIIa inhibition vs heparin with routine glycoprotein IIb/IIIa inhibition for early invasive management of acute coronary syndromes.  J Am Coll Cardiol. 2008;52(22):1758-1768
Link to Article
Ndrepepa G, Berger PB, Mehilli J,  et al.  Periprocedural bleeding and 1-year outcome after percutaneous coronary interventions: appropriateness of including bleeding as a component of a quadruple end point.  J Am Coll Cardiol. 2008;51(7):690-697
Link to Article
Stone GW, Bertrand ME, Moses JW,  et al.  Routine upstream initiation vs deferred selective use of glycoprotein IIb/IIIa inhibitors in acute coronary syndromes: the ACUITY Timing trial.  JAMA. 2007;297(6):591-602
Link to Article
Lindsey JB, Marso SP, Pencina M,  et al.  Prognostic impact of periprocedural bleeding and myocardial infarction after percutaneous coronary intervention in unselected patients: results from the EVENT (Evaluation of Drug-Eluting Stents and Ischemic Events) registry.  J Am Coll Cardiol. 2009;2(11):1074-1082
Link to Article
Mehran R, Stone GW. New paradigms of care for STEMI focusing on mortality and attributable death analysis: what do device and drug trials teach us?  Eur Heart J Suppl. 2009;11:(suppl C)  C4-C8
Link to Article
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. 2011;4(2):191-197
Link to Article
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Supplementary Online Content

Chhatriwalla AK, Amin AP, Kennedy KF, et al; for the National Cardiovascular Data Registry. Association between bleeding events and in-hospital mortality after percutaneous coronary intervention. JAMA. 2013. doi: 10.1001/jama.2013.1556

eFigure. Standardized Difference Plot for Selected Variables Before and After Propensity Matching Values Less Than 10% and Closer to Zero Demonstrate a More Balanced Cohort

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