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Prediction of bleeding in patients with acute coronary syndromes: Applying the new BARC definition
Thrombosis Research 133 (2014) 952–954
Contents lists available at ScienceDirect
Thrombosis Research journal homepage: www.elsevier.com/locate/thromres
Letter to the Editors-in-Chief Prediction of bleeding in patients with acute coronary syndromes: Applying the new BARC definition
Dear Editors, Major bleeding events are associated with worse outcomes in patients with acute coronary syndromes (ACS). There is significant heterogeneity among the bleeding definitions utilized in clinical trials and registries. The new Bleeding Academic Research Consortium (BARC) definition [1] is an important step to overcome this limitations. This definition was validated in patients from clinical trials [2], but little information exists about about its application in patients from routine clinical practice. In addition, most of the predictive models of bleeding in ACS [3] were derived from populations included in clinical trials in which high-risk patients are clearly underrepresented. The aim of this study was to assess the impact on survival of inhospital BARC bleeding and to perform a predictive model for BARC bleeding in non selected ACS patients from routine clinical practice. All ACS patients admitted to the Coronary Care Unit of our center between October 2009 and April 2012 were prospectively included. Data were collected on site using a standardized case report form. All elements included in the BARC categories were included in the case report form, thus being prospectively collected. Bleeding events were recorded using the BARC definition [1]. Since the main aim of the present analysis was to identify bleeding non related to surgery, BARC 4 category was excluded. In-hospital and mid-term mortality was also assessed. Information on deaths was obtained from hospital records, death certificates, or telephone contact. The impact of in-hospital bleeding (BARC 1,2 and 3 categories) on 6 month mortality was assessed by Cox regression method. BARC 5 category was excluded because of its obvious relationship to mortality. Criteria for including potential confounders in the multivariate analysis were: 1) a significant association (p b = 0.2) both with exposition (BARC bleeding categories) and effect (6 month mortality); 2) a clinically reasonable potential confounding effect between bleeding and mortality; and 3) not being an intermediate variable in the association between exposition and effect. Unadjusted and adjusted hazard ratios of mortality were calculated for each BARC category, using patients without bleeding (BARC 0) as reference category. Afterwards a predictive model for severe bleeding (3 and 5 BARC categories) was built using variables with a significance p b 0.2 in univariate analysis (Table 1). The selection of variables was performed using binary logistic regression. Predictive accuracy of the model was assessed by the area under the ROC curve (AUC). For model validation the sample was randomly divided in 2 (derivation sample, n = 861 and validation sample, n = 857), calculating AUC of the model for each sample.
http://dx.doi.org/10.1016/j.thromres.2014.02.019 0049-3848/© 2014 Elsevier Ltd. All rights reserved.
We included 1718 patients, 1328 male (77.3%), with mean age of 62.3 years. The incidence of in-hospital bleeding events was as follows: BARC bleeding: class 1: n = 64 (3.7%)); class 2: n = 33 (1.9%); class 3a: n = 19 (1.1%); class 3b: n = 14 (0.8%); class 3c: n = 4 (0.2%); class 5: n = 1 (0.1%). BARC 3/5 bleeding location was as follows: (n = 38): angiography site 13 (34%), urinary 6 (15.8%), digestive 6 (15.8%), respiratory 5 (13.2%), intracranial 5 (13.2%), retroperitoneal 1 (2.6%) and other location 2 (5.3%).
Table 1 Baseline characteristics and in-hospital treatments according to BARC 3/5 bleeding in the derivation cohort (univariate analysis). Predictor
Bleeding (n = 20)
No bleeding (n = 841)
p
Age (years) Sex (Male) Active Smoking Body mass index Body surface area Diabetes mellitus Hypertension Dislypidemia Previous stroke Previous myocardial infarction Previous bleeding Creatinin Clearance STEMIa Femoral access SBPb (mmHg) Heart rate (bpm) Killip class I II III IV LVEFc (%) Baseline Haematocrit (%) Aspirin Clopidogrel Unfractionated heparin Enoxaparin Bivalirudin GPIIbIIIa inhibitors Angiography Number of vessels affected 1 2 3 Intra-aortic balloon counterpulsation Invasive ventilation
64.7 (12.6) 11 (55.0) 4 (20.0) 26.8 (4.5) 1.78 (0.18) 10 (50.0) 10 (50.0) 15 (75.0) 2 (10.0) 3 (15.0) 2 (10.0%) 71.5 (28.8) 13 (65.0) 11 (84.7) 124.5 (28.1) 89.7 (23.9)
62.1 (12.9) 654 (77.9) 381 (45.9) 27.5 (4.3) 1.89 (0.20) 238 (28.3) 480 (57.1) 459 (54.6) 61 (7.3) 118 (14.0) 32 (3.8%) 93.0 (39.1) 649 (77.1) 314 (38.3) 128.8 (26.5) 79.9 (16.8)
0.357 0.021 0.049 0.430 0.023 0.034 0.649 0.070 0.484 0.753 0.185 0.012 0.224 0.026 0.471 0.064 0.004
11 (57.9) 3 (15.8) 2 (10.5) 3 (15.8) 43.4 (11.3) 39.4 (6.6) 20 (100.0) 20 (100.0) 13 (65.0) 10 (50.0) 1 (5.0) 9 (45.0) 17 (85.0)
688 (82.2) 95 (11.4) 37 (4.4) 17 (2.0) 51.2 (11.1) 41.2 (5.4) 829 (98.9) 817 (97.5) 531 (63.4) 332 (39.6) 120 (14.3) 194 (23.1) 820 (97.5)
7 (41.2) 4 (23.5) 6 (35.3) 7 (35.0) 5 (25.0)
407 (49.6) 215 (26.2) 164 (20.0) 37 (4.4) 35 (4.4)
0.007 0.147 0.808 0.606 0.542 0.363 0.200 0.027 0.012 0.267
0.001 0.002
Data are presented as n (%) in qualitative variables and mean (SD) in quantitative variables. aSTEMI: ST segment elevation myocardial infarction bSBP: Systolic blood pressure; cLVEF: Left ventricular ejection fraction.
Letter to the Editors-in-Chief
953
Fig. 1. Overall mortality according to Bleeding Academic Research Consortium (BARC) bleeding categories.
We obtained data on follow up in 1602 patients (93.2%). Mean follow up was 359 days. Mortality according to BARC bleeding categories is shown in Fig. 1. Patients with in-hospital BARC 3 bleeding episodes had a significantly higher mortality than patients without bleeding. After adjusting for several potential confounders the association between BARC 3 bleeding and mortality remained significant. This association was not observed for BARC 1 and 2 bleeding episodes (Table 2). Independent predictors of bleeding were low body surface area, heart rate on admission, previous bleeding, intra-aortic counterpulsation, GPIIbIIIa inhibitors and femoral access site (Table 3). The model showed an optimal predictive ability both in the derivation cohort (AUC 0.88; 95% CI 0.81-0.95) and in the validation cohort (AUC 0.86; 95% CI 0.790.93). There were no significant differences between both groups. The BARC definition was previously validated [2] in a cohort from 6 randomized trials, finding a higher mortality in patients with BARC bleeding = N2. Information about the impact of BARC bleeding in patients from the routine clinical practice is scarce. Only one recent study [4] showed a worse prognosis in patients with BARC =N3 bleeding from from a single center registry of STEMI patients. BARC 2 bleeding episodes were not reported. In addition, almost all patients underwent femoral angiography, and patients with cardiogenic shock were excluded. No other study has analyzed the impact of BARC bleeding in patients from routine clinical practice. Data from our study revealed a 5.63 fold increase in mortality in patients with BARC 3 bleeding. This association remained significant after adjusting for confounding factors. Unlike previous data [2], we did not Table 2 Hazard ratios of mortality related to each BARC bleeding category.
BARC 1 BARC 2 BARC 3
Unadjusted hazard ratio (95% CI)
p
Adjusted⁎ hazard ratio (95% CI)
p
0.97 (0.39-2.38) 1.24 (0.39-3.91) 5.63 (3.28-9.67)
0.944 0.712 0.001
0.62 (0,25-1.53) 1.59 (0,49-5.14) 3.09 (1.74-5.51)
0.297 0.436 0.001
⁎ Potential confounders included in the mutivariate analysis of the association between BARC bleeding and mid-term mortality: Age, sex, body surface area diabetes mellitus, creatinin clearance, previous stroke, baseline haematocrit, left ventricular ejection fraction, number of vessels affeced, Killip class on admission.
find a significant association between BARC 2 bleeding and mortality. Differences in baseline characteristics of patients or in clinical practice in some of the items that define the BARC 2 bleeding category (requirement of non-surgical medical intervention, need for early evaluation) could explain these different findings. On the other hand, most of the previous predictive models of bleeding used different definitions, which makes it difficult to generalize results to different populations. Our predictive model was performed using the new BARC definition in a cohort of all ACS spectrum, showing an excellent predictive ability. Some of the predictors of bleeding in our series are usually markers of frailty and comorbidities and are not common in populations from clinical trials, such as low body surface area and previous bleeding. Like in our patients, previous bleeding was identified as a strong predictor of bleeding in a large population from the GRACE registry [5]. The inclusion of the access site on angiography is one of the most interesting contributions of our model. Radial approach has demonstrated in several studies a reduction in bleeding complications [6]. Preexisting predictive models of bleeding in ACS did not include this variable because most were made in populations with very low representation of the radial access site. This study has the inherent limitations of being a single center registry, with a relatively homogenous management. The number of patients was relatively small. In addition, the use of prasugrel or ticagrelor was virtually absent in our patients In spite of these limitations, our results clearly show a significant association between BARC bleeding categories and mid term mortality in Table 3 Final predictive model. Variable
Odds Ratio (95% Confidence interval)
p
Body surface area Heart rate (for beat/minute) Previous bleeding Intra-aortic balloon counterpulsation Femoral access GP IIbIIIa inhibitors treatment
0.17 (0.02-1.35) 1.03 (1.01-1.05) 5.12 (1.50-17.47) 4.54 (1.80-11.47) 4.30 (1.77-10.47) 5.13 (2.39-11.02)
0,094 0,022 0,009 0,001 0,001 0,001
954
Letter to the Editors-in-Chief
non selected ACS patients from our series. In addition, we obtained a novel predictive model of BARC bleeding in ACS with an excellent predictive power. The BARC initiative was set in motion to establish a consistent bleeding definition for clinical trials and also to provide a level foundation for regulatory bodies assessing the safety profile of antithrombotic drugs being considered for approval. Taking into account our findings, we believe that the assessment of the predictive value of the BARC score in routine clinical practice should be considered in phase 4 (post-approval) programs, possibly as a surrogate for hard clinic endpoints. References [1] Mehran R, Rao SV, Bhatt DL, Gibson CM, Caixeta A, Eikelboom J, et al. Standardized Bleeding Definitions for Cardiovascular Clinical Trials A Consensus Report From the Bleeding Academic Research Consortium. Circulation 2011;123:2736–47. [2] Ndrepepa G, Schuster T, Hadamitzky M, Byrne RA, Mehilli J, Neumann FJ, et al. Validation of the Bleeding Academic Research Consortium Definition of Bleeding in Patients with Coronary Artery Disease Undergoing Percutaneous Coronary Intervention. Circulation 2012;125:1424–31. [3] Mehran R, Pocock SJ, Nikolsky E, Clayton T, Dangas GD, Kirtane AJ, et al. A risk score to predict bleeding in patients with acute coronary syndromes. J Am Coll Cardiol 2010;55:2556–66. [4] Mrdovic I, Savic L, Krljanac G, Asanin M, Lasica R, Djuricic N, et al. Simple Risk Algorithm to Predict Serious Bleeding in Patients With ST-Segment Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention. Circ J 2013;77: 1719–27.
[5] Moscucci M, Fox KA, Cannon CP, Klein W, López-Sendón J, Montalescot G, et al. Predictors of major bleeding in acute coronary syndromes: the Global Registry of Acute Coronary Events (GRACE). Eur Heart J 2003;24:1815–23. [6] Jolly SS, Yusuf S, Cairns J, Niemelä K, Xavier D, Widimsky P, et al. RIVAL trial group. Radial versus femoral access for coronary angiography and intervention in patients with acute coronary syndromes (RIVAL): a randomised, parallel group, multicentre trial. Lancet 2011;377:1409–20.
Albert Ariza-Solé ⁎ José L. Ferreiro José C. Sánchez-Salado Victòria Lorente Joel Salazar-Mendiguchía Marcos Ñato Andrea Di Marco Joan A. Gomez-Hospital Ángel Cequier Bellvitge University Hospital, L’Hospitalet de Llobregat, Barcelona, Spain ⁎ Corresponding author at: Coronary Care Unit, Bellvitge University Hospital, Feixa Llarga, sn. 08907, Hospitalet de Llobregat, Barcelona, Spain. Tel.: +34 932607924; fax: +34 932607541. E-mail address: [email protected] (A. Ariza-Solé). 6 February 2014
Contents lists available at ScienceDirect
Thrombosis Research journal homepage: www.elsevier.com/locate/thromres
Letter to the Editors-in-Chief Prediction of bleeding in patients with acute coronary syndromes: Applying the new BARC definition
Dear Editors, Major bleeding events are associated with worse outcomes in patients with acute coronary syndromes (ACS). There is significant heterogeneity among the bleeding definitions utilized in clinical trials and registries. The new Bleeding Academic Research Consortium (BARC) definition [1] is an important step to overcome this limitations. This definition was validated in patients from clinical trials [2], but little information exists about about its application in patients from routine clinical practice. In addition, most of the predictive models of bleeding in ACS [3] were derived from populations included in clinical trials in which high-risk patients are clearly underrepresented. The aim of this study was to assess the impact on survival of inhospital BARC bleeding and to perform a predictive model for BARC bleeding in non selected ACS patients from routine clinical practice. All ACS patients admitted to the Coronary Care Unit of our center between October 2009 and April 2012 were prospectively included. Data were collected on site using a standardized case report form. All elements included in the BARC categories were included in the case report form, thus being prospectively collected. Bleeding events were recorded using the BARC definition [1]. Since the main aim of the present analysis was to identify bleeding non related to surgery, BARC 4 category was excluded. In-hospital and mid-term mortality was also assessed. Information on deaths was obtained from hospital records, death certificates, or telephone contact. The impact of in-hospital bleeding (BARC 1,2 and 3 categories) on 6 month mortality was assessed by Cox regression method. BARC 5 category was excluded because of its obvious relationship to mortality. Criteria for including potential confounders in the multivariate analysis were: 1) a significant association (p b = 0.2) both with exposition (BARC bleeding categories) and effect (6 month mortality); 2) a clinically reasonable potential confounding effect between bleeding and mortality; and 3) not being an intermediate variable in the association between exposition and effect. Unadjusted and adjusted hazard ratios of mortality were calculated for each BARC category, using patients without bleeding (BARC 0) as reference category. Afterwards a predictive model for severe bleeding (3 and 5 BARC categories) was built using variables with a significance p b 0.2 in univariate analysis (Table 1). The selection of variables was performed using binary logistic regression. Predictive accuracy of the model was assessed by the area under the ROC curve (AUC). For model validation the sample was randomly divided in 2 (derivation sample, n = 861 and validation sample, n = 857), calculating AUC of the model for each sample.
http://dx.doi.org/10.1016/j.thromres.2014.02.019 0049-3848/© 2014 Elsevier Ltd. All rights reserved.
We included 1718 patients, 1328 male (77.3%), with mean age of 62.3 years. The incidence of in-hospital bleeding events was as follows: BARC bleeding: class 1: n = 64 (3.7%)); class 2: n = 33 (1.9%); class 3a: n = 19 (1.1%); class 3b: n = 14 (0.8%); class 3c: n = 4 (0.2%); class 5: n = 1 (0.1%). BARC 3/5 bleeding location was as follows: (n = 38): angiography site 13 (34%), urinary 6 (15.8%), digestive 6 (15.8%), respiratory 5 (13.2%), intracranial 5 (13.2%), retroperitoneal 1 (2.6%) and other location 2 (5.3%).
Table 1 Baseline characteristics and in-hospital treatments according to BARC 3/5 bleeding in the derivation cohort (univariate analysis). Predictor
Bleeding (n = 20)
No bleeding (n = 841)
p
Age (years) Sex (Male) Active Smoking Body mass index Body surface area Diabetes mellitus Hypertension Dislypidemia Previous stroke Previous myocardial infarction Previous bleeding Creatinin Clearance STEMIa Femoral access SBPb (mmHg) Heart rate (bpm) Killip class I II III IV LVEFc (%) Baseline Haematocrit (%) Aspirin Clopidogrel Unfractionated heparin Enoxaparin Bivalirudin GPIIbIIIa inhibitors Angiography Number of vessels affected 1 2 3 Intra-aortic balloon counterpulsation Invasive ventilation
64.7 (12.6) 11 (55.0) 4 (20.0) 26.8 (4.5) 1.78 (0.18) 10 (50.0) 10 (50.0) 15 (75.0) 2 (10.0) 3 (15.0) 2 (10.0%) 71.5 (28.8) 13 (65.0) 11 (84.7) 124.5 (28.1) 89.7 (23.9)
62.1 (12.9) 654 (77.9) 381 (45.9) 27.5 (4.3) 1.89 (0.20) 238 (28.3) 480 (57.1) 459 (54.6) 61 (7.3) 118 (14.0) 32 (3.8%) 93.0 (39.1) 649 (77.1) 314 (38.3) 128.8 (26.5) 79.9 (16.8)
0.357 0.021 0.049 0.430 0.023 0.034 0.649 0.070 0.484 0.753 0.185 0.012 0.224 0.026 0.471 0.064 0.004
11 (57.9) 3 (15.8) 2 (10.5) 3 (15.8) 43.4 (11.3) 39.4 (6.6) 20 (100.0) 20 (100.0) 13 (65.0) 10 (50.0) 1 (5.0) 9 (45.0) 17 (85.0)
688 (82.2) 95 (11.4) 37 (4.4) 17 (2.0) 51.2 (11.1) 41.2 (5.4) 829 (98.9) 817 (97.5) 531 (63.4) 332 (39.6) 120 (14.3) 194 (23.1) 820 (97.5)
7 (41.2) 4 (23.5) 6 (35.3) 7 (35.0) 5 (25.0)
407 (49.6) 215 (26.2) 164 (20.0) 37 (4.4) 35 (4.4)
0.007 0.147 0.808 0.606 0.542 0.363 0.200 0.027 0.012 0.267
0.001 0.002
Data are presented as n (%) in qualitative variables and mean (SD) in quantitative variables. aSTEMI: ST segment elevation myocardial infarction bSBP: Systolic blood pressure; cLVEF: Left ventricular ejection fraction.
Letter to the Editors-in-Chief
953
Fig. 1. Overall mortality according to Bleeding Academic Research Consortium (BARC) bleeding categories.
We obtained data on follow up in 1602 patients (93.2%). Mean follow up was 359 days. Mortality according to BARC bleeding categories is shown in Fig. 1. Patients with in-hospital BARC 3 bleeding episodes had a significantly higher mortality than patients without bleeding. After adjusting for several potential confounders the association between BARC 3 bleeding and mortality remained significant. This association was not observed for BARC 1 and 2 bleeding episodes (Table 2). Independent predictors of bleeding were low body surface area, heart rate on admission, previous bleeding, intra-aortic counterpulsation, GPIIbIIIa inhibitors and femoral access site (Table 3). The model showed an optimal predictive ability both in the derivation cohort (AUC 0.88; 95% CI 0.81-0.95) and in the validation cohort (AUC 0.86; 95% CI 0.790.93). There were no significant differences between both groups. The BARC definition was previously validated [2] in a cohort from 6 randomized trials, finding a higher mortality in patients with BARC bleeding = N2. Information about the impact of BARC bleeding in patients from the routine clinical practice is scarce. Only one recent study [4] showed a worse prognosis in patients with BARC =N3 bleeding from from a single center registry of STEMI patients. BARC 2 bleeding episodes were not reported. In addition, almost all patients underwent femoral angiography, and patients with cardiogenic shock were excluded. No other study has analyzed the impact of BARC bleeding in patients from routine clinical practice. Data from our study revealed a 5.63 fold increase in mortality in patients with BARC 3 bleeding. This association remained significant after adjusting for confounding factors. Unlike previous data [2], we did not Table 2 Hazard ratios of mortality related to each BARC bleeding category.
BARC 1 BARC 2 BARC 3
Unadjusted hazard ratio (95% CI)
p
Adjusted⁎ hazard ratio (95% CI)
p
0.97 (0.39-2.38) 1.24 (0.39-3.91) 5.63 (3.28-9.67)
0.944 0.712 0.001
0.62 (0,25-1.53) 1.59 (0,49-5.14) 3.09 (1.74-5.51)
0.297 0.436 0.001
⁎ Potential confounders included in the mutivariate analysis of the association between BARC bleeding and mid-term mortality: Age, sex, body surface area diabetes mellitus, creatinin clearance, previous stroke, baseline haematocrit, left ventricular ejection fraction, number of vessels affeced, Killip class on admission.
find a significant association between BARC 2 bleeding and mortality. Differences in baseline characteristics of patients or in clinical practice in some of the items that define the BARC 2 bleeding category (requirement of non-surgical medical intervention, need for early evaluation) could explain these different findings. On the other hand, most of the previous predictive models of bleeding used different definitions, which makes it difficult to generalize results to different populations. Our predictive model was performed using the new BARC definition in a cohort of all ACS spectrum, showing an excellent predictive ability. Some of the predictors of bleeding in our series are usually markers of frailty and comorbidities and are not common in populations from clinical trials, such as low body surface area and previous bleeding. Like in our patients, previous bleeding was identified as a strong predictor of bleeding in a large population from the GRACE registry [5]. The inclusion of the access site on angiography is one of the most interesting contributions of our model. Radial approach has demonstrated in several studies a reduction in bleeding complications [6]. Preexisting predictive models of bleeding in ACS did not include this variable because most were made in populations with very low representation of the radial access site. This study has the inherent limitations of being a single center registry, with a relatively homogenous management. The number of patients was relatively small. In addition, the use of prasugrel or ticagrelor was virtually absent in our patients In spite of these limitations, our results clearly show a significant association between BARC bleeding categories and mid term mortality in Table 3 Final predictive model. Variable
Odds Ratio (95% Confidence interval)
p
Body surface area Heart rate (for beat/minute) Previous bleeding Intra-aortic balloon counterpulsation Femoral access GP IIbIIIa inhibitors treatment
0.17 (0.02-1.35) 1.03 (1.01-1.05) 5.12 (1.50-17.47) 4.54 (1.80-11.47) 4.30 (1.77-10.47) 5.13 (2.39-11.02)
0,094 0,022 0,009 0,001 0,001 0,001
954
Letter to the Editors-in-Chief
non selected ACS patients from our series. In addition, we obtained a novel predictive model of BARC bleeding in ACS with an excellent predictive power. The BARC initiative was set in motion to establish a consistent bleeding definition for clinical trials and also to provide a level foundation for regulatory bodies assessing the safety profile of antithrombotic drugs being considered for approval. Taking into account our findings, we believe that the assessment of the predictive value of the BARC score in routine clinical practice should be considered in phase 4 (post-approval) programs, possibly as a surrogate for hard clinic endpoints. References [1] Mehran R, Rao SV, Bhatt DL, Gibson CM, Caixeta A, Eikelboom J, et al. Standardized Bleeding Definitions for Cardiovascular Clinical Trials A Consensus Report From the Bleeding Academic Research Consortium. Circulation 2011;123:2736–47. [2] Ndrepepa G, Schuster T, Hadamitzky M, Byrne RA, Mehilli J, Neumann FJ, et al. Validation of the Bleeding Academic Research Consortium Definition of Bleeding in Patients with Coronary Artery Disease Undergoing Percutaneous Coronary Intervention. Circulation 2012;125:1424–31. [3] Mehran R, Pocock SJ, Nikolsky E, Clayton T, Dangas GD, Kirtane AJ, et al. A risk score to predict bleeding in patients with acute coronary syndromes. J Am Coll Cardiol 2010;55:2556–66. [4] Mrdovic I, Savic L, Krljanac G, Asanin M, Lasica R, Djuricic N, et al. Simple Risk Algorithm to Predict Serious Bleeding in Patients With ST-Segment Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention. Circ J 2013;77: 1719–27.
[5] Moscucci M, Fox KA, Cannon CP, Klein W, López-Sendón J, Montalescot G, et al. Predictors of major bleeding in acute coronary syndromes: the Global Registry of Acute Coronary Events (GRACE). Eur Heart J 2003;24:1815–23. [6] Jolly SS, Yusuf S, Cairns J, Niemelä K, Xavier D, Widimsky P, et al. RIVAL trial group. Radial versus femoral access for coronary angiography and intervention in patients with acute coronary syndromes (RIVAL): a randomised, parallel group, multicentre trial. Lancet 2011;377:1409–20.
Albert Ariza-Solé ⁎ José L. Ferreiro José C. Sánchez-Salado Victòria Lorente Joel Salazar-Mendiguchía Marcos Ñato Andrea Di Marco Joan A. Gomez-Hospital Ángel Cequier Bellvitge University Hospital, L’Hospitalet de Llobregat, Barcelona, Spain ⁎ Corresponding author at: Coronary Care Unit, Bellvitge University Hospital, Feixa Llarga, sn. 08907, Hospitalet de Llobregat, Barcelona, Spain. Tel.: +34 932607924; fax: +34 932607541. E-mail address: [email protected] (A. Ariza-Solé). 6 February 2014