Does anemia affect the predictive ability of bleeding risk scores in patients with acute coronary syndromes? | Revista Portuguesa de Cardiologia (English edition)

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AUC: area under the curve." ] ] ] "textoCompleto" => "IntroductionThe incidence of major bleeding in patients with acute coronary syndromes (ACS) ranges from 3 to 5%.<a class="elsevierStyleCrossRefs" href="#bib0125">1–5</a> Major bleeding events are associated with worse outcomes in this clinical setting.<a class="elsevierStyleCrossRefs" href="#bib0245">6–8</a> Several bleeding risk scores<a class="elsevierStyleCrossRefs" href="#bib0150">9–11</a> have been developed in recent years in order to properly predict bleeding complications in patients with ACS, and their use is recommended in the current clinical guidelines.<a class="elsevierStyleCrossRef" href="#bib0165">12</a> However, it has been suggested that these scores have lower predictive ability in the elderly<a class="elsevierStyleCrossRef" href="#bib0170">13</a> and in patients with comorbidities.Anemia is a common comorbidity among patients with ACS,<a class="elsevierStyleCrossRef" href="#bib0175">14</a> and its prevalence is expected to increase due to the aging of the population. Anemia is associated with higher risk for both bleeding and ischemic complications in patients with ACS.<a class="elsevierStyleCrossRef" href="#bib0180">15</a> Information on the predictive ability of bleeding risk scores in patients with anemia is scarce; no study has assessed the performance of bleeding risk scores according to hematocrit status in patients with ACS. The aim of this study was to assess the predictive ability of the most widely used bleeding risk scores according to anemia status in a series of consecutive patients with ACS from routine clinical practice.MethodsStudy design and populationThis is an observational single-center registry, conducted at a tertiary care hospital in Spain (Hospital Universitari de Bellvitge, l’Hospitalet de Llobregat, Barcelona). All consecutive ACS patients admitted to the coronary care unit between October 2009 and April 2014 were prospectively included. Informed consent was provided by all patients before their inclusion in the study. Confidential patient data were protected according to current national directives. This manuscript was revised for publication by the Clinical Research Ethics Committee of Bellvitge University Hospital (IRB00005523).The primary outcome was in-hospital major bleeding according to the CRUSADE,<a class="elsevierStyleCrossRef" href="#bib0150">9</a> Mehran<a class="elsevierStyleCrossRef" href="#bib0155">10</a> and ACTION<a class="elsevierStyleCrossRef" href="#bib0160">11</a> definitions.Definitions, data collection and managementNon-ST-segment elevation ACS was defined as the presence of chest pain during the previous 48 hours with ST-segment changes on the electrocardiogram indicating ischemia or a positive troponin test. ST-segment elevation ACS was defined as the presence of chest pain with persistent ST-segment elevation of at least 0.1 mV in at least two contiguous leads or new left bundle branch block. Patients were classified as having anemia using the definition of the World Health Organization (WHO): hemoglobin <13.0 g/dl in men, and <12.0 g/dl in women.<a class="elsevierStyleCrossRef" href="#bib0185">16</a> Severe anemia was defined as hemoglobin <11.0 g/dl for the purpose of this study.Data were prospectively collected on site by trained physicians using a standardized case report form. Baseline characteristics, medical history, biochemical and electrocardiographic findings, treatments administered during hospitalization, incidence of in-hospital bleeding events and their anatomic location were collected. All elements included in the CRUSADE,<a class="elsevierStyleCrossRef" href="#bib0150">9</a> Mehran<a class="elsevierStyleCrossRef" href="#bib0155">10</a> and ACTION<a class="elsevierStyleCrossRef" href="#bib0160">11</a> bleeding risk scores were included in the case report forms. The development of these scores has been described in detail previously. The CRUSADE, Mehran and ACTION bleeding risk scores were all prospectively calculated for each patient.In-hospital major bleeding events were recorded using the CRUSADE,<a class="elsevierStyleCrossRef" href="#bib0150">9</a> TIMI,<a class="elsevierStyleCrossRef" href="#bib0165">12</a> Mehran,<a class="elsevierStyleCrossRef" href="#bib0155">10</a> ACTION<a class="elsevierStyleCrossRef" href="#bib0160">11</a> and Bleeding Academic Research Consortium (BARC)<a class="elsevierStyleCrossRef" href="#bib0190">17</a> definitions. For reasons of clinical relevance, BARC categories 3 and 5 were considered severe BARC bleeding for this study. Since the main aim of the present analysis was to identify the risk of bleeding unrelated to surgery, bleeding in patients who underwent coronary artery bypass graft surgery was included in the analysis only if it occurred before surgery. Thus, BARC category 4 was excluded.All elements included in the BARC category 2 criteria (need for nonsurgical medical intervention, need for increased level of care, prompting evaluation, baseline and lowest recorded hemoglobin, need for transfusion or surgery, requirement for intravenous vasoactive drugs) were included in the case report form, the data thus being prospectively collected. However, since the BARC definition was not available until 2011, BARC bleeding events were retrospectively assigned. ACTION, CRUSADE and Mehran major bleeding events were prospectively adjudicated.The quality of data collection was assessed by checking source documentation in random samples. Hemodynamic parameters (heart rate and systolic blood pressure) and Killip class were measured at admission. Creatinine clearance was calculated using the Cockcroft-Gault formula<a class="elsevierStyleCrossRef" href="#bib0195">18</a> and body surface area by the Mosteller formula.<a class="elsevierStyleCrossRef" href="#bib0200">19</a> Patients were managed according to current clinical guidelines.Information on deaths was obtained from hospital records, death certificates, or telephone contact with relatives of the patients or their referring physician.Statistical analysisQuantitative variables were expressed as mean and standard deviation. For baseline variables, the Student's t test was used for comparison of quantitative variables and the chi-square test or Fisher's exact test, when appropriate, were used for categorical variables (PASW Statistics 18, Chicago, IL, USA). The normality of distribution of variables was assessed using the Kolmogorov-Smirnov test.Patients with missing hemoglobin values and those lost to follow-up were excluded from the analysis. Baseline patient characteristics were analyzed in order to assess the impact of this exclusion. No significant differences were observed.The ability of the CRUSADE, Mehran and ACTION bleeding risk scores to predict major in-hospital bleeding according to different definitions was assessed by binary logistic regression, calculating receiver operating characteristic (ROC) curves and their corresponding area under the curve (AUC). The non-parametric method described by DeLong<a class="elsevierStyleCrossRef" href="#bib0205">20</a> was used to compare the predictive ability of the different bleeding risk scores.ResultsDuring the study period 2255 patients were admitted with a diagnosis of ACS. Their mean age was 62.4 years, and almost 77% were male. Anemia was present in 550 patients (24.4%). Patients with anemia were significantly older, less often male and had a significantly higher prevalence of comorbidities (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>).<elsevierMultimedia ident="tbl0005"></elsevierMultimedia>Patients with anemia also had poorer renal function at admission, a lower incidence of ST-segment elevation myocardial infarction and a higher percentage of signs of heart failure at presentation. Values of CRUSADE, Mehran and ACTION bleeding risk scores were all significantly higher in patients with anemia.Significant differences were also observed regarding clinical management (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). Patients with anemia were less often treated with clopidogrel and abciximab, and more often needed invasive procedures during hospitalization, such as intra-aortic balloon counterpulsation, renal replacement therapy, invasive mechanical ventilation and temporary pacing. Patients with anemia less often underwent an invasive strategy, and in patients undergoing angiography a radial approach was used less often than in patients without anemia.<elsevierMultimedia ident="tbl0010"></elsevierMultimedia>These patients had also a higher incidence of in-hospital complications such as atrial fibrillation, atrioventricular block, contrast-induced nephropathy and infectious complications requiring antibiotics. Bleeding was significantly more common in patients with anemia regardless of the definition used. In-hospital mortality was almost three times higher in patients with anemia.Bleeding risk prediction according to anemia statusThe three bleeding risk scores adequately predicted major bleeding according to their own definitions in the whole cohort (<a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a>). No significant differences were observed regarding the predictive ability of the bleeding risk scores in patients with and without anemia. While the predictive ability of the CRUSADE score was almost the same as for the whole cohort, the AUC of the ACTION score was slightly greater and that of the Mehran score was slightly smaller, without reaching statistical significance. <a class="elsevierStyleCrossRef" href="#fig0010">Figure 2</a> shows the AUCs of each of the three bleeding risk scores in patients with and without anemia.<elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia>The assessment of patients with severe anemia showed different findings. The predictive ability of both the CRUSADE and ACTION scores decreased slightly in patients with severe anemia, but without significant difference. In contrast, the predictive ability of the Mehran score showed a significant decrease in patients with severe anemia. <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> shows the AUC values of each of the bleeding risk scores in patients without anemia, with anemia according to the WHO criteria, and with severe anemia.<elsevierMultimedia ident="tbl0015"></elsevierMultimedia>DiscussionThe main findings from our study are: (a) a high prevalence of anemia was found in a series of unselected ACS patients from routine clinical practice; (b) no significant differences were observed regarding the predictive ability of the three bleeding risk scores in patients with or without anemia according to the WHO criteria; and (c) only the Mehran risk score showed a significantly poorer predictive ability in patients with severe anemia.Anemia is a common comorbidity in ACS, and is strongly associated with higher mortality and morbidity in this setting.<a class="elsevierStyleCrossRefs" href="#bib0180">15,21,22</a> The reasons for this association have not been clearly elucidated. Anemia worsens myocardial ischemia by reducing oxygen delivery to the injured myocardium as well as by increasing myocardial oxygen demands due to a larger stroke volume and higher heart rate. At the same time, anemia potentially reflects occult disease, such as malignancy or kidney disease, and can have unfavorable effects on the clinical course of noncardiac disease. Recent data suggest that causes of mortality in patients with ACS and anemia may differ according to age subgroups.<a class="elsevierStyleCrossRef" href="#bib0220">23</a> As reported above, patients with anemia from our series were significantly older, with a higher incidence of comorbidities and higher in-hospital mortality.In addition, anemia is a well-known predictor of bleeding<a class="elsevierStyleCrossRef" href="#bib0225">24</a> in patients with ACS. However, although currently available bleeding risk scores have been successfully validated in different subsets,<a class="elsevierStyleCrossRefs" href="#bib0230">25–27</a> there is little information on bleeding risk stratification in patients with anemia. Our group previously described<a class="elsevierStyleCrossRef" href="#bib0170">13</a> a poorer performance of the three bleeding risk scores in predicting major in-hospital bleeding in patients with ACS aged 75 years or older. One possible explanation for these findings is that certain aging-related variables such as frailty, disability, or comorbidities, which are rarely assessed in trials and registries in the cardiovascular area, might hamper bleeding risk stratification in this clinical scenario. In addition, most registries show a strong association between anemia and other comorbidities in different clinical settings. In the previously mentioned paper,<a class="elsevierStyleCrossRef" href="#bib0170">13</a> elderly patients with ACS had a significantly higher prevalence of anemia compared to younger patients. Therefore, poorer predictive ability could also be expected in patients with anemia. However, data from our series showed no significant differences regarding the predictive ability of the bleeding risk scores in patients with and without anemia.An important point when analyzing our findings is the role of hematocrit status in the composition of the different bleeding risk scores. Hemoglobin level is part of the CRUSADE,<a class="elsevierStyleCrossRef" href="#bib0150">9</a> Mehran<a class="elsevierStyleCrossRef" href="#bib0155">10</a> and ACTION<a class="elsevierStyleCrossRef" href="#bib0160">11</a> risk scores, but the contribution of hemoglobin values to these scores varies. While in CRUSADE and Mehran hemoglobin levels account for around 10% of the overall points, in ACTION they represent almost 20% of the score. Assessment of the predictive ability of these bleeding risk scores stratified by one of their components might in itself lead to a reduction in the AUC. In our opinion, the non-significant reduction in AUC values in patients with anemia may at least in part be due to this fact, especially for the ACTION score, given the greater role of hemoglobin status in the overall composition of the score.The differences in therapeutic management according to anemia status in our series also deserves particular mention. Patients with anemia underwent more conservative antithrombotic management, thus possibly leading to a reduction in the rate of bleeding and potentially affecting the performance of bleeding risk scores. However, this management was performed according to current recommendations, since patients with anemia are usually considered at higher bleeding risk and a more conservative antithrombotic approach is encouraged in this scenario. In our opinion, one of the strengths of this work is its assessment of the performance of the current bleeding risk scores in patients from our routine clinical practice managed according to the clinical judgment of the attending physician, taking into account current recommendations and patients’ profile in terms of ischemic and bleeding risk.The different characteristics of the populations from whom these scores were derived also deserve special comment. The CRUSADE and ACTION scores were based on large American registries (89134 patients for CRUSADE and 90273 for ACTION) with baseline characteristics that were probably similar to patients included in our study. In contrast, the Mehran score was based on 17421 patients included in two clinical trials (ACUITY and HORIZONS AMI). Populations from clinical trials usually have a low prevalence of comorbidities and patients at higher risk are usually under-represented. The prevalence of anemia in Mehran et al.’s series was clearly lower (14.5%) than in ours. The poorer predictive ability of the Mehran score in patients with severe anemia may be related to this fact. However, these data must be interpreted cautiously due to the small sample size of this subgroup.Our study has several limitations. This is a single-center study and therefore our conclusions should be applied only to similar populations undergoing similar clinical management. Since this is an observational study we cannot rule out the possibility of selection bias and residual confounding. The cutoff point of <11.0 g/dl for defining severe anemia was selected by investigators after assessing the distribution of hemoglobin in our series. The number of bleeding events was relatively small. In addition, the use of novel antithrombotic drugs like prasugrel or ticagrelor was uncommon. On the other hand, excluding patients with missing bleeding scores might have led to a certain bias. However, analysis of the baseline characteristics of these patients showed no significant differences from the other patients. Finally, the Mehran bleeding risk score was designed to predict bleeding during the first 30 days and not only during hospitalization as in our study.In spite of these limitations, we believe that our findings show reasonably acceptable predictive ability in the main available bleeding risk scores in a series of consecutive ACS patients with and without anemia from routine clinical practice.ConclusionsThe prevalence of anemia was high in our series of unselected ACS patients from routine clinical practice. The ability of the most important currently available bleeding risk scores to predict in-hospital major bleeding was acceptable in patients with anemia, especially in patients with mild anemia. The progressive aging of the population makes it particularly important to improve risk stratification in patients with comorbidities.Ethical disclosuresProtection of human and animal subjectsThe authors declare that no experiments were performed on humans or animals for this study.Confidentiality of dataThe authors declare that they have followed the protocols of their work center on the publication of patient data.Right to privacy and informed consentThe authors declare that no patient data appear in this article.Conflicts of interestThe authors have no conflicts of interest to declare." "textoCompletoSecciones" => array:1 [ "secciones" => array:13 [ 0 => array:3 [ "identificador" => "xres798183" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec796149" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres798184" "titulo" => "Resumo" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introdução e objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusões" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec796150" "titulo" => "Palavras-chave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Methods" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Study design and population" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Definitions, data collection and management" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Statistical analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0030" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0035" "titulo" => "Bleeding risk prediction according to anemia status" ] 8 => array:2 [ "identificador" => "sec0040" "titulo" => "Discussion" ] 9 => array:2 [ "identificador" => "sec0045" "titulo" => "Conclusions" ] 10 => array:3 [ "identificador" => "sec0050" "titulo" => "Ethical disclosures" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0055" "titulo" => "Protection of human and animal subjects" ] 1 => array:2 [ "identificador" => "sec0060" "titulo" => "Confidentiality of data" ] 2 => array:2 [ "identificador" => "sec0065" "titulo" => "Right to privacy and informed consent" ] ] ] 11 => array:2 [ "identificador" => "sec0070" "titulo" => "Conflicts of interest" ] 12 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2016-04-24" "fechaAceptado" => "2016-06-13" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec796149" "palabras" => array:4 [ 0 => "Bleeding risk stratification" 1 => "Acute coronary syndromes" 2 => "Anemia" 3 => "Comorbidities" ] ] ] "pt" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palavras-chave" "identificador" => "xpalclavsec796150" "palabras" => array:4 [ 0 => "Estratificação de risco de hemorragia" 1 => "Síndromes coronárias agudas" 2 => "Anemia" 3 => "Comorbilidades" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "Introduction and objectiveAnemia is a common comorbidity in patients with acute coronary syndromes (ACS), and is associated with higher risk for both bleeding and ischemic complications. We aimed to assess the predictive ability of bleeding risk scores (Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines [CRUSADE], Mehran and Acute Coronary Treatment and Intervention Outcomes Network [ACTION]) in ACS patients with anemia. MethodsAll consecutive ACS patients were prospectively included. The primary outcome was in-hospital major bleeding according to the CRUSADE, Mehran and ACTION definitions. Anemia was defined as hemoglobin <130 g/l in men and <120 g/l in women. The predictive ability of the bleeding risk scores was assessed by binary logistic regression, calculating receiver operating characteristic (ROC) curves and their corresponding area under the curve (AUC). ResultsWe included 2255 patients, mean age 62.4 years. Anemia was present in 550 patients (24.4%). Patients with anemia had a significantly higher prevalence of comorbidities. The three bleeding risk scores adequately predicted major bleeding in the whole cohort.No significant differences were observed regarding the predictive ability of each of the scores in patients with and without anemia (CRUSADE: AUC 0.73 without anemia vs. 0.74 with anemia, p=0.913; ACTION: AUC 0.68 without anemia vs. 0.73 with anemia, p=0.353; Mehran: AUC 0.69 without anemia vs. 0.61 with anemia, p=0.210). Only the Mehran score showed significantly lower predictive ability in patients with hemoglobin <11 g/dl (AUC 0.51, p=0.044). ConclusionsAnemia was a common comorbidity in patients with ACS from our series. Currently available bleeding risk scores showed an adequate predictive ability in patients with mild anemia." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "pt" => array:3 [ "titulo" => "Resumo" "resumen" => "Introdução e objetivoA anemia é uma comorbilidade frequente em doentes com síndromes coronárias agudas (SCA) e associa-se tanto a um maior risco de hemorragia, como de complicações isquémicas. O nosso objetivo foi avaliar a capacidade preditiva de scores de risco de hemorragia (Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines [CRUSADE], Mehran e Acute Coronary Treatment and Intervention Outcomes Network [ACTION]) em doentes com SCA e anemia. MétodosTodos os doentes consecutivos com SCA foram prospetivamente incluídos. O resultado primário foi a hemorragia intra-hospitalar major, de acordo com as definições CRUSADE, Mehran e ACTION. A anemia foi definida como uma concentração de hemoglobina <130 g/L em homens e <120 g/L em mulheres. A capacidade preditiva dos scores de risco de hemorragia foi avaliada pelo método de regressão logística binária, calculando curvas ROC e a sua área correspondente sob a curva (AUC). ResultadosForam incluídos 2255 doentes. A média de idades foi de 62,4 anos. A anemia estava presente em 550 doentes (24,4%). Doentes com anemia apresentaram uma prevalência significativamente maior de comorbilidades. Os três scores de risco previram corretamente hemorragia major no conjunto da coorte.Não se observaram diferenças significativas em relação à capacidade preditiva de cada um dos scores de risco de hemorragia em pacientes com e sem anemia (CRUSADE AUC 0,73 sem anemia versus 0,74 com anemia; p<0,913; ACTION AUC 0,68 sem anemia versus 0,73 com anemia; p<0,353; Mehran AUC 0,69 sem anemia versus 0,61 com anemia p<0,210). Apenas o score Mehran mostrou uma capacidade preditiva significativamente menor nos doentes com hemoglobina <11 g/dL (AUC 0,51, p<0,044). ConclusõesNa nossa amostra, a anemia foi uma comorbilidade frequente em doentes com SCA. Os scores de risco de hemorragia estudados mostraram uma capacidade de previsão adequada em doentes com anemia leve." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introdução e objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusões" ] ] ] ] "multimedia" => array:5 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 859 "Ancho" => 930 "Tamanyo" => 92720 ] ] "descripcion" => array:1 [ "en" => "Receiver operating characteristic curves of each of the bleeding risk scores for predicting major bleeding in the whole cohort. AUC: area under the curve." ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1709 "Ancho" => 1587 "Tamanyo" => 202227 ] ] "descripcion" => array:1 [ "en" => "Receiver operating characteristic curves of each of the bleeding risk scores for predicting major bleeding in patients with and without anemia according to the World Health Organization criteria. AUC: area under the curve." ] ] 2 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "BMI: body mass index; BSA: body surface area; HR: heart rate; LVEF: left ventricular ejection fraction; MI: myocardial infarction; PAD: peripheral arterial disease; PCI: percutaneous coronary intervention; SBP: systolic blood pressure; STEMI: ST-segment elevation myocardial infarction.Categorical variables are expressed as n (%). Quantitative variables are expressed as mean (SD)." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Whole cohort (n=2255) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Anemia (n=550) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia (n=1705) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">p \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Male \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1727 (76.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">372 (67.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1355 (795) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Age \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">62.4 (13) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">68.9 (12) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">60.3 (13) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">BMI (kg/m2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27.9 (4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27.8 (5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27.9 (4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.532 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">BSA (m2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.9 (0.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.8 (0.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.9 (0.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Diabetes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">674 (29.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">260 (47.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">414 (24.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Hypertension \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1335 (59.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">407 (74) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">928 (54.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Dyslipidemia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1302 (57.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">350 (63.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">952 (55.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Active smoking \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">947 (42.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">127 (23.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">820 (48.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Previous MI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">315 (14) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">126 (22.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">189 (11.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Previous PCI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">264 (11.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">123 (22.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">141 (8.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Previous stroke \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">157 (6.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">73 (13.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">84 (4.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">PAD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">256 (11.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">121 (22) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">135 (7.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Hematocrit (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">41 (5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">34 (4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">43 (4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Creatinine clearance (ml/min) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">91.7 (40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">70.4 (36) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">98.5 (39) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Previous bleeding \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">78 (3.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">37 (6.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">41 (2.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">STEMI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1461 (64.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">304 (55.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1157 (67.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Killip class >I \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">441 (19.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">173 (31.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">268 (15.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">SBP (mmHg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">130 (34) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">129 (28) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">130 (36) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.476 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">HR (bpm) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">80 (17) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">81 (18) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">79 (17) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.090 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">LVEF (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">51.9 (11) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">49.9 (11) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">52.5 (11) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CRUSADE score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">26 (16) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">39 (17) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">22 (14) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Mehran score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">17 (8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">24 (7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14 (7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">ACTION score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30 (8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">35 (7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">28 (7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">GRACE score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">124 (36) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">138 (37) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">118 (34) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Invasive strategy \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2170 (96.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">516 (93.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1654 (97) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Radial approach \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1455 (67.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">296 (57.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1159 (70.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1338706.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Baseline and clinical characteristics according to hematocrit status." ] ] 3 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "AV: atrioventricular block; BARC: Bleeding Academic Research Consortium; CIN: contrast-induced nephropathy; IABP: intra-aortic balloon pump; MR: mitral regurgitation; UF: unfractionated heparin; VSD: ventricular septal defect. Infections were defined as infectious complications requiring antibiotics.Categorical variables are expressed as n (%). Quantitative variables are expressed as mean (SD)." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Whole cohort (n=2255) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Anemia (n=550) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia (n=1705) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">p \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " colspan="5" align="left" valign="top">Anti-thrombotic treatments and in-hospital procedures</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Aspirin \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2186 (97.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">525 (96) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1661 (97.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.057 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Clopidogrel \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2083 (92.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">489 (89.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1594 (93.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Prasugrel \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">36 (1.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11 (2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">25 (1.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.379 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Ticagrelor \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">73 (3.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11 (2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">62 (3.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.061 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Enoxaparin \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1020 (45.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">259 (47.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">761 (44.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.276 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">UFH \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1168 (51.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">261 (47.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">907 (53.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.024 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Bivalirudin \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">236 (10.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">49 (9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">187 (11) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.179 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Abciximab \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">328 (14.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">47 (8.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">281 (16.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">IABP \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">156 (6.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">68 (12.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">88 (5.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Swan-Ganz catheter \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">54 (2.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">22 (4.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">32 (2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.005 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Hemodialysis \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29 (1.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">25 (4.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4 (0.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Invasive mechanical ventilation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">124 (5.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">40 (7.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">84 (4.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.036 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Temporary pacemaker \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">69 (3.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">28 (5.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">41 (2.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="5" align="left" valign="top"></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="5" align="left" valign="top">In-hospital clinical course</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">AV block \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">156 (7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">54 (10.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">102 (6.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Atrial fibrillation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">158 (7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">63 (11.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">95 (5.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Ventricular fibrillation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">138 (6.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">32 (6.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">106 (6.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.769 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Reinfarction \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">17 (0.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 (1.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11 (0.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.216 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">VSD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5 (0.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">- \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5 (0.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.247 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CIN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">98 (4.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">48 (8.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">50 (2.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Ischemic MR \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10 (0.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 (1.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4 (0.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.009 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Cardiac rupture \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14 (0.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4 (0.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10 (0.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.267 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Infections \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">120 (5.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">57 (10.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">63 (3.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">All bleeding \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">164 (7.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">55 (10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">109 (6.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.005 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Transfusion \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">64 (2.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">44 (6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 (1.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CRUSADE major bleeding \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">115 (5.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">39 (7.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">76 (4.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.015 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Mehran major bleeding \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">136 (6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">49 (8.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">87 (5.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">ACTION major bleeding \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">116 (5.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">40 (7.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">76 (4.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.009 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">BARC 3/5 bleeding \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">61 (2.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">24 (4.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">37 (2.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.006 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">In-hospital mortality \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">90 (4.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">43 (8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">47 (2.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1338705.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Management and clinical course according to hematocrit status." ] ] 4 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "AUC: area under the curve; CI: confidence interval; WHO: World Health Organization." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Bleeding risk score \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia, AUC (95% CI) (n=1705) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Anemia by WHO criteria, AUC (95% CI) (n=550) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Severe anemia,<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> AUC (95% CI) (n=173) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia vs. anemia by WHO criteria, p \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia vs. severe anemia,<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> p \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CRUSADE \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.73 (0.67-0.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.74 (0.65-0.83) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.62 (0.49-0.76) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.913 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.552 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Mehran \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.69 (0.63-0.75) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.61 (0.51-0.71) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.51 (0.38-0.65) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.210 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.044 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">ACTION \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.68 (0.60-0.75) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.73 (0.64-0.82) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.61 (0.50-0.72) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.353 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.181 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1338707.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "Hemoglobin <11 g/dl." ] ] ] "descripcion" => array:1 [ "en" => "Predictive ability of each of the bleeding risk scores according to different anemia categories." ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:27 [ 0 => array:3 [ "identificador" => "bib0125" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Bleeding and transfusion in acute coronary syndromes: a shift in the paradigm" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "J.P. Bassand" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1136/hrt.2007.125047" "Revista" => array:6 [ "tituloSerie" => "Heart" "fecha" => "2008" "volumen" => "94" "paginaInicial" => "661" "paginaFinal" => "666" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/18411361" "web" => "Medline" ] ] ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0130" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Predictors of major bleeding in acute coronary syndromes: the Global Registry of Acute Coronary Events (GRACE)" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:3 [ 0 => "M. Moscucci" 1 => "K.A. Fox" 2 => "C.P. 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Original Article

Does anemia affect the predictive ability of bleeding risk scores in patients with acute coronary syndromes?

Em doentes com síndromes coronárias agudas, a presença de anemia altera a capacidade preditiva de scores de risco hemorrágico?

Alberto Garay, Albert Ariza-Solé

Corresponding author

aariza@bellvitgehospital.cat

Corresponding author.

, Francesc Formiga, Victoria Lorente, José C. Sánchez-Salado, Joel Salazar-Mendiguchía, Gerard Roura, Guillem Muntané, Oriol Alegre, Lara Fuentes, Joan A. Gómez-Hospital, Angel Cequier

Bellvitge University Hospital, L’Hospitalet de Llobregat, Barcelona, Spain

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          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Receiver operating characteristic curves of each of the bleeding risk scores for predicting major bleeding in the whole cohort&#46; AUC&#58; area under the curve&#46;</p>"
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">The incidence of major bleeding in patients with acute coronary syndromes &#40;ACS&#41; ranges from 3 to 5&#37;&#46;<a class="elsevierStyleCrossRefs" href="#bib0125"><span class="elsevierStyleSup">1&#8211;5</span></a> Major bleeding events are associated with worse outcomes in this clinical setting&#46;<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">6&#8211;8</span></a> Several bleeding risk scores<a class="elsevierStyleCrossRefs" href="#bib0150"><span class="elsevierStyleSup">9&#8211;11</span></a> have been developed in recent years in order to properly predict bleeding complications in patients with ACS&#44; and their use is recommended in the current clinical guidelines&#46;<a class="elsevierStyleCrossRef" href="#bib0165"><span class="elsevierStyleSup">12</span></a> However&#44; it has been suggested that these scores have lower predictive ability in the elderly<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">13</span></a> and in patients with comorbidities&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">Anemia is a common comorbidity among patients with ACS&#44;<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">14</span></a> and its prevalence is expected to increase due to the aging of the population&#46; Anemia is associated with higher risk for both bleeding and ischemic complications in patients with ACS&#46;<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">15</span></a> Information on the predictive ability of bleeding risk scores in patients with anemia is scarce&#59; no study has assessed the performance of bleeding risk scores according to hematocrit status in patients with ACS&#46; The aim of this study was to assess the predictive ability of the most widely used bleeding risk scores according to anemia status in a series of consecutive patients with ACS from routine clinical practice&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Study design and population</span><p id="par0015" class="elsevierStylePara elsevierViewall">This is an observational single-center registry&#44; conducted at a tertiary care hospital in Spain &#40;Hospital Universitari de Bellvitge&#44; l&#8217;Hospitalet de Llobregat&#44; Barcelona&#41;&#46; All consecutive ACS patients admitted to the coronary care unit between October 2009 and April 2014 were prospectively included&#46; Informed consent was provided by all patients before their inclusion in the study&#46; Confidential patient data were protected according to current national directives&#46; This manuscript was revised for publication by the Clinical Research Ethics Committee of Bellvitge University Hospital &#40;IRB00005523&#41;&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The primary outcome was in-hospital major bleeding according to the CRUSADE&#44;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">9</span></a> Mehran<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">10</span></a> and ACTION<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">11</span></a> definitions&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Definitions&#44; data collection and management</span><p id="par0025" class="elsevierStylePara elsevierViewall">Non-ST-segment elevation ACS was defined as the presence of chest pain during the previous 48 hours with ST-segment changes on the electrocardiogram indicating ischemia or a positive troponin test&#46; ST-segment elevation ACS was defined as the presence of chest pain with persistent ST-segment elevation of at least 0&#46;1 mV in at least two contiguous leads or new left bundle branch block&#46; Patients were classified as having anemia using the definition of the World Health Organization &#40;WHO&#41;&#58; hemoglobin &#60;13&#46;0 g&#47;dl in men&#44; and &#60;12&#46;0 g&#47;dl in women&#46;<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">16</span></a> Severe anemia was defined as hemoglobin &#60;11&#46;0 g&#47;dl for the purpose of this study&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">Data were prospectively collected on site by trained physicians using a standardized case report form&#46; Baseline characteristics&#44; medical history&#44; biochemical and electrocardiographic findings&#44; treatments administered during hospitalization&#44; incidence of in-hospital bleeding events and their anatomic location were collected&#46; All elements included in the CRUSADE&#44;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">9</span></a> Mehran<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">10</span></a> and ACTION<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">11</span></a> bleeding risk scores were included in the case report forms&#46; The development of these scores has been described in detail previously&#46; The CRUSADE&#44; Mehran and ACTION bleeding risk scores were all prospectively calculated for each patient&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">In-hospital major bleeding events were recorded using the CRUSADE&#44;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">9</span></a> TIMI&#44;<a class="elsevierStyleCrossRef" href="#bib0165"><span class="elsevierStyleSup">12</span></a> Mehran&#44;<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">10</span></a> ACTION<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">11</span></a> and Bleeding Academic Research Consortium &#40;BARC&#41;<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">17</span></a> definitions&#46; For reasons of clinical relevance&#44; BARC categories 3 and 5 were considered severe BARC bleeding for this study&#46; Since the main aim of the present analysis was to identify the risk of bleeding unrelated to surgery&#44; bleeding in patients who underwent coronary artery bypass graft surgery was included in the analysis only if it occurred before surgery&#46; Thus&#44; BARC category 4 was excluded&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">All elements included in the BARC category 2 criteria &#40;need for nonsurgical medical intervention&#44; need for increased level of care&#44; prompting evaluation&#44; baseline and lowest recorded hemoglobin&#44; need for transfusion or surgery&#44; requirement for intravenous vasoactive drugs&#41; were included in the case report form&#44; the data thus being prospectively collected&#46; However&#44; since the BARC definition was not available until 2011&#44; BARC bleeding events were retrospectively assigned&#46; ACTION&#44; CRUSADE and Mehran major bleeding events were prospectively adjudicated&#46;</p><p id="par0045" class="elsevierStylePara elsevierViewall">The quality of data collection was assessed by checking source documentation in random samples&#46; Hemodynamic parameters &#40;heart rate and systolic blood pressure&#41; and Killip class were measured at admission&#46; Creatinine clearance was calculated using the Cockcroft-Gault formula<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">18</span></a> and body surface area by the Mosteller formula&#46;<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">19</span></a> Patients were managed according to current clinical guidelines&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">Information on deaths was obtained from hospital records&#44; death certificates&#44; or telephone contact with relatives of the patients or their referring physician&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Statistical analysis</span><p id="par0055" class="elsevierStylePara elsevierViewall">Quantitative variables were expressed as mean and standard deviation&#46; For baseline variables&#44; the Student&#39;s t test was used for comparison of quantitative variables and the chi-square test or Fisher&#39;s exact test&#44; when appropriate&#44; were used for categorical variables &#40;PASW Statistics 18&#44; Chicago&#44; IL&#44; USA&#41;&#46; The normality of distribution of variables was assessed using the Kolmogorov-Smirnov test&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">Patients with missing hemoglobin values and those lost to follow-up were excluded from the analysis&#46; Baseline patient characteristics were analyzed in order to assess the impact of this exclusion&#46; No significant differences were observed&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">The ability of the CRUSADE&#44; Mehran and ACTION bleeding risk scores to predict major in-hospital bleeding according to different definitions was assessed by binary logistic regression&#44; calculating receiver operating characteristic &#40;ROC&#41; curves and their corresponding area under the curve &#40;AUC&#41;&#46; The non-parametric method described by DeLong<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">20</span></a> was used to compare the predictive ability of the different bleeding risk scores&#46;</p></span></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Results</span><p id="par0070" class="elsevierStylePara elsevierViewall">During the study period 2255 patients were admitted with a diagnosis of ACS&#46; Their mean age was 62&#46;4 years&#44; and almost 77&#37; were male&#46; Anemia was present in 550 patients &#40;24&#46;4&#37;&#41;&#46; Patients with anemia were significantly older&#44; less often male and had a significantly higher prevalence of comorbidities &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0075" class="elsevierStylePara elsevierViewall">Patients with anemia also had poorer renal function at admission&#44; a lower incidence of ST-segment elevation myocardial infarction and a higher percentage of signs of heart failure at presentation&#46; Values of CRUSADE&#44; Mehran and ACTION bleeding risk scores were all significantly higher in patients with anemia&#46;</p><p id="par0080" class="elsevierStylePara elsevierViewall">Significant differences were also observed regarding clinical management &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; Patients with anemia were less often treated with clopidogrel and abciximab&#44; and more often needed invasive procedures during hospitalization&#44; such as intra-aortic balloon counterpulsation&#44; renal replacement therapy&#44; invasive mechanical ventilation and temporary pacing&#46; Patients with anemia less often underwent an invasive strategy&#44; and in patients undergoing angiography a radial approach was used less often than in patients without anemia&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">These patients had also a higher incidence of in-hospital complications such as atrial fibrillation&#44; atrioventricular block&#44; contrast-induced nephropathy and infectious complications requiring antibiotics&#46; Bleeding was significantly more common in patients with anemia regardless of the definition used&#46; In-hospital mortality was almost three times higher in patients with anemia&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Bleeding risk prediction according to anemia status</span><p id="par0090" class="elsevierStylePara elsevierViewall">The three bleeding risk scores adequately predicted major bleeding according to their own definitions in the whole cohort &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a>&#41;&#46; No significant differences were observed regarding the predictive ability of the bleeding risk scores in patients with and without anemia&#46; While the predictive ability of the CRUSADE score was almost the same as for the whole cohort&#44; the AUC of the ACTION score was slightly greater and that of the Mehran score was slightly smaller&#44; without reaching statistical significance&#46; <a class="elsevierStyleCrossRef" href="#fig0010">Figure 2</a> shows the AUCs of each of the three bleeding risk scores in patients with and without anemia&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0095" class="elsevierStylePara elsevierViewall">The assessment of patients with severe anemia showed different findings&#46; The predictive ability of both the CRUSADE and ACTION scores decreased slightly in patients with severe anemia&#44; but without significant difference&#46; In contrast&#44; the predictive ability of the Mehran score showed a significant decrease in patients with severe anemia&#46; <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> shows the AUC values of each of the bleeding risk scores in patients without anemia&#44; with anemia according to the WHO criteria&#44; and with severe anemia&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Discussion</span><p id="par0100" class="elsevierStylePara elsevierViewall">The main findings from our study are&#58; &#40;a&#41; a high prevalence of anemia was found in a series of unselected ACS patients from routine clinical practice&#59; &#40;b&#41; no significant differences were observed regarding the predictive ability of the three bleeding risk scores in patients with or without anemia according to the WHO criteria&#59; and &#40;c&#41; only the Mehran risk score showed a significantly poorer predictive ability in patients with severe anemia&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">Anemia is a common comorbidity in ACS&#44; and is strongly associated with higher mortality and morbidity in this setting&#46;<a class="elsevierStyleCrossRefs" href="#bib0180"><span class="elsevierStyleSup">15&#44;21&#44;22</span></a> The reasons for this association have not been clearly elucidated&#46; Anemia worsens myocardial ischemia by reducing oxygen delivery to the injured myocardium as well as by increasing myocardial oxygen demands due to a larger stroke volume and higher heart rate&#46; At the same time&#44; anemia potentially reflects occult disease&#44; such as malignancy or kidney disease&#44; and can have unfavorable effects on the clinical course of noncardiac disease&#46; Recent data suggest that causes of mortality in patients with ACS and anemia may differ according to age subgroups&#46;<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">23</span></a> As reported above&#44; patients with anemia from our series were significantly older&#44; with a higher incidence of comorbidities and higher in-hospital mortality&#46;</p><p id="par0110" class="elsevierStylePara elsevierViewall">In addition&#44; anemia is a well-known predictor of bleeding<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">24</span></a> in patients with ACS&#46; However&#44; although currently available bleeding risk scores have been successfully validated in different subsets&#44;<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">25&#8211;27</span></a> there is little information on bleeding risk stratification in patients with anemia&#46; Our group previously described<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">13</span></a> a poorer performance of the three bleeding risk scores in predicting major in-hospital bleeding in patients with ACS aged 75 years or older&#46; One possible explanation for these findings is that certain aging-related variables such as frailty&#44; disability&#44; or comorbidities&#44; which are rarely assessed in trials and registries in the cardiovascular area&#44; might hamper bleeding risk stratification in this clinical scenario&#46; In addition&#44; most registries show a strong association between anemia and other comorbidities in different clinical settings&#46; In the previously mentioned paper&#44;<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">13</span></a> elderly patients with ACS had a significantly higher prevalence of anemia compared to younger patients&#46; Therefore&#44; poorer predictive ability could also be expected in patients with anemia&#46; However&#44; data from our series showed no significant differences regarding the predictive ability of the bleeding risk scores in patients with and without anemia&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">An important point when analyzing our findings is the role of hematocrit status in the composition of the different bleeding risk scores&#46; Hemoglobin level is part of the CRUSADE&#44;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">9</span></a> Mehran<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">10</span></a> and ACTION<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">11</span></a> risk scores&#44; but the contribution of hemoglobin values to these scores varies&#46; While in CRUSADE and Mehran hemoglobin levels account for around 10&#37; of the overall points&#44; in ACTION they represent almost 20&#37; of the score&#46; Assessment of the predictive ability of these bleeding risk scores stratified by one of their components might in itself lead to a reduction in the AUC&#46; In our opinion&#44; the non-significant reduction in AUC values in patients with anemia may at least in part be due to this fact&#44; especially for the ACTION score&#44; given the greater role of hemoglobin status in the overall composition of the score&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">The differences in therapeutic management according to anemia status in our series also deserves particular mention&#46; Patients with anemia underwent more conservative antithrombotic management&#44; thus possibly leading to a reduction in the rate of bleeding and potentially affecting the performance of bleeding risk scores&#46; However&#44; this management was performed according to current recommendations&#44; since patients with anemia are usually considered at higher bleeding risk and a more conservative antithrombotic approach is encouraged in this scenario&#46; In our opinion&#44; one of the strengths of this work is its assessment of the performance of the current bleeding risk scores in patients from our routine clinical practice managed according to the clinical judgment of the attending physician&#44; taking into account current recommendations and patients&#8217; profile in terms of ischemic and bleeding risk&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">The different characteristics of the populations from whom these scores were derived also deserve special comment&#46; The CRUSADE and ACTION scores were based on large American registries &#40;89<span class="elsevierStyleHsp" style=""></span>134 patients for CRUSADE and 90<span class="elsevierStyleHsp" style=""></span>273 for ACTION&#41; with baseline characteristics that were probably similar to patients included in our study&#46; In contrast&#44; the Mehran score was based on 17<span class="elsevierStyleHsp" style=""></span>421 patients included in two clinical trials &#40;ACUITY and HORIZONS AMI&#41;&#46; Populations from clinical trials usually have a low prevalence of comorbidities and patients at higher risk are usually under-represented&#46; The prevalence of anemia in Mehran et al&#46;&#8217;s series was clearly lower &#40;14&#46;5&#37;&#41; than in ours&#46; The poorer predictive ability of the Mehran score in patients with severe anemia may be related to this fact&#46; However&#44; these data must be interpreted cautiously due to the small sample size of this subgroup&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">Our study has several limitations&#46; This is a single-center study and therefore our conclusions should be applied only to similar populations undergoing similar clinical management&#46; Since this is an observational study we cannot rule out the possibility of selection bias and residual confounding&#46; The cutoff point of &#60;11&#46;0 g&#47;dl for defining severe anemia was selected by investigators after assessing the distribution of hemoglobin in our series&#46; The number of bleeding events was relatively small&#46; In addition&#44; the use of novel antithrombotic drugs like prasugrel or ticagrelor was uncommon&#46; On the other hand&#44; excluding patients with missing bleeding scores might have led to a certain bias&#46; However&#44; analysis of the baseline characteristics of these patients showed no significant differences from the other patients&#46; Finally&#44; the Mehran bleeding risk score was designed to predict bleeding during the first 30 days and not only during hospitalization as in our study&#46;</p><p id="par0135" class="elsevierStylePara elsevierViewall">In spite of these limitations&#44; we believe that our findings show reasonably acceptable predictive ability in the main available bleeding risk scores in a series of consecutive ACS patients with and without anemia from routine clinical practice&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Conclusions</span><p id="par0140" class="elsevierStylePara elsevierViewall">The prevalence of anemia was high in our series of unselected ACS patients from routine clinical practice&#46; The ability of the most important currently available bleeding risk scores to predict in-hospital major bleeding was acceptable in patients with anemia&#44; especially in patients with mild anemia&#46; The progressive aging of the population makes it particularly important to improve risk stratification in patients with comorbidities&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Ethical disclosures</span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Protection of human and animal subjects</span><p id="par0145" class="elsevierStylePara elsevierViewall">The authors declare that no experiments were performed on humans or animals for this study&#46;</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Confidentiality of data</span><p id="par0150" class="elsevierStylePara elsevierViewall">The authors declare that they have followed the protocols of their work center on the publication of patient data&#46;</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Right to privacy and informed consent</span><p id="par0155" class="elsevierStylePara elsevierViewall">The authors declare that no patient data appear in this article&#46;</p></span></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Conflicts of interest</span><p id="par0160" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare&#46;</p></span></span>"
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        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction and objective</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Anemia is a common comorbidity in patients with acute coronary syndromes &#40;ACS&#41;&#44; and is associated with higher risk for both bleeding and ischemic complications&#46; We aimed to assess the predictive ability of bleeding risk scores &#40;Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC&#47;AHA guidelines &#91;CRUSADE&#93;&#44; Mehran and Acute Coronary Treatment and Intervention Outcomes Network &#91;ACTION&#93;&#41; in ACS patients with anemia&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">All consecutive ACS patients were prospectively included&#46; The primary outcome was in-hospital major bleeding according to the CRUSADE&#44; Mehran and ACTION definitions&#46; Anemia was defined as hemoglobin &#60;130 g&#47;l in men and &#60;120 g&#47;l in women&#46; The predictive ability of the bleeding risk scores was assessed by binary logistic regression&#44; calculating receiver operating characteristic &#40;ROC&#41; curves and their corresponding area under the curve &#40;AUC&#41;&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">We included 2255 patients&#44; mean age 62&#46;4 years&#46; Anemia was present in 550 patients &#40;24&#46;4&#37;&#41;&#46; Patients with anemia had a significantly higher prevalence of comorbidities&#46; The three bleeding risk scores adequately predicted major bleeding in the whole cohort&#46;</p><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">No significant differences were observed regarding the predictive ability of each of the scores in patients with and without anemia &#40;CRUSADE&#58; AUC 0&#46;73 without anemia vs&#46; 0&#46;74 with anemia&#44; p&#61;0&#46;913&#59; ACTION&#58; AUC 0&#46;68 without anemia vs&#46; 0&#46;73 with anemia&#44; p&#61;0&#46;353&#59; Mehran&#58; AUC 0&#46;69 without anemia vs&#46; 0&#46;61 with anemia&#44; p&#61;0&#46;210&#41;&#46; Only the Mehran score showed significantly lower predictive ability in patients with hemoglobin &#60;11 g&#47;dl &#40;AUC 0&#46;51&#44; p&#61;0&#46;044&#41;&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Anemia was a common comorbidity in patients with ACS from our series&#46; Currently available bleeding risk scores showed an adequate predictive ability in patients with mild anemia&#46;</p></span>"
        "secciones" => array:4 [
          0 => array:2 [
            "identificador" => "abst0005"
            "titulo" => "Introduction and objective"
          ]
          1 => array:2 [
            "identificador" => "abst0010"
            "titulo" => "Methods"
          ]
          2 => array:2 [
            "identificador" => "abst0015"
            "titulo" => "Results"
          ]
          3 => array:2 [
            "identificador" => "abst0020"
            "titulo" => "Conclusions"
          ]
        ]
      ]
      "pt" => array:3 [
        "titulo" => "Resumo"
        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introdu&#231;&#227;o e objetivo</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">A anemia &#233; uma comorbilidade frequente em doentes com s&#237;ndromes coron&#225;rias agudas &#40;SCA&#41; e associa-se tanto a um maior risco de hemorragia&#44; como de complica&#231;&#245;es isqu&#233;micas&#46; O nosso objetivo foi avaliar a capacidade preditiva de <span class="elsevierStyleItalic">scores</span> de risco de hemorragia &#40;<span class="elsevierStyleItalic">Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC&#47;AHA guidelines</span> &#91;CRUSADE&#93;&#44; <span class="elsevierStyleItalic">Mehran e Acute Coronary Treatment and Intervention Outcomes Network</span> &#91;ACTION&#93;&#41; em doentes com SCA e anemia&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">M&#233;todos</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Todos os doentes consecutivos com SCA foram prospetivamente inclu&#237;dos&#46; O resultado prim&#225;rio foi a hemorragia intra-hospitalar <span class="elsevierStyleItalic">major</span>&#44; de acordo com as defini&#231;&#245;es CRUSADE&#44; Mehran e ACTION&#46; A anemia foi definida como uma concentra&#231;&#227;o de hemoglobina &#60;130 g&#47;L em homens e &#60;120 g&#47;L em mulheres&#46; A capacidade preditiva dos <span class="elsevierStyleItalic">scores</span> de risco de hemorragia foi avaliada pelo m&#233;todo de regress&#227;o log&#237;stica bin&#225;ria&#44; calculando curvas ROC e a sua &#225;rea correspondente sob a curva &#40;AUC&#41;&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Foram inclu&#237;dos 2255 doentes&#46; A m&#233;dia de idades foi de 62&#44;4 anos&#46; A anemia estava presente em 550 doentes &#40;24&#44;4&#37;&#41;&#46; Doentes com anemia apresentaram uma preval&#234;ncia significativamente maior de comorbilidades&#46; Os tr&#234;s <span class="elsevierStyleItalic">scores</span> de risco previram corretamente hemorragia <span class="elsevierStyleItalic">major</span> no conjunto da coorte&#46;</p><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">N&#227;o se observaram diferen&#231;as significativas em rela&#231;&#227;o &#224; capacidade preditiva de cada um dos <span class="elsevierStyleItalic">scores</span> de risco de hemorragia em pacientes com e sem anemia &#40;CRUSADE AUC 0&#44;73 sem anemia <span class="elsevierStyleItalic">versus</span> 0&#44;74 com anemia&#59; p&#60;0&#44;913&#59; ACTION AUC 0&#44;68 sem anemia <span class="elsevierStyleItalic">versus</span> 0&#44;73 com anemia&#59; p&#60;0&#44;353&#59; Mehran AUC 0&#44;69 sem anemia <span class="elsevierStyleItalic">versus</span> 0&#44;61 com anemia p&#60;0&#44;210&#41;&#46; Apenas o <span class="elsevierStyleItalic">score</span> Mehran mostrou uma capacidade preditiva significativamente menor nos doentes com hemoglobina &#60;11 g&#47;dL &#40;AUC 0&#44;51&#44; p&#60;0&#44;044&#41;&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclus&#245;es</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Na nossa amostra&#44; a anemia foi uma comorbilidade frequente em doentes com SCA&#46; Os <span class="elsevierStyleItalic">scores</span> de risco de hemorragia estudados mostraram uma capacidade de previs&#227;o adequada em doentes com anemia leve&#46;</p></span>"
        "secciones" => array:4 [
          0 => array:2 [
            "identificador" => "abst0025"
            "titulo" => "Introdu&#231;&#227;o e objetivo"
          ]
          1 => array:2 [
            "identificador" => "abst0030"
            "titulo" => "M&#233;todos"
          ]
          2 => array:2 [
            "identificador" => "abst0035"
            "titulo" => "Resultados"
          ]
          3 => array:2 [
            "identificador" => "abst0040"
            "titulo" => "Conclus&#245;es"
          ]
        ]
      ]
    ]
    "multimedia" => array:5 [
      0 => array:7 [
        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr1.jpeg"
            "Alto" => 859
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        "descripcion" => array:1 [
          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Receiver operating characteristic curves of each of the bleeding risk scores for predicting major bleeding in the whole cohort&#46; AUC&#58; area under the curve&#46;</p>"
        ]
      ]
      1 => array:7 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr2.jpeg"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Receiver operating characteristic curves of each of the bleeding risk scores for predicting major bleeding in patients with and without anemia according to the World Health Organization criteria&#46; AUC&#58; area under the curve&#46;</p>"
        ]
      ]
      2 => array:8 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at1"
            "detalle" => "Table "
            "rol" => "short"
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        "tabla" => array:2 [
          "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">BMI&#58; body mass index&#59; BSA&#58; body surface area&#59; HR&#58; heart rate&#59; LVEF&#58; left ventricular ejection fraction&#59; MI&#58; myocardial infarction&#59; PAD&#58; peripheral arterial disease&#59; PCI&#58; percutaneous coronary intervention&#59; SBP&#58; systolic blood pressure&#59; STEMI&#58; ST-segment elevation myocardial infarction&#46;</p><p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Categorical variables are expressed as n &#40;&#37;&#41;&#46; Quantitative variables are expressed as mean &#40;SD&#41;&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Whole cohort &#40;n&#61;2255&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Anemia &#40;n&#61;550&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia &#40;n&#61;1705&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">p&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Male&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1727 &#40;76&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">372 &#40;67&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1355 &#40;795&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">62&#46;4 &#40;13&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">68&#46;9 &#40;12&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">60&#46;3 &#40;13&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">BMI &#40;kg&#47;m<span class="elsevierStyleSup">2</span>&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">27&#46;9 &#40;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">27&#46;8 &#40;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">27&#46;9 &#40;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;532&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">BSA &#40;m<span class="elsevierStyleSup">2</span>&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;9 &#40;0&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;8 &#40;0&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1&#46;9 &#40;0&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Diabetes&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">674 &#40;29&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">260 &#40;47&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">414 &#40;24&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Hypertension&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1335 &#40;59&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">407 &#40;74&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">928 &#40;54&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Dyslipidemia&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1302 &#40;57&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">350 &#40;63&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">952 &#40;55&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Active smoking&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">947 &#40;42&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">127 &#40;23&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">820 &#40;48&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Previous MI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">315 &#40;14&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">126 &#40;22&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">189 &#40;11&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Previous PCI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">264 &#40;11&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">123 &#40;22&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">141 &#40;8&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Previous stroke&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">157 &#40;6&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">73 &#40;13&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">84 &#40;4&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">PAD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">256 &#40;11&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">121 &#40;22&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">135 &#40;7&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Hematocrit &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">41 &#40;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">34 &#40;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">43 &#40;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Creatinine clearance &#40;ml&#47;min&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">91&#46;7 &#40;40&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">70&#46;4 &#40;36&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">98&#46;5 &#40;39&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Previous bleeding&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">78 &#40;3&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">37 &#40;6&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">41 &#40;2&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">STEMI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1461 &#40;64&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">304 &#40;55&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1157 &#40;67&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Killip class &#62;I&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">441 &#40;19&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">173 &#40;31&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">268 &#40;15&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">SBP &#40;mmHg&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">130 &#40;34&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">129 &#40;28&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">130 &#40;36&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;476&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">HR &#40;bpm&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">80 &#40;17&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">81 &#40;18&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">79 &#40;17&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;090&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">LVEF &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">51&#46;9 &#40;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">49&#46;9 &#40;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">52&#46;5 &#40;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CRUSADE score&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">26 &#40;16&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">39 &#40;17&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">22 &#40;14&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Mehran score&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">17 &#40;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">24 &#40;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">14 &#40;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">ACTION score&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">30 &#40;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">35 &#40;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">28 &#40;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">GRACE score&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">124 &#40;36&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">138 &#40;37&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">118 &#40;34&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Invasive strategy&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2170 &#40;96&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">516 &#40;93&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1654 &#40;97&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Radial approach&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1455 &#40;67&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">296 &#40;57&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1159 &#40;70&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
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          "leyenda" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">AV&#58; atrioventricular block&#59; BARC&#58; Bleeding Academic Research Consortium&#59; CIN&#58; contrast-induced nephropathy&#59; IABP&#58; intra-aortic balloon pump&#59; MR&#58; mitral regurgitation&#59; UF&#58; unfractionated heparin&#59; VSD&#58; ventricular septal defect&#46; Infections were defined as infectious complications requiring antibiotics&#46;</p><p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">Categorical variables are expressed as n &#40;&#37;&#41;&#46; Quantitative variables are expressed as mean &#40;SD&#41;&#46;</p>"
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                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head  " align="" valign="top" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Whole cohort &#40;n&#61;2255&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Anemia &#40;n&#61;550&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia &#40;n&#61;1705&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">p&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry  " colspan="5" align="left" valign="top"><span class="elsevierStyleItalic">Anti-thrombotic treatments and in-hospital procedures</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Aspirin&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2186 &#40;97&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">525 &#40;96&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1661 &#40;97&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;057&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Clopidogrel&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2083 &#40;92&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">489 &#40;89&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1594 &#40;93&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Prasugrel&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">36 &#40;1&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">11 &#40;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">25 &#40;1&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;379&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Ticagrelor&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">73 &#40;3&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">11 &#40;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">62 &#40;3&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;061&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Enoxaparin&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1020 &#40;45&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">259 &#40;47&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">761 &#40;44&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;276&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>UFH&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">1168 &#40;51&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">261 &#40;47&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">907 &#40;53&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;024&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Bivalirudin&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">236 &#40;10&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">49 &#40;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">187 &#40;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;179&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Abciximab&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">328 &#40;14&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">47 &#40;8&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">281 &#40;16&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>IABP&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">156 &#40;6&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">68 &#40;12&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">88 &#40;5&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Swan-Ganz catheter&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">54 &#40;2&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">22 &#40;4&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">32 &#40;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;005&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Hemodialysis&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">29 &#40;1&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">25 &#40;4&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4 &#40;0&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Invasive mechanical ventilation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">124 &#40;5&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">40 &#40;7&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">84 &#40;4&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;036&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Temporary pacemaker&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">69 &#40;3&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">28 &#40;5&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">41 &#40;2&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry  " colspan="5" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry  " colspan="5" align="left" valign="top"><span class="elsevierStyleItalic">In-hospital clinical course</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>AV block&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">156 &#40;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">54 &#40;10&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">102 &#40;6&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;002&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Atrial fibrillation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">158 &#40;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">63 &#40;11&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">95 &#40;5&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Ventricular fibrillation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">138 &#40;6&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">32 &#40;6&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">106 &#40;6&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;769&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Reinfarction&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">17 &#40;0&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6 &#40;1&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">11 &#40;0&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;216&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>VSD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5 &#40;0&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">-&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5 &#40;0&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;247&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>CIN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">98 &#40;4&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">48 &#40;8&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">50 &#40;2&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Ischemic MR&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">10 &#40;0&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6 &#40;1&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4 &#40;0&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;009&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Cardiac rupture&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">14 &#40;0&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4 &#40;0&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">10 &#40;0&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;267&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Infections&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">120 &#40;5&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">57 &#40;10&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">63 &#40;3&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>All bleeding&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">164 &#40;7&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">55 &#40;10&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">109 &#40;6&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;005&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Transfusion&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">64 &#40;2&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">44 &#40;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">20 &#40;1&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>CRUSADE major bleeding&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">115 &#40;5&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">39 &#40;7&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">76 &#40;4&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;015&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Mehran major bleeding&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">136 &#40;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">49 &#40;8&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">87 &#40;5&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>ACTION major bleeding&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">116 &#40;5&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">40 &#40;7&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">76 &#40;4&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;009&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>BARC 3&#47;5 bleeding&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">61 &#40;2&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">24 &#40;4&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">37 &#40;2&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;006&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>In-hospital mortality&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">90 &#40;4&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">43 &#40;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">47 &#40;2&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
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                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Bleeding risk score&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia&#44; AUC &#40;95&#37; CI&#41; &#40;n&#61;1705&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Anemia by WHO criteria&#44; AUC &#40;95&#37; CI&#41; &#40;n&#61;550&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Severe anemia&#44;<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> AUC &#40;95&#37; CI&#41; &#40;n&#61;173&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia vs&#46; anemia by WHO criteria&#44; p&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th><th class="td" title="table-head  " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">No anemia vs&#46; severe anemia&#44;<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> p&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">CRUSADE&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;73 &#40;0&#46;67-0&#46;80&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;74 &#40;0&#46;65-0&#46;83&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;62 &#40;0&#46;49-0&#46;76&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;913&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;552&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Mehran&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;69 &#40;0&#46;63-0&#46;75&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;61 &#40;0&#46;51-0&#46;71&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;51 &#40;0&#46;38-0&#46;65&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;210&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">0&#46;044&nbsp;\t\t\t\t\t\t\n
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Article information

ISSN: 21742049
Original language: English

DOI:10.1016/j.repce.2016.11.004

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Year/Month	Html	Pdf	Total

2024 November	6	3	9
2024 October	36	34	70
2024 September	48	27	75
2024 August	44	25	69
2024 July	58	34	92
2024 June	34	26	60
2024 May	35	26	61
2024 April	25	29	54
2024 March	38	24	62
2024 February	39	29	68
2024 January	45	28	73
2023 December	48	24	72
2023 November	45	28	73
2023 October	24	21	45
2023 September	31	22	53
2023 August	34	15	49
2023 July	28	8	36
2023 June	38	17	55
2023 May	45	32	77
2023 April	51	4	55
2023 March	72	30	102
2023 February	68	17	85
2023 January	36	19	55
2022 December	48	22	70
2022 November	64	30	94
2022 October	49	39	88
2022 September	21	34	55
2022 August	30	33	63
2022 July	27	41	68
2022 June	26	26	52
2022 May	25	43	68
2022 April	33	29	62
2022 March	21	38	59
2022 February	23	31	54
2022 January	33	31	64
2021 December	23	33	56
2021 November	38	39	77
2021 October	42	42	84
2021 September	26	37	63
2021 August	37	32	69
2021 July	24	31	55
2021 June	30	17	47
2021 May	37	37	74
2021 April	40	25	65
2021 March	60	26	86
2021 February	51	18	69
2021 January	42	18	60
2020 December	49	10	59
2020 November	30	10	40
2020 October	36	12	48
2020 September	49	23	72
2020 August	19	9	28
2020 July	29	17	46
2020 June	40	15	55
2020 May	39	10	49
2020 April	28	8	36
2020 March	37	11	48
2020 February	63	38	101
2020 January	36	3	39
2019 December	24	5	29
2019 November	37	12	49
2019 October	37	9	46
2019 September	18	15	33
2019 August	45	9	54
2019 July	30	5	35
2019 June	25	13	38
2019 May	35	13	48
2019 April	21	15	36
2019 March	24	14	38
2019 February	31	5	36
2019 January	29	16	45
2018 December	56	9	65
2018 November	41	9	50
2018 October	53	10	63
2018 September	23	8	31
2018 August	33	6	39
2018 July	22	3	25
2018 June	30	9	39
2018 May	57	10	67
2018 April	46	6	52
2018 March	80	8	88
2018 February	44	4	48
2018 January	79	4	83
2017 December	125	6	131
2017 November	45	8	53
2017 October	20	14	34
2017 September	14	8	22
2017 August	18	5	23
2017 July	15	4	19
2017 June	28	10	38
2017 May	29	8	37
2017 April	20	2	22
2017 March	33	5	38
2017 February	46	9	55
2017 January	28	8	36
2016 December	25	12	37

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