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Validation of two US risk scores for percutaneous coronary intervention in a single-center Portuguese population of patients with acute coronary syndrome
Validação de dois scores de risco americanos para a intervenção coronária percutânea num estudo unicêntrico da população portuguesa para doentes com síndrome coronária aguda
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GRACE: Global Registry of Acute Coronary Events risk score; MCRS: Mayo Clinic risk score; NCDR: National Cardiovascular Data Registry risk score.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Risk stratification is essential in the management of acute coronary syndromes (ACS), particularly in non-ST-elevation ACS. ACS patients are a heterogeneous group of patients in terms of risk, which has important implications for management strategies. Early risk stratification is therefore essential.</p><p id="par0010" class="elsevierStylePara elsevierViewall">The Global Registry of Acute Coronary Events (GRACE) score is the most widely used risk score due to its high predictive accuracy.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">1</span></a> Two groups in the US have developed more recent risk scores for stratification of patients undergoing coronary angioplasty, including patients with ACS.<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">2,3</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">We sought to validate these US risk scores in a European population of patients with ACS and to compare their predictive accuracy with that of the classical GRACE risk score.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Methods</span><p id="par0020" class="elsevierStylePara elsevierViewall">All consecutive adult patients (aged ≥18 years) included in a single-center registry on ACS between January 1, 2005 and October 31, 2013 were eligible. This is a continuous, prospective and observational registry. Inclusion criteria were a history of chest pain at rest or other symptoms suggestive of an ACS within 24 hours before admission associated with new or presumed new ECG changes (significant ST-T wave changes or left bundle branch block) and/or dynamic changes in levels of biomarkers of myocardial necrosis. ST-elevation myocardial infarction (STEMI) was defined as persistent (>20 minutes) ST-segment elevation. All other cases were considered non-ST-elevation ACS.</p><p id="par0025" class="elsevierStylePara elsevierViewall">Data were recorded in a database that included demographic, clinical and patient management characteristics, as well as hospital outcome. Hypertension, diabetes and hyperlipidemia were defined as either previously known or on specific therapy. Patients were classified as smokers if they had smoked during the previous six months and were self-reported.</p><p id="par0030" class="elsevierStylePara elsevierViewall">Decisions regarding patient management strategy, including referral for coronary angiography and myocardial revascularization, either by percutaneous coronary intervention (PCI) or by coronary artery bypass grafting (CABG), were left to the discretion of the attending physician. Only patients undergoing PCI were eligible for the present study.</p><p id="par0035" class="elsevierStylePara elsevierViewall">For each patient a score was retrospectively assigned according to the National Cardiovascular Data Registry (NCDR) risk score, the Mayo Clinic risk score (MCRS) and the Global Registry of Acute Coronary Events (GRACE) risk score.<a class="elsevierStyleCrossRefs" href="#bib0075"><span class="elsevierStyleSup">1–3</span></a></p><p id="par0040" class="elsevierStylePara elsevierViewall">The primary endpoint was all-cause mortality during the index hospitalization.</p><p id="par0045" class="elsevierStylePara elsevierViewall">Other outcomes were also recorded in the study population: stroke/transient ischemic attack (TIA) and major bleeding. Stroke/TIA was defined as the presence of new neurological symptoms with signs of ischemia or bleeding on computed tomography or magnetic resonance imaging. Major bleeding was defined according to the Global Use of Strategies to Open Occluded Coronary Arteries (GUSTO) criteria as intracranial bleeding or bleeding that caused hemodynamic compromise requiring intervention.<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">4</span></a></p><p id="par0050" class="elsevierStylePara elsevierViewall">Informed consent was obtained from each patient. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the institution's human research committee.</p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Statistical analysis</span><p id="par0055" class="elsevierStylePara elsevierViewall">Categorical variables are reported as percentages and continuous variables as means (standard deviation [SD]).</p><p id="par0060" class="elsevierStylePara elsevierViewall">Concerning the variables included in the risk scores, we found missing data in 6.6% of patients for heart rate, 6.3% for systolic blood pressure and 3.7% for creatinine on admission in the study population. Qualitative left ventricular ejection fraction was also missing in 8.3%. A missing values analysis was performed, which showed that these values were missing completely at random, which was confirmed by Little's MCAR test (p=0.137). A multiple imputation technique based on a Markov chain Monte Carlo approach was used to estimate the missing values for each of the missing data points in the study, including death. A primary model included imputed variables, with a secondary model that excluded patients with missing variables to determine the consistency of the general findings with each approach. The imputation of missing values did not change the means of the variables.</p><p id="par0065" class="elsevierStylePara elsevierViewall">Goodness of fit for each model was assessed with the Hosmer-Lemeshow test. In this test, a high (non-significant) p-value indicates that the model is performing well and has a good fit. Since we aimed to compare different models using the same data and predicting the same outcome, we also analyzed and compared pseudo R-squared statistics. A higher pseudo R-squared value indicates which model better predicts the outcome. Discriminative ability was assessed by the area under the ROC (receiver operating characteristic) curve (AUC). The AUC provides a measure of the model's ability to discriminate between subjects who experience the outcome of interest and those who do not. To compare the AUC of each of these models, the method described by DeLong et al. was used.<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">5</span></a> Calibration was tested with a comparison of the observed frequencies of patients with the event of interest with the expected frequencies based on the values of the estimated probabilities obtained by the logistic regression models.</p><p id="par0070" class="elsevierStylePara elsevierViewall">Continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices were also calculated. The net proportion of patients who died (with events) with higher probabilities of death (NRI<span class="elsevierStyleInf">events</span>) and of patients who did not die (without events) with lower probabilities of death (NRI<span class="elsevierStyleInf">nonevents</span>) were calculated considering each group of models. NRI<span class="elsevierStyleInf">total</span> is the sum of NRI<span class="elsevierStyleInf">events</span> and NRI<span class="elsevierStyleInf">nonevents</span> and quantifies the correctness of upward and downward reclassification or movement of predicted probabilities as a result of using a different risk score.<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">6</span></a> IDI is a measure of the improvement in prediction and may be viewed as the difference between improvement in average sensitivity and average 1-specificity.<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">6</span></a></p><p id="par0075" class="elsevierStylePara elsevierViewall">IBM SPSS Statistics software (version 21, Armonk, NY) was used for all statistical analyses. Model comparisons were made using the R Project for Statistical Computing open source programming language.<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">7–9</span></a> All statistical tests were two-sided with a value of 0.05 for statistical significance.</p></span></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Results</span><p id="par0080" class="elsevierStylePara elsevierViewall">A total of 2148 patients were included in the present study. Baseline characteristics are detailed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>. Risk factors and previous cardiovascular disease distribution were similar to other studies and ACS registries. There was a predominance of STEMI. Treatment and outcome are presented in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. There were 96 in-hospital deaths (4.5%) in the study sample.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">The MCRS showed adequate goodness of fit (p=0.337) and discriminative accuracy (AUC 0.85, 95% confidence interval [CI] 0.81–0.90) (<a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a>). Calibration was also good, except for patients with high scores, in whom predicted mortality was lower than that observed (<a class="elsevierStyleCrossRef" href="#fig0010">Figure 2</a>). The NCDR risk score also presented adequate goodness of fit (p=0.474) and good discrimination (AUC 0.87, 95% CI 0.83–0.91). Calibration was almost perfect. Finally, the GRACE risk score also showed adequate goodness of fit (p=0.482) and the highest discriminative ability (AUC 0.94, 95% 0.91–0.96) with excellent calibration. Comparing the GRACE risk score with the other scores, GRACE enabled significantly better reclassification of patients in their risk class, with a NRI >75% (<a class="elsevierStyleCrossRefs" href="#tbl0015">Tables 3 and 4</a>). Pseudo R-squared statistics confirmed that GRACE is the best predictive risk score: GRACE R<span class="elsevierStyleSup">2</span>=0.497; NCDR R<span class="elsevierStyleSup">2</span>=0.337; MCRS R<span class="elsevierStyleSup">2</span>=0.339.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Discussion</span><p id="par0090" class="elsevierStylePara elsevierViewall">Risk stratification is an important step in the management of ACS patients, who are a heterogeneous group in term of characteristics and risk, particularly the subgroup with non-ST-elevation ACS. It is thus essential to perform early risk stratification to optimize decision-making for appropriate management of these patients, including the best time for revascularization and the ideal combination of antithrombotic therapies. Although these treatments improve outcome, they can increase both complications and costs. For this reason, informed decisions are important, and for this risk scores are helpful tools.</p><p id="par0095" class="elsevierStylePara elsevierViewall">Several risk stratification scores for ACS have been published since the 1990s. The first was the TIMI risk scores for STEMI and unstable angina/non-STEMI.<a class="elsevierStyleCrossRefs" href="#bib0120"><span class="elsevierStyleSup">10,11</span></a> These were developed from cohorts of patients included in randomized clinical trials (RCTs). The later PURSUIT risk score was also based on an RCT, which can limit its application and use because real-world cohorts have different characteristics.<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">12</span></a> The GRACE risk score<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">1</span></a> was published in 2003, based on the GRACE multicenter ACS registry database that included the whole spectrum of ACS, reflecting a real-world cohort of patients. Due to its high predictive accuracy it is nowadays the most widely used risk score in ACS.<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">13</span></a></p><p id="par0100" class="elsevierStylePara elsevierViewall">More recently, both the Mayo Clinic and the National Cardiovascular Data Registry developed risk scores for patients undergoing coronary angioplasty.<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">2,3</span></a> Both scores included patients with ACS. Although these US scores were developed from PCI registries, the variables selected were mainly clinical in nature; no angiographic data was used for risk scoring. Thus, the variables selected for these new risk scores are very similar to those used in the GRACE risk score. Both scores have been validated in US populations,<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">14</span></a> but not in European populations, particularly in the context of ACS, and so we sought to validate both risk scores and to compare them with the classical GRACE risk score.</p><p id="par0105" class="elsevierStylePara elsevierViewall">All the scores showed moderate predictive accuracy for in-hospital all-cause mortality. However, the GRACE score had the best performance, with significantly better discriminative accuracy as shown by ROC curve analysis. Since ROC curve analysis is considered insufficient to compare different prediction models, it is recommended to perform a reclassification study of one model compared to another. Patient reclassification was better for GRACE according to their observed risk, that is, patients with events were more often correctly reclassified to a higher risk level and patients without events to a lower risk level.</p><p id="par0110" class="elsevierStylePara elsevierViewall">There are several possible explanations for these results. Firstly, the US scores were not developed from cohorts of patients with ACS but from larger registries of patients undergoing PCI. In these study groups, only 25–40% of the patients had ACS.<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">2,3,14</span></a> This explains several different clinical characteristics of the patients in both US registries compared to our population of patients with ACS: our population has fewer females, fewer diabetic patients and less previous history of myocardial infarction, PCI, peripheral arterial disease and cerebrovascular disease. There was also a significant proportion of patients with STEMI, which is also an important difference compared to other cohorts of ACS patients. In addition, the scores were developed for medium-term mortality (30-day and 1-year) and we validated them for short-term mortality (in-hospital).</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Limitations</span><p id="par0115" class="elsevierStylePara elsevierViewall">Our study has some limitations. Although our study population was obtained from a clinical registry as for the Mayo Clinic, NCDR and GRACE risk scores, the observational and non-randomized nature of the study might limit our conclusions, particularly for other European cohorts with different characteristics. Only patients undergoing PCI were included in the study cohort. Thus our results do not apply to ACS patients who were not revascularized. Furthermore, our study population is somewhat different from other cohorts, particularly its predominance of STEMI patients (70.9%), and thus our results cannot be directly extrapolated to other cohorts.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Conclusions</span><p id="par0120" class="elsevierStylePara elsevierViewall">In a large European cohort of patients with ACS, the NCDR score and the MCRS are useful for in-hospital mortality risk stratification in patients undergoing PCI. However, the GRACE risk score has the best predictive accuracy and as such it should remain the preferred risk stratification score.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Conflicts of interest</span><p id="par0125" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:12 [ 0 => array:3 [ "identificador" => "xres818328" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec815359" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres818327" "titulo" => "Resumo" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introdução" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusão" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec815358" "titulo" => "Palavras-chave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Methods" "secciones" => array:1 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Statistical analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0020" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0025" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0030" "titulo" => "Limitations" ] 9 => array:2 [ "identificador" => "sec0035" "titulo" => "Conclusions" ] 10 => array:2 [ "identificador" => "sec0040" "titulo" => "Conflicts of interest" ] 11 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2015-05-04" "fechaAceptado" => "2015-09-13" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec815359" "palabras" => array:4 [ 0 => "Risk stratification" 1 => "Scores" 2 => "Acute coronary syndromes" 3 => "Coronary angioplasty" ] ] ] "pt" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palavras-chave" "identificador" => "xpalclavsec815358" "palabras" => array:4 [ 0 => "Estratificação de risco" 1 => "<span class="elsevierStyleItalic">Scores</span>" 2 => "Síndromes coronárias agudas" 3 => "Angioplastia coronária" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">New scores have been developed and validated in the US for in-hospital mortality risk stratification in patients undergoing coronary angioplasty: the National Cardiovascular Data Registry (NCDR) risk score and the Mayo Clinic Risk Score (MCRS). We sought to validate these scores in a European population with acute coronary syndrome (ACS) and to compare their predictive accuracy with that of the GRACE risk score.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">In a single-center ACS registry of patients undergoing coronary angioplasty, we used the area under the receiver operating characteristic curve (AUC), a graphical representation of observed vs. expected mortality, and net reclassification improvement (NRI)/integrated discrimination improvement (IDI) analysis to compare the scores.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">A total of 2148 consecutive patients were included, mean age 63 years (SD 13), 74% male and 71% with ST-segment elevation ACS. In-hospital mortality was 4.5%. The GRACE score showed the best AUC (0.94, 95% CI 0.91–0.96) compared with NCDR (0.87, 95% CI 0.83–0.91, p=0.0003) and MCRS (0.85, 95% CI 0.81–0.90, p=0.0003). In model calibration analysis, GRACE showed the best predictive power. With GRACE, patients were more often correctly classified than with MCRS (NRI 78.7, 95% CI 59.6–97.7; IDI 0.136, 95% CI 0.073–0.199) or NCDR (NRI 79.2, 95% CI 60.2–98.2; IDI 0.148, 95% CI 0.087–0.209).</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">The NCDR and Mayo Clinic risk scores are useful for risk stratification of in-hospital mortality in a European population of patients with ACS undergoing coronary angioplasty. However, the GRACE score is still to be preferred.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] "pt" => array:3 [ "titulo" => "Resumo" "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introdução</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Foram validados novos <span class="elsevierStyleItalic">scores</span> nos EU para estratificação de risco de mortalidade hospitalar em doentes submetidos a angioplastia coronária: da <span class="elsevierStyleItalic">National Cardiovascular Data Registry</span> (NCDR) e da Mayo Clinic (MC). Procurámos validar estes <span class="elsevierStyleItalic">scores</span> numa população Europeia com Síndrome Coronária Aguda (SCA) e comparar a sua acuidade preditiva com o <span class="elsevierStyleItalic">score</span> de GRACE.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Métodos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Registo de SCA de um único centro de doentes submetidos a angioplastia coronária. Utilizaram-se as curvas <span class="elsevierStyleItalic">Receiver Operating Characteristics</span> (ROC) e a <span class="elsevierStyleItalic">Area Under Curve</span> (AUC), a mortalidade observada e esperada e a análise do <span class="elsevierStyleItalic">Net Reclassification Index (NRI)/Integrated Discrimination Improvement (IDI).</span></p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Foram incluídos 2148 doentes consecutivos. Idade média de 63 (DP 13) anos, 74% do sexo masculino e 71% com SCA com elevação ST. A mortalidade hospitalar foi de 4,5%. O <span class="elsevierStyleItalic">score</span> GRACE foi o que mostrou melhor AUC (0,94, IC 95% 0,91 – 0,96) comparativamente com o NCDR (0,87, IC 95% 0,83 – 0,91, p=0,0003) e o MC (0,85, IC 95% 0,81 – 0,90, p=0,0003). Na análise da calibração, o GRACE mostrou o melhor poder preditivo. Com o <span class="elsevierStyleItalic">score</span> GRACE, os doentes foram mais corretamente classificados comparativamente com o da Mayo Clinic (NRI 78,7, IC 95% 59,6 – 97,7; IDI 0,136, IC 95% 0,073 – 0,199) e NCDR (NRI 79,2, IC 95% 60,2 – 98,2; IDI 0,148, IC 95% 0,087 – 0,209).</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusão</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Os <span class="elsevierStyleItalic">scores</span> NCDR e MC são úteis na estratificação de risco para mortalidade hospitalar numa população europeia de doentes com SCA submetidos a angioplastia coronária. Contudo, o <span class="elsevierStyleItalic">score</span> GRACE continua a ser o ideal.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introdução" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusão" ] ] ] ] "multimedia" => array:6 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1341 "Ancho" => 1604 "Tamanyo" => 138174 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Area under the curve for the three models. GRACE: Global Registry of Acute Coronary Events risk score; MCRS: Mayo Clinic risk score; NCDR: National Cardiovascular Data Registry risk score.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 774 "Ancho" => 3400 "Tamanyo" => 211752 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Calibration of the three scores (plots of predicted vs. observed probability of death for the three scores). NCDR: National Cardiovascular Data Registry.</p>" ] ] 2 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">CABG: coronary artery bypass grafting; MI: myocardial infarction; PAD: peripheral arterial disease; PCI: percutaneous coronary intervention; SBP: systolic blood pressure; STEMI: ST-elevation myocardial infarction; TIA: transient ischemic attack.</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"> \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">Overall (n=2148) \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"><span class="elsevierStyleItalic">Age (years)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">62 (13) \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="elsevierStyleItalic">Male gender (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">74.1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" 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="2" align="left" valign="top"><span class="elsevierStyleItalic">Risk factors (%)</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>Hypertension \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">62.8 \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>Hyperlipidemia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">50.0 \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>Diabetes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">23.7 \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>Smoking \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">41.9 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" 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="2" align="left" valign="top"><span class="elsevierStyleItalic">Previous history (%)</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>MI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.5 \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>PCI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.7 \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>CABG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.7 \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>PAD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.8 \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>Stroke/TIA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.6 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" 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="2" align="left" valign="top"><span class="elsevierStyleItalic">Initial presentation</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>Heart rate (bpm) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">78 (19) \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>SBP (mmHg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">135 (28) \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>Killip class >1 (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9.7 \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>STEMI (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">70.9 \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>Creatinine (mg/dl) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.0 (0.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1375483.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Baseline characteristics of the study population.</p>" ] ] 3 => array:7 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; CABG: coronary artery bypass grafting; PCI: percutaneous coronary intervention.</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"> \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">Overall (n=2148) \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="2" align="left" valign="top"><span class="elsevierStyleItalic">Treatment (%)</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 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">99.4 \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 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">97.9 \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>ACEI/ARB \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">87.9 \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>Beta-blocker \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">84.5 \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>Statin \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">94.6 \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>PCI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">100.0 \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>CABG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.2 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" 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="2" align="left" valign="top"><span class="elsevierStyleItalic">Complications (%)</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>Stroke \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.1 \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>Mechanical complication \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.3 \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>Major bleeding \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.1 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1375484.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Treatment and complications.</p>" ] ] 4 => array:7 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:3 [ "leyenda" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">95% confidence intervals are shown in brackets.</p><p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">AUC: area under the receiver operator characteristic curve; IDI: integrated discrimination improvement; MCRS: Mayo Clinic risk score; NRI: net reclassification improvement.</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=""><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleItalic">Continuous NRI (%)</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>NRI<span class="elsevierStyleInf">events</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27.1 (8.3–45.9) \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>NRI<span class="elsevierStyleInf">nonevents</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">51.6 (48.6–54.6) \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>NRI<span class="elsevierStyleInf">total</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">78.7 (59.6–97.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></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="elsevierStyleItalic">IDI statistics</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.136 (0.073–0.199) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleItalic">AUC (95% CI)</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>MCRS \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.85 (0.81–0.90) \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>GRACE risk score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.94 (0.91–0.96) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></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="elsevierStyleItalic">Difference (p)</span> \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"><span class="elsevierStyleItalic">Goodness of fit (MCRS)</span><a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.337 \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="elsevierStyleItalic">Goodness of fit (GRACE risk score)</span><a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.482 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1375482.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Hosmer-Lemeshow goodness of fit test (p-value).</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Statistics for model improvement (comparing GRACE and Mayo Clinic risk scores).</p>" ] ] 5 => array:7 [ "identificador" => "tbl0020" "etiqueta" => "Table 4" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:3 [ "leyenda" => "<p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">95% confidence intervals are shown in brackets.</p><p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">AUC: area under the receiver operator characteristic curve; CI: confidence interval; IDI: integrated discrimination improvement; NCDR: National Cardiovascular Data Registry; NRI: net reclassification improvement.</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=""><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleItalic">Continuous NRI (%)</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>NRI<span class="elsevierStyleInf">events</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">26.0 (7.3–44.8) \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>NRI<span class="elsevierStyleInf">nonevents</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">53.2 (50.2–56.1) \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>NRI<span class="elsevierStyleInf">total</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">79.2 (60.2–98.2) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></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="elsevierStyleItalic">IDI statistics</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.148 (0.087–0.209) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleItalic">AUC (95% CI)</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>NCDR risk score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.87 (0.83–0.91) \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>GRACE risk score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.94 (0.91–0.96) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></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="elsevierStyleItalic">Difference (p)</span> \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"><span class="elsevierStyleItalic">Goodness of fit (NCDR risk score)</span><a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.474 \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="elsevierStyleItalic">Goodness of fit (GRACE risk score)</span><a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.482 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1375481.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Hosmer-Lemeshow goodness of fit test (p-value).</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">Statistics for model improvement (comparing GRACE and National Cardiovascular Data Registry risk scores).</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:14 [ 0 => array:3 [ "identificador" => "bib0075" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Predictors of hospital mortality in the Global Registry of Acute Coronary Events" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:3 [ 0 => "C.B. 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| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 November | 5 | 4 | 9 |
| 2024 October | 30 | 32 | 62 |
| 2024 September | 37 | 19 | 56 |
| 2024 August | 49 | 22 | 71 |
| 2024 July | 36 | 34 | 70 |
| 2024 June | 25 | 21 | 46 |
| 2024 May | 33 | 17 | 50 |
| 2024 April | 39 | 21 | 60 |
| 2024 March | 37 | 18 | 55 |
| 2024 February | 28 | 22 | 50 |
| 2024 January | 21 | 28 | 49 |
| 2023 December | 24 | 24 | 48 |
| 2023 November | 32 | 30 | 62 |
| 2023 October | 22 | 14 | 36 |
| 2023 September | 16 | 21 | 37 |
| 2023 August | 24 | 17 | 41 |
| 2023 July | 21 | 19 | 40 |
| 2023 June | 31 | 16 | 47 |
| 2023 May | 41 | 32 | 73 |
| 2023 April | 17 | 13 | 30 |
| 2023 March | 30 | 20 | 50 |
| 2023 February | 31 | 22 | 53 |
| 2023 January | 19 | 10 | 29 |
| 2022 December | 36 | 18 | 54 |
| 2022 November | 38 | 25 | 63 |
| 2022 October | 29 | 18 | 47 |
| 2022 September | 23 | 43 | 66 |
| 2022 August | 31 | 52 | 83 |
| 2022 July | 22 | 30 | 52 |
| 2022 June | 18 | 17 | 35 |
| 2022 May | 21 | 30 | 51 |
| 2022 April | 34 | 22 | 56 |
| 2022 March | 20 | 33 | 53 |
| 2022 February | 15 | 23 | 38 |
| 2022 January | 31 | 30 | 61 |
| 2021 December | 15 | 33 | 48 |
| 2021 November | 43 | 31 | 74 |
| 2021 October | 30 | 35 | 65 |
| 2021 September | 23 | 32 | 55 |
| 2021 August | 30 | 31 | 61 |
| 2021 July | 20 | 19 | 39 |
| 2021 June | 22 | 17 | 39 |
| 2021 May | 27 | 26 | 53 |
| 2021 April | 29 | 26 | 55 |
| 2021 March | 52 | 12 | 64 |
| 2021 February | 35 | 13 | 48 |
| 2021 January | 26 | 16 | 42 |
| 2020 December | 35 | 13 | 48 |
| 2020 November | 27 | 13 | 40 |
| 2020 October | 16 | 14 | 30 |
| 2020 September | 52 | 11 | 63 |
| 2020 August | 26 | 12 | 38 |
| 2020 July | 43 | 8 | 51 |
| 2020 June | 31 | 9 | 40 |
| 2020 May | 33 | 3 | 36 |
| 2020 April | 28 | 8 | 36 |
| 2020 March | 34 | 11 | 45 |
| 2020 February | 75 | 14 | 89 |
| 2020 January | 24 | 4 | 28 |
| 2019 December | 34 | 2 | 36 |
| 2019 November | 38 | 7 | 45 |
| 2019 October | 27 | 14 | 41 |
| 2019 September | 14 | 7 | 21 |
| 2019 August | 25 | 3 | 28 |
| 2019 July | 36 | 8 | 44 |
| 2019 June | 18 | 12 | 30 |
| 2019 May | 29 | 8 | 37 |
| 2019 April | 21 | 14 | 35 |
| 2019 March | 58 | 10 | 68 |
| 2019 February | 54 | 16 | 70 |
| 2019 January | 25 | 7 | 32 |
| 2018 December | 36 | 9 | 45 |
| 2018 November | 79 | 12 | 91 |
| 2018 October | 163 | 16 | 179 |
| 2018 September | 52 | 18 | 70 |
| 2018 August | 31 | 27 | 58 |
| 2018 July | 16 | 7 | 23 |
| 2018 June | 25 | 7 | 32 |
| 2018 May | 30 | 11 | 41 |
| 2018 April | 26 | 15 | 41 |
| 2018 March | 45 | 9 | 54 |
| 2018 February | 14 | 4 | 18 |
| 2018 January | 23 | 6 | 29 |
| 2017 December | 38 | 7 | 45 |
| 2017 November | 23 | 8 | 31 |
| 2017 October | 27 | 17 | 44 |
| 2017 September | 26 | 12 | 38 |
| 2017 August | 28 | 17 | 45 |
| 2017 July | 22 | 12 | 34 |
| 2017 June | 28 | 6 | 34 |
| 2017 May | 31 | 6 | 37 |
| 2017 April | 13 | 4 | 17 |
| 2017 March | 25 | 4 | 29 |
| 2017 February | 34 | 8 | 42 |
| 2017 January | 25 | 2 | 27 |
| 2016 December | 19 | 8 | 27 |
| 2016 November | 25 | 5 | 30 |
| 2016 October | 23 | 7 | 30 |
| 2016 September | 18 | 5 | 23 |
| 2016 August | 5 | 3 | 8 |
| 2016 July | 13 | 3 | 16 |
| 2016 June | 6 | 23 | 29 |
| 2016 May | 11 | 4 | 15 |
| 2016 April | 11 | 1 | 12 |
| 2016 March | 71 | 27 | 98 |
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