ProACS risk score: An early and simple score for risk stratification of patients with acute coronary syndromes

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Several risk scores have been developed for this purpose, from both clinical trials and registries. They differ in predictive accuracy as well as in the number and type of variables included. The first to be developed was the TIMI risk score,<a class="elsevierStyleCrossRefs" href="#bib0130">6,7</a> but its predictive accuracy is usually lower than more recent scores. The most recent and most widely used is the Global Registry of Acute Coronary Events (GRACE) risk score, developed from the GRACE registry.<a class="elsevierStyleCrossRefs" href="#bib0140">8,9</a> This has very high predictive accuracy, but includes many variables with significant complexity, which may explain why it is often underused.<a class="elsevierStyleCrossRefs" href="#bib0150">10,11</a>Previously, our group and others have demonstrated that these risk scores can be simplified, with a slight reduction in predictive accuracy compared to the GRACE score, but that can be considered acceptable.<a class="elsevierStyleCrossRefs" href="#bib0160">12,13</a> These simplified scores show similar accuracy to the TIMI risk score.<a class="elsevierStyleCrossRefs" href="#bib0130">6,7,14</a>Our objective was to develop a simple score for risk stratification of in-hospital mortality in patients with ACS, to be used very early in patient management, including at pre-hospital level.MethodsThe Portuguese Registry on Acute Coronary Syndromes (ProACS) is a multicenter nationwide registry of ACS. It is a prospective, continuous observational registry, with 33 participating cardiology departments from Portugal (mainland and islands).<a class="elsevierStyleCrossRef" href="#bib0175">15</a> Patient inclusion in the registry began on January 1, 2002, and all consecutive adult patients (≥18 years) registered until October 31, 2014 were included in the present study. Criteria for inclusion in the registry were a history of chest pain at rest or other symptoms suggestive of an ACS (with the most recent episode occurring within 48 hours of admission) with or without new or presumed new significant ST-segment or T-wave changes, new left bundle branch block or elevated biomarkers of myocardial damage with a rise and/or fall in levels. Acute myocardial infarction (MI) was defined according to the universal definition of type 1 myocardial infarction.<a class="elsevierStyleCrossRef" href="#bib0180">16</a> A diagnosis of ST-elevation myocardial infarction (STEMI) was made in the presence of persistent (>30 min) ST-segment elevation. All other cases with elevated biomarkers of myocardial damage were considered non-STEMI (NSTEMI).Data were collected in a dedicated computer database, and included demographic, clinical and patient management-related characteristics, as well as clinical outcome. Hypertension, diabetes and hyperlipidemia were defined as either previously known or on specific therapy. If the patients had smoked during the previous 30 days they were classified as smokers and were self-reported. Decisions on patient management strategy, including referral for coronary angiography and mode of myocardial revascularization, if any, were left to the attending physician and site-specific protocols. The primary endpoint was all-cause mortality during the index hospitalization.From the main study population (40415 records), we obtained three cohorts of patients (<a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a>). From January 1, 2002 to June 30, 2011, 31829 patients were included in the registry and were randomly divided into a cohort for risk score development (60% of the patients) and the cohort for internal validation (the remaining 40%). All patients included between July 2011 and October 2014 (8586 patients) were included in the external validation cohort. Patients with missing values for the main variables were excluded (2955 patients – 7.3% of the initial patient sample).<elsevierMultimedia ident="fig0005"></elsevierMultimedia>The study complies with the 1975 Declaration of Helsinki. The national data protection committee for clinical research approved the study protocol and informed consent was obtained from each patient. The registry is registered at ClinicalTrials.gov with the identification number <a id="intr0005" class="elsevierStyleInterRef" href="https://clinicaltrials.gov/NCT01642329">NCT01642329</a>.This manuscript was written in accordance with the Transparent Reporting of a Multivariable Predictor Model for Individual Prognosis or Diagnosis (TRIPOD) statement on appropriate description of studies for score validation.<a class="elsevierStyleCrossRef" href="#bib0185">17</a>Statistical analysisCategorical variables are reported as percentages and differences between groups were tested with the chi-square test or Fisher's exact test, as appropriate. Continuous variables are reported as means and standard deviation. Normality was tested with the Kolmogorov-Smirnov test. One-way ANOVA was used to compare continuous variables with normal distribution. Continuous variables without normal distribution are reported as medians and interquartile range and were compared with the Kruskal-Wallis test.Multivariable stepwise forward logistic regression models were used to select the four variables with the highest predictive potential for score development. Variables were removed from the model when the p-value exceeded 0.10 and were kept in the final model when less than 0.05. The variables included were age, admission heart rate, systolic blood pressure and Killip class, creatinine, ST-segment deviation, gender, diabetes, smoking status, previous myocardial infarction, percutaneous coronary intervention (PCI), coronary artery bypass grafting, stroke or peripheral arterial disease. The selected continuous variables were categorized (dichotomized) by receiver operating characteristic (ROC) curve analysis. Cut-offs were obtained by maximizing the sum of sensitivity and specificity. For each variable, appropriate weights were determined based on the regression coefficient of the final logistic model (to the closest whole number). Points were summed to obtain the final risk score. ROC curve analysis and the area under the curve (AUC) were used to study the predictive value of the new risk score in each cohort. Bootstrap techniques were used for ROC curve comparison and for confidence intervals.IBM SPSS Statistics software (version 19.0.0.2) was used for all statistical analyses. All statistical tests were two-sided with a critical value of 0.05 for statistical significance.ResultsPatients were included in each cohort according to the flowchart presented in <a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a>. Patients in the development cohort had fewer risk factors and less past history of coronary artery disease compared to the internal and external validation cohorts (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). On admission they had higher heart rate and systolic blood pressure and worse Killip class and STEMI was more frequent, particularly compared to the external validation cohort. They less often underwent coronary angiography and revascularization procedures. In-hospital mortality was lower in the validation cohorts, particularly in the external validation cohort.<elsevierMultimedia ident="tbl0005"></elsevierMultimedia>In the multivariate model, the variables with the highest predictive ability were age, systolic blood pressure on admission, Killip class on admission and ST-segment elevation. The cut-off obtained for age was 72 years and for systolic blood pressure it was 116 mmHg. The ProACS score was developed according to these cut-offs with a simple scoring system (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).<elsevierMultimedia ident="tbl0010"></elsevierMultimedia>The ProACS score was then tested in the development cohort as well as in the internal and external validation cohorts. In all cohorts the discriminative ability was also very good (AUC >0.75) and similar between all cohorts, although slightly higher in the external validation cohort (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>). The fit of the developed logistic model was also good in the development cohort (<a class="elsevierStyleCrossRef" href="#fig0010">Figure 2</a>). Performance was similar comparing patients with STEMI and non-ST-elevation acute coronary syndrome (NSTACS). However, compared to the GRACE risk score, the predictive ability was slightly lower (<a class="elsevierStyleCrossRef" href="#fig0015">Figure 3</a>).<elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia>On the ProACS risk score, patients with a score 0 have a low risk of in-hospital death (<1%) and those with a score ≥3 have a high risk (>4%). Patients with score 1-2 have intermediate risk (1-4%) (<a class="elsevierStyleCrossRef" href="#fig0020">Figure 4</a>).<elsevierMultimedia ident="fig0020"></elsevierMultimedia>DiscussionIn the past 20 years, invasive strategies for the treatment of ACS have had a significant impact on prognosis.<a class="elsevierStyleCrossRefs" href="#bib0105">1–5</a> It is thus important to identify and select the patients who can derive the most benefit from these strategies. In STEMI primary PCI is now generally recommended<a class="elsevierStyleCrossRefs" href="#bib0105">1,3</a> and so risk stratification for these patients is less important, but some cases require urgent pre-hospital referral to a more specialized center. NSTACS patients are a heterogeneous group and invasive strategies are recommended in high- and intermediate-risk patients.<a class="elsevierStyleCrossRefs" href="#bib0110">2,4</a> For this reason, several risk scores have been developed in the past 15 years. The TIMI risk scores for STEMI and unstable angina/NSTEMI were developed from large clinical trials.<a class="elsevierStyleCrossRefs" href="#bib0130">6,7</a> These scores were simple and intuitive with simple variables, and their use spread rapidly worldwide. Other risk scores were also developed but the TIMI score remained the most used until 2003, when the GRACE registry group published a risk score for in-hospital mortality in patients with ACS and later for six-month mortality.<a class="elsevierStyleCrossRefs" href="#bib0140">8,9</a> This score was developed from a registry that represented real-world patients instead of the highly controlled and selected populations of clinical trials, from which elderly patients and those with severe renal dysfunction were usually excluded. The GRACE risk score showed high predictive accuracy compared to previous risk scores; it soon became the most used score, and was subsequently updated.<a class="elsevierStyleCrossRef" href="#bib0190">18</a> However, the GRACE score's nomogram includes several variables with a wide range of categories, some of which can only be obtained after initial diagnostic tests. This adds complexity to risk scoring and means it cannot be used at an early stage. This might explain its underuse in clinical practice in spite of the development of several tools.<a class="elsevierStyleCrossRefs" href="#bib0150">10,11</a>Our group demonstrated in a single-center study that it is possible to simplify these risk scores with simple and fewer variables, with similar predictive accuracy, although we did not set out to propose a new risk score due to the inherent limitations of our study.<a class="elsevierStyleCrossRef" href="#bib0160">12</a> A Canadian group has developed another score, the Canada Acute Coronary Syndrome risk score (C-ACS), based on large ACS registries in Canada.<a class="elsevierStyleCrossRef" href="#bib0165">13</a> The selected variables were age, Killip class, systolic blood pressure and heart rate, with a simple dichotomization of continuous variables. This score enables rapid stratification of patients with ACS, even by health care professionals without advanced medical training, in pre-hospital settings or emergency departments. It showed a good predictive value for both short- and long-term mortality and in both STEMI and NSTACS. The c-statistic of this new score is adequate (≥0.75) but slightly lower than classical risk scores for the prediction of short- and long-term mortality.<a class="elsevierStyleCrossRef" href="#bib0165">13</a>Since North American populations have different characteristics from other populations, including European and particularly Mediterranean (low-risk) countries, we thought it would be valuable to develop a similar risk score for early use in ACS that would be simple and easy to memorize.<a class="elsevierStyleCrossRefs" href="#bib0195">19,20</a> The ProACS risk score was developed from a nationwide registry of ACS in Portugal. The sample is large and is representative of real-world patients. In our score, we used simple variables (age, systolic blood pressure, ST-segment elevation and Killip class on admission), similar to the C-ACS score, and confirmed the validity of the selected variables (those with the highest predictive value). It also showed good predictive accuracy, which although lower than the GRACE risk score, is still acceptable. It is also easy to calculate and can be applied in pre-hospital settings or emergency departments by non-medical healthcare professionals. It improves referral of high- and intermediate-risk patients to hospitals with catheterization facilities, particularly in NSTACS. External validation was performed in a more recent cohort of patients, which may explain some of the differences observed, compared with the development and internal validation cohorts, particularly the lower proportion of STEMI and higher rate of coronary angiography and revascularization. It is also important to mention that predictive accuracy was higher in the external validation cohort, which highlights its utility in contemporary cohorts. Predictive accuracy was similar in patients with STEMI and NSTACS, suggesting that it can also be used in STEMI patients, although in this group the recommended strategy is primary PCI.LimitationsThis score was developed and validated in a Portuguese population. It requires further validation in other populations, preferably in contemporary cohorts.We chose to exclude from the analysis all patients with missing values of the score variables. Overall, only 7.3% of the patients were excluded, which is acceptable because of our large sample size. Data from the external validation cohort were more complete and fewer patients were excluded from this cohort.We could not test goodness of fit with the new risk score because this is a categorical score and is thus not suitable for this type of analysis.ConclusionThe ProACS risk score enables easy and simple risk stratification for in-hospital mortality at the first medical contact of patients with ACS. It also has excellent predictive ability in a contemporary population of patients with ACS, although slightly lower than the GRACE risk score. Its simplicity can improve implementation of scores for risk stratification in clinical practice, although further validation in other populations and countries is warranted.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" => "xres799836" "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" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec798345" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres799835" "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" => "Conclusões" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec798344" "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" => "Conclusion" ] 10 => array:3 [ "identificador" => "sec0040" "titulo" => "Ethical disclosures" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0045" "titulo" => "Protection of human and animal subjects" ] 1 => array:2 [ "identificador" => "sec0050" "titulo" => "Confidentiality of data" ] 2 => array:2 [ "identificador" => "sec0055" "titulo" => "Right to privacy and informed consent" ] ] ] 11 => array:2 [ "identificador" => "sec0060" "titulo" => "Conflicts of interest" ] 12 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2016-03-29" "fechaAceptado" => "2016-05-18" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec798345" "palabras" => array:3 [ 0 => "Risk stratification score" 1 => "Acute coronary syndromes" 2 => "Prognosis" ] ] ] "pt" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palavras-chave" "identificador" => "xpalclavsec798344" "palabras" => array:3 [ 0 => "Score de estratificação de risco" 1 => "Síndromes coronárias agudas" 2 => "Prognóstico" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "IntroductionThere are barriers to proper implementation of risk stratification scores in patients with acute coronary syndromes (ACS), including their complexity. Our objective was to develop a simple score for risk stratification of all-cause in-hospital mortality in a population of patients with ACS. MethodsThe score was developed from a nationwide ACS registry. The development and internal validation cohorts were obtained from the first 31829 patients, randomly separated (60% and 40%, respectively). The external validation cohort consisted of the last 8586 patients included in the registry. This cohort is significantly different from the other cohorts in terms of baseline characteristics, treatment and mortality. Multivariate logistic regression analysis was used to select four variables with the highest predictive potential. A score was allocated to each parameter based on the regression coefficient of each variable in the logistic regression model: 1 point for systolic blood pressure ≤116 mmHg, Killip class 2 or 3, and ST-segment elevation; 2 points for age ≥72 years; and 3 points for Killip class 4. ResultsThe new score had good discriminative ability in the development cohort (area under the curve [AUC] 0.796), and it was similar in the internal validation cohort (AUC 0.785, p=0.333). In the external validation cohort, there was also excellent discriminative ability (AUC 0.815), with an adequate fit. ConclusionsThe ProACS risk score enables easy and simple risk stratification of patients with ACS for in-hospital mortality that can be used at the first medical contact, with excellent predictive ability in a contemporary population." "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" => "Conclusions" ] ] ] "pt" => array:3 [ "titulo" => "Resumo" "resumen" => "IntroduçãoExistem algumas barreiras à implementação adequada dos scores de estratificação de risco em doentes com síndrome coronária aguda (SCA), tais como a sua complexidade. O nosso objetivo foi desenvolver um score simples para estratificação de risco de mortalidade hospitalar de todas as causas numa população de doentes com SCA. MétodosO score foi desenvolvido a partir de um registo nacional de SCA. A coorte de desenvolvimento e de validação interna foi obtida a partir dos primeiros 31829 doentes, aleatoriamente separados (60 e 40%, respetivamente). A coorte de validação externa é composta pelos últimos 8586 doentes incluídos no registo. Esta coorte é significativamente diferente das restantes (características basais, tratamento e mortalidade). Foi utilizada análise de regressão logística multivariada para selecionar as quatro variáveis com maior potencial preditivo e foi atribuída uma pontuação baseada no coeficiente de regressão de cada variável no modelo de regressão logística: um ponto para TAS ≤ 116 mmHg, classe Killip 2 ou 3, e elevação segmento ST, dois pontos para idade ≥ 72 anos e três pontos para classe Killip 4. ResultadosO novo score tem uma boa capacidade preditiva na coorte de desenvolvimento (area under curve [AUC] 0,796), semelhante à coorte de validação interna (AUC 0,785, p=0,333). Na coorte de validação externa também apresentou uma excelente capacidade discriminativa (AUC 0,815), com calibração adequada. ConclusõesO score de risco ProACS permite uma estratificação de risco precoce e simples em doentes com SCA para mortalidade hospitalar, que pode ser utilizada no primeiro contacto médico, com excelente capacidade preditiva numa população contemporânea." "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" => "Conclusões" ] ] ] ] "multimedia" => array:7 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1495 "Ancho" => 2317 "Tamanyo" => 151115 ] ] "descripcion" => array:1 [ "en" => "Flowchart of patient inclusion." ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1270 "Ancho" => 1608 "Tamanyo" => 74518 ] ] "descripcion" => array:1 [ "en" => "Fit of the final multivariate logistic regression model in the development cohort. df: degrees of freedom." ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1625 "Ancho" => 1557 "Tamanyo" => 129131 ] ] "descripcion" => array:1 [ "en" => "Comparison of ProACS and GRACE risk scores in the external validation cohort. ROC: receiver operating characteristic." ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Figure 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 955 "Ancho" => 1529 "Tamanyo" => 45510 ] ] "descripcion" => array:1 [ "en" => "Observed in-hospital mortality in the external validation cohort according to the risk cut-offs." ] ] 4 => 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:3 [ "leyenda" => "BMI: body mass index; CABG: coronary artery bypass grafting; COPD: chronic obstructive pulmonary disease; LVEF: left ventricular ejection fraction; PAD: peripheral arterial disease; PCI: percutaneous coronary intervention; SBP: systolic blood pressure; STEMI: ST-elevation myocardial infarction; TIA: transient ischemic attack." "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">Development cohort (n=17380) \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">Internal validation cohort (n=11548) \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">External validation cohort (n=8532) \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">Age, years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">66 (13) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">66 (13) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">66 (13) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.155 \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">Male (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">69.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">70.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">72.1 \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">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 (4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27 (4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27 (4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.785 \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">Previous history (%)</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">62.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">62.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">69.8 \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">Smoking \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">24.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">24.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27.9 \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">27.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30.4 \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">Hyperlipidemia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">45.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">45.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">57.5 \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">Myocardial infarction \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20.2 \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">PCI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14.5 \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">CABG \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.0 \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">Stroke/TIA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.132 \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">3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.8 \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">COPD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.195 \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">Admission</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">43.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">42.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">40.8 \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">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">79 (20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">79 (20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">77 (19) \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">141 (30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">141 (30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">138 (29) \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 ≥2 (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15.5 \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">Killip class 4 (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.801 \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 (mg/dl) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.9 (0.8-1.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.9 (0.8-1.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.9 (0.8-1.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.130 \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">Blood glucose (mg/dl) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">130 (108-170) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">130 (107-175) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">129 (106-174) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.739 \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">Hemoglobin (g/dl) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.7 (1.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.8 (1.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.7 (1.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.618 \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">Coronary angiography (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">68.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">69.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">86.8 \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">Multivessel disease<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">50.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">50.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">50.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.994 \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">PCI (%) \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">43.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">65.0 \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">CABG (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.5 \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">LVEF <50% (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">39.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">39.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">36.7 \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" 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">Outcome (%)</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Re-infarction \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.120 \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">Stroke/TIA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.807 \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">Major bleeding \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.298 \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">Death \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.0 \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 => "xTab1341864.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "Percentage of patients who underwent coronary angiography." ] ] ] "descripcion" => array:1 [ "en" => "Clinical characteristics of the patients in each cohort." ] ] 5 => 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" => "SBP: systolic blood pressure." "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">Variable \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">Cut-off \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">Points \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">Age \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><72 years ≥72 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0 2 \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 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><116 mmHg ≥116 mmHg \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1 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">Killip class \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1 2 3 4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0 1 1 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">ST-segment elevation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">No Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0 1 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1341863.png" ] ] ] ] "descripcion" => array:1 [ "en" => "ProACS risk score." ] ] 6 => 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:2 [ "leyenda" => "CI: confidence interval; NSTACS: non-ST-elevation acute coronary syndrome; STEMI: ST-elevation myocardial infarction." "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">Cohort \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">C-statistic (95% CI) \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">Development \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.796 (0.782-0.810) \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">Internal validation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.785 (0.767-0.803) \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">External validation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.815 (0.793-0.837) \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">0.799 (0.768-0.830) \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">NSTACS \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.809 (0.774-0.845) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1341865.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Area under the curve for the ProACS risk score in the different cohorts." ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:20 [ 0 => array:3 [ "identificador" => "bib0105" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:3 [ "comentario" => "Task Force on the management of ST-segment elevation acute myocardial infarction of the European Society of Cardiology (ESC)" "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:3 [ 0 => "G. 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Original Article

Score de risco ProACS: em score simples e precoce para estratificação de risco em doentes com síndromes coronárias agudas

Ana Teresa Timóteoa,

Autor para correspondência

ana_timoteo@yahoo.com

Corresponding author.

, Sílvia Aguiar Rosaa, Marta Afonso Nogueiraa, Adriana Belob, Rui Cruz Ferreiraa, on behalf of the ProACS Investigators

a Cardiology Department, Santa Marta Hospital, Centro Hospitalar de Lisboa Central, Lisbon, Portugal

b National Center for Data Collection in Cardiology, Portuguese Society of Cardiology, Coimbra, Portugal

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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 management of acute coronary syndromes &#40;ACS&#41; has changed dramatically in the past 20 years following demonstration of the benefits associated with invasive strategies&#44; particularly in high-risk patients&#46;<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">1&#8211;5</span></a> Selection for these strategies is an important task in every patient with ACS&#46; Several risk scores have been developed for this purpose&#44; from both clinical trials and registries&#46; They differ in predictive accuracy as well as in the number and type of variables included&#46; The first to be developed was the TIMI risk score&#44;<a class="elsevierStyleCrossRefs" href="#bib0130"><span class="elsevierStyleSup">6&#44;7</span></a> but its predictive accuracy is usually lower than more recent scores&#46; The most recent and most widely used is the Global Registry of Acute Coronary Events &#40;GRACE&#41; risk score&#44; developed from the GRACE registry&#46;<a class="elsevierStyleCrossRefs" href="#bib0140"><span class="elsevierStyleSup">8&#44;9</span></a> This has very high predictive accuracy&#44; but includes many variables with significant complexity&#44; which may explain why it is often underused&#46;<a class="elsevierStyleCrossRefs" href="#bib0150"><span class="elsevierStyleSup">10&#44;11</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Previously&#44; our group and others have demonstrated that these risk scores can be simplified&#44; with a slight reduction in predictive accuracy compared to the GRACE score&#44; but that can be considered acceptable&#46;<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">12&#44;13</span></a> These simplified scores show similar accuracy to the TIMI risk score&#46;<a class="elsevierStyleCrossRefs" href="#bib0130"><span class="elsevierStyleSup">6&#44;7&#44;14</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Our objective was to develop a simple score for risk stratification of in-hospital mortality in patients with ACS&#44; to be used very early in patient management&#44; including at pre-hospital level&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Methods</span><p id="par0020" class="elsevierStylePara elsevierViewall">The Portuguese Registry on Acute Coronary Syndromes &#40;ProACS&#41; is a multicenter nationwide registry of ACS&#46; It is a prospective&#44; continuous observational registry&#44; with 33 participating cardiology departments from Portugal &#40;mainland and islands&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">15</span></a> Patient inclusion in the registry began on January 1&#44; 2002&#44; and all consecutive adult patients &#40;&#8805;18 years&#41; registered until October 31&#44; 2014 were included in the present study&#46; Criteria for inclusion in the registry were a history of chest pain at rest or other symptoms suggestive of an ACS &#40;with the most recent episode occurring within 48 hours of admission&#41; with or without new or presumed new significant ST-segment or T-wave changes&#44; new left bundle branch block or elevated biomarkers of myocardial damage with a rise and&#47;or fall in levels&#46; Acute myocardial infarction &#40;MI&#41; was defined according to the universal definition of type 1 myocardial infarction&#46;<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">16</span></a> A diagnosis of ST-elevation myocardial infarction &#40;STEMI&#41; was made in the presence of persistent &#40;&#62;30 min&#41; ST-segment elevation&#46; All other cases with elevated biomarkers of myocardial damage were considered non-STEMI &#40;NSTEMI&#41;&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Data were collected in a dedicated computer database&#44; and included demographic&#44; clinical and patient management-related characteristics&#44; as well as clinical outcome&#46; Hypertension&#44; diabetes and hyperlipidemia were defined as either previously known or on specific therapy&#46; If the patients had smoked during the previous 30 days they were classified as smokers and were self-reported&#46; Decisions on patient management strategy&#44; including referral for coronary angiography and mode of myocardial revascularization&#44; if any&#44; were left to the attending physician and site-specific protocols&#46; The primary endpoint was all-cause mortality during the index hospitalization&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">From the main study population &#40;40<span class="elsevierStyleHsp" style=""></span>415 records&#41;&#44; we obtained three cohorts of patients &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a>&#41;&#46; From January 1&#44; 2002 to June 30&#44; 2011&#44; 31<span class="elsevierStyleHsp" style=""></span>829 patients were included in the registry and were randomly divided into a cohort for risk score development &#40;60&#37; of the patients&#41; and the cohort for internal validation &#40;the remaining 40&#37;&#41;&#46; All patients included between July 2011 and October 2014 &#40;8586 patients&#41; were included in the external validation cohort&#46; Patients with missing values for the main variables were excluded &#40;2955 patients &#8211; 7&#46;3&#37; of the initial patient sample&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0035" class="elsevierStylePara elsevierViewall">The study complies with the 1975 Declaration of Helsinki&#46; The national data protection committee for clinical research approved the study protocol and informed consent was obtained from each patient&#46; The registry is registered at ClinicalTrials&#46;gov with the identification number <a id="intr0005" class="elsevierStyleInterRef" href="https://clinicaltrials.gov/NCT01642329">NCT01642329</a>&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">This manuscript was written in accordance with the Transparent Reporting of a Multivariable Predictor Model for Individual Prognosis or Diagnosis &#40;TRIPOD&#41; statement on appropriate description of studies for score validation&#46;<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">17</span></a></p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Statistical analysis</span><p id="par0045" class="elsevierStylePara elsevierViewall">Categorical variables are reported as percentages and differences between groups were tested with the chi-square test or Fisher&#39;s exact test&#44; as appropriate&#46; Continuous variables are reported as means and standard deviation&#46; Normality was tested with the Kolmogorov-Smirnov test&#46; One-way ANOVA was used to compare continuous variables with normal distribution&#46; Continuous variables without normal distribution are reported as medians and interquartile range and were compared with the Kruskal-Wallis test&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">Multivariable stepwise forward logistic regression models were used to select the four variables with the highest predictive potential for score development&#46; Variables were removed from the model when the p-value exceeded 0&#46;10 and were kept in the final model when less than 0&#46;05&#46; The variables included were age&#44; admission heart rate&#44; systolic blood pressure and Killip class&#44; creatinine&#44; ST-segment deviation&#44; gender&#44; diabetes&#44; smoking status&#44; previous myocardial infarction&#44; percutaneous coronary intervention &#40;PCI&#41;&#44; coronary artery bypass grafting&#44; stroke or peripheral arterial disease&#46; The selected continuous variables were categorized &#40;dichotomized&#41; by receiver operating characteristic &#40;ROC&#41; curve analysis&#46; Cut-offs were obtained by maximizing the sum of sensitivity and specificity&#46; For each variable&#44; appropriate weights were determined based on the regression coefficient of the final logistic model &#40;to the closest whole number&#41;&#46; Points were summed to obtain the final risk score&#46; ROC curve analysis and the area under the curve &#40;AUC&#41; were used to study the predictive value of the new risk score in each cohort&#46; Bootstrap techniques were used for ROC curve comparison and for confidence intervals&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">IBM SPSS Statistics software &#40;version 19&#46;0&#46;0&#46;2&#41; was used for all statistical analyses&#46; All statistical tests were two-sided with a critical value of 0&#46;05 for statistical significance&#46;</p></span></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Results</span><p id="par0060" class="elsevierStylePara elsevierViewall">Patients were included in each cohort according to the flowchart presented in <a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a>&#46; Patients in the development cohort had fewer risk factors and less past history of coronary artery disease compared to the internal and external validation cohorts &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#46; On admission they had higher heart rate and systolic blood pressure and worse Killip class and STEMI was more frequent&#44; particularly compared to the external validation cohort&#46; They less often underwent coronary angiography and revascularization procedures&#46; In-hospital mortality was lower in the validation cohorts&#44; particularly in the external validation cohort&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0065" class="elsevierStylePara elsevierViewall">In the multivariate model&#44; the variables with the highest predictive ability were age&#44; systolic blood pressure on admission&#44; Killip class on admission and ST-segment elevation&#46; The cut-off obtained for age was 72 years and for systolic blood pressure it was 116 mmHg&#46; The ProACS score was developed according to these cut-offs with a simple scoring system &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0070" class="elsevierStylePara elsevierViewall">The ProACS score was then tested in the development cohort as well as in the internal and external validation cohorts&#46; In all cohorts the discriminative ability was also very good &#40;AUC &#62;0&#46;75&#41; and similar between all cohorts&#44; although slightly higher in the external validation cohort &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41;&#46; The fit of the developed logistic model was also good in the development cohort &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Figure 2</a>&#41;&#46; Performance was similar comparing patients with STEMI and non-ST-elevation acute coronary syndrome &#40;NSTACS&#41;&#46; However&#44; compared to the GRACE risk score&#44; the predictive ability was slightly lower &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Figure 3</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0075" class="elsevierStylePara elsevierViewall">On the ProACS risk score&#44; patients with a score 0 have a low risk of in-hospital death &#40;&#60;1&#37;&#41; and those with a score &#8805;3 have a high risk &#40;&#62;4&#37;&#41;&#46; Patients with score 1-2 have intermediate risk &#40;1-4&#37;&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0020">Figure 4</a>&#41;&#46;</p><elsevierMultimedia ident="fig0020"></elsevierMultimedia></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Discussion</span><p id="par0080" class="elsevierStylePara elsevierViewall">In the past 20 years&#44; invasive strategies for the treatment of ACS have had a significant impact on prognosis&#46;<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">1&#8211;5</span></a> It is thus important to identify and select the patients who can derive the most benefit from these strategies&#46; In STEMI primary PCI is now generally recommended<a class="elsevierStyleCrossRefs" href="#bib0105"><span class="elsevierStyleSup">1&#44;3</span></a> and so risk stratification for these patients is less important&#44; but some cases require urgent pre-hospital referral to a more specialized center&#46; NSTACS patients are a heterogeneous group and invasive strategies are recommended in high- and intermediate-risk patients&#46;<a class="elsevierStyleCrossRefs" href="#bib0110"><span class="elsevierStyleSup">2&#44;4</span></a> For this reason&#44; several risk scores have been developed in the past 15 years&#46; The TIMI risk scores for STEMI and unstable angina&#47;NSTEMI were developed from large clinical trials&#46;<a class="elsevierStyleCrossRefs" href="#bib0130"><span class="elsevierStyleSup">6&#44;7</span></a> These scores were simple and intuitive with simple variables&#44; and their use spread rapidly worldwide&#46; Other risk scores were also developed but the TIMI score remained the most used until 2003&#44; when the GRACE registry group published a risk score for in-hospital mortality in patients with ACS and later for six-month mortality&#46;<a class="elsevierStyleCrossRefs" href="#bib0140"><span class="elsevierStyleSup">8&#44;9</span></a> This score was developed from a registry that represented real-world patients instead of the highly controlled and selected populations of clinical trials&#44; from which elderly patients and those with severe renal dysfunction were usually excluded&#46; The GRACE risk score showed high predictive accuracy compared to previous risk scores&#59; it soon became the most used score&#44; and was subsequently updated&#46;<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">18</span></a> However&#44; the GRACE score&#39;s nomogram includes several variables with a wide range of categories&#44; some of which can only be obtained after initial diagnostic tests&#46; This adds complexity to risk scoring and means it cannot be used at an early stage&#46; This might explain its underuse in clinical practice in spite of the development of several tools&#46;<a class="elsevierStyleCrossRefs" href="#bib0150"><span class="elsevierStyleSup">10&#44;11</span></a></p><p id="par0085" class="elsevierStylePara elsevierViewall">Our group demonstrated in a single-center study that it is possible to simplify these risk scores with simple and fewer variables&#44; with similar predictive accuracy&#44; although we did not set out to propose a new risk score due to the inherent limitations of our study&#46;<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">12</span></a> A Canadian group has developed another score&#44; the Canada Acute Coronary Syndrome risk score &#40;C-ACS&#41;&#44; based on large ACS registries in Canada&#46;<a class="elsevierStyleCrossRef" href="#bib0165"><span class="elsevierStyleSup">13</span></a> The selected variables were age&#44; Killip class&#44; systolic blood pressure and heart rate&#44; with a simple dichotomization of continuous variables&#46; This score enables rapid stratification of patients with ACS&#44; even by health care professionals without advanced medical training&#44; in pre-hospital settings or emergency departments&#46; It showed a good predictive value for both short- and long-term mortality and in both STEMI and NSTACS&#46; The c-statistic of this new score is adequate &#40;&#8805;0&#46;75&#41; but slightly lower than classical risk scores for the prediction of short- and long-term mortality&#46;<a class="elsevierStyleCrossRef" href="#bib0165"><span class="elsevierStyleSup">13</span></a></p><p id="par0090" class="elsevierStylePara elsevierViewall">Since North American populations have different characteristics from other populations&#44; including European and particularly Mediterranean &#40;low-risk&#41; countries&#44; we thought it would be valuable to develop a similar risk score for early use in ACS that would be simple and easy to memorize&#46;<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">19&#44;20</span></a> The ProACS risk score was developed from a nationwide registry of ACS in Portugal&#46; The sample is large and is representative of real-world patients&#46; In our score&#44; we used simple variables &#40;age&#44; systolic blood pressure&#44; ST-segment elevation and Killip class on admission&#41;&#44; similar to the C-ACS score&#44; and confirmed the validity of the selected variables &#40;those with the highest predictive value&#41;&#46; It also showed good predictive accuracy&#44; which although lower than the GRACE risk score&#44; is still acceptable&#46; It is also easy to calculate and can be applied in pre-hospital settings or emergency departments by non-medical healthcare professionals&#46; It improves referral of high- and intermediate-risk patients to hospitals with catheterization facilities&#44; particularly in NSTACS&#46; External validation was performed in a more recent cohort of patients&#44; which may explain some of the differences observed&#44; compared with the development and internal validation cohorts&#44; particularly the lower proportion of STEMI and higher rate of coronary angiography and revascularization&#46; It is also important to mention that predictive accuracy was higher in the external validation cohort&#44; which highlights its utility in contemporary cohorts&#46; Predictive accuracy was similar in patients with STEMI and NSTACS&#44; suggesting that it can also be used in STEMI patients&#44; although in this group the recommended strategy is primary PCI&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Limitations</span><p id="par0095" class="elsevierStylePara elsevierViewall">This score was developed and validated in a Portuguese population&#46; It requires further validation in other populations&#44; preferably in contemporary cohorts&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">We chose to exclude from the analysis all patients with missing values of the score variables&#46; Overall&#44; only 7&#46;3&#37; of the patients were excluded&#44; which is acceptable because of our large sample size&#46; Data from the external validation cohort were more complete and fewer patients were excluded from this cohort&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">We could not test goodness of fit with the new risk score because this is a categorical score and is thus not suitable for this type of analysis&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Conclusion</span><p id="par0110" class="elsevierStylePara elsevierViewall">The ProACS risk score enables easy and simple risk stratification for in-hospital mortality at the first medical contact of patients with ACS&#46; It also has excellent predictive ability in a contemporary population of patients with ACS&#44; although slightly lower than the GRACE risk score&#46; Its simplicity can improve implementation of scores for risk stratification in clinical practice&#44; although further validation in other populations and countries is warranted&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Ethical disclosures</span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Protection of human and animal subjects</span><p id="par0115" class="elsevierStylePara elsevierViewall">The authors declare that no experiments were performed on humans or animals for this study&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Confidentiality of data</span><p id="par0120" 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="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Right to privacy and informed consent</span><p id="par0125" class="elsevierStylePara elsevierViewall">The authors declare that no patient data appear in this article&#46;</p></span></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Conflicts of interest</span><p id="par0130" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare&#46;</p></span></span>"
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        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">There are barriers to proper implementation of risk stratification scores in patients with acute coronary syndromes &#40;ACS&#41;&#44; including their complexity&#46; Our objective was to develop a simple score for risk stratification of all-cause in-hospital mortality in a population of patients with ACS&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">The score was developed from a nationwide ACS registry&#46; The development and internal validation cohorts were obtained from the first 31<span class="elsevierStyleHsp" style=""></span>829 patients&#44; randomly separated &#40;60&#37; and 40&#37;&#44; respectively&#41;&#46; The external validation cohort consisted of the last 8586 patients included in the registry&#46; This cohort is significantly different from the other cohorts in terms of baseline characteristics&#44; treatment and mortality&#46; Multivariate logistic regression analysis was used to select four variables with the highest predictive potential&#46; A score was allocated to each parameter based on the regression coefficient of each variable in the logistic regression model&#58; 1 point for systolic blood pressure &#8804;116 mmHg&#44; Killip class 2 or 3&#44; and ST-segment elevation&#59; 2 points for age &#8805;72 years&#59; and 3 points for Killip class 4&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The new score had good discriminative ability in the development cohort &#40;area under the curve &#91;AUC&#93; 0&#46;796&#41;&#44; and it was similar in the internal validation cohort &#40;AUC 0&#46;785&#44; p&#61;0&#46;333&#41;&#46; In the external validation cohort&#44; there was also excellent discriminative ability &#40;AUC 0&#46;815&#41;&#44; with an adequate fit&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">The ProACS risk score enables easy and simple risk stratification of patients with ACS for in-hospital mortality that can be used at the first medical contact&#44; with excellent predictive ability in a contemporary population&#46;</p></span>"
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        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Introdu&#231;&#227;o</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Existem algumas barreiras &#224; implementa&#231;&#227;o adequada dos <span class="elsevierStyleItalic">scores</span> de estratifica&#231;&#227;o de risco em doentes com s&#237;ndrome coron&#225;ria aguda &#40;SCA&#41;&#44; tais como a sua complexidade&#46; O nosso objetivo foi desenvolver um <span class="elsevierStyleItalic">score</span> simples para estratifica&#231;&#227;o de risco de mortalidade hospitalar de todas as causas numa popula&#231;&#227;o de doentes com SCA&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">M&#233;todos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">O <span class="elsevierStyleItalic">score</span> foi desenvolvido a partir de um registo nacional de SCA&#46; A coorte de desenvolvimento e de valida&#231;&#227;o interna foi obtida a partir dos primeiros 31<span class="elsevierStyleHsp" style=""></span>829 doentes&#44; aleatoriamente separados &#40;60 e 40&#37;&#44; respetivamente&#41;&#46; A coorte de valida&#231;&#227;o externa &#233; composta pelos &#250;ltimos 8586 doentes inclu&#237;dos no registo&#46; Esta coorte &#233; significativamente diferente das restantes &#40;caracter&#237;sticas basais&#44; tratamento e mortalidade&#41;&#46; Foi utilizada an&#225;lise de regress&#227;o log&#237;stica multivariada para selecionar as quatro vari&#225;veis com maior potencial preditivo e foi atribu&#237;da uma pontua&#231;&#227;o baseada no coeficiente de regress&#227;o de cada vari&#225;vel no modelo de regress&#227;o log&#237;stica&#58; um ponto para TAS &#8804; 116 mmHg&#44; classe Killip 2 ou 3&#44; e eleva&#231;&#227;o segmento ST&#44; dois pontos para idade &#8805; 72 anos e tr&#234;s pontos para classe Killip 4&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">O novo <span class="elsevierStyleItalic">score</span> tem uma boa capacidade preditiva na coorte de desenvolvimento &#40;<span class="elsevierStyleItalic">area under curve</span> &#91;AUC&#93; 0&#44;796&#41;&#44; semelhante &#224; coorte de valida&#231;&#227;o interna &#40;AUC 0&#44;785&#44; p&#61;0&#44;333&#41;&#46; Na coorte de valida&#231;&#227;o externa tamb&#233;m apresentou uma excelente capacidade discriminativa &#40;AUC 0&#44;815&#41;&#44; com calibra&#231;&#227;o adequada&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclus&#245;es</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">O <span class="elsevierStyleItalic">score</span> de risco ProACS permite uma estratifica&#231;&#227;o de risco precoce e simples em doentes com SCA para mortalidade hospitalar&#44; que pode ser utilizada no primeiro contacto m&#233;dico&#44; com excelente capacidade preditiva numa popula&#231;&#227;o contempor&#226;nea&#46;</p></span>"
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          "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Fit of the final multivariate logistic regression model in the development cohort&#46; df&#58; degrees of freedom&#46;</p>"
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        "etiqueta" => "Figure 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
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          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Comparison of ProACS and GRACE risk scores in the external validation cohort&#46; ROC&#58; receiver operating characteristic&#46;</p>"
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      3 => array:7 [
        "identificador" => "fig0020"
        "etiqueta" => "Figure 4"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
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          "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Observed in-hospital mortality in the external validation cohort according to the risk cut-offs&#46;</p>"
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        "tipo" => "MULTIMEDIATABLA"
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          "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">BMI&#58; body mass index&#59; CABG&#58; coronary artery bypass grafting&#59; COPD&#58; chronic obstructive pulmonary disease&#59; LVEF&#58; left ventricular ejection fraction&#59; PAD&#58; peripheral arterial disease&#59; PCI&#58; percutaneous coronary intervention&#59; SBP&#58; systolic blood pressure&#59; STEMI&#58; ST-elevation myocardial infarction&#59; TIA&#58; transient ischemic attack&#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">Development cohort &#40;n&#61;17<span class="elsevierStyleHsp" style=""></span>380&#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">Internal validation cohort &#40;n&#61;11<span class="elsevierStyleHsp" style=""></span>548&#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">External validation cohort &#40;n&#61;8532&#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"><span class="elsevierStyleItalic">Age&#44; years</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">66 &#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">66 &#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">66 &#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;155&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="elsevierStyleItalic">Male &#40;&#37;&#41;</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">69&#46;9&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;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">72&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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="elsevierStyleItalic">BMI &#40;kg&#47;m</span><span class="elsevierStyleSup"><span class="elsevierStyleItalic">2</span></span><span class="elsevierStyleItalic">&#41;</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">27 &#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 &#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 &#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;785&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">Previous history &#40;&#37;&#41;</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&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;2&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;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">69&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>Smoking&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">24&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">24&#46;5&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&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>Diabetes&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;0&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&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">30&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>Hyperlipidemia&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">45&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">45&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">57&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>Myocardial infarction&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">18&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">18&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">20&#46;2&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>PCI&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">8&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">14&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>CABG&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;0&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>Stroke&#47;TIA&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">7&#46;6&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;132&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>PAD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">3&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>COPD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">6&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;5&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;195&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">Admission</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>STEMI &#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">43&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">42&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">40&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>Heart rate &#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">79 &#40;20&#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;20&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">77 &#40;19&#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;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>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">141 &#40;30&#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;30&#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;29&#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;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>Killip class &#8805;2 &#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">18&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">19&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">15&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>Killip class 4 &#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">2&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">2&#46;0&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&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;801&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>Creatinine &#40;mg&#47;dl&#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;9 &#40;0&#46;8-1&#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;9 &#40;0&#46;8-1&#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;9 &#40;0&#46;8-1&#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;130&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>Blood glucose &#40;mg&#47;dl&#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;108-170&#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;107-175&#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;106-174&#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;739&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>Hemoglobin &#40;g&#47;dl&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">13&#46;7 &#40;1&#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">13&#46;8 &#40;1&#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">13&#46;7 &#40;1&#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;618&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>Coronary angiography &#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">68&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">69&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">86&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>Multivessel disease<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> &#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">50&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">50&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">50&#46;7&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;994&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>PCI &#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">43&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">43&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">65&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>CABG &#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">1&#46;4&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;5&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;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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>LVEF &#60;50&#37; &#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">39&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">39&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">36&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;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">Outcome &#40;&#37;&#41;</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>Re-infarction&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;5&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;4&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;2&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;120&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>Stroke&#47;TIA&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;8&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;9&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;8&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;807&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>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">1&#46;3&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;2&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;4&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;298&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>Death&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">5&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">4&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="char" valign="top">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
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            0 => array:3 [
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              "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Percentage of patients who underwent coronary angiography&#46;</p>"
            ]
          ]
        ]
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          "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Clinical characteristics of the patients in each cohort&#46;</p>"
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          0 => array:3 [
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          "leyenda" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">SBP&#58; systolic blood pressure&#46;</p>"
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                0 => """
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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">Variable&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">Cut-off&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">Points&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">Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">&#60;72 years<br>&#8805;72 years&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">0<br>2&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&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">1<br>0&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&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td><td class="td" title="table-entry  " align="left" valign="top">0<br>1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
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          "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">ProACS risk score&#46;</p>"
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                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">Cohort&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">C-statistic &#40;95&#37; CI&#41;&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">Development&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;796 &#40;0&#46;782-0&#46;810&#41;&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">Internal validation&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;785 &#40;0&#46;767-0&#46;803&#41;&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">External validation&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;815 &#40;0&#46;793-0&#46;837&#41;&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">0&#46;799 &#40;0&#46;768-0&#46;830&#41;&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">NSTACS&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;809 &#40;0&#46;774-0&#46;845&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
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          "en" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Area under the curve for the ProACS risk score in the different cohorts&#46;</p>"
        ]
      ]
    ]
    "bibliografia" => array:2 [
      "titulo" => "References"
      "seccion" => array:1 [
        0 => array:2 [
          "identificador" => "bibs0005"
          "bibliografiaReferencia" => array:20 [
            0 => array:3 [
              "identificador" => "bib0105"
              "etiqueta" => "1"
              "referencia" => array:1 [
                0 => array:3 [
                  "comentario" => "Task Force on the management of ST-segment elevation acute myocardial infarction of the European Society of Cardiology &#40;ESC&#41;"
                  "contribucion" => array:1 [
                    0 => array:2 [
                      "titulo" => "ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation"
                      "autores" => array:1 [
                        0 => array:2 [
                          "etal" => true
                          "autores" => array:3 [
                            0 => "G&#46; Steg"
                            1 => "S&#46;K&#46; James"
                            2 => "D&#46; Atar"
                          ]
                        ]
                      ]
                    ]
                  ]
                  "host" => array:1 [
                    0 => array:2 [
                      "doi" => "10.1093/eurheartj/ehs215"
                      "Revista" => array:6 [
                        "tituloSerie" => "Eur Heart J"
                        "fecha" => "2012"
                        "volumen" => "33"
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Informação do artigo

ISSN: 08702551
Idioma original: Inglês

DOI:10.1016/j.repc.2016.05.010

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2024 Novembro	12	2	14
2024 Outubro	82	37	119
2024 Setembro	129	25	154
2024 Agosto	88	26	114
2024 Julho	90	28	118
2024 Junho	98	40	138
2024 Maio	66	23	89
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2024 Janeiro	76	27	103
2023 Dezembro	89	20	109
2023 Novembro	64	27	91
2023 Outubro	41	19	60
2023 Setembro	48	22	70
2023 Agosto	45	21	66
2023 Julho	43	13	56
2023 Junho	43	22	65
2023 Maio	71	29	100
2023 Abril	54	8	62
2023 Mar�o	58	25	83
2023 Fevereiro	58	27	85
2023 Janeiro	29	26	55
2022 Dezembro	54	39	93
2022 Novembro	58	35	93
2022 Outubro	44	29	73
2022 Setembro	44	29	73
2022 Agosto	30	23	53
2022 Julho	50	48	98
2022 Junho	34	37	71
2022 Maio	34	26	60
2022 Abril	56	40	96
2022 Mar�o	60	68	128
2022 Fevereiro	33	53	86
2022 Janeiro	57	46	103
2021 Dezembro	39	36	75
2021 Novembro	44	64	108
2021 Outubro	41	42	83
2021 Setembro	34	34	68
2021 Agosto	46	36	82
2021 Julho	21	30	51
2021 Junho	30	28	58
2021 Maio	50	43	93
2021 Abril	63	38	101
2021 Mar�o	64	36	100
2021 Fevereiro	56	22	78
2021 Janeiro	72	12	84
2020 Dezembro	42	16	58
2020 Novembro	45	21	66
2020 Outubro	55	22	77
2020 Setembro	43	12	55
2020 Agosto	39	9	48
2020 Julho	49	16	65
2020 Junho	48	24	72
2020 Maio	47	8	55
2020 Abril	50	15	65
2020 Mar�o	49	14	63
2020 Fevereiro	69	21	90
2020 Janeiro	29	10	39
2019 Dezembro	40	11	51
2019 Novembro	38	9	47
2019 Outubro	42	8	50
2019 Setembro	33	20	53
2019 Agosto	28	8	36
2019 Julho	39	14	53
2019 Junho	46	20	66
2019 Maio	48	12	60
2019 Abril	40	21	61
2019 Mar�o	56	16	72
2019 Fevereiro	78	10	88
2019 Janeiro	55	9	64
2018 Dezembro	78	19	97
2018 Novembro	125	13	138
2018 Outubro	229	28	257
2018 Setembro	49	9	58
2018 Agosto	36	12	48
2018 Julho	21	3	24
2018 Junho	45	8	53
2018 Maio	52	9	61
2018 Abril	55	8	63
2018 Mar�o	72	14	86
2018 Fevereiro	69	7	76
2018 Janeiro	30	7	37
2017 Dezembro	113	10	123
2017 Novembro	64	15	79
2017 Outubro	41	10	51
2017 Setembro	20	9	29
2017 Agosto	29	14	43
2017 Julho	17	25	42
2017 Junho	30	15	45
2017 Maio	46	19	65
2017 Abril	28	13	41
2017 Mar�o	59	24	83
2017 Fevereiro	82	58	140
2017 Janeiro	0	1	1

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