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array:24 [ "pii" => "S2254887418300018" "issn" => "22548874" "doi" => "10.1016/j.rceng.2017.12.001" "estado" => "S300" "fechaPublicacion" => "2018-03-01" "aid" => "1458" "copyright" => "Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI)" "copyrightAnyo" => "2017" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Clin Esp. 2018;218:49-57" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 2 "PDF" => 2 ] "Traduccion" => array:1 [ "es" => array:19 [ "pii" => "S0014256517302898" "issn" => "00142565" "doi" => "10.1016/j.rce.2017.12.004" "estado" => "S300" "fechaPublicacion" => "2018-03-01" "aid" => "1458" "copyright" => "Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI)" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Rev Clin Esp. 2018;218:49-57" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 87 "formatos" => array:2 [ "HTML" => 65 "PDF" => 22 ] ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">ORIGINAL</span>" "titulo" => "Propuesta de una escala clínica para el diagnóstico de síndrome coronario agudo en pacientes con electrocardiograma y biomarcadores de lesión miocárdica no concluyentes" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "49" "paginaFinal" => "57" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Proposed clinical scale for the diagnosis of acute coronary syndrome in patients with an inconclusive electrocardiogram and myocardial injury biomarkers" ] ] "contieneResumen" => array:2 [ "es" => true "en" => true ] "contieneTextoCompleto" => array:1 [ "es" => true ] "contienePdf" => array:1 [ "es" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figura 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1031 "Ancho" => 1585 "Tamanyo" => 98905 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Algoritmo de flujo de inclusión de pacientes en el estudio.</p> <p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">ECG: electrocardiograma; SCA: síndrome coronario agudo.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "F.J. Montero-Pérez, F.B. Quero-Espinosa, M.J. Clemente-Millán, J.A. Castro-Giménez, J. de Burgos-Marín, M.Á. Romero-Moreno" "autores" => array:6 [ 0 => array:2 [ "nombre" => "F.J." "apellidos" => "Montero-Pérez" ] 1 => array:2 [ "nombre" => "F.B." "apellidos" => "Quero-Espinosa" ] 2 => array:2 [ "nombre" => "M.J." "apellidos" => "Clemente-Millán" ] 3 => array:2 [ "nombre" => "J.A." "apellidos" => "Castro-Giménez" ] 4 => array:2 [ "nombre" => "J." "apellidos" => "de Burgos-Marín" ] 5 => array:2 [ "nombre" => "M.Á." "apellidos" => "Romero-Moreno" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2254887418300018" "doi" => "10.1016/j.rceng.2017.12.001" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2254887418300018?idApp=WRCEE" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0014256517302898?idApp=WRCEE" "url" => "/00142565/0000021800000002/v1_201802240414/S0014256517302898/v1_201802240414/es/main.assets" ] ] "itemSiguiente" => array:19 [ "pii" => "S2254887417301364" "issn" => "22548874" "doi" => "10.1016/j.rceng.2017.10.006" "estado" => "S300" "fechaPublicacion" => "2018-03-01" "aid" => "1447" "copyright" => "Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI)" "documento" => "article" "crossmark" => 1 "subdocumento" => "sco" "cita" => "Rev Clin Esp. 2018;218:58-60" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 2 "PDF" => 2 ] "en" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Brief Original</span>" "titulo" => "Does nutritional intervention maintain its prognostic benefit in the long term for malnourished patients hospitalised for heart failure?" 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Bonilla-Palomas, A.L. Gámez-López, J.C. Castillo-Domínguez, M. Moreno-Conde, M.C. López-Ibáñez, M. Anguita-Sánchez" "autores" => array:6 [ 0 => array:2 [ "nombre" => "J.L." "apellidos" => "Bonilla-Palomas" ] 1 => array:2 [ "nombre" => "A.L." "apellidos" => "Gámez-López" ] 2 => array:2 [ "nombre" => "J.C." "apellidos" => "Castillo-Domínguez" ] 3 => array:2 [ "nombre" => "M." "apellidos" => "Moreno-Conde" ] 4 => array:2 [ "nombre" => "M.C." "apellidos" => "López-Ibáñez" ] 5 => array:2 [ "nombre" => "M." "apellidos" => "Anguita-Sánchez" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S0014256517302588" "doi" => "10.1016/j.rce.2017.10.005" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0014256517302588?idApp=WRCEE" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2254887417301364?idApp=WRCEE" "url" => "/22548874/0000021800000002/v1_201802230423/S2254887417301364/v1_201802230423/en/main.assets" ] "asociados" => array:1 [ 0 => array:19 [ "pii" => "S2254887418300249" "issn" => "22548874" "doi" => "10.1016/j.rceng.2018.01.003" "estado" => "S300" "fechaPublicacion" => "2018-03-01" "aid" => "1466" "copyright" => "Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI)" "documento" => "simple-article" "crossmark" => 1 "subdocumento" => "edi" "cita" => "Rev Clin Esp. 2018;218:72-3" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 1 "PDF" => 1 ] "en" => array:10 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Editorial</span>" "titulo" => "Clinical scales: Tools for the diagnosis of unstable angina" "tienePdf" => "en" "tieneTextoCompleto" => "en" "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "72" "paginaFinal" => "73" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Escalas clínicas: una herramienta para el diagnóstico de angina inestable" ] ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "A. Alquézar-Arbé, B. Lopez Barbeito" "autores" => array:2 [ 0 => array:2 [ "nombre" => "A." "apellidos" => "Alquézar-Arbé" ] 1 => array:2 [ "nombre" => "B." "apellidos" => "Lopez Barbeito" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S0014256518300250" "doi" => "10.1016/j.rce.2018.01.005" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0014256518300250?idApp=WRCEE" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2254887418300249?idApp=WRCEE" "url" => "/22548874/0000021800000002/v1_201802230423/S2254887418300249/v1_201802230423/en/main.assets" ] ] "en" => array:21 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original article</span>" "titulo" => "Proposed clinical scale for the diagnosis of acute coronary syndrome in patients with an inconclusive electrocardiogram and myocardial injury biomarkers" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "49" "paginaFinal" => "57" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "F.J. Montero-Pérez, F.B. Quero-Espinosa, M.J. Clemente-Millán, J.A. Castro-Giménez, J. de Burgos-Marín, M.Á. Romero-Moreno" "autores" => array:6 [ 0 => array:3 [ "nombre" => "F.J." "apellidos" => "Montero-Pérez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 1 => array:4 [ "nombre" => "F.B." "apellidos" => "Quero-Espinosa" "email" => array:1 [ 0 => "borjaquero@gmail.com" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 2 => array:3 [ "nombre" => "M.J." "apellidos" => "Clemente-Millán" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "J.A." "apellidos" => "Castro-Giménez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 4 => array:3 [ "nombre" => "J." "apellidos" => "de Burgos-Marín" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 5 => array:3 [ "nombre" => "M.Á." "apellidos" => "Romero-Moreno" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "Unidad de Gestión Clínica de Urgencias, Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Universitario Reina Sofía, Universidad de Córdoba, Córdoba, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Unidad de Gestión Clínica de Cardiología, Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Hospital Universitario Reina Sofía, Universidad de Córdoba, Córdoba, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Propuesta de una escala clínica para el diagnóstico de síndrome coronario agudo en pacientes con electrocardiograma y biomarcadores de lesión miocárdica no concluyentes" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1031 "Ancho" => 1585 "Tamanyo" => 104920 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Flow algorithm for study patient enrollment.</p> <p id="spar0060" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>: ACS, acute coronary syndrome; ECG, electrocardiogram.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Background</span><p id="par0005" class="elsevierStylePara elsevierViewall">Acute chest pain (ACP) is a common symptom in hospital emergency department (HED) consultations. ACP represents 5–15% of emergency department consultations<a class="elsevierStyleCrossRefs" href="#bib0210"><span class="elsevierStyleSup">1,2</span></a> and creates a significant burden of care and diagnostic problems.<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">3</span></a> In Spain, 50% of patients hospitalized for suspected acute coronary syndrome (ACS) are discharged without a diagnostic confirmation.<a class="elsevierStyleCrossRefs" href="#bib0210"><span class="elsevierStyleSup">1,4,5</span></a> Moreover, 2–10% of cases with ACS are sent home after considering the ACP as noncoronary, resulting in a poorer prognosis than for those with ACS identified early.<a class="elsevierStyleCrossRefs" href="#bib0210"><span class="elsevierStyleSup">1,4,5</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The diagnostic challenge of ACP lies in the fact that its presentation, physical examination and additional tests (electrocardiogram [ECG] and serum biomarkers) are insufficient for discerning whether its origin is coronary. Therefore, there have been efforts to obtain clinical prediction tools based on scales that can help reduce diagnostic errors.</p><p id="par0015" class="elsevierStylePara elsevierViewall">Most of the scales, such as the Thrombolysis in Myocardial Infarction (TIMI) score,<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">6</span></a> HEART score,<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">7</span></a> Global Registry of Acute Coronary Events (GRACE),<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">8</span></a> Banach Score<a class="elsevierStyleCrossRef" href="#bib0250"><span class="elsevierStyleSup">9</span></a> and Emergency Department Assessment of Chest Pain Score-Accelerated Diagnostic Protocol (EDACS-ADP),<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">10</span></a> have been designed for risk stratification, prediction of adverse cardiovascular events and mortality or to decide on the initial therapeutic strategies for patients with ACS but have not been validated as diagnostic methods. Geleijnse et al.<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">6</span></a> (<a class="elsevierStyleCrossRef" href="#sec0055">Appendix 1</a>) and, more recently, Boubaker et al.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">12</span></a> investigated new scales as clinical tools for determining the coronary or noncoronary origin of an ACP.</p><p id="par0020" class="elsevierStylePara elsevierViewall">In Spain, we have studies by Sanchís et al.<a class="elsevierStyleCrossRefs" href="#bib0270"><span class="elsevierStyleSup">13–16</span></a> on the prognostic assessment of long-term coronary risk, but these studies were also not designed to determine the probability of ACS in patients with ACP.</p><p id="par0025" class="elsevierStylePara elsevierViewall">The main objective of this study was to obtain a scale based on clinical variables that is predictive for the diagnosis of ACS and applicable to patients in HEDs who have ACP, whose ECG and troponin values do not confirm the diagnosis.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Material and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Study design and population</span><p id="par0030" class="elsevierStylePara elsevierViewall">A prospective cohort study was conducted with patients who attended an HED for ACP between June 2013 and May 2014. Patient selection was conducted using sampling by batches. Each batch included all patients who consecutively visited for ACP (except for trauma) during the participating researchers’ workday. Of these patients, we included those with normal troponin levels and nondiagnostic ECG (normal tracing, ST reduction <0.5<span class="elsevierStyleHsp" style=""></span>mm or asymmetric T-wave inversion in less than 2 contiguous leads). We excluded patients who did not give their consent, those with increased serial troponin levels at 0, 6 and 12<span class="elsevierStyleHsp" style=""></span>h of the onset of pain and those who had a diagnostic ECG (ST-elevation in 2 or more contiguous leads, complete unknown left bundle branch block, ST-segment reduction or dynamic T-wave inversion).<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">17</span></a> We analyzed all ECGs performed on each patient.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Study variables and basic definitions</span><p id="par0035" class="elsevierStylePara elsevierViewall">The clinical variables included in the study are shown in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>, which includes the results of the coronary angiography (normal/pathological), maximum conventional serum troponin I peak at 0, 6 and 12<span class="elsevierStyleHsp" style=""></span>h of the onset of pain (negative/positive), computed tomography angiography (CTA) (normal/pathological), final diagnostic group (ACS/non-ACS) and etiological diagnosis.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0040" class="elsevierStylePara elsevierViewall">The gestures considered in the study corresponded to the 3 most often described<a class="elsevierStyleCrossRefs" href="#bib0295"><span class="elsevierStyleSup">18,19</span></a> as associated with coronary ACP, although they have shown limited diagnostic validity<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">20</span></a> for diagnosing ACS in previous studies.</p><p id="par0045" class="elsevierStylePara elsevierViewall">The quantitative variable “age” was recategorized according to the median of its data distribution. The variable “Geleijnse scale” was categorized according to the originally established cutoff.<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">6</span></a> The cardiovascular risk factors are shown in <a class="elsevierStyleCrossRef" href="#sec0060">Appendix 2</a> and were considered according to internationally established standards.<a class="elsevierStyleCrossRefs" href="#bib0310"><span class="elsevierStyleSup">21–24</span></a> The results of the coronary angiography were considered pathological when there was an obstructive lesion >50% of the lumen in epicardial vessels larger than 2<span class="elsevierStyleHsp" style=""></span>mm in diameter.<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">25</span></a></p><p id="par0050" class="elsevierStylePara elsevierViewall">Serum troponin I levels were measured by chemiluminescence and considered positive for values >0.30<span class="elsevierStyleHsp" style=""></span>ng/mL (corresponding to a value above the 99 percentile of the upper reference limit). The coronary artery CTA was performed with a multidetector 64-slice CT system, using contrast with iopromide 370.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Data collection system</span><p id="par0055" class="elsevierStylePara elsevierViewall">The clinical variables were collected by 5 HED physicians during the first contact with the patient. These physicians were previously trained through a correlation series on the same cases until a kappa index of at least 80% was achieved. Subsequently, 2 researchers analyzed the additional tests to assign the diagnostic group; in case of discrepancy, an expert cardiologist decided the final diagnosis.</p><p id="par0060" class="elsevierStylePara elsevierViewall">The Geleijnse scale<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">11</span></a> was calculated by an independent researcher who was unaware of the study database. The study was approved by the hospital center's ethics committee (reference 2221).</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Study protocol and patient follow-up</span><p id="par0065" class="elsevierStylePara elsevierViewall">The patients were assigned to 2 diagnostic groups: the ACS group (when the coronary origin was confirmed) and the non-ACS group (when another origin was demonstrated). The classification as coronary origin and the assignment to one of its categories (stable/unstable angina or acute myocardial infarction) was performed following the current clinically based criteria, troponin values, ECG and imaging studies (CTA and coronary angiography).<a class="elsevierStyleCrossRefs" href="#bib0335"><span class="elsevierStyleSup">26–31</span></a> The diagnosis of ACS was defined by 2 emergency department physicians and endorsed by an expert cardiologist, in case of discrepancy. The patients diagnosed with hemodynamic angina were encompassed within the non-ACS group, except when there was evidence of a coronary lesion in the imaging techniques.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Statistical analysis</span><p id="par0070" class="elsevierStylePara elsevierViewall">The sample size was calculated to determine the proportion of a finite population, considering that 5% of patients treated in an HED are there for chest pain (in our case, <span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>6250, corresponding to 125,000 emergencies/year) and that the proportion of ischemic heart disease is 10%. We accepted a maximum imprecision of ±3% and a 95% confidence level in the calculation. The minimum sample size obtained was 362 cases.</p><p id="par0075" class="elsevierStylePara elsevierViewall">The variables were entered into the Statistical Package for the Social Sciences 17.0 for Windows (IBM SPSS Software). We performed a univariate study using an absolute and relative frequency distribution for the qualitative variables. The continuous variables are expressed with the arithmetic mean, accompanied by the 95% confidence intervals (95% CI) and were compared using Student's <span class="elsevierStyleItalic">t</span>-test. To analyze the association between the qualitative variables and the dependent variable “final diagnostic group”, we employed the chi-squared test of bilateral significance, with Fisher's exact test if necessary. We considered a <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>.05 significant.</p><p id="par0080" class="elsevierStylePara elsevierViewall">Those variables that in the bivariate study showed a statistically significant (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>.05) or almost significant (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>.1) association with the dependent variable “final diagnostic group (ACS/non-ACS)” and with a clinically significant trend to association were inserted into a logistic regression model to find the independent predictors of the diagnosis of ACS. The indicator variables (also known as dummy variables) were constructed according to the partial method, considering the reference category as that with the least association with the presence of ACS. The variable “gesture type” performed by the patient with their hands was entered into the initial model because we wanted to explore its influence in the multivariate model.</p><p id="par0085" class="elsevierStylePara elsevierViewall">The multiple logistic regression analysis was initially conducted using the stepwise-forward method and then with the methodical selection method (Enter), determining the possible existence of confounding variables and interactions. We used an entry probability of 0.25 and an exit probability of 0.80 for the model. The goodness of fit of the logistic regression model (i.e., the comparison between the observed effects and those predicted by the predictive model) was assessed using the Hosmer–Lemeshow test.</p><p id="par0090" class="elsevierStylePara elsevierViewall">After identifying the independent predictors of the diagnosis of ACS, we assigned a score to each predictor, dividing the coefficient of each (based on the logistic regression analysis) by the coefficient of the smaller value. We then rounded each quotient to the closest whole number. Lastly, we added up the points and calculated a prediction score for each patient. The diagnostic validity was measured using the calculation of sensitivity, specificity, positive predictive value, negative predictive value, positive and negative likelihood ratios and the overall diagnostic value,<a class="elsevierStyleCrossRef" href="#bib0365"><span class="elsevierStyleSup">32</span></a> with their respective 95% CI.<a class="elsevierStyleCrossRef" href="#bib0370"><span class="elsevierStyleSup">33</span></a></p><p id="par0095" class="elsevierStylePara elsevierViewall">We calculated the area under the receiver operating characteristic (ROC) curve, with its corresponding 95% CI, to assess whether the model's prediction was better than that due to random chance and to compare the models. The comparison of the area under the ROC curve for each group was performed using MedCalc™ software version 17 (Ostend, Belgium) by the Delong et al. method<a class="elsevierStyleCrossRef" href="#bib0375"><span class="elsevierStyleSup">34</span></a> and considering a significance level of <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>.05. We considered the result good when the area was at least 80%; i.e., when 80% of paired cases thus chosen were correctly classified by the model.<a class="elsevierStyleCrossRef" href="#bib0375"><span class="elsevierStyleSup">34</span></a></p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Results</span><p id="par0100" class="elsevierStylePara elsevierViewall">We initially considered 383 patients for the study. The reasons for exclusion were high troponin levels (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>22), abnormal ECG results (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>20), high troponin levels and abnormal ECG results (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>34) and lost cases (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>21) (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). The final sample consisted of 286 cases. The diagnosis of ACS was confirmed in 103 (36%) cases (27 categorized as stable angina, 66 as unstable and 10 as hemodynamic angina with demonstrated coronary lesions), which constituted the ACS group. The diagnosis of ACS was rejected in 183 (64%) cases, which comprised the non-ACS group (15 cases of noncoronary cardiac origin, 19 of pulmonary causes, 5 of gastrointestinal etiology, 24 of muscle-skeletal origin, 11 of psychogenic etiology, 1 of otolaryngologic origin and 108 of indeterminate causes). The overall mean age was 64.15 years (95% CI 62.43–65.88); 70 years (95% CI 67.7–72.2) for the ACS group and 61 years for the non-ACS group (95% CI 58.63–63.16) (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>.05). The sample's median age was 65 years, with an interquartile range of 23. The distribution of cases according to the different variables (overall and by diagnostic groups) is shown in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>, along with the statistical significance of the bivariate analysis.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0105" class="elsevierStylePara elsevierViewall">The results of the final multiple logistic regression model are shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. The multivariate analysis identified the following independent diagnostic predictors of ACS: a history of coronary artery disease, dyslipidemia and a score ≥6 points on the Geleijnse scale (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>). There was no evidence of a lack of fit in the model (the goodness of fit measured by Pearson's chi-squared statistic achieved a <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>.29). Based on the logistic regression coefficients, we assigned the corresponding score to each variable that ultimately shaped the model, as explained above (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0110" class="elsevierStylePara elsevierViewall">After examining the various prediction scores (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>), we determined that the optimal cutoff was 5 points or more for the final model, due to its greater overall diagnosis value or diagnostic accuracy. The model's area under the ROC curve was 0.89 (95% CI 0.85–0.93) (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> and <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). Considering the Geleijnse scale independently as the only predictor of a diagnosis of ACS, its area under the ROC curve was 0.60 (95% CI 0.53–0.66) (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> and <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). We observed statistically significant differences when comparing the 2 curves (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>.001) (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> and <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>).</p><elsevierMultimedia ident="tbl0020"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Discussion</span><p id="par0115" class="elsevierStylePara elsevierViewall">The present study proposes a clinical assessment scale with a positive likelihood ratio that allows for its use in HEDs as a diagnostic tool for patients who present ACP and inconclusive biomarkers and ECG, which can occur in up to 33% of cases of ACS.<a class="elsevierStyleCrossRef" href="#bib0380"><span class="elsevierStyleSup">35</span></a> One of the components of this scale is the Geleijnse scale<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">11</span></a> and includes the presence of a history of coronary disease and dyslipidemia. These 2 risk factors presented the greatest statistical significance, above that of nicotine addiction, diabetes and arterial hypertension.</p><p id="par0120" class="elsevierStylePara elsevierViewall">The diagnostic validity of the proposed scale, based on the area under the ROC curve, was superior to that of the Geleijnse scale alone (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> and <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). The cutoff of 5 points on our proposed scale achieved sufficient power to discern whether a patient with ACP has ACS or not (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a>), with a specificity of 91.8% (95% CI 87–95%) and a positive likelihood ratio of 9.36 (95% CI 5.70–15.4).</p><p id="par0125" class="elsevierStylePara elsevierViewall">The objective of most current scales is the quantification of risk and the prediction of adverse cardiovascular events and mortality.<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">6–10</span></a> Although the use of these scales is consolidated in patients already diagnosed with ACS, there are few studies that have investigated the scales’ usefulness in helping clinicians determine whether an ACP is an ACS or not.<a class="elsevierStyleCrossRefs" href="#bib0385"><span class="elsevierStyleSup">36–39</span></a> Additionally, their results have not been validated and have not demonstrated sufficient diagnostic validity.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">12</span></a> Recently, Boubaker et al.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">12</span></a> explored this aspect by developing a scale consisting of the sum of scores on 2 other scales: the TIMI scale<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">6</span></a> and the Geleijnse scale.<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">11</span></a> The authors obtained an area under the ROC curve of 0.85 (95% CI 0.60–0.90), while the areas under the ROC curves of the original scales, considered independently, were 0.74 (95% CI 0.67–0.81) and 0.79 (95% CI 0.74–0.84), respectively. The joint scale by Boubaker et al.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">12</span></a> achieved a sensitivity of 92%, a specificity of 62% and a positive predictive value of 23%. Compared with our study, these authors’ results were inferior in terms of diagnostic validity, coupled with the greater complexity resulting from adding 2 different scales.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">12</span></a></p><p id="par0130" class="elsevierStylePara elsevierViewall">The Geleijnse<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">11</span></a> scale is based exclusively on the characteristics of the ACP without considering other data, which limits its usefulness as a diagnostic instrument.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">12</span></a> This limitation could explain the low discriminative power exhibited in the study by Boubaker et al.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">12</span></a> and in ours. The improvement in the diagnostic capacity in our study could be explained by the inclusion of elements in addition to the characteristics of the ACP, such as the history of coronary disease and dyslipidemia.</p><p id="par0135" class="elsevierStylePara elsevierViewall">With regard to the HEART scale,<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">7</span></a> several studies have supported its use in the decision-making process for patients with ACP in the HED. Nevertheless, the scale is a scoring system that uses 5 more complex items, including myocardial lesion biomarkers and electrocardiographic abnormalities. However, the scale does not behave like a diagnostic tool, because patients are still admitted despite achieving scores classified as low risk.<a class="elsevierStyleCrossRef" href="#bib0405"><span class="elsevierStyleSup">40</span></a></p><p id="par0140" class="elsevierStylePara elsevierViewall">Our study has a number of limitations. The study was performed in a single HED, and the sample size was small (smaller than that determined as optimal). The sampling by batches probably left out eligible patients who might have had the same profile as those selected for the study. Another possible bias was the assignment to diagnostic groups, although being reviewed by an expert cardiologist following criteria defined by scientific societies minimizes the possible error. It is possible that the recoding of the variables used to build the predictive model might have affected its predictive capacity for ACS by overestimating a number of parameters. However, we believe that the recoding did not significantly alter the conclusions.</p><p id="par0145" class="elsevierStylePara elsevierViewall">The proposed scale is superior to the Geleijnse scale and easier to implement than the scale by Boubaker et al.,<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">12</span></a> although, as with the latter scale, the proposed scale requires external validation in a different sample and from various centers to verify its clinical applicability.</p><p id="par0150" class="elsevierStylePara elsevierViewall">If we consider that many patients with ACP and suspected ACS are admitted to hospitals for diagnostic tests and that on other occasions the patients are improperly sent home, there is no doubt that a clinical tool that reduces these risks would be useful in the HED. The proposed scale contains variables that are easy to acquire during the consultation, which is an essential condition for its applicability in daily clinical practice. These tools are obviously needed in the HED when there a lack of more innovative diagnostic procedures, such as coronary CTA and ultrasensitive troponin, which could help better stratify the coronary risk in the future.<a class="elsevierStyleCrossRef" href="#bib0410"><span class="elsevierStyleSup">41</span></a></p><p id="par0155" class="elsevierStylePara elsevierViewall">In conclusion, we propose a predictive model for ACS, based on purely clinical variables, with an acceptable diagnostic validity to determine whether a patient with ACP who is admitted to an HED with inconclusive ECG and serum troponin levels is experiencing an ACS. It is clear that the model's interpretation should be integrated in the medical history dataset, without ruling out other already proven risk scales for the prognosis.</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Conflicts of interest</span><p id="par0160" class="elsevierStylePara elsevierViewall">The authors declare that they have no conflicts of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:10 [ 0 => array:3 [ "identificador" => "xres991056" "titulo" => "Abstract" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Rationale" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Objectives" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Methods" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Results" ] 4 => array:2 [ "identificador" => "abst0025" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec954978" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres991055" "titulo" => "Resumen" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "abst0030" "titulo" => "Justificación" ] 1 => array:2 [ "identificador" => "abst0035" "titulo" => "Objetivos" ] 2 => array:2 [ "identificador" => "abst0040" "titulo" => "Métodos" ] 3 => array:2 [ "identificador" => "abst0045" "titulo" => "Resultados" ] 4 => array:2 [ "identificador" => "abst0050" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec954979" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Background" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Material and methods" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Study design and population" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Study variables and basic definitions" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Data collection system" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Study protocol and patient follow-up" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Statistical analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0040" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0045" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0050" "titulo" => "Conflicts of interest" ] 9 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2017-09-20" "fechaAceptado" => "2017-12-15" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec954978" "palabras" => array:3 [ 0 => "Chest pain" 1 => "Acute coronary syndrome" 2 => "Emergency department" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec954979" "palabras" => array:3 [ 0 => "Dolor torácico" 1 => "Síndrome coronario agudo" 2 => "Servicio de Urgencias" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Rationale</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Acute coronary syndrome (ACS) requires improved diagnostic accuracy through useful, safe and easy-to-apply tools.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Objectives</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">To obtain an assessment scale for the diagnosis of ACS in patients with chest pain and nondiagnostic electrocardiogram and troponin concentrations.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Methods</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">A prospective cohort study included 286 patients treated in the emergency department for chest pain, with normal electrocardiogram and troponin levels. Using multiple logistic regression, we obtained the independent predictors for the diagnosis of ACS. The assessment scale's discriminative power was assessed with the area under the ROC curve.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Results</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">The diagnosis of ACS was confirmed in 103 patients (36%). The final predictive model included 3 endpoints: a history of coronary artery disease, hyperlipidaemia and a score<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>6 points on the Geleijnse scale. The area under the ROC curve for the final model was 0.90 (95% confidence interval [95% CI] 0.85–0.93). A threshold of 5 points achieved a sensitivity of 76.7% (95% CI 68–84), a specificity of 91.8% (95% CI 87–95), a positive likelihood ratio of 9.36 (95% CI 5.70–15.40), a negative likelihood ratio of 0.25 (95% CI 18.00–36.00) and an overall diagnostic accuracy of 86.4% (95% CI 82–90). The predictive model was superior to the Geleijnse scale alone.</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Conclusions</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">The final scale showed good discriminative capacity for diagnosing ACS and could therefore be of interest for identifying ACS in emergency departments. Nevertheless, the scale needs to be validated in larger multicentre studies.</p></span>" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Rationale" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Objectives" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Methods" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Results" ] 4 => array:2 [ "identificador" => "abst0025" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Justificación</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">El síndrome coronario agudo (SCA) requiere una mejora de la precisión diagnóstica mediante herramientas útiles, seguras y fácilmente aplicables.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Objetivos</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Obtener una escala de valoración para el diagnóstico de SCA en pacientes con dolor torácico y electrocardiograma y concentraciones de troponina no diagnósticas.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Métodos</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Estudio de cohortes prospectivo que incluyó 286 pacientes atendidos en Urgencias por dolor torácico, con electrocardiograma y troponina normales. Mediante regresión logística múltiple se obtuvieron las variables independientes predictoras del diagnóstico de SCA. El poder discriminativo de la escala de valoración se evaluó mediante el área bajo la curva ROC.</p></span> <span id="abst0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Resultados</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">El diagnóstico de SCA se confirmó en 103 pacientes (36%). El modelo predictivo final incluyó 3 variables: antecedentes personales de enfermedad coronaria, hiperlipidemia y una puntuación en la escala de Geleijnse<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>6 puntos. El área bajo la curva ROC para el modelo obtenido fue de 0,90 (intervalo de confianza al 95% [IC 95%] 0,85-0,93). Un umbral de 5 puntos obtuvo una sensibilidad del 76,7% (IC 95% 68-84), una especificidad del 91,8% (IC 95% 87-95), un cociente de probabilidad positivo de 9,36 (IC 95% 5,70-15,40), un cociente de probabilidad negativo de 0,25 (18,00-36,00) y una precisión diagnóstica global del 86,4% (IC 95% 82-90).El modelo predictivo fue superior a la escala de Geleijnse aislada.</p></span> <span id="abst0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Conclusiones</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">La escala de puntuación obtenida mostró una buena capacidad discriminativa para el diagnóstico de SCA, por lo que podría ser de interés para identificar el SCA en los servicios de Urgencias. No obstante, se precisa su validación mediante estudios multicéntricos más amplios.</p></span>" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "abst0030" "titulo" => "Justificación" ] 1 => array:2 [ "identificador" => "abst0035" "titulo" => "Objetivos" ] 2 => array:2 [ "identificador" => "abst0040" "titulo" => "Métodos" ] 3 => array:2 [ "identificador" => "abst0045" "titulo" => "Resultados" ] 4 => array:2 [ "identificador" => "abst0050" "titulo" => "Conclusiones" ] ] ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0015">Please cite this article as: Montero-Pérez FJ, Quero-Espinosa FB, Clemente-Millán MJ, Castro-Giménez JA, de Burgos-Marín J, Romero-Moreno MÁ. Propuesta de una escala clínica para el diagnóstico de síndrome coronario agudo en pacientes con electrocardiograma y biomarcadores de lesión miocárdica no concluyentes. Rev Clin Esp. 2018;218:49–57.</p>" ] ] "apendice" => array:1 [ 0 => array:1 [ "seccion" => array:1 [ 0 => array:4 [ "apendice" => "<p id="par0170" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="upi0010"></elsevierMultimedia></p>" "etiqueta" => "Appendix" "titulo" => "Supplementary data" "identificador" => "sec0060" ] ] ] ] "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" => 1031 "Ancho" => 1585 "Tamanyo" => 104920 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Flow algorithm for study patient enrollment.</p> <p id="spar0060" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>: ACS, acute coronary syndrome; ECG, electrocardiogram.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1062 "Ancho" => 1411 "Tamanyo" => 113961 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Areas under the ROC curve of the Geleijnse scale and of the proposed scale in this study.</p>" ] ] 2 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>: 95% CI, 95% confidence intervals; ACS, acute coronary syndrome; BMI, body mass index; ULE, upper left extremity; VAS, visual analog scale.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td-with-role" title="table-head ; entry_with_role_rowhead " align="left" valign="top" scope="col">Variables \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col">Patients \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">Diagnostic groups</th><th class="td" title="table-head " align="" valign="top" scope="col"> \t\t\t\t\t\t\n \t\t\t\t</th></tr><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"><span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>286 \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">ACS<br><span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>103 \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">Non-ACS<br><span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>183 \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"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " colspan="4" align="left" valign="top"><span class="elsevierStyleItalic">Age, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top"><.01 \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><65 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">145 (50.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">38 (36.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">107 (58.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>>65 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">141 (49.30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">65 (63.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">76 (41.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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="4" align="left" valign="top"><span class="elsevierStyleItalic">Sex, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top">.53 \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>Female \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">132 (46.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">45 (43.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">87 (47.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Male \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">154 (53.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">58 (56.30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">96 (52.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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">History of ischemic heart disease, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">95 (33.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">79 (76.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16 (8.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><.01 \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">Family history of ischemic heart disease, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">53 (18.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27 (26.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">26 (14.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.012 \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">Overweight <span class="elsevierStyleItalic">(BMI</span><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">></span><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">25), n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">198 (81.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">72 (83.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">126 (80.30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.50 \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">Arterial hypertension, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">187 (65.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">84 (81.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">103 (56.30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><.01 \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">Diabetes mellitus, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">73 (25.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">37 (35.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">36 (19.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.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"><span class="elsevierStyleItalic">Dyslipidemia, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">152 (53.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">79 (76.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">73 (39.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><.01 \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">Active smoker, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">71 (24.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 (19.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">51 (27.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.11 \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">Passive smoking, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30 (13.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10 (11.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 (15.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.55 \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">Physical inactivity, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">96 (33.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">41 (39.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">55 (30.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.09 \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">Chest pain present at the time of the interview, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">99 (34.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19 (18.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">80 (43.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleItalic">First episode of chest pain in the patient's life, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">111 (38.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 (8.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">102 (55.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><.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"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="4" align="left" valign="top"><span class="elsevierStyleItalic">Number of episodes, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top">.086 \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>One \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">249 (87.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">85 (82.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">164 (89.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>More than one \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">37 (12.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18 (17.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19 (10.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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="4" align="left" valign="top"><span class="elsevierStyleItalic">Type of onset of the chest pain, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top">.26 \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>Resting \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">250 (87.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">87 (84.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">163 (89.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Exercise \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">36 (12.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16 (15.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 (10.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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="4" align="left" valign="top"><span class="elsevierStyleItalic">Verbal expression of the pain, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top">.32 \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>Overwhelming/oppressive \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">230 (80.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">86 (83.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">134 (78.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Others \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">56 (19.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">17 (16.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">39 (21.30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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="4" align="left" valign="top"><span class="elsevierStyleItalic">Location of the pain, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top">.007 \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>Midthoracic \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">218 (76.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">87 (84.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">131 (71.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Other locations \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">68 (23.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">16 (15.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">52 (28.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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="4" align="left" valign="top"><span class="elsevierStyleItalic">Pain radiation, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top">.57 \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>Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">163 (57.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">61 (59.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">102 (55.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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="4" align="left" valign="top"><span class="elsevierStyleItalic">Location of the radiation, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top">1.00 \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>ULE \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">75 (26.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27 (26.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">48 (26.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Others or no radiation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">211 (73.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">76 (73.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">135 (73.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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="4" align="left" valign="top"><span class="elsevierStyleItalic">Pain intensity by VAS, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top">.95 \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>Mild (1–4 points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">58 (20.30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">21 (20.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">37 (20.30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Moderate (5–7 points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">134 (46.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">47 (45.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">87 (47.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Intense (8–10 points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">94 (32.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">35 (34.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">59 (32.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleItalic">Presence of accompanying symptoms, n (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">195 (68.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">76 (73.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">119 (65.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.13 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Sweats \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">114 (39.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">55 (53.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">59 (32.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Syncope \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5 (1.75) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 (1.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4 (2.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.66 \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>Dyspnea \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">84 (29.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">33 (32.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">51 (27.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.46 \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>Nausea or vomiting \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">69 (24.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30 (29.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">39 (21.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">.15 \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="4" align="left" valign="top"><span class="elsevierStyleItalic">Geleijnse scale, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top"><.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>≤5 points \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">55 (19.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7 (6.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">48 (26.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>≥6 points \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">231 (80.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">96 (93.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">135 (73.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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="4" align="left" valign="top"><span class="elsevierStyleItalic">Hand gestures, n (%)</span></td><td class="td" title="table-entry " align="left" valign="top">.52 \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>Gesture 1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">32 (11.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13 (12.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">19 (10.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Gesture 2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">171 (59.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">63 (61.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">106 (57.60) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Gesture 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">57 (19.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">21 (20.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">36 (19.70) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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>Other gestures \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">26 (9.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 (5.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 (10.90) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1678898.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Description of the study variables and the results of the univariate and bivariate analysis.</p>" ] ] 3 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>: OR, odds ratio; 95% CI, 95% confidence interval.</p><p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">Hosmer–Lemeshow Fit Test. <span class="elsevierStyleItalic">χ</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>4.99, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>.29.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="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">Logistic regression coefficient <span class="elsevierStyleItalic">β</span> \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">Standard error of the logistic regression coefficient <span class="elsevierStyleItalic">β</span> \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">Wald <span class="elsevierStyleItalic">p</span>-value \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">Significance \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">OR (95% CI)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \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">Score<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">b</span></a> \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">Personal history of coronary artery disease \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">80.70 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.00 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30.40 (14.40–64.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Dyslipidemia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.90 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5.70 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.017 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.40 (1.20–64.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Geleijnse score<span class="elsevierStyleHsp" style=""></span>≥6<span class="elsevierStyleHsp" style=""></span>points \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.90 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.60 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10.60 \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><td class="td" title="table-entry " align="char" valign="top">6.60 (2.12–20.50) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1678900.png" ] ] ] "notaPie" => array:2 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">The OR of each independent variable inserted in the model can be obtained directly from the regression coefficients (<span class="elsevierStyleItalic">β</span>) of the independent variables. The OR corresponds to the risk of having an evaluated result or effect for a specific value (<span class="elsevierStyleItalic">x</span>) compared with the value decreased by 1 unit (<span class="elsevierStyleItalic">x</span><span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>1) or in the case of qualitative variables versus the complementary category (e.g., having dyslipidemia vs. not having it).</p>" ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "b" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Each coefficient was divided by 0.90, and the quotient was rounded up to the nearest whole number to determine the number of points assigned to each independent predictor.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">Predictors of acute coronary syndrome. Results of the final model of the multivariate analysis.</p>" ] ] 4 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0100" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>: OR, odds ratio; 95% CI, 95% confidence interval; ACS, acute coronary syndrome.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">ACS prediction score \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Patients with ACS/total patients with this prediction score \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Proportion of ACS, % \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">OR (95% CI) \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"><span class="elsevierStyleItalic">p</span> \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">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0/34 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.00 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0/10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.00 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">7/80 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.80 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.11 (0.05–0.25) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">.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">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">17/67 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">25.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.53 (0.29–0.97) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">.038 \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">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 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.00 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \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">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">7/10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">70.00 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.40 (1.11–17.30) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">.023 \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">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">20/22 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">90.90 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">23.20 (5.30–101.20) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">.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">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">52/62 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">83.90 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">17.00 (8.05–35.80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1678899.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">Probability of ACS based on the score observed in all study patients.</p>" ] ] 5 => array:8 [ "identificador" => "tbl0020" "etiqueta" => "Table 4" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at4" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0110" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>: CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Geleijnse scale \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">Proposed scale \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">Cutoff \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">≥6 points \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">≥5 points \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">Area under the ROC curve (95% CI)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.60 (0.53–0.66) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.90 (0.85–0.93) \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">p-Value of the area under the ROC curve</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.006 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.000 \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">Sensitivity, %</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">93.20 (87.00–97.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">76.70 (68.00–84.00) \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">Specificity, %</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">26.20 (21.00–33.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">91.80 (87.00–95.00) \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">PPV, %</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">41.60 (35.00–48.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">84.00 (75.00–90.00) \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">NPV, %</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">87.30 (76.00–94.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">87.50 (82.00–91.00) \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">Positive likelihood ratio</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.26 (1.14–1.40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">9.36 (5.70–15.40) \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">Negative likelihood ratio</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.26 (0.12–0.55) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.25 (18.00–36.00) \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">Overall diagnostic value, %</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">50 (45.00–56.00) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">86.40 (82–90) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleItalic">Comparison of ROC curves</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>Difference between areas \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " colspan="2" align="center" valign="top">0.30</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>95% CI \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " colspan="2" align="center" valign="top">0.25–0.35</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>Z statistic \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " colspan="2" align="center" valign="top">11.401</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>Significance level \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " colspan="2" align="center" valign="top"><span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>.0001</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1678897.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Comparison of the diagnostic validity of acute coronary syndrome according to our scale and the Geleijnse scale.</p>" ] ] 6 => array:5 [ "identificador" => "upi0010" "tipo" => "MULTIMEDIAECOMPONENTE" "mostrarFloat" => false "mostrarDisplay" => true "Ecomponente" => array:2 [ "fichero" => "mmc1.pdf" "ficheroTamanyo" => 264926 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:41 [ 0 => array:3 [ "identificador" => "bib0210" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Dolor torácico en urgencias: frecuencia, perfil clínico y estratificación de riesgo" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "M. 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