Discrepancy between stress electrocardiographic changes and nuclear myocardial perfusion defects in the prognostic assessment of patients with chest pain | Revista Portuguesa de Cardiologia (English edition)

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"tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "761" "paginaFinal" => "768" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Sergio Raposeiras-Roubin, Miguel Garrido-Pumar, Virginia Pubulnuñez, Carlos Peña-Gil, Sonia Argibay-Vázquez, Rosa María Agra Bermejo, Emad Abuassi, Amparo Martínez-Monzonís, Marino Vega, Álvaro Rubial-Morell, José Ramón González-Juanatey" "autores" => array:11 [ 0 => array:4 [ "nombre" => "Sergio" "apellidos" => "Raposeiras-Roubin" "email" => array:1 [ 0 => "raposeiras26@hotmail.com" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "¿" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Miguel" "apellidos" => "Garrido-Pumar" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "Virginia" "apellidos" => "Pubulnuñez" "referencia" => array:1 [ 0 => array:2 [ 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"titulosAlternativos" => array:1 [ "pt" => array:1 [ "titulo" => "Discrepância entre alterações eletrocardiográficas no esforço e defeitos de perfusão miocárdica na cintigrafia, na avaliação de doentes com dor torácica" ] ] "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" => 2499 "Ancho" => 1538 "Tamanyo" => 267889 ] ] "descripcion" => array:1 [ "en" => "Temporal trends in the occurrence of cardiac events in the groups with positive and negative ECG response (A) and SPECT MPI result (B)." ] ] ] "textoCompleto" => "1IntroductionThe application of prognostic testing in patients with known or suspected coronary artery disease (CAD) is based on the premise of risk stratification.<a class="elsevierStyleCrossRef" href="#bib0005">1</a> Ideally, the prognostic utility of a test is to distinguish patients with a high likelihood of future major cardiac events – who may benefit from an invasive strategy – from low-risk patients in whom invasive examination is not recommended.<a class="elsevierStyleCrossRef" href="#bib0010">2</a>The standard test for the initial evaluation of low-risk patients who have a normal baseline electrocardiogram (ECG) is exercise treadmill testing (ETT).<a class="elsevierStyleCrossRef" href="#bib0015">3</a> This remains an inexpensive, safe, and effective exam.<a class="elsevierStyleCrossRef" href="#bib0020">4</a> From the standpoint of clinical management, when clinical cardiologists treat patients with chest pain, they are interested in knowing with high sensitivity and high specificity which patients are at high coronary risk, since cardiac catheterization is not without risks for the patient, as well as economic costs. Myocardial perfusion imaging (MPI) has shown its prognostic value for the prediction of future cardiac events beyond established risk factors and clinical presentation.<a class="elsevierStyleCrossRef" href="#bib0025">5</a> Clinicians sometimes find discrepancies between the ECG (ECG) response in ETT and MPI.<a class="elsevierStyleCrossRefs" href="#bib0030">6,7</a> In such cases there may be doubts concerning the strategy to adopt in the management of patients.The aim of the present study was to determine the prognostic value provided by the exercise ECG response to single-photon emission computed tomography (SPECT) MPI in the evaluation of patients with chest pain, focusing on patients with a discrepancy between the two tests (especially patients with positive ECG response and negative MPI).2Methods2.1Study populationWe identified 4230 consecutive patients with an intermediate or high likelihood of CAD who underwent SPECT between January 1, 2003 and March 1, 2011. The indication for the test was the presence of ischemic equivalent, consisting of symptoms or ECG findings consistent with CAD. Patients with previous myocardial infarction or revascularization were considered to have known CAD and were excluded from this study, as were patients with valvular heart disease or primary cardiomyopathy. Patients with a non-diagnostic ECG but with underlying ECG abnormalities, including left bundle branch block, and those with pacemakers were also excluded, as were patients unable to exercise. Of the remaining patients (1520), 60 (3.9%) were lost to follow-up. The study conformed to the ethical standards of the Declaration of Helsinki.2.2Perfusion myocardial images and ECG responseSubjects underwent technetium (Tc)-99m sestamibi rest-stress gated SPECT MPI with either a 1- or 2-day protocol. All patients performed a symptom-limited treadmill exercise test using standard protocols. During exercise, 12-lead ECG recording was performed at each minute of stress with continuous monitoring. At near maximal exercise, a weight-adjusted dose of Tc-99m sestamibi was injected and exercise continued for 1 min after injection. Tc-99m sestamibi SPECT imaging was begun 30 min after isotope injection. SPECT MPI was performed using an elliptical 180° acquisition for 64 projections at 20 s per projection. No attenuation or scatter correction was used. After filtered back-projection, short-axis, vertical, and horizontal long-axis tomograms were generated. SPECT data were analyzed by two imaging cardiologists with regard to the presence of reversible and/or fixed perfusion defects in short-axis and horizontal and vertical long-axis slices, as well as on polar maps, with a commercially available quantitative gated and perfusion SPECT software package, which also enabled assessment of left ventricular ejection fraction (LVEF). In addition, MPI scans were assessed semiquantitatively by segmental visual interpretation, using short-axis and vertical long-axis tomograms divided into 20 segments. Each segment was scored by consensus of two experienced observers using a five-point scoring system (0 = normal, 1 = equivocal, 2 = moderate, 3 = severe reduction of tracer uptake, and 4 = absence of detectable uptake). A summed stress score (SSS) was obtained by adding the scores of the 20 segments of the stress sestamibi images; SSS <3 was considered normal.2.3DefinitionsCigarette smoking was defined as active smoking in the last six months. Diabetes was defined as increased fasting plasma glucose of ≥126 mg/dl, HbA1c of ≥6.5%, or undergoing treatment with insulin or hypoglycemic agents. Hypertension was defined as systolic/diastolic blood pressure >140/90 mmHg or current use of any antihypertensive medication. Dyslipidemia was defined as total cholesterol ≥220 mg/dl, triglycerides ≥150 mg/dl, high-density lipoprotein (HDL) cholesterol <40 mg/dl, or current use of lipid-lowering drugs. These four variables (cigarette smoking, diabetes, hypertension and dyslipidemia) were considered as cardiovascular risk factors, together with the presence of previous myocardial infarction or of peripheral arterial disease. Age >45 years (males) or >55 years (females) was also considered a cardiovascular risk factor. An ECG stress test was considered positive if there was a horizontal or downsloping ST-segment depression of ≥0.1 mV at 0.08 s after the J-point.2.4Follow-upEvents were defined as cardiac death (confirmed by review of death certificate, hospital chart, or physician's records), non-fatal myocardial infarction (documented by appropriate cardiac enzyme and electrocardiographic changes), revascularization or admission due to new-onset heart failure. Events at one year and during follow-up were analyzed. The median follow-up was 16.8 months (interquartile range: 14.0-39.6 months).2.5Statistical analysisThe statistical analyses were performed with SPSS (Statistical Package for the Social Sciences), version 17.0. Categorical or dichotomous variables are expressed as absolute values and percentages, and were compared with Pearson's chi-square test. Continuous variables with normal distribution are expressed as mean ± standard deviation, and the Student's t test was used for comparisons between groups. Continuous data from more than two groups were compared with ANOVA. A binomial logistic regression model was used to evaluate the independent contribution of ECG response and MPI to the risk of events at one year. Age in years, number of cardiovascular risk factors, LVEF as a continuous variable, ECG response and MPI results were included in the model in backward stepwise analysis. C statistics, as a measure of discriminative power, were calculated for predicting cardiac events in the first year after the test. A Cox proportional hazard analysis was carried out to assess the independent role of both tests (ECG and SPECT) for predicting mortality during follow-up using the same variables as in the binomial logistic regression. Adjusted odds and hazard ratios and 95% confidence intervals (CI) were presented. Kaplan-Meier curves were constructed to evaluate prognosis during follow-up of different groups based on ECG response and MPI results, and the results were analyzed using the log-rank test. Receiver operating characteristic (ROC) curve analysis was performed to establish the diagnostic value of both tests (ECG and SPECT) to predict events. A p value <0.05 was considered statistically significant.3Results3.1Baseline characteristicsA total of 1460 patients (777 female; mean age 62.6±11.4 years) were included in the study. <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> shows their baseline clinical characteristics. Ischemic ECG changes were observed during stress testing in 271 patients (18.5%) and 362 patients (24.7%) had positive MPI results (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).<elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia>3.2Events during follow-upDuring the follow-up period (1-92 months), 224 (15.3%) of the total population presented cardiac events. Compared to patients who were still alive at the end of follow-up, those who died had a higher incidence of positive ECG response and positive MPI results (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>). There were no statistically significant differences between groups (with and without events) in terms of other variables (age and gender, cardiovascular risk factors and LVEF). The hazard ratios (HR) of ECG changes and MPI results to predict events were 1.506 (95% CI: 1.113-2.039, p=0.008) and 10.481 (95% CI: 7.799-14.080, p=0.001), respectively (<a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a>). For comparison of different groups (based on ECG and MPI results), survival curves were constructed for the four groups (<a class="elsevierStyleCrossRef" href="#fig0010">Figure 2</a>). As can be seen, the prognosis of patients with positive MPI was clearly worse than that of patients with negative MPI, independently of ECG changes during stress. In multivariate analysis (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a>), adjusted for age, gender, number of cardiovascular risk factors and LVEF, MPI proved an independent long-term prognostic factor (HR 10.536, 95% CI: 7.759-14.308; p=0.001), but not ECG changes (HR 1.356, 95% CI: 0.994-1.850; p=0.055).<elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia>3.3Events at one yearAt one year after testing, 127 (8.7%) of the total population presented events. Patients with ECG changes during exercise testing were at greater risk of events at one year than those without (OR: 1.768, 95% CI: 1.169-2.675; p=0.007), as were patients with positive SPECT (OR 13.684, 95% CI: 8.851-21.155; p=0.001). The area under the curve (AUC) for MPI results to predict events at one year (0.787: 95% CI: 0.743-0.831) was significantly larger than for ECG changes (0.549: 95% CI: 0.495-0.604) (p=0.002). The combined model of ECG changes and MPI results increased the AUC for the prediction of events to 0.802 (95% CI: 0.760-0.844), without reaching statistical significance compared to the AUC for MPI results (p=0.691). In multivariate analysis (<a class="elsevierStyleCrossRef" href="#tbl0025">Table 5</a>), adjusted for age, gender, number of cardiovascular risk factors and LVEF, both ECG changes and MPI results proved independent predictors of 1-year outcomes (OR 1.737, 95% CI: 1.091-2.767; p=0.020 for ECG changes, and OR 13.670, 95% CI: 8.691-21.503; p=0.001 for MPI result). The C statistic of the model was 0.811.<elsevierMultimedia ident="tbl0025"></elsevierMultimedia>3.4Discrepant groupsNormal MPI with a positive ECG in the exercise test was observed in 190 patients (13.0%) and the opposite (positive MPI and negative ECG) in 281 patients (19.2%). No statistically significant differences were found between the clinical characteristics of either of these groups compared to the rest of the study population. <a class="elsevierStyleCrossRef" href="#fig0015">Figure 3</a> shows the survival curves in the discrepant groups adjusted by gender and hypertension. Of the 1098 patients (75.2%) with negative SPECT, the presence of ECG changes during stress testing was not associated with more events at one year (OR 1.857, 95% CI: 0.810-4.257; p=0.244) or more events throughout the follow-up period (HR 1.214, 95% CI: 0.646-2.282; p=0.547). The same was true for the 362 patients (24.8%) with positive SPECT (OR for events at one year 1.479, 95% CI: 0.866-2.524, p=0.151, and HR for follow-up events 1.203, 95% CI: 0.848-1.705; p=0.307). However, in the latter group (positive MPI) we found that non-hypertensive patients (n=110) with ischemic ECG changes during stress testing (n=20) had a worse prognosis (percentage of events 70% vs. 37.8% in hypertensive patients with positive MPI and ischemic ECG changes) (HR for positive ECG response 1.937, 95% CI: 1.030-3.642, p=0.009).<elsevierMultimedia ident="fig0015"></elsevierMultimedia>In addition we focused on patients with negative MPI and positive ECG. In this subgroup, 11 (6.3%, 0.75% of the total study population) had cardiac events during follow-up. Of these, seven were undergoing cardiac catheterization, and multivessel disease was detected in all except one (who had proximal left anterior descending disease). In these patients, the ECG response was markedly positive (widespread ST depression ≥2 mm).4DiscussionThe most striking result of our study is the low prognostic value added by the ECG response to that of MPI in patients with chest pain undergoing exercise stress testing. MPI results (normal vs. abnormal) had strong predictive value, but discrepant ECG results had no significant additive prognostic value (HR 10.4 vs. 1.5). Besides the general implications of this result, particular consideration should be given to two particular groups: firstly patients with multivessel CAD, in whom MPI results may be misleadingly balanced, and in whom the ECG response is often overly positive (widespread ST-segment depression >2 mm); and secondly non-hypertensive patients, in whom, even if they had a positive response on MPI, a positive ECG response suggests a worse prognosis.Significant perfusion abnormalities result in myocardial ischemia that affects the ST segment on the ECG tracing, evaluated during stress testing (exercise or pharmacological stress with dipyridamole, adenosine, or dobutamine).<a class="elsevierStyleCrossRefs" href="#bib0030">6,7</a> Abnormal ECG findings may result from ischemia, but they may also reflect other factors (leading to false positive results), particularly in women and patients with hypertension<a class="elsevierStyleCrossRefs" href="#bib0040">8–12</a>. In our study we found no differences in the percentage of positive ECG responses according to gender and hypertension, unlike other studies, despite the high percentage of female (>50%) and hypertensive (>65%) patients.The prognostic value of the ECG response to exercise and MPI has been evaluated in multiple independent studies.<a class="elsevierStyleCrossRefs" href="#bib0065">13,14</a> By the 1990s SPECT imaging was considered to have superior value compared to ECG, associated with a minimal mortality risk of <1% per year.<a class="elsevierStyleCrossRefs" href="#bib0075">15–17</a> However, in 2003 Abbott et al.<a class="elsevierStyleCrossRef" href="#bib0035">7</a> and Klodas et al.<a class="elsevierStyleCrossRef" href="#bib0090">18</a> questioned this belief, showing a significant risk of a cardiac event (with a yearly risk of death or myocardial infarction of 5-10%) in patients with no perfusion abnormalities on SPECT but with “ischemic ST depression” during dipyridamole or adenosine stress testing. They explained these results on the basis of multivessel CAD that resulted in true positive ECG findings and false negative perfusion imaging with SPECT. One year later, Candell et al.<a class="elsevierStyleCrossRef" href="#bib0095">19</a> retrospectively analyzed 33 patients (of 7350 myocardial perfusion SPECT studies) who had normal scintigraphic findings with a positive ECG in the exercise test and coronary angiography. Twenty-one of the 26 women (81%) and one of the seven men (14%) had normal coronary angiographic findings (p=0.004). Seven out of 11 patients with CAD had left main coronary artery or multivessel disease. The authors concluded that the “normal SPECT with positive ECG” pattern is infrequent, and is observed predominantly in women with normal coronary angiographic findings. Later, Hage et al.<a class="elsevierStyleCrossRef" href="#bib0100">20</a> confirmed the superior prognostic value of SPECT over ECG: with normal (true negative) SPECT findings, the presence of concomitant ischemic (false positive) ST changes on the ECG does not have an adverse effect on prognosis. They presented the results of 73 patients with ischemic ST depression but without perfusion abnormalities detected by SPECT during adenosine stress, followed for five years. The cardiac mortality in this group of patients was less than 1.3%.We focused on patients with chest pain and discrepancies between the two tests: positive SPECT MPI with a negative ECG response and vice versa, negative MPI with a positive ECG response. We found no differences in baseline characteristics of patients in the different groups. In patients with negative SPECT the event rate was very low, regardless of the ECG response. There was a small proportion of patients (<1%) with negative MPI and multivessel CAD in whom the ECG response was very positive. Although the literature reports an overall sensitivity of SPECT MPI for detecting multivessel disease ranging from 80% to 95%,<a class="elsevierStyleCrossRef" href="#bib0105">21</a> in cases with evenly distributed ischemia without inducible flow heterogeneity among the different vessel territories, balanced ischemia has been hypothesised to explain lack of perfusion defects.<a class="elsevierStyleCrossRef" href="#bib0025">5</a> By its nature, SPECT only allows detection of relative perfusion defects. Therefore, the true extent of myocardial ischemia may be underestimated, particularly in vascular territories adjacent to areas with more severe coronary stenosis. It may be necessary to specify different degrees of positivity or the time of normalization of ECG response in order to determine that a negative result of ischemia by nuclear MPI is not a false positive.In addition, Ghadri et al.<a class="elsevierStyleCrossRef" href="#bib0110">22</a> recently showed that in patients with normal SPECT MPI, a coronary artery calcium score (AAC) >1000 confers high added diagnostic value for detecting CAD, predominantly in patients with one- and two-vessel disease, and state that this is not based solely on unmasking balanced ischemia due to three-vessel disease. According to the authors, balanced ischemia may not be solely due to balanced epicardial coronary lesion severity, but could also be caused by microcirculatory dysfunction in which global hyperemic myocardial flow response and myocardial flow reserve are blunted. A massive increase in CAC would – although measured in the epicardial vessel – then reflect involvement of the coronary microcirculatory tree, potentially associated with a decrease in myocardial flow reserve, favoring a balanced hyperemic response due to blunted flow heterogeneity.On the other hand patients with positive MPI have a poorer prognosis, independently of ECG response. Special mention should be made of non-hypertensive patients, in whom the ECG result cannot be distorted by signs of left ventricular hypertrophy with systolic overload. In these patients (positive MPI) the ECG response identifies a subgroup of patients with very high risk of cardiac events (more than two-thirds).5ConclusionsThis study addresses a frequent clinical dilemma: what to do with discrepant ECG and MPI findings? After analyzing the prognostic value of MPI and ECG response to exercise testing in 1460 consecutive patients with intermediate or high likelihood of CAD, we concluded that when the results of MPI and ECG are discordant, it is correct to consider only the MPI results.6Ethical disclosures6.1Protection of human and animal subjectsThe authors declare that no experiments were performed on humans or animals for this study.6.2Confidentiality of dataThe authors declare that they have followed the protocols of their work center on the publication of patient data and that all the patients included in the study received sufficient information and gave their written informed consent to participate in the study.6.3Right to privacy and informed consentThe authors have obtained the written informed consent of the patients or subjects mentioned in the article. The corresponding author is in possession of this document.7Conflicts of interestThe authors have no conflicts of interest to declare." "textoCompletoSecciones" => array:1 [ "secciones" => array:12 [ 0 => array:2 [ "identificador" => "xres298794" "titulo" => array:5 [ 0 => "Abstract" 1 => "Objectives" 2 => "Methods" 3 => "Results" 4 => "Conclusion" ] ] 1 => array:2 [ "identificador" => "xpalclavsec281818" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xres298795" "titulo" => array:5 [ 0 => "Resumo" 1 => "Objetivos" 2 => "Método" 3 => "Resultados" 4 => "Conclusão" ] ] 3 => array:2 [ "identificador" => "xpalclavsec281819" "titulo" => "Palavras-chave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Methods" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Study population" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Perfusion myocardial images and ECG response" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Definitions" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Follow-up" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Statistical analysis" ] ] ] 6 => array:3 [ "identificador" => "sec0040" "titulo" => "Results" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0045" "titulo" => "Baseline characteristics" ] 1 => array:2 [ "identificador" => "sec0050" "titulo" => "Events during follow-up" ] 2 => array:2 [ "identificador" => "sec0055" "titulo" => "Events at one year" ] 3 => array:2 [ "identificador" => "sec0060" "titulo" => "Discrepant groups" ] ] ] 7 => array:2 [ "identificador" => "sec0065" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0070" "titulo" => "Conclusions" ] 9 => array:3 [ "identificador" => "sec0075" "titulo" => "Ethical disclosures" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0080" "titulo" => "Protection of human and animal subjects" ] 1 => array:2 [ "identificador" => "sec0085" "titulo" => "Confidentiality of data" ] 2 => array:2 [ "identificador" => "sec0090" "titulo" => "Right to privacy and informed consent" ] ] ] 10 => array:2 [ "identificador" => "sec0095" "titulo" => "Conflicts of interest" ] 11 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2012-09-02" "fechaAceptado" => "2012-11-08" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec281818" "palabras" => array:4 [ 0 => "Electrocardiogram" 1 => "Myocardial perfusion imaging" 2 => "Stress test" 3 => "Prognosis" ] ] ] "pt" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palavras-chave" "identificador" => "xpalclavsec281819" "palabras" => array:4 [ 0 => "ECG" 1 => "Imagens de perfusão miocárdica" 2 => "Prova de esforço" 3 => "Prognóstico" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "ObjectivesThe aim of the present study was to determine the long-term prognostic value provided by the exercise electrocardiographic (ECG) response to nuclear myocardial perfusion imaging (MPI) in the evaluation of patients with chest pain, focusing on patients with a discrepancy between the two tests. MethodsA total of 1460 consecutive patients (777 female; 62.6±11.4 years) undergoing exercise myocardial single-photon emission computed tomography (SPECT) were included. The endpoint was the occurrence of acute coronary syndrome, heart failure or cardiac death during follow-up. ResultsIschemic ECG changes were observed during stress testing in 271 patients (18.5%) and 362 patients (24.7%) had positive (abnormal) exercise MPI results. There was a discrepancy between ECG and SPECT findings in 471 patients (32.2%). During the follow-up period (14.0-39.6 months), 224 patients (15.3%) presented cardiac events. The hazard ratios (HR) of ECG and MPI results to predict events were 1.506 (95% CI: 1.113-2.039) and 10.481 (95% CI: 7.799-14.080), respectively. In patients with negative MPI, the ECG response did not predict events (HR 1.214 [95% CI: 0.646-2.282]), the same as in patients with positive MPI (HR 1.203 [95% CI: 0.848-1.705]). Only in hypertensive patients with positive SPECT did the ECG show significant prognostic value (HR 1.937 [95% CI: 1.030-3.642]). In multivariate analysis, positive MPI proved an independent long-term prognostic factor (HR 10.536 [95% CI: 7.759-14.308]), but not ECG (HR 1.356 [95% CI: 0.994-1.850]). ConclusionMPI results (normal vs. abnormal) had strong predictive value and discrepant ECG results had no significant additive prognostic value." ] "pt" => array:2 [ "titulo" => "Resumo" "resumen" => "ObjetivosO objetivo do presente estudo, foi determinar o valor prognóstico a longo prazo, fornecido pelo eletrocardiograma (ECG) em resposta ao esforço na cintigrafia de perfusão miocárdica (CPM) na avaliação de doentes com dor torácica, com ênfase para os grupos de doentes com discrepância entre os dois testes. MétodoForam incluídos 1460 doentes consecutivos (777 do sexo feminino; 62,6 ± 11,4 anos), submetidos a exercício na cintigrafia de perfusão miocárdica com SPECT. O endpoint foi a presença de síndrome coronária aguda, insuficiência cardíaca ou morte cardíaca durante o seguimento. ResultadosDuzentos e setenta e um doentes (18,5%) apresentaram alterações eletrocardiográficas de isquemia durante o teste de esforço e 362 doentes (24,7%) tinham resultados positivos (anormais) na CPM. A discrepância entre os resultados do ECG e CPM apareceu em 471 doentes (32,2%). Durante o período de seguimento (14,0-39,6 meses), 224 (15,3%) apresentaram eventos cardíacos. O hazard ratio (HR) para predizer eventos dos resultados do ECG e CPM foi de 1,506 (1,113-2,039) e 10,481 (7,799-14,080), respetivamente. No grupo de doentes com CPM negativo, o ECG não prediz eventos [HR 1,214 (0,646-2,282)],e o mesmo acontece no grupo de doentes com CPM positivo [HR 1,203 (0,848-1,705)]. Apenas em doentes hipertensos com CPM positivo, o ECG mostrou valor prognóstico significativo [HR 1,937 (1,030-3,642)]. Na análise multivariada, a CPM positiva foi um fator independente de prognóstico a longo prazo [HR 10,536 (7,759-14,308)], mas não o ECG [HR 1,356 (0,994-1,850)]. ConclusãoO resultado da CPM (normal versus anormal) teve um forte valor preditivo e o resultado discrepante no ECG não teve um valor prognóstico adicional significativo." ] ] "multimedia" => array:8 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 2499 "Ancho" => 1538 "Tamanyo" => 267889 ] ] "descripcion" => array:1 [ "en" => "Temporal trends in the occurrence of cardiac events in the groups with positive and negative ECG response (A) and SPECT MPI result (B)." ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1195 "Ancho" => 1490 "Tamanyo" => 166221 ] ] "descripcion" => array:1 [ "en" => "Kaplan-Meier survival curves according to ECG and SPECT findings." ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1743 "Ancho" => 2118 "Tamanyo" => 320079 ] ] "descripcion" => array:1 [ "en" => "Survival curves in the discrepant groups adjusted by gender and hypertension." ] ] 3 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "−: negative; +: positive; ECG: electrocardiographic result; LVEF: left ventricular ejection fraction; METs: metabolic equivalents; SPECT: single-photon emission computed tomography result." "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"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Total \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">SPECT −, ECG − \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">SPECT −, ECG + \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">SPECT +, ECG − \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">SPECT +, ECG + \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">p \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.6±11.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.8±11.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63.0±12.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.9±11.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.2±12.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.638 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Female (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.899 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Current smoking (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.652 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hypertension (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">66.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">65.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">68.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">75.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.243 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Dyslipidemia (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">59.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.843 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Diabetes (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">24.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.223 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Peripheral arterial disease (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.451 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Atrial fibrillation (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.061 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LVEF (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.0±11.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">60.9±11.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">60.9±13.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.1±12.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.3±12.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.797 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Energy cost (METs) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.7±3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.7±3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.4±3.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.6±3.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9.2±3.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.361 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Events at six months (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">33.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Events during follow-up (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">43.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab437337.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Baseline characteristics of the overall population and of different groups according to SPECT and ECG results." ] ] 4 => array:7 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "−: negative; +: positive; ECG: electrocardiographic result; SPECT: single-photon emission computed tomography result." "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"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">ECG − \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">ECG + \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SPECT − \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">908 (62.3%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">190 (13.0%) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SPECT + \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">281 (19.2%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">81 (5.5%) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab437338.png" ] ] ] ] "descripcion" => array:1 [ "en" => "ECG and SPECT results in the total population." ] ] 5 => array:7 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "+: positive; ECG: electrocardiographic result; LVEF: left ventricular ejection fraction; METs: metabolic equivalents; SPECT: single-photon emission computed tomography result." "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"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Events \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">No events \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">p \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.7±11.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.6±11.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.937 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Female (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.909 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Current smoking (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.804 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hypertension (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">70.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">65.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.127 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Dyslipidemia (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.235 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Diabetes (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">24.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.649 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Peripheral arterial disease (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.695 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Atrial fibrillation (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.357 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LVEF (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.0±12.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.0±11.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.987 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Energy cost (METs) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.6±3.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.7±3.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.504 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SPECT + (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">72.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">ECG + (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.007 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab437340.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Baseline characteristics of the study population, stratified by presence of cardiac death and cardiac events." ] ] 6 => array:7 [ "identificador" => "tbl0020" "etiqueta" => "Table 4" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "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"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">VARIABLES \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">HAZARD RATIO \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">CI 95% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">P value \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.004 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.991–1.017 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.573 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Number of cardiovascular risk factors \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.167 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.027–1.326 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.018 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Left ventricular ejection fraction \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.996 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.984–1.007 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.455 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SPECT + \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.356 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.994–1.850 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.055 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">EKG + \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10.536 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.759–14.308 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab437339.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Multivariate analysis to predict long-term outcomes." ] ] 7 => array:7 [ "identificador" => "tbl0025" "etiqueta" => "Table 5" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "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"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">VARIABLES \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">ODDS RATIO \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">CI 95% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">P value \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.997 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.977–1.017 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.794 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Number of cardiovascular risk factors \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.192 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.987–1.440 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.069 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Left ventricular ejection fraction \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.997 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.980–1.013 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.699 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SPECT + \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.737 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.091–2.767 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.020 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">EKG + \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13.670 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8.691–21.503 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab437336.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Multivariate analysis to predict events in the first year." ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:22 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Assessment of prognosis in chronic coronary artery disease" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "T.M. 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Original Article

Discrepancy between stress electrocardiographic changes and nuclear myocardial perfusion defects in the prognostic assessment of patients with chest pain

Discrepância entre alterações eletrocardiográficas no esforço e defeitos de perfusão miocárdica na cintigrafia, na avaliação de doentes com dor torácica

Sergio Raposeiras-Roubina,

Corresponding author

raposeiras26@hotmail.com

Corresponding author.

, Miguel Garrido-Pumarb, Virginia Pubulnuñezb, Carlos Peña-Gilc, Sonia Argibay-Vázquezb, Rosa María Agra Bermejoa, Emad Abuassia, Amparo Martínez-Monzonísa, Marino Vegaa, Álvaro Rubial-Morellb, José Ramón González-Juanateya

a Department of Cardiology, University Clinical Hospital of Santiago de Compostela, Spain

b Department of Nuclear Medicine, University Clinical Hospital of Santiago de Compostela, Spain

c Department of Cardiology, Hospital Meixoeiro, Vigo, Spain

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is exercise treadmill testing &#40;ETT&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> This remains an inexpensive&#44; safe&#44; and effective exam&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> From the standpoint of clinical management&#44; when clinical cardiologists treat patients with chest pain&#44; they are interested in knowing with high sensitivity and high specificity which patients are at high coronary risk&#44; since cardiac catheterization is not without risks for the patient&#44; as well as economic costs&#46; Myocardial perfusion imaging &#40;MPI&#41; has shown its prognostic value for the prediction of future cardiac events beyond established risk factors and clinical presentation&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> Clinicians sometimes find discrepancies between the ECG &#40;ECG&#41; response in ETT and MPI&#46;<a class="elsevierStyleCrossRefs" href="#bib0030"><span class="elsevierStyleSup">6&#44;7</span></a> In such cases there may be doubts concerning the strategy to adopt in the management of patients&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">The aim of the present study was to determine the prognostic value provided by the exercise ECG response to single-photon emission computed tomography &#40;SPECT&#41; MPI in the evaluation of patients with chest pain&#44; focusing on patients with a discrepancy between the two tests &#40;especially patients with positive ECG response and negative MPI&#41;&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2</span><span class="elsevierStyleSectionTitle" id="sect0070">Methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;1</span><span class="elsevierStyleSectionTitle" id="sect0075">Study population</span><p id="par0020" class="elsevierStylePara elsevierViewall">We identified 4230 consecutive patients with an intermediate or high likelihood of CAD who underwent SPECT between January 1&#44; 2003 and March 1&#44; 2011&#46; The indication for the test was the presence of ischemic equivalent&#44; consisting of symptoms or ECG findings consistent with CAD&#46; Patients with previous myocardial infarction or revascularization were considered to have known CAD and were excluded from this study&#44; as were patients with valvular heart disease or primary cardiomyopathy&#46; Patients with a non-diagnostic ECG but with underlying ECG abnormalities&#44; including left bundle branch block&#44; and those with pacemakers were also excluded&#44; as were patients unable to exercise&#46; Of the remaining patients &#40;1520&#41;&#44; 60 &#40;3&#46;9&#37;&#41; were lost to follow-up&#46; The study conformed to the ethical standards of the Declaration of Helsinki&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;2</span><span class="elsevierStyleSectionTitle" id="sect0080">Perfusion myocardial images and ECG response</span><p id="par0025" class="elsevierStylePara elsevierViewall">Subjects underwent technetium &#40;Tc&#41;-99m sestamibi rest-stress gated SPECT MPI with either a 1- or 2-day protocol&#46; All patients performed a symptom-limited treadmill exercise test using standard protocols&#46; During exercise&#44; 12-lead ECG recording was performed at each minute of stress with continuous monitoring&#46; At near maximal exercise&#44; a weight-adjusted dose of Tc-99m sestamibi was injected and exercise continued for 1 min after injection&#46; Tc-99m sestamibi SPECT imaging was begun 30 min after isotope injection&#46; SPECT MPI was performed using an elliptical 180&#176; acquisition for 64 projections at 20 s per projection&#46; No attenuation or scatter correction was used&#46; After filtered back-projection&#44; short-axis&#44; vertical&#44; and horizontal long-axis tomograms were generated&#46; SPECT data were analyzed by two imaging cardiologists with regard to the presence of reversible and&#47;or fixed perfusion defects in short-axis and horizontal and vertical long-axis slices&#44; as well as on polar maps&#44; with a commercially available quantitative gated and perfusion SPECT software package&#44; which also enabled assessment of left ventricular ejection fraction &#40;LVEF&#41;&#46; In addition&#44; MPI scans were assessed semiquantitatively by segmental visual interpretation&#44; using short-axis and vertical long-axis tomograms divided into 20 segments&#46; Each segment was scored by consensus of two experienced observers using a five-point scoring system &#40;0 &#61; normal&#44; 1 &#61; equivocal&#44; 2 &#61; moderate&#44; 3 &#61; severe reduction of tracer uptake&#44; and 4 &#61; absence of detectable uptake&#41;&#46; A summed stress score &#40;SSS&#41; was obtained by adding the scores of the 20 segments of the stress sestamibi images&#59; SSS &#60;3 was considered normal&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;3</span><span class="elsevierStyleSectionTitle" id="sect0085">Definitions</span><p id="par0030" class="elsevierStylePara elsevierViewall">Cigarette smoking was defined as active smoking in the last six months&#46; Diabetes was defined as increased fasting plasma glucose of &#8805;126 mg&#47;dl&#44; HbA1c of &#8805;6&#46;5&#37;&#44; or undergoing treatment with insulin or hypoglycemic agents&#46; Hypertension was defined as systolic&#47;diastolic blood pressure &#62;140&#47;90 mmHg or current use of any antihypertensive medication&#46; Dyslipidemia was defined as total cholesterol &#8805;220 mg&#47;dl&#44; triglycerides &#8805;150 mg&#47;dl&#44; high-density lipoprotein &#40;HDL&#41; cholesterol &#60;40 mg&#47;dl&#44; or current use of lipid-lowering drugs&#46; These four variables &#40;cigarette smoking&#44; diabetes&#44; hypertension and dyslipidemia&#41; were considered as cardiovascular risk factors&#44; together with the presence of previous myocardial infarction or of peripheral arterial disease&#46; Age &#62;45 years &#40;males&#41; or &#62;55 years &#40;females&#41; was also considered a cardiovascular risk factor&#46; An ECG stress test was considered positive if there was a horizontal or downsloping ST-segment depression of &#8805;0&#46;1 mV at 0&#46;08 s after the J-point&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;4</span><span class="elsevierStyleSectionTitle" id="sect0090">Follow-up</span><p id="par0035" class="elsevierStylePara elsevierViewall">Events were defined as cardiac death &#40;confirmed by review of death certificate&#44; hospital chart&#44; or physician&#39;s records&#41;&#44; non-fatal myocardial infarction &#40;documented by appropriate cardiac enzyme and electrocardiographic changes&#41;&#44; revascularization or admission due to new-onset heart failure&#46; Events at one year and during follow-up were analyzed&#46; The median follow-up was 16&#46;8 months &#40;interquartile range&#58; 14&#46;0-39&#46;6 months&#41;&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;5</span><span class="elsevierStyleSectionTitle" id="sect0095">Statistical analysis</span><p id="par0040" class="elsevierStylePara elsevierViewall">The statistical analyses were performed with SPSS &#40;Statistical Package for the Social Sciences&#41;&#44; version 17&#46;0&#46; Categorical or dichotomous variables are expressed as absolute values and percentages&#44; and were compared with Pearson&#39;s chi-square test&#46; Continuous variables with normal distribution are expressed as mean &#177; standard deviation&#44; and the Student&#39;s t test was used for comparisons between groups&#46; Continuous data from more than two groups were compared with ANOVA&#46; A binomial logistic regression model was used to evaluate the independent contribution of ECG response and MPI to the risk of events at one year&#46; Age in years&#44; number of cardiovascular risk factors&#44; LVEF as a continuous variable&#44; ECG response and MPI results were included in the model in backward stepwise analysis&#46; C statistics&#44; as a measure of discriminative power&#44; were calculated for predicting cardiac events in the first year after the test&#46; A Cox proportional hazard analysis was carried out to assess the independent role of both tests &#40;ECG and SPECT&#41; for predicting mortality during follow-up using the same variables as in the binomial logistic regression&#46; Adjusted odds and hazard ratios and 95&#37; confidence intervals &#40;CI&#41; were presented&#46; Kaplan-Meier curves were constructed to evaluate prognosis during follow-up of different groups based on ECG response and MPI results&#44; and the results were analyzed using the log-rank test&#46; Receiver operating characteristic &#40;ROC&#41; curve analysis was performed to establish the diagnostic value of both tests &#40;ECG and SPECT&#41; to predict events&#46; A p value &#60;0&#46;05 was considered statistically significant&#46;</p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3</span><span class="elsevierStyleSectionTitle" id="sect0100">Results</span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3&#46;1</span><span class="elsevierStyleSectionTitle" id="sect0105">Baseline characteristics</span><p id="par0045" class="elsevierStylePara elsevierViewall">A total of 1460 patients &#40;777 female&#59; mean age 62&#46;6&#177;11&#46;4 years&#41; were included in the study&#46; <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> shows their baseline clinical characteristics&#46; Ischemic ECG changes were observed during stress testing in 271 patients &#40;18&#46;5&#37;&#41; and 362 patients &#40;24&#46;7&#37;&#41; had positive MPI results &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3&#46;2</span><span class="elsevierStyleSectionTitle" id="sect0110">Events during follow-up</span><p id="par0050" class="elsevierStylePara elsevierViewall">During the follow-up period &#40;1-92 months&#41;&#44; 224 &#40;15&#46;3&#37;&#41; of the total population presented cardiac events&#46; Compared to patients who were still alive at the end of follow-up&#44; those who died had a higher incidence of positive ECG response and positive MPI results &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41;&#46; There were no statistically significant differences between groups &#40;with and without events&#41; in terms of other variables &#40;age and gender&#44; cardiovascular risk factors and LVEF&#41;&#46; The hazard ratios &#40;HR&#41; of ECG changes and MPI results to predict events were 1&#46;506 &#40;95&#37; CI&#58; 1&#46;113-2&#46;039&#44; p&#61;0&#46;008&#41; and 10&#46;481 &#40;95&#37; CI&#58; 7&#46;799-14&#46;080&#44; p&#61;0&#46;001&#41;&#44; respectively &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a>&#41;&#46; For comparison of different groups &#40;based on ECG and MPI results&#41;&#44; survival curves were constructed for the four groups &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Figure 2</a>&#41;&#46; As can be seen&#44; the prognosis of patients with positive MPI was clearly worse than that of patients with negative MPI&#44; independently of ECG changes during stress&#46; In multivariate analysis &#40;<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a>&#41;&#44; adjusted for age&#44; gender&#44; number of cardiovascular risk factors and LVEF&#44; MPI proved an independent long-term prognostic factor &#40;HR 10&#46;536&#44; 95&#37; CI&#58; 7&#46;759-14&#46;308&#59; p&#61;0&#46;001&#41;&#44; but not ECG changes &#40;HR 1&#46;356&#44; 95&#37; CI&#58; 0&#46;994-1&#46;850&#59; p&#61;0&#46;055&#41;&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3&#46;3</span><span class="elsevierStyleSectionTitle" id="sect0115">Events at one year</span><p id="par0055" class="elsevierStylePara elsevierViewall">At one year after testing&#44; 127 &#40;8&#46;7&#37;&#41; of the total population presented events&#46; Patients with ECG changes during exercise testing were at greater risk of events at one year than those without &#40;OR&#58; 1&#46;768&#44; 95&#37; CI&#58; 1&#46;169-2&#46;675&#59; p&#61;0&#46;007&#41;&#44; as were patients with positive SPECT &#40;OR 13&#46;684&#44; 95&#37; CI&#58; 8&#46;851-21&#46;155&#59; p&#61;0&#46;001&#41;&#46; The area under the curve &#40;AUC&#41; for MPI results to predict events at one year &#40;0&#46;787&#58; 95&#37; CI&#58; 0&#46;743-0&#46;831&#41; was significantly larger than for ECG changes &#40;0&#46;549&#58; 95&#37; CI&#58; 0&#46;495-0&#46;604&#41; &#40;p&#61;0&#46;002&#41;&#46; The combined model of ECG changes and MPI results increased the AUC for the prediction of events to 0&#46;802 &#40;95&#37; CI&#58; 0&#46;760-0&#46;844&#41;&#44; without reaching statistical significance compared to the AUC for MPI results &#40;p&#61;0&#46;691&#41;&#46; In multivariate analysis &#40;<a class="elsevierStyleCrossRef" href="#tbl0025">Table 5</a>&#41;&#44; adjusted for age&#44; gender&#44; number of cardiovascular risk factors and LVEF&#44; both ECG changes and MPI results proved independent predictors of 1-year outcomes &#40;OR 1&#46;737&#44; 95&#37; CI&#58; 1&#46;091-2&#46;767&#59; p&#61;0&#46;020 for ECG changes&#44; and OR 13&#46;670&#44; 95&#37; CI&#58; 8&#46;691-21&#46;503&#59; p&#61;0&#46;001 for MPI result&#41;&#46; The C statistic of the model was 0&#46;811&#46;</p><elsevierMultimedia ident="tbl0025"></elsevierMultimedia></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3&#46;4</span><span class="elsevierStyleSectionTitle" id="sect0120">Discrepant groups</span><p id="par0060" class="elsevierStylePara elsevierViewall">Normal MPI with a positive ECG in the exercise test was observed in 190 patients &#40;13&#46;0&#37;&#41; and the opposite &#40;positive MPI and negative ECG&#41; in 281 patients &#40;19&#46;2&#37;&#41;&#46; No statistically significant differences were found between the clinical characteristics of either of these groups compared to the rest of the study population&#46; <a class="elsevierStyleCrossRef" href="#fig0015">Figure 3</a> shows the survival curves in the discrepant groups adjusted by gender and hypertension&#46; Of the 1098 patients &#40;75&#46;2&#37;&#41; with negative SPECT&#44; the presence of ECG changes during stress testing was not associated with more events at one year &#40;OR 1&#46;857&#44; 95&#37; CI&#58; 0&#46;810-4&#46;257&#59; p&#61;0&#46;244&#41; or more events throughout the follow-up period &#40;HR 1&#46;214&#44; 95&#37; CI&#58; 0&#46;646-2&#46;282&#59; p&#61;0&#46;547&#41;&#46; The same was true for the 362 patients &#40;24&#46;8&#37;&#41; with positive SPECT &#40;OR for events at one year 1&#46;479&#44; 95&#37; CI&#58; 0&#46;866-2&#46;524&#44; p&#61;0&#46;151&#44; and HR for follow-up events 1&#46;203&#44; 95&#37; CI&#58; 0&#46;848-1&#46;705&#59; p&#61;0&#46;307&#41;&#46; However&#44; in the latter group &#40;positive MPI&#41; we found that non-hypertensive patients &#40;n&#61;110&#41; with ischemic ECG changes during stress testing &#40;n&#61;20&#41; had a worse prognosis &#40;percentage of events 70&#37; vs&#46; 37&#46;8&#37; in hypertensive patients with positive MPI and ischemic ECG changes&#41; &#40;HR for positive ECG response 1&#46;937&#44; 95&#37; CI&#58; 1&#46;030-3&#46;642&#44; p&#61;0&#46;009&#41;&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0065" class="elsevierStylePara elsevierViewall">In addition we focused on patients with negative MPI and positive ECG&#46; In this subgroup&#44; 11 &#40;6&#46;3&#37;&#44; 0&#46;75&#37; of the total study population&#41; had cardiac events during follow-up&#46; Of these&#44; seven were undergoing cardiac catheterization&#44; and multivessel disease was detected in all except one &#40;who had proximal left anterior descending disease&#41;&#46; In these patients&#44; the ECG response was markedly positive &#40;widespread ST depression &#8805;2 mm&#41;&#46;</p></span></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4</span><span class="elsevierStyleSectionTitle" id="sect0125">Discussion</span><p id="par0070" class="elsevierStylePara elsevierViewall">The most striking result of our study is the low prognostic value added by the ECG response to that of MPI in patients with chest pain undergoing exercise stress testing&#46; MPI results &#40;normal vs&#46; abnormal&#41; had strong predictive value&#44; but discrepant ECG results had no significant additive prognostic value &#40;HR 10&#46;4 vs&#46; 1&#46;5&#41;&#46; Besides the general implications of this result&#44; particular consideration should be given to two particular groups&#58; firstly patients with multivessel CAD&#44; in whom MPI results may be misleadingly balanced&#44; and in whom the ECG response is often overly positive &#40;widespread ST-segment depression &#62;2 mm&#41;&#59; and secondly non-hypertensive patients&#44; in whom&#44; even if they had a positive response on MPI&#44; a positive ECG response suggests a worse prognosis&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">Significant perfusion abnormalities result in myocardial ischemia that affects the ST segment on the ECG tracing&#44; evaluated during stress testing &#40;exercise or pharmacological stress with dipyridamole&#44; adenosine&#44; or dobutamine&#41;&#46;<a class="elsevierStyleCrossRefs" href="#bib0030"><span class="elsevierStyleSup">6&#44;7</span></a> Abnormal ECG findings may result from ischemia&#44; but they may also reflect other factors &#40;leading to false positive results&#41;&#44; particularly in women and patients with hypertension<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8&#8211;12</span></a>&#46; In our study we found no differences in the percentage of positive ECG responses according to gender and hypertension&#44; unlike other studies&#44; despite the high percentage of female &#40;&#62;50&#37;&#41; and hypertensive &#40;&#62;65&#37;&#41; patients&#46;</p><p id="par0080" class="elsevierStylePara elsevierViewall">The prognostic value of the ECG response to exercise and MPI has been evaluated in multiple independent studies&#46;<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13&#44;14</span></a> By the 1990s SPECT imaging was considered to have superior value compared to ECG&#44; associated with a minimal mortality risk of &#60;1&#37; per year&#46;<a class="elsevierStyleCrossRefs" href="#bib0075"><span class="elsevierStyleSup">15&#8211;17</span></a> However&#44; in 2003 Abbott et al&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> and Klodas et al&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> questioned this belief&#44; showing a significant risk of a cardiac event &#40;with a yearly risk of death or myocardial infarction of 5-10&#37;&#41; in patients with no perfusion abnormalities on SPECT but with &#8220;ischemic ST depression&#8221; during dipyridamole or adenosine stress testing&#46; They explained these results on the basis of multivessel CAD that resulted in true positive ECG findings and false negative perfusion imaging with SPECT&#46; One year later&#44; Candell et al&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> retrospectively analyzed 33 patients &#40;of 7350 myocardial perfusion SPECT studies&#41; who had normal scintigraphic findings with a positive ECG in the exercise test and coronary angiography&#46; Twenty-one of the 26 women &#40;81&#37;&#41; and one of the seven men &#40;14&#37;&#41; had normal coronary angiographic findings &#40;p&#61;0&#46;004&#41;&#46; Seven out of 11 patients with CAD had left main coronary artery or multivessel disease&#46; The authors concluded that the &#8220;normal SPECT with positive ECG&#8221; pattern is infrequent&#44; and is observed predominantly in women with normal coronary angiographic findings&#46; Later&#44; Hage et al&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> confirmed the superior prognostic value of SPECT over ECG&#58; with normal &#40;true negative&#41; SPECT findings&#44; the presence of concomitant ischemic &#40;false positive&#41; ST changes on the ECG does not have an adverse effect on prognosis&#46; They presented the results of 73 patients with ischemic ST depression but without perfusion abnormalities detected by SPECT during adenosine stress&#44; followed for five years&#46; The cardiac mortality in this group of patients was less than 1&#46;3&#37;&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">We focused on patients with chest pain and discrepancies between the two tests&#58; positive SPECT MPI with a negative ECG response and vice versa&#44; negative MPI with a positive ECG response&#46; We found no differences in baseline characteristics of patients in the different groups&#46; In patients with negative SPECT the event rate was very low&#44; regardless of the ECG response&#46; There was a small proportion of patients &#40;&#60;1&#37;&#41; with negative MPI and multivessel CAD in whom the ECG response was very positive&#46; Although the literature reports an overall sensitivity of SPECT MPI for detecting multivessel disease ranging from 80&#37; to 95&#37;&#44;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a> in cases with evenly distributed ischemia without inducible flow heterogeneity among the different vessel territories&#44; balanced ischemia has been hypothesised to explain lack of perfusion defects&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> By its nature&#44; SPECT only allows detection of relative perfusion defects&#46; Therefore&#44; the true extent of myocardial ischemia may be underestimated&#44; particularly in vascular territories adjacent to areas with more severe coronary stenosis&#46; It may be necessary to specify different degrees of positivity or the time of normalization of ECG response in order to determine that a negative result of ischemia by nuclear MPI is not a false positive&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">In addition&#44; Ghadri et al&#46;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a> recently showed that in patients with normal SPECT MPI&#44; a coronary artery calcium score &#40;AAC&#41; &#62;1000 confers high added diagnostic value for detecting CAD&#44; predominantly in patients with one- and two-vessel disease&#44; and state that this is not based solely on unmasking balanced ischemia due to three-vessel disease&#46; According to the authors&#44; balanced ischemia may not be solely due to balanced epicardial coronary lesion severity&#44; but could also be caused by microcirculatory dysfunction in which global hyperemic myocardial flow response and myocardial flow reserve are blunted&#46; A massive increase in CAC would &#8211; although measured in the epicardial vessel &#8211; then reflect involvement of the coronary microcirculatory tree&#44; potentially associated with a decrease in myocardial flow reserve&#44; favoring a balanced hyperemic response due to blunted flow heterogeneity&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">On the other hand patients with positive MPI have a poorer prognosis&#44; independently of ECG response&#46; Special mention should be made of non-hypertensive patients&#44; in whom the ECG result cannot be distorted by signs of left ventricular hypertrophy with systolic overload&#46; In these patients &#40;positive MPI&#41; the ECG response identifies a subgroup of patients with very high risk of cardiac events &#40;more than two-thirds&#41;&#46;</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">5</span><span class="elsevierStyleSectionTitle" id="sect0130">Conclusions</span><p id="par0100" class="elsevierStylePara elsevierViewall">This study addresses a frequent clinical dilemma&#58; what to do with discrepant ECG and MPI findings&#63; After analyzing the prognostic value of MPI and ECG response to exercise testing in 1460 consecutive patients with intermediate or high likelihood of CAD&#44; we concluded that when the results of MPI and ECG are discordant&#44; it is correct to consider only the MPI results&#46;</p></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">6</span><span class="elsevierStyleSectionTitle" id="sect0135">Ethical disclosures</span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">6&#46;1</span><span class="elsevierStyleSectionTitle" id="sect0140">Protection of human and animal subjects</span><p id="par0105" class="elsevierStylePara elsevierViewall">The authors declare that no experiments were performed on humans or animals for this study&#46;</p></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">6&#46;2</span><span class="elsevierStyleSectionTitle" id="sect0145">Confidentiality of data</span><p id="par0110" class="elsevierStylePara elsevierViewall">The authors declare that they have followed the protocols of their work center on the publication of patient data and that all the patients included in the study received sufficient information and gave their written informed consent to participate in the study&#46;</p></span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">6&#46;3</span><span class="elsevierStyleSectionTitle" id="sect0150">Right to privacy and informed consent</span><p id="par0115" class="elsevierStylePara elsevierViewall">The authors have obtained the written informed consent of the patients or subjects mentioned in the article&#46; The corresponding author is in possession of this document&#46;</p></span></span><span id="sec0095" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">7</span><span class="elsevierStyleSectionTitle" id="sect0155">Conflicts of interest</span><p id="par0120" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare&#46;</p></span></span>"
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          ]
        ]
      ]
      "pt" => array:1 [
        0 => array:4 [
          "clase" => "keyword"
          "titulo" => "Palavras-chave"
          "identificador" => "xpalclavsec281819"
          "palabras" => array:4 [
            0 => "ECG"
            1 => "Imagens de perfus&#227;o mioc&#225;rdica"
            2 => "Prova de esfor&#231;o"
            3 => "Progn&#243;stico"
          ]
        ]
      ]
    ]
    "tieneResumen" => true
    "resumen" => array:2 [
      "en" => array:2 [
        "titulo" => "Abstract"
        "resumen" => "<span class="elsevierStyleSectionTitle" id="sect0010">Objectives</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">The aim of the present study was to determine the long-term prognostic value provided by the exercise electrocardiographic &#40;ECG&#41; response to nuclear myocardial perfusion imaging &#40;MPI&#41; in the evaluation of patients with chest pain&#44; focusing on patients with a discrepancy between the two tests&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">A total of 1460 consecutive patients &#40;777 female&#59; 62&#46;6&#177;11&#46;4 years&#41; undergoing exercise myocardial single-photon emission computed tomography &#40;SPECT&#41; were included&#46; The endpoint was the occurrence of acute coronary syndrome&#44; heart failure or cardiac death during follow-up&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Ischemic ECG changes were observed during stress testing in 271 patients &#40;18&#46;5&#37;&#41; and 362 patients &#40;24&#46;7&#37;&#41; had positive &#40;abnormal&#41; exercise MPI results&#46; There was a discrepancy between ECG and SPECT findings in 471 patients &#40;32&#46;2&#37;&#41;&#46; During the follow-up period &#40;14&#46;0-39&#46;6 months&#41;&#44; 224 patients &#40;15&#46;3&#37;&#41; presented cardiac events&#46; The hazard ratios &#40;HR&#41; of ECG and MPI results to predict events were 1&#46;506 &#40;95&#37; CI&#58; 1&#46;113-2&#46;039&#41; and 10&#46;481 &#40;95&#37; CI&#58; 7&#46;799-14&#46;080&#41;&#44; respectively&#46; In patients with negative MPI&#44; the ECG response did not predict events &#40;HR 1&#46;214 &#91;95&#37; CI&#58; 0&#46;646-2&#46;282&#93;&#41;&#44; the same as in patients with positive MPI &#40;HR 1&#46;203 &#91;95&#37; CI&#58; 0&#46;848-1&#46;705&#93;&#41;&#46; Only in hypertensive patients with positive SPECT did the ECG show significant prognostic value &#40;HR 1&#46;937 &#91;95&#37; CI&#58; 1&#46;030-3&#46;642&#93;&#41;&#46; In multivariate analysis&#44; positive MPI proved an independent long-term prognostic factor &#40;HR 10&#46;536 &#91;95&#37; CI&#58; 7&#46;759-14&#46;308&#93;&#41;&#44; but not ECG &#40;HR 1&#46;356 &#91;95&#37; CI&#58; 0&#46;994-1&#46;850&#93;&#41;&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">MPI results &#40;normal vs&#46; abnormal&#41; had strong predictive value and discrepant ECG results had no significant additive prognostic value&#46;</p>"
      ]
      "pt" => array:2 [
        "titulo" => "Resumo"
        "resumen" => "<span class="elsevierStyleSectionTitle" id="sect0035">Objetivos</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">O objetivo do presente estudo&#44; foi determinar o valor progn&#243;stico a longo prazo&#44; fornecido pelo eletrocardiograma &#40;ECG&#41; em resposta ao esfor&#231;o na cintigrafia de perfus&#227;o mioc&#225;rdica &#40;CPM&#41; na avalia&#231;&#227;o de doentes com dor tor&#225;cica&#44; com &#234;nfase para os grupos de doentes com discrep&#226;ncia entre os dois testes&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0040">M&#233;todo</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Foram inclu&#237;dos 1460 doentes consecutivos &#40;777 do sexo feminino&#59; 62&#44;6 &#177; 11&#44;4 anos&#41;&#44; submetidos a exerc&#237;cio na cintigrafia de perfus&#227;o mioc&#225;rdica com SPECT&#46; O <span class="elsevierStyleItalic">endpoint</span> foi a presen&#231;a de s&#237;ndrome coron&#225;ria aguda&#44; insufici&#234;ncia card&#237;aca ou morte card&#237;aca durante o seguimento&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Duzentos e setenta e um doentes &#40;18&#44;5&#37;&#41; apresentaram altera&#231;&#245;es eletrocardiogr&#225;ficas de isquemia durante o teste de esfor&#231;o e 362 doentes &#40;24&#44;7&#37;&#41; tinham resultados positivos &#40;anormais&#41; na CPM&#46; A discrep&#226;ncia entre os resultados do ECG e CPM apareceu em 471 doentes &#40;32&#44;2&#37;&#41;&#46; Durante o per&#237;odo de seguimento &#40;14&#44;0-39&#44;6 meses&#41;&#44; 224 &#40;15&#44;3&#37;&#41; apresentaram eventos card&#237;acos&#46; O <span class="elsevierStyleItalic">hazard ratio</span> &#40;HR&#41; para predizer eventos dos resultados do ECG e CPM foi de 1&#44;506 &#40;1&#44;113-2&#44;039&#41; e 10&#44;481 &#40;7&#44;799-14&#44;080&#41;&#44; respetivamente&#46; No grupo de doentes com CPM negativo&#44; o ECG n&#227;o prediz eventos &#91;HR 1&#44;214 &#40;0&#44;646-2&#44;282&#41;&#93;&#44;e o mesmo acontece no grupo de doentes com CPM positivo &#91;HR 1&#44;203 &#40;0&#44;848-1&#44;705&#41;&#93;&#46; Apenas em doentes hipertensos com CPM positivo&#44; o ECG mostrou valor progn&#243;stico significativo &#91;HR 1&#44;937 &#40;1&#44;030-3&#44;642&#41;&#93;&#46; Na an&#225;lise multivariada&#44; a CPM positiva foi um fator independente de progn&#243;stico a longo prazo &#91;HR 10&#44;536 &#40;7&#44;759-14&#44;308&#41;&#93;&#44; mas n&#227;o o ECG &#91;HR 1&#44;356 &#40;0&#44;994-1&#44;850&#41;&#93;&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0050">Conclus&#227;o</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">O resultado da CPM &#40;normal <span class="elsevierStyleItalic">versus</span> anormal&#41; teve um forte valor preditivo e o resultado discrepante no ECG n&#227;o teve um valor progn&#243;stico adicional significativo&#46;</p>"
      ]
    ]
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      0 => array:7 [
        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr1.jpeg"
            "Alto" => 2499
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            "Tamanyo" => 267889
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        "descripcion" => array:1 [
          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Temporal trends in the occurrence of cardiac events in the groups with positive and negative ECG response &#40;A&#41; and SPECT MPI result &#40;B&#41;&#46;</p>"
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      1 => array:7 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr2.jpeg"
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            "Tamanyo" => 166221
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        "descripcion" => array:1 [
          "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Kaplan-Meier survival curves according to ECG and SPECT findings&#46;</p>"
        ]
      ]
      2 => array:7 [
        "identificador" => "fig0015"
        "etiqueta" => "Figure 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr3.jpeg"
            "Alto" => 1743
            "Ancho" => 2118
            "Tamanyo" => 320079
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        "descripcion" => array:1 [
          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Survival curves in the discrepant groups adjusted by gender and hypertension&#46;</p>"
        ]
      ]
      3 => array:7 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:2 [
          "leyenda" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">&#8722;&#58; negative&#59; &#43;&#58; positive&#59; ECG&#58; electrocardiographic result&#59; LVEF&#58; left ventricular ejection fraction&#59; METs&#58; metabolic equivalents&#59; SPECT&#58; single-photon emission computed tomography result&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Variables&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Total&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">SPECT &#8722;&#44; ECG &#8722;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">SPECT &#8722;&#44; ECG &#43;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">SPECT &#43;&#44; ECG &#8722;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">SPECT &#43;&#44; ECG &#43;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">p&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age &#40;years&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">62&#46;6&#177;11&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">62&#46;8&#177;11&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">63&#46;0&#177;12&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">61&#46;9&#177;11&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">62&#46;2&#177;12&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;638&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Female &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">53&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">53&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">54&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">51&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">54&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;899&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Current smoking &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;652&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Hypertension &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">66&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">65&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">64&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">68&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">75&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;243&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Dyslipidemia &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">58&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">59&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">57&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">58&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">55&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;843&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Diabetes &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">24&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">26&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">20&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">21&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;223&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Peripheral arterial disease &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;451&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Atrial fibrillation &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">11&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;061&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">LVEF &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">61&#46;0&#177;11&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">60&#46;9&#177;11&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">60&#46;9&#177;13&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">61&#46;1&#177;12&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">62&#46;3&#177;12&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;797&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Energy cost &#40;METs&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;7&#177;3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;7&#177;3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;4&#177;3&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;6&#177;3&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9&#46;2&#177;3&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;361&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Events at six months &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">25&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">33&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Events during follow-up <span class="elsevierStyleItalic">&#40;&#37;&#41;</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">43&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">54&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
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                0 => "xTab437337.png"
              ]
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          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Baseline characteristics of the overall population and of different groups according to SPECT and ECG results&#46;</p>"
        ]
      ]
      4 => array:7 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:2 [
          "leyenda" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">&#8722;&#58; negative&#59; &#43;&#58; positive&#59; ECG&#58; electrocardiographic result&#59; SPECT&#58; single-photon emission computed tomography result&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">ECG &#8722;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">ECG &#43;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">SPECT &#8722;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">908 &#40;62&#46;3&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">190 &#40;13&#46;0&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">SPECT &#43;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">281 &#40;19&#46;2&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">81 &#40;5&#46;5&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab437338.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">ECG and SPECT results in the total population&#46;</p>"
        ]
      ]
      5 => array:7 [
        "identificador" => "tbl0015"
        "etiqueta" => "Table 3"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:2 [
          "leyenda" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">&#43;&#58; positive&#59; ECG&#58; electrocardiographic result&#59; LVEF&#58; left ventricular ejection fraction&#59; METs&#58; metabolic equivalents&#59; SPECT&#58; single-photon emission computed tomography result&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
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                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Variables&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Events&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">No events&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">p&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age &#40;years&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">62&#46;7&#177;11&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">62&#46;6&#177;11&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;937&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Female &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">53&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Current smoking &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Hypertension &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;127&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Dyslipidemia &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">62&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">58&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;235&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Diabetes &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">25&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">24&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;649&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Peripheral arterial disease &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;695&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Atrial fibrillation &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;357&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">LVEF &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">61&#46;0&#177;12&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">61&#46;0&#177;11&#46;9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;987&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Energy cost &#40;METs&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;6&#177;3&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;7&#177;3&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;504&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">SPECT &#43; &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">72&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">ECG <span class="elsevierStyleItalic">&#43; &#40;&#37;&#41;</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">25&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;007&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
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                0 => "xTab437340.png"
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        ]
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          "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">Baseline characteristics of the study population&#44; stratified by presence of cardiac death and cardiac events&#46;</p>"
        ]
      ]
      6 => array:7 [
        "identificador" => "tbl0020"
        "etiqueta" => "Table 4"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
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        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
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                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">VARIABLES&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">HAZARD RATIO&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">CI 95&#37;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">P value&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;004&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;991&#8211;1&#46;017&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;573&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Number of cardiovascular risk factors&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;167&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;027&#8211;1&#46;326&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;018&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Left ventricular ejection fraction&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;996&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;984&#8211;1&#46;007&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;455&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">SPECT &#43;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;356&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;994&#8211;1&#46;850&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;055&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">EKG &#43;</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;536&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;759&#8211;14&#46;308&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab437339.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">Multivariate analysis to predict long-term outcomes&#46;</p>"
        ]
      ]
      7 => array:7 [
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Article information

ISSN: 21742049
Original language: English

DOI:10.1016/j.repce.2012.11.023

The statistics are updated each day

Year/Month	Html	Pdf	Total

2024 November	11	4	15
2024 October	33	34	67
2024 September	40	43	83
2024 August	42	40	82
2024 July	35	34	69
2024 June	28	17	45
2024 May	62	22	84
2024 April	29	23	52
2024 March	27	14	41
2024 February	28	21	49
2024 January	23	28	51
2023 December	34	30	64
2023 November	31	20	51
2023 October	23	16	39
2023 September	23	23	46
2023 August	31	16	47
2023 July	30	7	37
2023 June	29	17	46
2023 May	33	24	57
2023 April	30	7	37
2023 March	27	30	57
2023 February	30	19	49
2023 January	26	23	49
2022 December	43	28	71
2022 November	43	28	71
2022 October	29	15	44
2022 September	30	30	60
2022 August	23	23	46
2022 July	29	31	60
2022 June	27	26	53
2022 May	26	36	62
2022 April	29	28	57
2022 March	26	25	51
2022 February	28	23	51
2022 January	27	20	47
2021 December	21	30	51
2021 November	38	35	73
2021 October	32	35	67
2021 September	35	27	62
2021 August	27	23	50
2021 July	25	27	52
2021 June	13	18	31
2021 May	36	34	70
2021 April	43	52	95
2021 March	34	17	51
2021 February	42	11	53
2021 January	22	15	37
2020 December	36	7	43
2020 November	20	13	33
2020 October	9	8	17
2020 September	45	8	53
2020 August	13	8	21
2020 July	46	4	50
2020 June	29	4	33
2020 May	39	7	46
2020 April	35	14	49
2020 March	41	5	46
2020 February	60	15	75
2020 January	33	7	40
2019 December	25	7	32
2019 November	121	9	130
2019 October	47	5	52
2019 September	14	11	25
2019 August	38	6	44
2019 July	35	13	48
2019 June	16	14	30
2019 May	29	10	39
2019 April	28	17	45
2019 March	38	11	49
2019 February	74	12	86
2019 January	54	8	62
2018 December	81	16	97
2018 November	259	11	270
2018 October	644	20	664
2018 September	127	11	138
2018 August	128	8	136
2018 July	34	5	39
2018 June	51	6	57
2018 May	46	5	51
2018 April	93	4	97
2018 March	46	6	52
2018 February	49	5	54
2018 January	35	14	49
2017 December	68	13	81
2017 November	37	7	44
2017 October	34	11	45
2017 September	40	6	46
2017 August	39	16	55
2017 July	23	7	30
2017 June	45	13	58
2017 May	37	6	43
2017 April	42	3	45
2017 March	35	62	97
2017 February	27	7	34
2017 January	41	2	43
2016 December	32	8	40
2016 November	19	5	24
2016 October	34	4	38
2016 September	25	12	37
2016 August	18	2	20
2016 July	16	3	19
2016 June	7	5	12
2016 May	14	2	16
2016 April	17	10	27
2016 March	50	17	67
2016 February	44	34	78
2016 January	44	18	62
2015 December	22	11	33
2015 November	31	12	43
2015 October	36	9	45
2015 September	30	9	39
2015 August	32	6	38
2015 July	22	6	28
2015 June	19	1	20
2015 May	26	6	32
2015 April	36	5	41
2015 March	37	7	44
2015 February	21	4	25
2015 January	24	4	28
2014 December	31	8	39
2014 November	24	5	29
2014 October	50	4	54
2014 September	93	6	99
2014 August	88	5	93
2014 July	67	8	75
2014 June	52	4	56
2014 May	27	8	35
2014 April	20	3	23
2014 March	25	10	35
2014 February	33	11	44
2014 January	38	14	52
2013 December	14	8	22

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