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2016;36:57-62" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:2 [ "total" => 9262 "formatos" => array:3 [ "EPUB" => 299 "HTML" => 8072 "PDF" => 891 ] ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original</span>" "titulo" => "Comparación entre bioimpedancia espectroscópica y fórmula de Watson para medición de volumen corporal en pacientes en diálisis peritoneal" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "57" "paginaFinal" => "62" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Comparison of bioimpedance spectroscopy and the Watson formula for measuring body volume in patients on peritoneal dialysis" ] ] "contieneResumen" => array:2 [ "es" => true "en" => true ] "contieneTextoCompleto" => array:1 [ "es" => true ] "contienePdf" => array:1 [ "es" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figura 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 943 "Ancho" => 1550 "Tamanyo" => 29699 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Volumen (l) calculado por fórmula de Watson (Vw) y BIS (Vbis) (p < 0,01).</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Gonzalo Martínez Fernández, Agustín Ortega Cerrato, Lourdes de la Vara Iniesta, Eva Oliver Galera, Carmen Gómez Roldán, Juan Pérez Martínez" "autores" => array:6 [ 0 => array:2 [ "nombre" => "Gonzalo" "apellidos" => "Martínez Fernández" ] 1 => array:2 [ "nombre" => "Agustín" "apellidos" => "Ortega Cerrato" ] 2 => array:2 [ "nombre" => "Lourdes" "apellidos" => "de la Vara Iniesta" ] 3 => array:2 [ "nombre" => "Eva" "apellidos" => "Oliver Galera" ] 4 => array:2 [ "nombre" => "Carmen" "apellidos" => "Gómez Roldán" ] 5 => array:2 [ "nombre" => "Juan" "apellidos" => "Pérez Martínez" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2013251416000225" "doi" => "10.1016/j.nefroe.2016.02.002" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2013251416000225?idApp=UINPBA000064" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0211699515002313?idApp=UINPBA000064" "url" => "/02116995/0000003600000001/v1_201602110042/S0211699515002313/v1_201602110042/es/main.assets" ] "itemAnterior" => array:20 [ "pii" => "S0211699515001903" "issn" => "02116995" "doi" => "10.1016/j.nefro.2015.10.005" "estado" => "S300" "fechaPublicacion" => "2016-01-01" "aid" => "123" "copyright" => "Sociedad Española de Nefrología" "documento" => "article" "crossmark" => 0 "licencia" => "http://creativecommons.org/licenses/by-nc-nd/4.0/" "subdocumento" => "fla" "cita" => "Nefrologia. 2016;36:42-50" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:2 [ "total" => 11925 "formatos" => array:3 [ "EPUB" => 326 "HTML" => 10566 "PDF" => 1033 ] ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original</span>" "titulo" => "Hiponatremia, mortalidad y hemodiálisis: una asociación no explicada" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "42" "paginaFinal" => "50" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Hyponatraemia, mortality and haemodialysis: An unexplained association" ] ] "contieneResumen" => array:2 [ "es" => true "en" => true ] "contieneTextoCompleto" => array:1 [ "es" => true ] "contienePdf" => array:1 [ "es" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figura 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 2833 "Ancho" => 2594 "Tamanyo" => 230032 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Curvas de supervivencia (Kaplan-Meier) según el cuartil de la natremia.</p> <p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Las curvas de supervivencia muestran el ritmo de fallecimientos según el cuartil (C) de Na en el que estuviese cada paciente a los 6 meses de entrada en las clínicas FMC.</p> <p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">El tiempo medio de supervivencia del C1 fue de 3,21 años IC (3,10-3,31), el del C2 de 3,42 años IC (3,32-3,51), el del C3 3,51 años IC (3,42-3,60) y por último el del C4 de 3,53 IC (3,44-3,61).</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Rafael Pérez-García, Inés Palomares, José Ignacio Merello, Rosa Ramos, Francisco Maduell, Manolo Molina, Pedro Aljama, Daniele Marcelli" "autores" => array:9 [ 0 => array:2 [ "nombre" => "Rafael" "apellidos" => "Pérez-García" ] 1 => array:2 [ "nombre" => "Inés" "apellidos" => "Palomares" ] 2 => array:2 [ "nombre" => "José Ignacio" "apellidos" => "Merello" ] 3 => array:2 [ "nombre" => "Rosa" "apellidos" => "Ramos" ] 4 => array:2 [ "nombre" => "Francisco" "apellidos" => "Maduell" ] 5 => array:2 [ "nombre" => "Manolo" "apellidos" => "Molina" ] 6 => array:2 [ "nombre" => "Pedro" "apellidos" => "Aljama" ] 7 => array:2 [ "nombre" => "Daniele" "apellidos" => "Marcelli" ] 8 => array:1 [ "colaborador" => "en representación del grupo ORD" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2013251416000262" "doi" => "10.1016/j.nefroe.2016.02.005" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2013251416000262?idApp=UINPBA000064" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0211699515001903?idApp=UINPBA000064" "url" => "/02116995/0000003600000001/v1_201602110042/S0211699515001903/v1_201602110042/es/main.assets" ] "en" => array:20 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original article</span>" "titulo" => "Creatine-kinase and dialysis patients, a helpful tool for stratifying cardiovascular risk?" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "51" "paginaFinal" => "56" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Borja Quiroga, Almudena Vega, Soraya Abad, Maite Villaverde, Javier Reque, Juan Manuel López-Gómez" "autores" => array:6 [ 0 => array:4 [ "nombre" => "Borja" "apellidos" => "Quiroga" "email" => array:1 [ 0 => "borjaqg@gmail.com" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:2 [ "nombre" => "Almudena" "apellidos" => "Vega" ] 2 => array:2 [ "nombre" => "Soraya" "apellidos" => "Abad" ] 3 => array:2 [ "nombre" => "Maite" "apellidos" => "Villaverde" ] 4 => array:2 [ "nombre" => "Javier" "apellidos" => "Reque" ] 5 => array:2 [ "nombre" => "Juan Manuel" "apellidos" => "López-Gómez" ] ] "afiliaciones" => array:1 [ 0 => array:2 [ "entidad" => "Servicio de Nefrología, Hospital General Universitario Gregorio Marañón, Madrid, Spain" "identificador" => "aff0005" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "<span class="elsevierStyleItalic">Corresponding author</span>." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Creatincinasa y pacientes en diálisis, ¿una herramienta útil para estratificar el riesgo cardiovascular?" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 893 "Ancho" => 1610 "Tamanyo" => 84690 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Kaplan–Meier plot illustrating cardiovascular events and CKMB values.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Chronic kidney disease (CKD) is a major cardiovascular risk factor similar to congestive heart failure, and this risk increases with the decline of renal function, being maximum in dialysis.<a class="elsevierStyleCrossRefs" href="#bib0125"><span class="elsevierStyleSup">1,2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Several strategies have been proposed in order to detect those patients at high risk for developing cardiovascular events and for detecting subclinical alterations to be treated.<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">3</span></a> Current guidelines recommend measuring cardiac biomarkers, specifically troponins and natriuretic peptides, as they are usually increased in our patients.<a class="elsevierStyleCrossRefs" href="#bib0125"><span class="elsevierStyleSup">1,4</span></a> If possible, ecocardiography should be performed periodically although recommended intervals vary in function of the guideline. Some studies have found a strong association between cardiac biomarkers, ecocardiographic findings and prognosis.<a class="elsevierStyleCrossRefs" href="#bib0135"><span class="elsevierStyleSup">3,5</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">On one hand, serum cardiac biomarkers are increased in virtually all CKD patients especially in dialysis and those who have higher values have poorer prognosis. On the other hand, when these biomarkers are adjusted by ecocardiographic findings (for example, diastolic and systolic dysfunction and left ventricular hypertrophy) they lose their independent prediction value, suggesting their role as observer of cardiac damage, usually subclinically.<a class="elsevierStyleCrossRefs" href="#bib0145"><span class="elsevierStyleSup">5,6</span></a> Most importantly, increased cardiac biomarkers seems to be universal with high sensitivity assays, but those with higher values have a better association to postmortem cardiac damage or with coronary lesions demonstrated by angiography.<a class="elsevierStyleCrossRefs" href="#bib0155"><span class="elsevierStyleSup">7,8</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">However, and although several authors have proposed different cut-offs for stratifying the cardiovascular risk, lack of agreement has been reached, suggesting that probably those markers must be interpreted as continuous variables.</p><p id="par0025" class="elsevierStylePara elsevierViewall">One important biomarker has not been widely studied in CKD patients until date, creatine kinase MB isoenzyme (CKMB). In general population, its sensitivity in acute coronary syndromes is inferior to troponins. However, due to its small half-life, current guidelines recommend its use for monitoring cardiac damage after revascularization as they present a good correlation with re-infarction.<a class="elsevierStyleCrossRefs" href="#bib0165"><span class="elsevierStyleSup">9,10</span></a> Many patients with CKD have been excluded from studies about CKMB due to its difficult interpretation when renal function is impaired. Published series yield controversial data in terms of prevalence of raise serum values and their use in ischemic heart disease. However, it seems to have the same value as prognosis marker in re-infarction in patients with renal impairment.<a class="elsevierStyleCrossRefs" href="#bib0175"><span class="elsevierStyleSup">11,12</span></a> The aim of the present study was to analyze the prognosis value of CKMB in a cohort of dialysis patients and also its related factors.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Materials and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Patients</span><p id="par0030" class="elsevierStylePara elsevierViewall">A total of 211 patients on hemodialysis in a single center were enrolled in the restrospective study. Stable patients with no cardiovascular events in the 4 weeks before serum determinations were included. During the follow up [39 (19–56) months], patients with changes in hemodialysis parameters, transferred to another center or transplanted were censored. Investigations were in accordance with the Declaration of Helsinki.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Baseline characteristics and measurements</span><p id="par0035" class="elsevierStylePara elsevierViewall">Baseline characteristics were recorded, including age, sex, presence of diabetes mellitus, peripheral vascular disease, previous cardiovascular disease (congestive heart failure determined by echocardiography within the three previous months, myocardial infarction, cerebrovascular disease), dialysis vintage and data regarding the vascular access. Basally, we measured C-reactive protein (CRP), high sensitivity troponin T (hsTnT), CK-MB and N-terminal prohormone of brain natriuretic peptide (Nt-proBNP). All included patients had the same hemodialysis therapy protocol: 4<span class="elsevierStyleHsp" style=""></span>h, three times per week. Routine clinical and biochemical variables were measured by standardized methods on autoanalyzers. CKMB and HsTnT were measured on a Roche/Hitachi Cobas E411 analyzer. Factors related to higher values of CKMB were analyzed.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Echocardiography</span><p id="par0040" class="elsevierStylePara elsevierViewall">Echocardiography was recorded in stable patients who had a less than 6-month one before obtaining the sample and within 24<span class="elsevierStyleHsp" style=""></span>h after the last hemodialysis on the day between mid-week dialysis sessions. Diastolic dysfunction was defined as <span class="elsevierStyleItalic">e</span>′ (early mitral annulus velocity) less than 8<span class="elsevierStyleHsp" style=""></span>cm/s, average <span class="elsevierStyleItalic">E</span> (early mitral flow)/<span class="elsevierStyleItalic">e</span>′ over 8, LAVi (left atrium volume index) over 28<span class="elsevierStyleHsp" style=""></span>mL/m<span class="elsevierStyleSup">2</span> or Ar<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>A (time difference between duration of the pulmonary venous atrial reversal wave and duration of the A wave) over 30<span class="elsevierStyleHsp" style=""></span>ms. Systolic dysfunction in turn was defined as a left ventricle ejection fraction of under 45%. The left ventricular mass index (LVMi) was estimated by Devereux's formula.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">13</span></a></p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Outcomes</span><p id="par0045" class="elsevierStylePara elsevierViewall">New cardiovascular events (ischemic or hemorrhagic cerebrovascular accident, cardiac event [including myocardial infarction and/or congestive heart failure], peripheral vascular events and other ischemic events) were recorded during follow-up. We analyzed the predictor value of CKMB for cardiovascular events.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Statistical procedures</span><p id="par0050" class="elsevierStylePara elsevierViewall">Values are expressed as the mean (SD) or median (IQR). We established linear regression models for evaluating the distribution of the studied variables according to CKMB levels. To assess the diagnosis value of the different cardiac markers we used the receiving operator characteristics (ROC) curve for each one. Correlations between cardiac markers were performed using Pearson test. Multivariate analysis was performed by Cox regression. Variables were analyzed and only those considered confounders were entered in the final Cox regression model. Different models were used, including CKMB for its different values, in order to determine the best cut-off to assess cardiovascular risk. Cardiovascular events were analyzed using Kaplan–Meier plots, and survival curves were compared using the log-rank test. We used the integrated discrimination improvement (IDI), as described by Pencina et al.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">14</span></a> to interpret the incremental value of CKMB<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>2<span class="elsevierStyleHsp" style=""></span>ng/mL added to a risk prediction model including age, sex, previous heart disease, peripheral vascular disease, diabetes mellitus, NtproBNP and HsTnT. IDI is a measure of improvement in model performance and represents the difference between discrimination slopes of two competing models. We calculated the relative IDI, which expresses the relative increase in separation of events and non-events from the separation achieved in the base model (i.e., the difference in discrimination slopes is expressed as a proportion of the discrimination slope of the base model).<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">15</span></a> All statistical analyses were performed with the SPSS<span class="elsevierStyleSup">®</span> 18.0 statistical package (Chicago, IL, USA). A <span class="elsevierStyleItalic">p</span>-value <0.05 was considered statistically significant.</p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Results</span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Baseline characteristics and factors associated with increased levels</span><p id="par0055" class="elsevierStylePara elsevierViewall">A total of 211 prevalent hemodialysis patients were included in this study to be followed for 39 (19–56) months. One hundred and twenty-three patients (58.3%) were male, with a median age of 73 (60–80). Baseline characteristic are showed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>. The median value of CKMB was 1 (1–2) ng/mL, with the following distribution: 13 patients (6.3%) have basal levels of 0<span class="elsevierStyleHsp" style=""></span>ng/mL, 121 (58.5) have 1<span class="elsevierStyleHsp" style=""></span>ng/mL, 56 (27.1%) have 2<span class="elsevierStyleHsp" style=""></span>ng/mL, 13 (6.3%) have 3<span class="elsevierStyleHsp" style=""></span>ng/dL and only 4 (1.9) have 4<span class="elsevierStyleHsp" style=""></span>ng/dL. In <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>, baseline characteristics are shown in the different strata according to CKMB values. No patients exceeded the normal range of our laboratory (0–4<span class="elsevierStyleHsp" style=""></span>ng/mL). Univariate analysis revealed that higher levels of CKMB were associated to previous heart disease, diabetes mellitus, peripheral vascular disease and systolic and diastolic dysfunction (data shown in <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>).</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Correlation with other cardiac biomarkers</span><p id="par0060" class="elsevierStylePara elsevierViewall">Correlation between cardiac biomarkers showed a positive and significant one between CKMB and NtproBNP (0.163, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.020) and between NtproBNP and HsTnT (0.635, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001). No significant correlation was established between CKMB and HsTnT.</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Cardiovascular events and predictive value of CKMB</span><p id="par0065" class="elsevierStylePara elsevierViewall">A total of 94 cardiovascular events were recorded during the follow up. Cardiac event was the most common event (79.8%), followed by peripheral vascular disease (8.5%), cerebrovascular event (6.4%). Diabetic patients did not show higher incidence of cardiovascular events in our cohort (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.3), although they have a trend of higher prevalence of diastolic dysfunction (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.056). The area under the ROC curve for cardiovascular events was greater for HsTnT (0.723) and Nt-proBNP (0.688) than CKMB (0.587). CKMB levels were associated to the development of cardiovascular events during follow up [HR 1.46 95% CI (1.15–1.87), <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.002] as well as the other factors shown in <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>. In <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>, the survival curve shows the association between cardiovascular events and the different CKMB values confirming that higher levels condition worse prognosis (logRank 8.8, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.01). Multivariate Cox regression model adjusted for several cofounders and variables showed that CKMB levels ≥2<span class="elsevierStyleHsp" style=""></span>ng/mL independently increased cardiovascular risk in our cohort of hemodialysis patients. A non-significant trend was observed if the CKMB cut-off was ≥1<span class="elsevierStyleHsp" style=""></span>ng/mL (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a>).</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia><p id="par0070" class="elsevierStylePara elsevierViewall">IDI analysis was performed to assess the improvement in risk discrimination of adding CKMB (≥2<span class="elsevierStyleHsp" style=""></span>ng/mL) to a cardiovascular event risk prediction model including age, sex, previous heart disease, peripheral vascular disease, diabetes mellitus, NtproBNP and HsTnT. This analysis comprised all individuals and used NtproBNP and HsTnT as dichotomous variables. Adding CKMB resulted in 17% improved risk discrimination for cardiovascular events (IDI 0.026 [0.004–0.053]; relative IDI 9.9%; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.04).</p></span></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Discussion</span><p id="par0075" class="elsevierStylePara elsevierViewall">Our study demonstrates that CK-MB is a useful marker for stratifying cardiovascular risk in dialysis patients, even in normal range values. Importantly for acute situations, hemodialysis patients do not have basally elevated higher levels, so this marker could be useful in the differential diagnosis of chest pain as marker of acute ischemia. Supporting this fact, and different from other cardiac markers, CKMB does not appear to be influenced by the dialysis, so its levels remain stable after and before.<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">16</span></a> However, and although CKMB has a short half-life, its levels are correlated to HsTnT and NtproBNP and their values are associated to other cardiovascular risk factors. One study conducted by McCullong et al. that included 817 consecutive patients with chest pain, shown that those with confirmed acute myocardial infarction (AMI) suffer a CKMB rise irrespective of the renal function.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">17</span></a> Curiously, in this study, those patients without final diagnosis of AMI showed significant different basal levels of CKMB between groups (inversely correlated with glomerular filtration and maximum in dialysis patients), unlike those with final diagnosis of AMI.</p><p id="par0080" class="elsevierStylePara elsevierViewall">The first report regarding the influence of hemodialysis in CKMB levels was published in 1984 by Jaffe et al.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">18</span></a> In this study that included 88 patients, authors demonstrated normal levels of CKMB for the most part of the studied sample. Although some authors differ in their opinions on this regard, the CHANCE study confirmed not only this situation but also that levels of CK-MB could be influenced by the presence of history of ischemic heart disease.<a class="elsevierStyleCrossRefs" href="#bib0215"><span class="elsevierStyleSup">19–22</span></a> Our results agree with this finding, and with the association to age (we could only demonstrate a trend with this value) and troponins. Cardiovascular risk factors as peripheral vascular disease, diabetes mellitus and Nt-proBNP seem to be associated to CKMB, above all with values ≥3<span class="elsevierStyleHsp" style=""></span>ng/mL.</p><p id="par0085" class="elsevierStylePara elsevierViewall">We assessed the predictive value of CKMB for cardiovascular events. Our data suggests that CKMB<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>2<span class="elsevierStyleHsp" style=""></span>ng/mL give a poor cardiovascular prognosis in dialysis patients. Results of CHANCE study agree with our results, showing an increased risk of major cardiovascular events in those patients with CKMB<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>3<span class="elsevierStyleHsp" style=""></span>ng/mL, in a 2-year follow up.<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">11</span></a></p><p id="par0090" class="elsevierStylePara elsevierViewall">However, CKMB is not the only marker but the less studied in this regard, although it is cheap and easily applicable in routine clinical. In adjusted multivariate analysis, those patients with HsTnT and NtproBNP over the median and CKMB<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>2<span class="elsevierStyleHsp" style=""></span>ng/mL enhanced its cardiovascular risk more than threefold, in comparison to CKMB<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>2<span class="elsevierStyleHsp" style=""></span>ng/mL alone. Several studies have found an association between cardiac markers and prognosis, and now we know that this situation reveals a true clinical or subclinical cardiac damage.<a class="elsevierStyleCrossRefs" href="#bib0155"><span class="elsevierStyleSup">7,23</span></a> In fact, in our study, CKMB demonstrated an association with previous heart disease and also with systolic and diastolic dysfunction. Previous published data has only been able to remark an association to LVH in patients with renal function impairment.</p><p id="par0095" class="elsevierStylePara elsevierViewall">The present study has some limitations. Firstly, the typical limitations of a retrospective design. Secondly, not all the patients had a echocardiograph image and they were performed by different specialists. This bias was partially avoided by the use of the same criteria for the definitions of each entity (systolic and diastolic dysfunction and LVH). Thirdly, vascular calcifications were not assessed as recommended in recent guidelines.<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">24</span></a> Lastly, the study was performed in one center, and results must be confirmed in bigger sample size.</p><p id="par0100" class="elsevierStylePara elsevierViewall">In conclusion, CKMB is a good marker for stratifying cardiovascular risk in hemodialysis patients even in the normal range of their values. We recommend measuring several cardiac markers, at least two, in order to get better predictive values. As basal levels of CKMB are not elevated in these patients, further studies are required to confirm their value in acute coronary syndromes.</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Conflict of interests</span><p id="par0105" class="elsevierStylePara elsevierViewall">The authors declare no conflict of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:11 [ 0 => array:3 [ "identificador" => "xres604096" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Background and aims" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec618270" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres604097" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Antecedentes y objetivos" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec618269" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Materials and methods" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Patients" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Baseline characteristics and measurements" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Echocardiography" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Outcomes" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Statistical procedures" ] ] ] 6 => array:3 [ "identificador" => "sec0040" "titulo" => "Results" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0045" "titulo" => "Baseline characteristics and factors associated with increased levels" ] 1 => array:2 [ "identificador" => "sec0050" "titulo" => "Correlation with other cardiac biomarkers" ] 2 => array:2 [ "identificador" => "sec0055" "titulo" => "Cardiovascular events and predictive value of CKMB" ] ] ] 7 => array:2 [ "identificador" => "sec0060" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0065" "titulo" => "Conflict of interests" ] 9 => array:2 [ "identificador" => "xack203332" "titulo" => "Acknowledgment" ] 10 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2014-12-10" "fechaAceptado" => "2015-03-06" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec618270" "palabras" => array:4 [ 0 => "Cardiac biomarkers" 1 => "Cardiovascular" 2 => "CKMB" 3 => "Hemodialysis" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec618269" "palabras" => array:4 [ 0 => "Biomarcadores cardiacos" 1 => "Cardiovascular" 2 => "CKMB" 3 => "Hemodiálisis" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Background and aims</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Hemodialysis patients have an enhanced risk for cardiovascular events. Cardiac biomarkers provide useful information for stratifying their risk. However the prognosis value of creatine kinase MB isoenzyme (CKMB) has not yet been validated in this population. The aim of the present study is to determine the predictable value of CK-MB in hemodialysis.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">A cohort of 211 hemodialysis patients (58.3% male, median age 73 (60–80) years) were followed for 39 (19–56) months. Cardiac biomarkers including CKMB were recorded at baseline. Factors associated to CKMB and prognosis value of this biomarker was studied.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The median value of CKMB was 1 (1–2) ng/mL with no patient exceeding normal laboratory values. Previous heart disease, diabetes mellitus, peripheral vascular disease and systolic and diastolic dysfunction were associated with higher levels of CKMB. Ninety-four patients (44.5%) cardiovascular events were recorded. CKMB levels ≥2<span class="elsevierStyleHsp" style=""></span>ng/mL was independently associated to cardiovascular events during the follow up after adjusting. Adding CKMB to a model including several variables for predicting cardiovascular events, resulted in 17% improvement in risk discrimination (IDI) with a relative IDI of 9.9% (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.04).</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">CKMB is a good marker for stratifying cardiovascular risk in hemodialysis patients and adds prognosis information to other well known independent predictors for cardiovascular events.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Background and aims" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Antecedentes y objetivos</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Los pacientes en hemodiálisis presentan un riesgo cardiovascular elevado. Los biomarcadores cardiacos otorgan información útil para estratificar dicho riesgo cardiovascular. Sin embargo, el valor pronóstico de la isoenzima MB de la creatincinasa (CKMB) no ha sido aún validado en esta población. El objetivo del presente trabajo es evaluar el valor predictivo de CKMB en una población en hemodiálisis.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Métodos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Una cohorte de 211 pacientes en hemodiálisis (58,3% varones, con una edad media de 73 [60–80] años) fueron seguidos durante 39 (19–56) meses. Se recogieron basalmente los valores de diferentes biomarcadores cardiacos incluyendo CKMB. Se evaluaron los factores asociados a niveles más elevados de CKMB, así como su valor predictivo independiente.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">La mediana de CKMB fue de 1 (1–2) ng/mL. Todos los pacientes presentaron valores dentro de los establecidos de referencia en la población normal. Los antecedentes de cardiopatía, diabetes mellitus, enfermedad periférica y la disfunción diastólica y sistólica se asociaron a niveles más elevados de CKMB. Un total de 94 pacientes (44,5%) presentaron un evento cardiovascular. Los niveles de CKMB<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>2<span class="elsevierStyleHsp" style=""></span>ng/mL se asociaron de manera independiente a presentar eventos cardiovasculares durante el seguimiento tras el ajuste para diferentes factores. La adición de CKMB a un modelo predictor con diferentes factores generó una mejoría del 17% en la estimación de la probabilidad de forma lineal (IDI) con un IDI relativo del 9,9% (p<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0,04).</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">CKMB es un buen marcador para estratificar el riesgo cardiovascular en los pacientes de hemodiálisis y añade información en cuanto al pronóstico cuando se combina con otros predictores de eventos cardiovasculares.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Antecedentes y objetivos" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "multimedia" => array:5 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 893 "Ancho" => 1610 "Tamanyo" => 84690 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Kaplan–Meier plot illustrating cardiovascular events and CKMB values.</p>" ] ] 1 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Age (years)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">73 (60-80)<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Sex male,</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">123 (58.3)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">History of heart disease,</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">90 (42.7)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Diabetes mellitus,</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">68 (32.2)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Peripheral vascular disease,</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">64 (30.3)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Previous echocardiogram,</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">166 (78.7)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">- Left ventricular hypertrophy,</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">106 (63.9)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">- Systolic dysfunction,</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">24 (14.4)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">- Diastolic dysfunction,</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">60 (36.1)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Previous dialysis vintage (months)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">83 (43-128)<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Vascular access</span><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleBold">- Autologous</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleBold">- PTFE</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleBold">- Permanent catheter</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">116 (55)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>65 (31)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>30 (14)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">HsTnT (ng/L)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">56 (35-90)<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">CKMB (ng/mL)</span><span class="elsevierStyleBold">- 0 ng/mL</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">,n</span></span><span class="elsevierStyleBold">(%)</span><span class="elsevierStyleBold">- 1 ng/mL</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">,n</span></span><span class="elsevierStyleBold">(%)</span><span class="elsevierStyleBold">- 2 ng/mL</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">,n</span></span><span class="elsevierStyleBold">(%)</span><span class="elsevierStyleBold">- 3 ng/mL</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">,n</span></span><span class="elsevierStyleBold">(%)</span><span class="elsevierStyleBold">- 4 ng/mL</span><span class="elsevierStyleItalic"><span class="elsevierStyleBold">,n</span></span><span class="elsevierStyleBold">(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1 (1-2)<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">*</span></a>13 (6.3)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>121 (58.5)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>56 (27.1)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>13 (6.3)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>4 (1.9)<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Nt-proBNP (ng/L)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">4994 (2237-15036)<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">CRP (mg/L)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">7.0 (4.0-15.0)<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab989330.png" ] ] ] "notaPie" => array:2 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Mean (standart deviation).</p>" ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Median (interquartile range). High sentivity troponin T (hsTnT), <span class="elsevierStyleItalic">Creatinekinase-MB(CK</span>-<span class="elsevierStyleItalic">MB</span>), N-terminal prohormone of brain <span class="elsevierStyleItalic">natriureticpeptide (</span>NT-proBNP), C- reactive protein (CRP).</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Baseline characteristics.</p>" ] ] 2 => array:7 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">CKMB = 0 ng/mL</span><span class="elsevierStyleItalic">(n=13)</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">CKMB = 1 ng/mL</span><span class="elsevierStyleItalic">(n=121)</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">CKMB = 2 ng/mL</span><span class="elsevierStyleItalic">(n=56)</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">CKMB ≥ 3 ng/mL</span><span class="elsevierStyleItalic">(n=17)</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">P for trend</span> \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Age (years)</span><a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup"><span class="elsevierStyleBold">*</span></span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">77 (62-81) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">75 (56-81) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">73 (63-78) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">66 (70-64) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.096 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Sex male (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">53.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">57.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">60.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">76.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.072 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Previous heart disease (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">36.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">50.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">70.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.030 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Diabetes mellitus (%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">28.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">37.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">52.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.013 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Peripheral vascular disease(%)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">23.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">24.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">41.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">41.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.027 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Left ventricular hypertrophy (%)</span><a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">44.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">64.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">57.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">84.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.181 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Systolic dysfunction (%)</span><a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">38.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.040 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Diastolic dysfunction (%)</span><a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">a</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">30.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">25.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">53.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.019 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Previous dialysis vintage (months)</span><a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup"><span class="elsevierStyleBold">*</span></span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">107 (38-132) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">82 (46-128) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">85 (31-148) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">62 (40-122) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.291 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">HsTnT (ng/L)</span><a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">71 (39-88) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">50 (30-74) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">67 (42-128) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">89 (44-144) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Nt-proBNP (ng/L)</span><a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6814 (3894-12466) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4112 (2159-14244) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7293 (2506-17008) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10524 (4265-20146) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.020 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">CRP (mg/L)</span><a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">*</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15.0 (6.5-27.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.0 (3.0-14.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.0 (4.0-12.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14.0 (6.0-22.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.298 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab989331.png" ] ] ] "notaPie" => array:2 [ 0 => array:3 [ "identificador" => "tblfn0015" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara" id="npar0015">Median (interquartile range).</p>" ] 1 => array:3 [ "identificador" => "tblfn0020" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0020">Percentage over the patients with a previous echocardiogram (166). Abbrev.: High sentivity troponin T (hsTnT), <span class="elsevierStyleItalic">Creatinekinase-MB(CK</span>-<span class="elsevierStyleItalic">MB</span>), N-terminal prohormone of brain <span class="elsevierStyleItalic">natriureticpeptide (</span>NT-proBNP), C- reactive protein (CRP).<span class="elsevierStyleVsp" style="height:0.5px"></span></p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Descriptive of baseline characteristics according to CKMB values.</p>" ] ] 3 => array:7 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:3 [ "leyenda" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Abbreviations: HR (95% CI) = Hazard ratio (95% Confidence interval). (F/M)=(female/male). <span class="elsevierStyleItalic">Creatinekinase-MB(CK</span>-<span class="elsevierStyleItalic">MB</span>), N-terminal prohormone of brain <span class="elsevierStyleItalic">natriureticpeptide (</span>NT-proBNP),High sentivity troponin T (hsTnT), C- reactive protein (CRP).</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">HR (95% CI) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Age (years)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.02 (1.01-1.03) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.027 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Sex (male)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.11 (0.73-1.67) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.619 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Previous Heart Disease</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">4.99 (3.16-7.66) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Diabetes Mellitus</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.23 (0.81-1.88) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.330 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Peripheral vascular disease</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.53 (1.01-2.32) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.049 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Dialysis Vintage (per month)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.00 (0.99-1.01) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.825 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Vascular access (autologus fistualae)</span><a class="elsevierStyleCrossRef" href="#tblfn0035"><span class="elsevierStyleSup"><span class="elsevierStyleBold">*</span></span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.96 (0.64-1.45) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.860 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Left ventricular hypertrophy</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.64 (0.94-2.85) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.080 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Systolic dysfunction</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">2.72 (1.52-4.85) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Diastolic dysfunction</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">2.59 (1.52-4.42) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">CK-MB (ng/mL)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.46 (1.15-1.87) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">HsTnT ≥ 56 ng/L (ng/L)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">2.52 (1.47-4.32) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">Nt-proBNP (mcg/L)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.01 (1.01-1.01) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">CRP (mg/dL)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.01 (0.96-1.07) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.578 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab989332.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0035" "etiqueta" => "*" "nota" => "<p class="elsevierStyleNotepara" id="npar0025">Autologous vascular access has been codified as 0 and non-autologous as 1.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Factors associated with cardiovascular events during follow up (unadjusted Cox regression).</p>" ] ] 4 => array:7 [ "identificador" => "tbl0020" "etiqueta" => "Table 4" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">HR (95% CI) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">P \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">CKMB = 0 (ng/mL)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><span class="elsevierStyleItalic">ref</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><span class="elsevierStyleItalic">ref</span> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">CKMB ≥ 1 ng/mL</span><a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">a</span></a><span class="elsevierStyleBold">- CKMB ≥ 1 ng/mL * NtproBNP ≥ 4994 ng/L</span><a class="elsevierStyleCrossRef" href="#tblfn0030"><span class="elsevierStyleSup">b</span></a><span class="elsevierStyleBold">- CKMB ≥ 1 ng/mL * HsTnT ≥ 56 ng/L</span><a class="elsevierStyleCrossRef" href="#tblfn0030"><span class="elsevierStyleSup"><span class="elsevierStyleBold">b</span></span></a><span class="elsevierStyleBold">- CKMB ≥ 1 ng/mL * NtproBNP ≥ 4994 ng/L * HsTnT ≥ 56 ng/L</span><a class="elsevierStyleCrossRef" href="#tblfn0030"><span class="elsevierStyleSup"><span class="elsevierStyleBold">b</span></span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">2.20 (0.88-5.51)2.84 (1.79-4.53)1.98 (1.23-3.17)2.71 (1.74-4.21) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.092<0.0010.005<0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleBold">CKMB ≥ 2 (ng/mL)</span><a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">a</span></a><span class="elsevierStyleBold">- CKMB ≥ 2 ng/mL * NtproBNP ≥ 4994 ng/L</span><a class="elsevierStyleCrossRef" href="#tblfn0030"><span class="elsevierStyleSup">b</span></a><span class="elsevierStyleBold">- CKMB ≥ 2 ng/mL * HsTnT ≥ 56 ng/L</span><a class="elsevierStyleCrossRef" href="#tblfn0030"><span class="elsevierStyleSup">b</span></a><span class="elsevierStyleBold">- CKMB ≥ 2 ng/mL * NtproBNP ≥ 4994 ng/L * HsTnT ≥ 56 ng/L</span><a class="elsevierStyleCrossRef" href="#tblfn0030"><span class="elsevierStyleSup">b</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.63 (1.06-2.53)3.01 (1.90-4.81)1.82 (1.11-2.99)3.24 (1.92-5.47) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.027<0.0010.016<0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab989333.png" ] ] ] "notaPie" => array:2 [ 0 => array:3 [ "identificador" => "tblfn0025" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0030">Cox regression model adjusted for age, sex, previous heart disease, peripheral vascular disease, diabetes mellitus, N-terminal prohormone of brain <span class="elsevierStyleItalic">natriureticpeptide</span> and high sentivity troponin T.</p>" ] 1 => array:3 [ "identificador" => "tblfn0030" "etiqueta" => "b" "nota" => "<p class="elsevierStyleNotepara" id="npar0035">Cox regression model adjusted for age, sex, previous heart disease, peripheral vascular disease, diabetes mellitus.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Predictor value of the different values of CK-MB for cardiovascular events during follow up (adjusted Cox regression) alone and in combination with the other cardiac biomarkers.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:24 [ 0 => array:3 [ "identificador" => "bib0125" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Cardiac troponin T predicts mortality in patients with end-stage renal disease" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "J. 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Pacheco for proofreading the manuscript.</p>" "vista" => "all" ] ] ] "idiomaDefecto" => "en" "url" => "/02116995/0000003600000001/v1_201602110042/S0211699515001897/v1_201602110042/en/main.assets" "Apartado" => array:4 [ "identificador" => "42448" "tipo" => "SECCION" "en" => array:2 [ "titulo" => "Originales" "idiomaDefecto" => true ] "idiomaDefecto" => "en" ] "PDF" => "https://static.elsevier.es/multimedia/02116995/0000003600000001/v1_201602110042/S0211699515001897/v1_201602110042/en/main.pdf?idApp=UINPBA000064&text.app=https://revistanefrologia.com/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0211699515001897?idApp=UINPBA000064" ]
año/Mes | Html | Total | |
---|---|---|---|
2024 Noviembre | 8 | 5 | 13 |
2024 Octubre | 53 | 30 | 83 |
2024 Septiembre | 73 | 22 | 95 |
2024 Agosto | 77 | 65 | 142 |
2024 Julio | 58 | 31 | 89 |
2024 Junio | 69 | 36 | 105 |
2024 Mayo | 56 | 35 | 91 |
2024 Abril | 68 | 51 | 119 |
2024 Marzo | 46 | 28 | 74 |
2024 Febrero | 38 | 32 | 70 |
2024 Enero | 45 | 29 | 74 |
2023 Diciembre | 38 | 20 | 58 |
2023 Noviembre | 72 | 38 | 110 |
2023 Octubre | 83 | 39 | 122 |
2023 Septiembre | 55 | 25 | 80 |
2023 Agosto | 58 | 33 | 91 |
2023 Julio | 64 | 32 | 96 |
2023 Junio | 103 | 18 | 121 |
2023 Mayo | 102 | 27 | 129 |
2023 Abril | 80 | 11 | 91 |
2023 Marzo | 92 | 26 | 118 |
2023 Febrero | 84 | 18 | 102 |
2023 Enero | 41 | 34 | 75 |
2022 Diciembre | 73 | 27 | 100 |
2022 Noviembre | 69 | 53 | 122 |
2022 Octubre | 73 | 43 | 116 |
2022 Septiembre | 56 | 33 | 89 |
2022 Agosto | 67 | 55 | 122 |
2022 Julio | 38 | 59 | 97 |
2022 Junio | 62 | 46 | 108 |
2022 Mayo | 65 | 47 | 112 |
2022 Abril | 59 | 60 | 119 |
2022 Marzo | 70 | 56 | 126 |
2022 Febrero | 61 | 44 | 105 |
2022 Enero | 47 | 51 | 98 |
2021 Diciembre | 72 | 49 | 121 |
2021 Noviembre | 63 | 48 | 111 |
2021 Octubre | 76 | 46 | 122 |
2021 Septiembre | 59 | 49 | 108 |
2021 Agosto | 115 | 30 | 145 |
2021 Julio | 49 | 42 | 91 |
2021 Junio | 54 | 32 | 86 |
2021 Mayo | 63 | 37 | 100 |
2021 Abril | 96 | 58 | 154 |
2021 Marzo | 79 | 54 | 133 |
2021 Febrero | 75 | 11 | 86 |
2021 Enero | 58 | 18 | 76 |
2020 Diciembre | 59 | 19 | 78 |
2020 Noviembre | 58 | 16 | 74 |
2020 Octubre | 28 | 30 | 58 |
2020 Septiembre | 49 | 14 | 63 |
2020 Agosto | 60 | 15 | 75 |
2020 Julio | 49 | 14 | 63 |
2020 Junio | 33 | 28 | 61 |
2020 Mayo | 77 | 28 | 105 |
2020 Abril | 54 | 23 | 77 |
2020 Marzo | 45 | 22 | 67 |
2020 Febrero | 64 | 20 | 84 |
2020 Enero | 69 | 48 | 117 |
2019 Diciembre | 53 | 36 | 89 |
2019 Noviembre | 53 | 31 | 84 |
2019 Octubre | 26 | 25 | 51 |
2019 Septiembre | 33 | 23 | 56 |
2019 Agosto | 30 | 23 | 53 |
2019 Julio | 15 | 21 | 36 |
2019 Junio | 32 | 30 | 62 |
2019 Mayo | 38 | 20 | 58 |
2019 Abril | 88 | 38 | 126 |
2019 Marzo | 51 | 31 | 82 |
2019 Febrero | 32 | 18 | 50 |
2019 Enero | 20 | 27 | 47 |
2018 Diciembre | 229 | 40 | 269 |
2018 Noviembre | 472 | 25 | 497 |
2018 Octubre | 366 | 31 | 397 |
2018 Septiembre | 97 | 19 | 116 |
2018 Agosto | 66 | 15 | 81 |
2018 Julio | 50 | 16 | 66 |
2018 Junio | 62 | 19 | 81 |
2018 Mayo | 118 | 8 | 126 |
2018 Abril | 132 | 20 | 152 |
2018 Marzo | 147 | 15 | 162 |
2018 Febrero | 141 | 13 | 154 |
2018 Enero | 86 | 10 | 96 |
2017 Diciembre | 123 | 9 | 132 |
2017 Noviembre | 73 | 12 | 85 |
2017 Octubre | 62 | 9 | 71 |
2017 Septiembre | 41 | 11 | 52 |
2017 Agosto | 42 | 11 | 53 |
2017 Julio | 62 | 14 | 76 |
2017 Junio | 64 | 15 | 79 |
2017 Mayo | 77 | 10 | 87 |
2017 Abril | 58 | 13 | 71 |
2017 Marzo | 43 | 17 | 60 |
2017 Febrero | 38 | 17 | 55 |
2017 Enero | 17 | 15 | 32 |
2016 Diciembre | 73 | 7 | 80 |
2016 Noviembre | 84 | 23 | 107 |
2016 Octubre | 119 | 15 | 134 |
2016 Septiembre | 128 | 11 | 139 |
2016 Agosto | 149 | 5 | 154 |
2016 Julio | 217 | 12 | 229 |
2016 Junio | 115 | 0 | 115 |
2016 Mayo | 202 | 0 | 202 |
2016 Abril | 165 | 0 | 165 |
2016 Marzo | 179 | 0 | 179 |
2016 Febrero | 195 | 0 | 195 |
2016 Enero | 59 | 0 | 59 |
2015 Diciembre | 21 | 0 | 21 |