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Dorado Díaz, Carmen Estébanez Álvarez, C. Estébanez Álvarez, Pilar Martín Pérez, P. Martín Pérez, Carlos Fernández Renedo, C. Fernández Renedo, Raquel González Fernández, R. González Fernández, Mª Purificación Galindo Villardón, M.P. Galindo Villardón, José Carlos Espinosa Gutiérrez, J.C. Espinosa Gutiérrez" "autores" => array:14 [ 0 => array:2 [ "nombre" => "Ana" "apellidos" => "Dorado Díaz" ] 1 => array:2 [ "Iniciales" => "A." "apellidos" => "Dorado Díaz" ] 2 => array:2 [ "nombre" => "Carmen" "apellidos" => "Estébanez Álvarez" ] 3 => array:2 [ "Iniciales" => "C." "apellidos" => "Estébanez Álvarez" ] 4 => array:2 [ "nombre" => "Pilar" "apellidos" => "Martín Pérez" ] 5 => array:2 [ "Iniciales" => "P." "apellidos" => "Martín Pérez" ] 6 => array:2 [ "nombre" => "Carlos" "apellidos" => "Fernández Renedo" ] 7 => array:2 [ "Iniciales" => "C." "apellidos" => "Fernández Renedo" ] 8 => array:2 [ "nombre" => "Raquel" "apellidos" => "González Fernández" ] 9 => array:2 [ "Iniciales" => "R." 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González-López, José Carlos Tutor, J.C. Tutor" "autores" => array:4 [ 0 => array:3 [ "nombre" => "Jaime" "apellidos" => "Gonzalez-López" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "affa" ] ] ] 1 => array:3 [ "Iniciales" => "J." "apellidos" => "González-López" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "affb" ] ] ] 2 => array:4 [ "nombre" => "José Carlos" "apellidos" => "Tutor" "email" => array:1 [ 0 => "josecarlsotutor@redfarma.org" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "affc" ] ] ] 3 => array:4 [ "Iniciales" => "J.C." "apellidos" => "Tutor" "email" => array:1 [ 0 => "josecarlsotutor@redfarma.org" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "affd" ] ] ] ] "afiliaciones" => array:4 [ 0 => array:3 [ "entidad" => "Servicio de Farmacia, Hospital Clínico Universitario, Instituto de Investigación Sanitaria (IDIS), Santiago de Compostela, A Coruña, Spain, " "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "affa" ] 1 => array:3 [ "entidad" => "Servicio de Farmacia, Hospital Clínico Universitario, Instituto de Investigación Sanitaria (IDIS), Santiago de Compostela, A Coruña, " "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "affb" ] 2 => array:3 [ "entidad" => "Unidad de Monitorización de Fármacos, Laboratorio Central, Hospital Clínico Universitario, Instituto de Investigación Sanitaria (IDIS), Santiago de Compostela, A Coruña, Spain, " "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "affc" ] 3 => array:3 [ "entidad" => "Unidad de Monitorización de Fármacos, Laboratorio Central, Hospital Clínico Universitario, Instituto de Investigación Sanitaria (IDIS), Santiago de Compostela, A Coruña, " "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "affd" ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Eficiencia diagnóstica de la predicción de concentraciones de digoxina en función de la tasa de filtración glomerular mediante un modelo hiperbólico" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig1" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "10893_108_21688_en_f110893.jpg" "Alto" => 1335 "Ancho" => 600 "Tamanyo" => 255536 ] ] "descripcion" => array:1 [ "en" => "Correlation between glomerular filtration rate and obtained digoxin concentration (A), digoxin clearance (B) and the ratio (digoxin clearance) / (glomerular filtration rate)." ] ] ] "textoCompleto" => "<p class="elsevierStylePara"><span class="elsevierStyleBold">INTRODUCTION</span></p><p class="elsevierStylePara">Digoxin has been used for over two centuries, and concern over its toxicity can be traced back to its introduction into therapy. However, considering its beneficial effect in reducing mortality and hospitalisation of patients with progressive heart failure, its cost-effectiveness and easy availability worldwide, digoxin should not be considered a drug of the past but rather a drug of the present and even one of the future.<span class="elsevierStyleSup">1</span></p><p class="elsevierStylePara">Digoxin is usually administered orally, reaching maximum serum concentrations two to three hours after administration. However, trough concentration (immediately before the next dose) is used for therapeutic monitoring, or at least once the distribution phase is complete.<span class="elsevierStyleSup">2</span> The determination of serum digoxin concentration is essential for safe and proper use of the drug, which has a narrow therapeutic index, and this is the primary cause of morbidity and mortality associated with its use. It is currently recommended that doses be set to reach serum concentrations between 0.5ng/ml and 1.1ng/ml.<span class="elsevierStyleSup">1,3-5</span> A significant fraction of the absorbed dose of digoxin is eliminated by the kidneys, with its systemic clearance being an important determinant of the maintenance dose.<span class="elsevierStyleSup">1,6</span> Renal function impairment therefore frequently causes the onset of toxic effects.<span class="elsevierStyleSup">1</span></p><p class="elsevierStylePara">Glomerular filtration rate (GFR) is widely accepted as an appropriate measure of renal function, and adjusting the dosage of digoxin based on GFR is widely recommended in the literature.<span class="elsevierStyleSup">6-11</span> However, recent studies suggest that inappropriate dosing of digoxin is common in patients with various degrees of renal failure.<span class="elsevierStyleSup">7,12</span> Comparative studies have been performed using conventional pharmacokinetic procedures for calculating clearance (CL) and the prediction of digoxin concentrations, estimating GFR based on cystatin C<span class="elsevierStyleSup">13</span> and creatinine using Cockcroft-Gault and MDRD (modification of diet in renal disease) formulas.<span class="elsevierStyleSup">13,14</span> This article, which employs data from 400 adult patients, indicates the Diagnostic efficiencyof a hyperbolic model for calculating digoxin concentration based on GFR.<span class="elsevierStyleSup">15</span></p><p class="elsevierStylePara"> </p><p class="elsevierStylePara"><span class="elsevierStyleBold">PATIENTS AND METHOD</span></p><p class="elsevierStylePara">We studied a group of 400 patients of both sexes (158 males and 242 females) with heart failure, with a mean age (± standard deviation of the mean [SDM]) of 78.6±0.64 years (range, 24-97 years), who were treated as outpatients in our hospital’s emergency, cardiology and internal medicine departments. All patients received digoxin orally in the form of tablets, with a dosage of 0.125mg to 0.25mg every 24 to 48 hours, which was not changed for at least 20 days before taking blood samples. The samples were taken after the distribution phase was complete, 24 to 48 hours after the last doses, which meant that digoxin levels corresponded to the steady-state minimum concentration (Css). The study was performed according to the good practice standards for human research of the Health Department of the Government of Galicia, and was approved by the Clinical Research Ethics Committee of the University Hospital Complex of Santiago de Compostela.  </p><p class="elsevierStylePara">Serum digoxin concentration was determined by fluorescence polarisation immunoassay, using reagents from Abbott Laboratories (Abbott Park, IL, USA). Determination of serum creatinine was performed using an Advia 2400 Chemistry System (Siemens Healthcare Diagnostics Inc., Newark, NJ, USA). Cystatin C was determined by PENIA (particle enhanced nephelometric immunoassay) in a BN ProSpec nephelometer (Siemens Healthcare Diagnostics, Inc.). GFR was calculated from the serum concentrations of creatinine and cystatin C using the Cockcroft-Gault<span class="elsevierStyleSup">16</span> and de Hoeck et al<span class="elsevierStyleSup">17</span> formulas, respectively. The apparent clearance of digoxin was calculated using a conventional pharmacokinetic procedure<span class="elsevierStyleSup">18</span>: CL=(dose/dosage interval)/Css, with the results expressed in ml/min. The prediction of digoxin concentration was made according to the Konishi et al hyperbolic model<span class="elsevierStyleSup">15</span>: Css/D=1/(2.22CGGFR+25.7), where Css= level of digoxin (ng/ml), D= dose (µg/day) and CGGFR=GFR calculated using the Cockcroft-Gault formula (ml/min).</p><p class="elsevierStylePara">Statistical analysis was performed using the StatGraphics Plus (v5.0) program, using the Shapiro-Wilks test for data distribution assessment. We used the Pearson’s correlation coefficient for Gaussian distributions, and the Spearman correlation for all other distributions. Regression analysis was performed using the non-parametric Passing-Bablock method. The hyperbolic fit of the relationship between the ratio Css/dose and CGGFR was performed using the Microcalc Origin<span class="elsevierStyleSup">®</span> (v8.0) program. </p><p class="elsevierStylePara"> </p><p class="elsevierStylePara"><span class="elsevierStyleBold">RESULTS  </span></p><p class="elsevierStylePara">In a group of 300 study patients (120 males and 180 females) with a mean age of 79.2±0.5 years (range, 24-97 years), there were significant correlations between digoxin Css, digoxin CL, and the ratio (digoxin CL)/(GFR), with GFR as indicated in Figure 1. According to earlier studies,<span class="elsevierStyleSup">9,11,12</span> digoxin Css was inversely correlated to GFR (Figure 1A). However, although digoxin CL is significantly dependent on GFR (Figure 1B), the modest coefficient of determination (r<span class="elsevierStyleSup">2</span>=0.257) between both variables indicated that only 26% of the interindividual variability of digoxin CL may be explained by changes in GFR.<span class="elsevierStyleSup">19</span> This determination coefficient was not significantly improved by a dichotomy of the data by gender, the normalisation of digoxin CL by kg of weight (ml/min/kg) or the use of MDRD equations with four or six variables for calculating GFR. Values of GFR under 70ml/min would produce an exponential increase in the relative proportion of non-renal CL, compared to the total digoxin CL (Figure 1C). </p><p class="elsevierStylePara">Serum concentrations of cystatin C were measured in 60 of these patients. The correlations obtained between digoxin CL and GFR calculated from creatinine (r=0.403; <span class="elsevierStyleItalic">P</span><.005) and cystatin C (r=0.500; <span class="elsevierStyleItalic">P</span><.005) suggest a modest improvement of the coefficient of determination between digoxin CL and GFR calculated by cystatin C.</p><p class="elsevierStylePara">Just as Konishi et al<span class="elsevierStyleSup">15</span> developed their predictive model on a group of Japanese people, we calculated the constants for the hyperbolic equation for our group of 300 patients with an eye towards optimisation, obtaining the following expression: Css/D=1/(2.42±0.18CGGFR+29.44±4.06). Figure 2 shows the results for the group of 100 remaining patients (38 males and 62 females) with a mean age of 77.2±1.0 years (range, 24 to 92 years) for the relationship between digoxin concentrations obtained experimentally and the predicted values using the original Konishi et al model<span class="elsevierStyleSup">15</span> and the model modified for our population group of 300 patients. Digoxin concentrations predicted by the original model were on average 10% higher than those provided by the model modified for our population group (Figure 2A). The proportional difference between the concentrations predicted by each model (expressed as a ratio) showed a highly significant negative correlation with GFR (Figure 3). </p><p class="elsevierStylePara">We obtained modest correlation coefficients and wide dispersion between obtained digoxin concentrations and those predicted by the original (Figure 2B) and modified (Figure 2C) estimation models. The obtained concentrations and those predicted by both models showed 65% concordance in their classification as subtherapeutic, therapeutic and supratherapeutic.</p><p class="elsevierStylePara"> </p><p class="elsevierStylePara"><span class="elsevierStyleBold">DISCUSSION</span></p><p class="elsevierStylePara">Currently, the US Food and Drug Administration, the National Kidney Foundation<span class="elsevierStyleSup">22</span> and various authors<span class="elsevierStyleSup">20-24 </span>recommend the use of the Cockcroft-Gault formula for calculating GFR in pharmacokinetic studies and for dosage adjustments.</p><p class="elsevierStylePara">Consequently, in this study, GFR calculation was performed using the Cockcroft-Gault formula,<span class="elsevierStyleSup">16</span> which is also the method used by Konishi et al<span class="elsevierStyleSup">15</span> in the development of their predictive model.</p><p class="elsevierStylePara">Recent studies have demonstrated that patients with varying degrees of renal failure often receive inappropriate doses of digoxin.<span class="elsevierStyleSup">7,12</span> The GFR calculation would provide better dosage adjustment for these cases.<span class="elsevierStyleSup">6-11</span> As with earlier studies,<span class="elsevierStyleSup">9,11,12</span> our patients had an inverse correlation between digoxin concentration and GFR (Figure 1A). There was a 54% prevalence of cases with improper dosages of digoxin (of which 94% had GFR<60ml/min) and with serum concentrations higher than 1.1ng/ml, which is considered the safety limit in chronic treatment.<span class="elsevierStyleSup">1,3-5</span> For this reason, it should be noted that elderly patients with renal dysfunction are particularly susceptible to high concentrations of digoxin, with increased susceptibility to cardiac toxicity, which consequently increases mortality risk.<span class="elsevierStyleSup">25</span></p><p class="elsevierStylePara">However, the results shown in Figure 1B indicate the poor coefficient of determination (r<span class="elsevierStyleSup">2</span><span class="elsevierStyleSup">=</span>0.257) between digoxin CL and GFR, which suggests that differences in this variable would only explain 26% of the interindividual variability of digoxin CL. Although the serum concentration of cystatin C has been reported as having certain advantages over that of creatinine in calculating GFR for predicting digoxin levels,<span class="elsevierStyleSup">10,13</span> the use of cystatin C in calculating GFR did not improve in a practical way the coefficient of determination between this variable and digoxin CL. Moreover, the results shown in Figure 1C show that, for a GFR<60-70ml/min, the interindividual variability of the ratio (digoxin CL)/GFR increased significantly, with a significant increase in the relative proportion of non-renal CL compared to the total CL of the drug. These results would indicate <span class="elsevierStyleItalic">a priori</span> that patients with GFR<60-70ml/min have clearly limiting factors for correctly predicting digoxin concentrations (or doses) based on GFR.</p><p class="elsevierStylePara">Digoxin concentrations predicted by the original Konishi et al model<span class="elsevierStyleSup">15</span> were, on average, 10% higher than those predicted by the model modified for our population group (Figure 2A). However, none of the cases reached a difference of 15%, which is the accepted limit of deviation according to the standards of validation of methods for the determination of drugs and their metabolites in biological media.<span class="elsevierStyleSup">26</span> Consideration should be given to the possibility of greater bioavailability of the tablets administered to the Japanese patients, as well as our different analytical methodology in determining digoxin. However, since the proportional difference between concentrations predicted by both models increases as GRF decreases (Figure 3), as does the relative proportion of non-renal CL compared to total digoxin CL (Figure 1C), the highest concentrations predicted by the original model may be due to a lower non-renal digoxin CL in the Japanese population.</p><p class="elsevierStylePara">The Diagnostic efficiencyof a laboratory test is the percentage of total results that are true (excluding false positives and false negatives), and it is accepted as a general rule that a test is not clinically valid if its Diagnostic efficiencyis below 80%.<span class="elsevierStyleSup">27</span> The agreement in the classification as subtherapeutic, therapeutic and supratherapeutic between the obtained and predicted digoxin concentrations was only 65%, which indicates that both the original predictive model and the model modified for our population have an unacceptable diagnostic accuracy.</p><p class="elsevierStylePara">In conclusion, although the importance of adjusting dosage based on GFR in order to avoid digoxin intoxication has been noted,<span class="elsevierStyleSup">7-9</span> this variable would only explain 25% of the interindividual variability of digoxin CL. Moreover, in cases with GFR<60-70ml/min, the non-renal CL significance increases very variably for digoxin elimination, as it depends on several factors<span class="elsevierStyleSup">28-30 </span>that are difficult to account for in the predictive models. These facts lead to the conclusion that the Diagnostic efficiencyof predictive models for digoxin levels (or dosage) based on GFR are clinically unacceptable. According to Schentang et al,<span class="elsevierStyleSup">31</span> it seems more practical to assume that digoxin bioavailability and CL (if there are no significant changes in GFR or concomitant medication with possible pharmacokinetic interaction) remain stable for the same patient in the medium term, and in a steady-state there is a linear relationship between dosage and Css. We could therefore, based on the Css obtained experimentally for a dose D that must be adjusted, make a prediction of the Css* for a new dose D* using the expression: Css*/D*=Css/D.</p><p class="elsevierStylePara"><a href="grande/10893_108_21688_en_f110893.jpg" class="elsevierStyleCrossRefs"><img src="10893_108_21688_en_f110893.jpg" alt="Correlation between glomerular filtration rate and obtained digoxin concentration (A), digoxin clearance (B) and the ratio (digoxin clearance) / (glomerular filtration rate)."></img></a></p><p class="elsevierStylePara">Figure 1. Correlation between glomerular filtration rate and obtained digoxin concentration (A), digoxin clearance (B) and the ratio (digoxin clearance) / (glomerular filtration rate).</p><p class="elsevierStylePara"><a href="grande/10893_108_21689_en_f210893.jpg" class="elsevierStyleCrossRefs"><img src="10893_108_21689_en_f210893.jpg" alt="Correlation and regression between predicted Css [OM] and predicted Css [MM] (A), predicted Css [OM] and obtained Css (B), and predicted Css [MM] and obtained Css (C)."></img></a></p><p class="elsevierStylePara">Figure 2. Correlation and regression between predicted Css [OM] and predicted Css [MM] (A), predicted Css [OM] and obtained Css (B), and predicted Css [MM] and obtained Css (C).</p><p class="elsevierStylePara"><a href="grande/10893_108_21690_en_f310893.jpg" class="elsevierStyleCrossRefs"><img src="10893_108_21690_en_f310893.jpg" alt="Correlation of the ratio (predicted Css [OM])/(predicted Css[MM]) with GFR"></img></a></p><p class="elsevierStylePara">Figure 3. Correlation of the ratio (predicted Css [OM])/(predicted Css[MM]) with GFR</p>" "pdfFichero" => "P1-E524-S3336-A10893-EN.pdf" "tienePdf" => true "PalabrasClave" => array:2 [ "es" => array:4 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec440599" "palabras" => array:1 [ 0 => "Aclaramiento de digoxina" ] ] 1 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec440601" "palabras" => array:1 [ 0 => "Tasa de filtración glomerular" ] ] 2 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec440603" "palabras" => array:1 [ 0 => "Ajuste de dosificación" ] ] 3 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec440605" "palabras" => array:1 [ 0 => "Concentraciones predichas" ] ] ] "en" => array:4 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec440600" "palabras" => array:1 [ 0 => "Digoxin clearance" ] ] 1 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec440602" "palabras" => array:1 [ 0 => "Glomerular filtration rate" ] ] 2 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec440604" "palabras" => array:1 [ 0 => "Dosage adjustment" ] ] 3 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec440606" "palabras" => array:1 [ 0 => "Predicted concentrations" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "es" => array:1 [ "resumen" => "<p class="elsevierStylePara"><span class="elsevierStyleBold">Objetivos:</span> Los pacientes con insuficiencia renal presentan una prevalencia elevada de dosis inapropiadas y de concentraciones elevadas de digoxina, y se ha recomendado el ajuste de la dosificación en función de la tasa de filtración glomerular (TFG). El objetivo de nuestro estudio fue evaluar el grado de dependencia con respecto a la TFG del aclaramiento total (CL) de digoxina, y la eficiencia diagnóstica de un modelo predictivo para la concentración sérica de digoxina en el estado de equilibrio (Css) en función de la TFG estimada por la ecuación de Cockcroft-Gault. <span class="elsevierStyleBold">Métodos:</span> En 400 pacientes ambulatorios tratados por vía oral con digoxina se determinaron las Css séricas (inmunoanálisis de polarización de fluorescencia, Abbott Laboratories), calculandose el CL total. La predicción de Css se hizo mediante un modelo hiperbólico desarrollado por Konishi, et al. (<span class="elsevierStyleItalic">J Clin Pharm Ther</span> 2002;27:257), y las constantes de la ecuación fueron modificadas para la población caucasiana. <span class="elsevierStyleBold">Resultados: </span>Sólo el 26% de la variabilidad interindividual del CL de digoxina puede ser explicado por diferencias de la TFG, y este hecho constituye una seria limitación para los modelos predictivos derivados. Se obtuvo una eficiencia diagnóstica del 65% para los modelos predictivos original y modificado en la clasificación correcta de las Css predichas como subterapéuticas, terapéuticas o supraterapéuticas con respecto a las Css obtenidas. <span class="elsevierStyleBold">Conclusiones:</span> La eficiencia diagnóstica obtenida en la predicción de las concentraciones séricas de digoxina en función de los valores estimados de TFG es inaceptable para el ajuste de la dosificación del fármaco en la práctica clínica.</p>" ] "en" => array:1 [ "resumen" => "<p class="elsevierStylePara"><span class="elsevierStyleItalic"><span class="elsevierStyleBold">Background:</span> Inappropriate doses and high serum concentrations of digoxin are highly prevalent in patients with renal impairment, and the drug dosage adjustment according to the glomerular filtration rate (GFR) is recommended. The aim of our study was to evaluate the dependence degree of digoxin total clearance (CL) on GFR, and the diagnostic efficiency of a predictive model for serum digoxin steady-state concentrations (Css) from estimated GFR by Cockcroft-Gault formula. <span class="elsevierStyleBold">Methods:</span> In 400 outpatients treated orally with digoxin, serum Css were determined (fluorescence polarization immunoassay from Abbott Laboratories), and total CL was calculated. The prediction of Css was carried out using a hyperbolic model developed by Konishi et al in Japan (</span>J Clin Pharm Ther <span class="elsevierStyleItalic">2002;27:257), and the constants of the equation were modified for a Caucasian population. <span class="elsevierStyleBold">Results:</span> Only 26% of the digoxin CL interindividual variability may be explained by differences in GFR, and this fact is a serious limitation for the derived predictive models. A 65% diagnostic efficiency was obtained for original and modified hyperbolic models in the correct classification of predicted Css as subtherapeutic, therapeutic or supratherapeutic with respect to obtained Css concentrations. <span class="elsevierStyleBold">Conclusions: </span>The diagnostic efficiency obtained in the prediction of serum digoxin concentrations from estimated GFR values is unacceptable for the drug dosage adjustment in clinical practice.</span></p>" ] ] "multimedia" => array:3 [ 0 => array:8 [ "identificador" => "fig1" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "10893_108_21688_en_f110893.jpg" "Alto" => 1335 "Ancho" => 600 "Tamanyo" => 255536 ] ] "descripcion" => array:1 [ "en" => "Correlation between glomerular filtration rate and obtained digoxin concentration (A), digoxin clearance (B) and the ratio (digoxin clearance) / (glomerular filtration rate)." ] ] 1 => array:8 [ "identificador" => "fig2" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "10893_108_21689_en_f210893.jpg" "Alto" => 1346 "Ancho" => 600 "Tamanyo" => 255804 ] ] "descripcion" => array:1 [ "en" => "Correlation and regression between predicted Css [OM] and predicted Css [MM] (A), predicted Css [OM] and obtained Css (B), and predicted Css [MM] and obtained Css (C)." ] ] 2 => array:8 [ "identificador" => "fig3" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "10893_108_21690_en_f310893.jpg" "Alto" => 500 "Ancho" => 507 "Tamanyo" => 28770 ] ] "descripcion" => array:1 [ "en" => "Correlation of the ratio (predicted Css [OM])/(predicted Css[MM]) with GFR" ] ] ] "bibliografia" => array:2 [ "titulo" => "Bibliography" "seccion" => array:1 [ 0 => array:1 [ "bibliografiaReferencia" => array:31 [ 0 => array:3 [ "identificador" => "bib1" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:3 [ "referenciaCompleta" => "Pervaiz MH, Dickinson MG, Yamani M. 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Year/Month | Html | Total | |
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2024 November | 6 | 7 | 13 |
2024 October | 64 | 55 | 119 |
2024 September | 56 | 34 | 90 |
2024 August | 75 | 89 | 164 |
2024 July | 62 | 34 | 96 |
2024 June | 61 | 56 | 117 |
2024 May | 90 | 36 | 126 |
2024 April | 79 | 43 | 122 |
2024 March | 55 | 26 | 81 |
2024 February | 60 | 45 | 105 |
2024 January | 62 | 28 | 90 |
2023 December | 62 | 28 | 90 |
2023 November | 72 | 40 | 112 |
2023 October | 77 | 34 | 111 |
2023 September | 65 | 32 | 97 |
2023 August | 65 | 18 | 83 |
2023 July | 61 | 27 | 88 |
2023 June | 39 | 28 | 67 |
2023 May | 59 | 38 | 97 |
2023 April | 42 | 19 | 61 |
2023 March | 49 | 19 | 68 |
2023 February | 48 | 29 | 77 |
2023 January | 50 | 24 | 74 |
2022 December | 45 | 35 | 80 |
2022 November | 46 | 42 | 88 |
2022 October | 42 | 51 | 93 |
2022 September | 51 | 23 | 74 |
2022 August | 52 | 47 | 99 |
2022 July | 32 | 61 | 93 |
2022 June | 54 | 39 | 93 |
2022 May | 47 | 30 | 77 |
2022 April | 74 | 47 | 121 |
2022 March | 47 | 54 | 101 |
2022 February | 40 | 39 | 79 |
2022 January | 27 | 38 | 65 |
2021 December | 57 | 52 | 109 |
2021 November | 48 | 40 | 88 |
2021 October | 45 | 47 | 92 |
2021 September | 33 | 43 | 76 |
2021 August | 37 | 36 | 73 |
2021 July | 27 | 41 | 68 |
2021 June | 18 | 19 | 37 |
2021 May | 32 | 41 | 73 |
2021 April | 45 | 73 | 118 |
2021 March | 54 | 36 | 90 |
2021 February | 54 | 25 | 79 |
2021 January | 32 | 16 | 48 |
2020 December | 29 | 12 | 41 |
2020 November | 51 | 27 | 78 |
2020 October | 30 | 14 | 44 |
2020 September | 32 | 16 | 48 |
2020 August | 38 | 15 | 53 |
2020 July | 50 | 5 | 55 |
2020 June | 36 | 6 | 42 |
2020 May | 38 | 20 | 58 |
2020 April | 40 | 21 | 61 |
2020 March | 41 | 8 | 49 |
2020 February | 42 | 19 | 61 |
2020 January | 47 | 26 | 73 |
2019 December | 44 | 27 | 71 |
2019 November | 43 | 16 | 59 |
2019 October | 17 | 7 | 24 |
2019 September | 19 | 14 | 33 |
2019 August | 13 | 6 | 19 |
2019 July | 33 | 24 | 57 |
2019 June | 26 | 14 | 40 |
2019 May | 34 | 17 | 51 |
2019 April | 99 | 41 | 140 |
2019 March | 35 | 14 | 49 |
2019 February | 21 | 13 | 34 |
2019 January | 48 | 21 | 69 |
2018 December | 112 | 37 | 149 |
2018 November | 115 | 18 | 133 |
2018 October | 108 | 15 | 123 |
2018 September | 108 | 15 | 123 |
2018 August | 41 | 42 | 83 |
2018 July | 63 | 17 | 80 |
2018 June | 50 | 14 | 64 |
2018 May | 51 | 18 | 69 |
2018 April | 48 | 6 | 54 |
2018 March | 46 | 7 | 53 |
2018 February | 49 | 4 | 53 |
2018 January | 53 | 8 | 61 |
2017 December | 60 | 10 | 70 |
2017 November | 57 | 14 | 71 |
2017 October | 42 | 5 | 47 |
2017 September | 66 | 16 | 82 |
2017 August | 70 | 11 | 81 |
2017 July | 77 | 8 | 85 |
2017 June | 85 | 13 | 98 |
2017 May | 90 | 11 | 101 |
2017 April | 52 | 12 | 64 |
2017 March | 64 | 11 | 75 |
2017 February | 24 | 5 | 29 |
2017 January | 34 | 9 | 43 |
2016 December | 48 | 8 | 56 |
2016 November | 60 | 8 | 68 |
2016 October | 93 | 7 | 100 |
2016 September | 97 | 5 | 102 |
2016 August | 189 | 7 | 196 |
2016 July | 143 | 12 | 155 |
2016 June | 121 | 0 | 121 |
2016 May | 143 | 0 | 143 |
2016 April | 108 | 0 | 108 |
2016 March | 100 | 0 | 100 |
2016 February | 122 | 0 | 122 |
2016 January | 119 | 0 | 119 |
2015 December | 134 | 0 | 134 |
2015 November | 112 | 0 | 112 |
2015 October | 83 | 0 | 83 |
2015 September | 80 | 0 | 80 |
2015 August | 74 | 0 | 74 |
2015 July | 90 | 0 | 90 |
2015 June | 35 | 0 | 35 |
2015 May | 45 | 0 | 45 |
2015 April | 5 | 0 | 5 |