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array:24 [ "pii" => "S2387020615000030" "issn" => "23870206" "doi" => "10.1016/j.medcle.2013.11.001" "estado" => "S300" "fechaPublicacion" => "2015-01-06" "aid" => "2808" "copyright" => "Elsevier España, S.L.U.. All rights reserved" "copyrightAnyo" => "2013" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Med Clin. 2015;144:1-8" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 238 "formatos" => array:2 [ "HTML" => 154 "PDF" => 84 ] ] "Traduccion" => array:1 [ "es" => array:18 [ "pii" => "S0025775313008440" "issn" => "00257753" "doi" => "10.1016/j.medcli.2013.11.014" "estado" => "S300" "fechaPublicacion" => "2015-01-06" "aid" => "2808" "copyright" => "Elsevier España, S.L.U." "documento" => "article" "subdocumento" => "fla" "cita" => "Med Clin. 2015;144:1-8" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 737 "formatos" => array:2 [ "HTML" => 577 "PDF" => 160 ] ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original</span>" "titulo" => "Umbrales de FRAX<span class="elsevierStyleSup">®</span> para identificar personas con alto o bajo riesgo de fractura osteoporótica en población femenina española" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "1" "paginaFinal" => "8" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "FRAX® thresholds to identify people with high or low risk of osteoporotic fracture in Spanish female population" ] ] "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" => 1457 "Ancho" => 2337 "Tamanyo" => 183581 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Flujo de selección de la cohorte de mujeres españolas que no recibieron tratamiento durante los 10 años de seguimiento.</p> <p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">anti-OP: tratamientos con medicamentos para la osteoporosis no suplementados con calcio/vitamina D; Fx OP: fractura osteoporótica; Fx ppal: fractura principal (<span class="elsevierStyleItalic">major osteoporotic</span> en FRAX<span class="elsevierStyleSup">®</span>).</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Rafael Azagra, Genís Roca, Juan Carlos Martín-Sánchez, Enrique Casado, Gloria Encabo, Marta Zwart, Amada Aguyé, Adolf Díez-Pérez" "autores" => array:9 [ 0 => array:2 [ "nombre" => "Rafael" "apellidos" => "Azagra" ] 1 => array:2 [ "nombre" => "Genís" "apellidos" => "Roca" ] 2 => array:2 [ "nombre" => "Juan Carlos" "apellidos" => "Martín-Sánchez" ] 3 => array:2 [ "nombre" => "Enrique" "apellidos" => "Casado" ] 4 => array:2 [ "nombre" => "Gloria" "apellidos" => "Encabo" ] 5 => array:2 [ "nombre" => "Marta" "apellidos" => "Zwart" ] 6 => array:2 [ "nombre" => "Amada" "apellidos" => "Aguyé" ] 7 => array:2 [ "nombre" => "Adolf" "apellidos" => "Díez-Pérez" ] 8 => array:1 [ "colaborador" => "en representación del grupo de investigación GROIMAP" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2387020615000030" "doi" => "10.1016/j.medcle.2013.11.001" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2387020615000030?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0025775313008440?idApp=UINPBA00004N" "url" => "/00257753/0000014400000001/v1_201412160101/S0025775313008440/v1_201412160101/es/main.assets" ] ] "itemSiguiente" => array:19 [ "pii" => "S2387020615000054" "issn" => "23870206" "doi" => "10.1016/j.medcle.2013.09.002" "estado" => "S300" "fechaPublicacion" => "2015-01-06" "aid" => "2847" "copyright" => "Elsevier España, S.L.U." 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Gisbert" "autores" => array:3 [ 0 => array:2 [ "nombre" => "Pablo" "apellidos" => "Miranda García" ] 1 => array:2 [ "nombre" => "María" "apellidos" => "Chaparro" ] 2 => array:2 [ "nombre" => "Javier P." "apellidos" => "Gisbert" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S0025775314000116" "doi" => "10.1016/j.medcli.2013.09.047" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0025775314000116?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2387020615000054?idApp=UINPBA00004N" "url" => "/23870206/0000014400000001/v1_201509042040/S2387020615000054/v1_201509042040/en/main.assets" ] "asociados" => array:1 [ 0 => array:19 [ "pii" => "S2387020615000029" "issn" => "23870206" "doi" => "10.1016/j.medcle.2014.01.001" "estado" => "S300" "fechaPublicacion" => "2015-01-06" "aid" => "2870" "copyright" => "Elsevier España, S.L.U." 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identify people with high or low risk of osteoporotic fracture in Spanish female population" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "1" "paginaFinal" => "8" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Rafael Azagra, Genís Roca, Juan Carlos Martín-Sánchez, Enrique Casado, Gloria Encabo, Marta Zwart, Amada Aguyé, Adolf Díez-Pérez" "autores" => array:9 [ 0 => array:4 [ "nombre" => "Rafael" "apellidos" => "Azagra" "email" => array:1 [ 0 => "rafael.azagra@uab.cat" ] "referencia" => array:4 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 2 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] 3 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Genís" "apellidos" => "Roca" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "aff0020" ] ] ] 2 => array:3 [ "nombre" => "Juan Carlos" "apellidos" => "Martín-Sánchez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">e</span>" "identificador" => "aff0025" ] ] ] 3 => array:3 [ "nombre" => "Enrique" "apellidos" => "Casado" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">f</span>" "identificador" => "aff0030" ] ] ] 4 => array:3 [ "nombre" => "Gloria" "apellidos" => "Encabo" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">g</span>" "identificador" => "aff0035" ] ] ] 5 => array:3 [ "nombre" => "Marta" "apellidos" => "Zwart" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">h</span>" "identificador" => "aff0040" ] ] ] 6 => array:3 [ "nombre" => "Amada" "apellidos" => "Aguyé" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">i</span>" "identificador" => "aff0045" ] ] ] 7 => array:3 [ "nombre" => "Adolf" "apellidos" => "Díez-Pérez" "referencia" => array:3 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">j</span>" "identificador" => "aff0050" ] 2 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">k</span>" "identificador" => "aff0055" ] ] ] 8 => array:1 [ "colaborador" => "representing the research group GROIMAP" ] ] "afiliaciones" => array:11 [ 0 => array:3 [ "entidad" => "Departamento de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Medicina de Familia, CAP Badía del Vallés, Institut Català de la Salut (ICS), USR MN-IDIAP Jordi Gol, Barcelona, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Departamento de Medicina, Universitat Internacional de Catalunya, Sant Cugat del Vallés, Barcelona, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Medicina de Familia, CAP Sant Llàtzer, Corporació Sanitària de Terrassa, Terrassa, Barcelona, Spain" "etiqueta" => "d" "identificador" => "aff0020" ] 4 => array:3 [ "entidad" => "Bioestadística, Departamento de Ciencias Básicas, Universitat Internacional de Catalunya, Campus Sant Cugat, Sant Cugat del Vallés, Barcelona, Spain" "etiqueta" => "e" "identificador" => "aff0025" ] 5 => array:3 [ "entidad" => "Reumatología, Hospital de Sabadell, Corporació Sanitària Parc Taulí, Sabadell, Barcelona, Spain" "etiqueta" => "f" "identificador" => "aff0030" ] 6 => array:3 [ "entidad" => "Medicina Nuclear, Hospital Universitari Vall d’Hebron, Institut Català de la Salut (ICS), Barcelona, Spain" "etiqueta" => "g" "identificador" => "aff0035" ] 7 => array:3 [ "entidad" => "Medicina de Familia, CAP Girona-2, Institut Català de la Salut (ICS)-USR Girona, IDIAP Jordi Gol, Girona, Spain" "etiqueta" => "h" "identificador" => "aff0040" ] 8 => array:3 [ "entidad" => "Medicina de Familia, CAP Granollers Centre, Institut Català de la Salut (ICS), Granollers, Barcelona, Spain" "etiqueta" => "i" "identificador" => "aff0045" ] 9 => array:3 [ "entidad" => "Departamento de Medicina Interna, URFOA, IMIM, Parc de Salut Mar, Barcelona, Spain" "etiqueta" => "j" "identificador" => "aff0050" ] 10 => array:3 [ "entidad" => "Red Temática de Investigación Cooperativa en Envejecimiento y Fragilidad (RETICEF), Instituto de Salud Carlos III-FEDER, Madrid, Spain" "etiqueta" => "k" "identificador" => "aff0055" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Umbrales de FRAX<span class="elsevierStyleSup">®</span> para identificar personas con alto o bajo riesgo de fractura osteoporótica en población femenina española" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1583 "Ancho" => 1578 "Tamanyo" => 134269 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Decision-making diagram according to the most cost-effective option in the FRIDEX cohort of Spanish women who did not receive treatment during the 10 years of follow-up DXA: dual energy X-ray absorptiometry.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Osteoporosis is a public health problem of great magnitude that leads to increased risk of fragility fractures in any location, particularly of the vertebrae, proximal femur, shoulder and wrist.<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">1</span></a> In general, fragility fractures entail a risk of complications, disabilities and, sometimes, also higher mortality.<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">2</span></a> In 1994,<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">3</span></a> a committee of experts from the World Health Organization (WHO) published certain criteria based on the measurement of bone mineral density (BMD) by dual-energy X-ray absorptiometry (DXA) to define osteoporosis. Since their publication, clinicians have incorporated these criteria into the daily management of osteoporosis and prevention of fragility fractures.<a class="elsevierStyleCrossRefs" href="#bib0165"><span class="elsevierStyleSup">4,5</span></a> Apart from BMD, diverse clinical risk factors have been identified as promoters of fragility fractures.<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">5</span></a> Among them, old age and previous fractures stand out, but there are also other factors whose correlation with fragility fractures has been demonstrated in large studies and/or meta-analyses, which have used different scales.<a class="elsevierStyleCrossRefs" href="#bib0170"><span class="elsevierStyleSup">5–8</span></a> For instance, the WHO have published the <span class="elsevierStyleItalic">Fracture Risk Assessment Tool</span><span class="elsevierStyleSup"><span class="elsevierStyleItalic">®</span></span> (FRAX<span class="elsevierStyleSup">®</span>)<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">9</span></a> based on research by a team of experts at the University of Sheffield. It is an algorithm that uses an online application to estimate 10-year absolute risk of osteoporotic fractures for male and female populations.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">9</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The tool includes 10 particular clinical risk factors, with the optional incorporation of the femoral neck T-score measured by DXA. This risk measure is expressed in absolute values (percentage) for major or frequent fracture (major osteoporotic fracture), which includes proximal femur, clinical vertebrae, proximal humerus and distal forearm or wrist fractures. It also individually expresses the absolute risk of proximal femur fracture (hip fracture). This tool is available in its version 3.8 for 47 countries in 20 languages. On the web, it has been recommended that each country should determine which high-risk levels of fracture will be treated surgically, based on a perspective of cost-effectiveness, the population and its resources (<a href="http://www.shef.ac.uk/FRAX/index.aspx?lang=sp">http://www.shef.ac.uk/FRAX/index.aspx?lang=sp</a>). The FRAX<span class="elsevierStyleSup">®</span> tool has been evaluated in many countries, and in some it has also been calibrated with adjustments.<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">10–12</span></a> The National Osteoporosis Guideline Group (NOGG) published the first cost-effectiveness study of the FRAX<span class="elsevierStyleSup">®</span> tool for the population of the United Kingdom based on clinical risk factors.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">13</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">This publication defined the FRAX<span class="elsevierStyleSup">®</span> values that were considered an intermediate risk of major fracture (for which DXA was recommended) and high risk factors (for which pharmacological treatment was recommended), with certain cost-effective criteria in the primary prevention of fragility fractures.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">13</span></a> More recently, other countries have published FRAX<span class="elsevierStyleSup">®</span> thresholds that adjust to their risk characteristics, particularly hip fracture.<a class="elsevierStyleCrossRefs" href="#bib0215"><span class="elsevierStyleSup">14,15</span></a> The realities of these countries, both in terms of fragility fracture incidence and health care resource distribution, differentiate them from southern European countries.<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">16</span></a> For the Spanish population, various studies have been published that analyse the discriminative and predictive capacity of the FRAX<span class="elsevierStyleSup">®</span> tool in 3 cohorts of Spanish women.<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">17–19</span></a> These studies agree that the FRAX<span class="elsevierStyleSup">®</span> tool has a moderate discriminative capacity to identify Spanish women at high risk of major fracture. Although the analyses are global, these studies agree that the evaluations of predictive capacity need to be adjusted or calibrated to better predict the individual risk at 10 years.<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">17–19</span></a> It has been demonstrated that, both in the Spanish and in other populations, FRAX<span class="elsevierStyleSup">®</span> is very well prepared to detect women that will experience densitometry osteoporosis.<a class="elsevierStyleCrossRefs" href="#bib0245"><span class="elsevierStyleSup">20,21</span></a> Thus, the establishment of FRAX<span class="elsevierStyleSup">®</span> values to identify the Spanish population at low or high risk of fracture in the next 10 years is still pending.<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">22,23</span></a> Common practice in Spain, which we will call the traditional model’, is based on patient selection and DXA testing according to clinical criteria and isolated risk factors, especially in primary prevention.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">24</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The main objective of this study is to identify FRAX<span class="elsevierStyleSup">®</span> cut-off points (FRAX<span class="elsevierStyleSup">®</span> calibrated model) in order to identify groups of women at low and high risk of fragility fractures in the following 10 years, while providing a cost-effective assessment and comparison of this model with current standard clinical practice.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Patients and methods</span><p id="par0025" class="elsevierStylePara elsevierViewall">The following is a prospective cohort study performed in the Fracture RIsk factors and bone DEnsitometry type central dual X-ray (FRIDEX) with a cohort of Spanish women.</p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Study population</span><p id="par0030" class="elsevierStylePara elsevierViewall">The characteristics of the FRIDEX cohort have been recently described.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">25</span></a> In short, it is a dynamic cohort of Spanish women aged ≥40 and ≤90 years old, whose physician had ordered DXA testing and who provided their consent to participate, pursuant to the rules accepted by the governing Clinical Investigation Ethics Committee. This study excluded cases derived from errors in the contact record, failure to answer 3 call attempts, refusal to participate, deceased persons and those with cancer or receiving osteoporotic medications at baseline, except supplements (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). The criteria were satisfied by 1308 women. Of these, 492 women were excluded as they had received osteoporotic treatment for at least 6 months during the 10 years of the study, not including calcium and/or vitamin D supplements.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0035" class="elsevierStylePara elsevierViewall">Using an initial and final structured questionnaire, the following baseline clinical variables were obtained: age, body mass index (BMI); dichotomous variables (yes/no): previous fracture, hip fracture in the parents, active smoking, glucocorticoids, excessive alcohol intake, rheumatoid arthritis and presence of secondary osteoporosis, as per the FRAX<span class="elsevierStyleSup">®</span> criteria.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Determination of bone mass</span><p id="par0040" class="elsevierStylePara elsevierViewall">Bone mass was determined by central DXA in a single densitometer model (GE Lunar Prodigy Advance™) with software version 11.4 and daily calibrations. According to the recommendations of the International Society for Clinical Densitometry (<a href="http://www.iscd.org/official-positions/">http://www.iscd.org/official-positions/</a>), the analysed regions were the entire L1–L4 lumbar spine and the total proximal femur and femoral neck values. The results were categorised as osteoporosis, osteopenia or normal, as per the WHO criteria from 1994.<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">3</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">The analyses included only the incident fractures that were able to be confirmedby patient medical files or hospital records. For the FRAX<span class="elsevierStyleSup">®</span> assessment analyses, only fractures considered major osteoporotic fractures according to the FRAX<span class="elsevierStyleSup">®</span> tool (hip, clinical vertebrae, proximal humerus and distal forearm) were included.</p><p id="par0050" class="elsevierStylePara elsevierViewall">The risk estimation for major fracture with FRAX<span class="elsevierStyleSup">®</span> of the participants was performed through the official web page for the Spanish population using data from the baseline interview. To calculate FRAX<span class="elsevierStyleSup">®</span> with BMD, the femoral neck T-score value from the baseline DXA was used. The calculations were made separately and in parallel by 2 researchers and subsequently reviewed by another 2.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Diagnostic reliability tests</span><p id="par0055" class="elsevierStylePara elsevierViewall">The discriminative capacity of the FRAX<span class="elsevierStyleSup">®</span> tool for 10-year major fracture risk (with and without BMD) was determined through the area under the curve (AUC) receiver operating characteristic (ROC) analysis. The predictive capacity of the FRAX<span class="elsevierStyleSup">®</span> tool was analysed through the ratio of observed fractures (ObsFx) during the 10-year follow-up of the FRIDEX cohort and the expected fractures (ExpFx) by the FRAX<span class="elsevierStyleSup">®</span> tool with the formula: [ObsFx/sum of the individual probability for fracture assigned by FRAX<span class="elsevierStyleSup">®</span> to women in the cohort/100].</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Detection of the best FRAX<span class="elsevierStyleSup">®</span> threshold values for calibration</span><p id="par0060" class="elsevierStylePara elsevierViewall">The process to determine the best cut-off points was developed in various phases. In the first phase, the accumulated incidence of fragility fractures was analysed in the cohort with 816 women who did not receive pharmacological treatment during the 10-year period. The cases of major osteoporotic fractures were observed, and the descriptive analysis of the different risk factors included in the FRAX<span class="elsevierStyleSup">®</span> tool was done, with the additional factor of falls in the previous year and the differences between women with fractures and those without fractures. Subsequently, the discriminative capacity of FRAX<span class="elsevierStyleSup">®</span> was analysed through the AUC-ROC. To end this phase, the predictive capacity of FRAX<span class="elsevierStyleSup">®</span> was evaluated with the ratio of the number of women with fractures (ObsFx) in the cohort and the number of women who could have fractures (ExpFx), estimated by FRAX<span class="elsevierStyleSup">®</span>.</p><p id="par0065" class="elsevierStylePara elsevierViewall">In the second phase, these 816 women were stratified into 3 risk groups (low, moderate and high), following the criterion used in the Canadian cohort CaMos,<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">26</span></a> which considers absolute 10-year fracture risk lower than 10% as low risk, values over 20% as high risk and values between 10 and 20% as moderate risk. In the third phase, the values equivalent to the 2 cut-points among the 3 groups were observed. That is to say, the threshold below which there were women with less than 10% of 10-year fragility fractures, and the threshold that determined the group of women with a fracture in more than 20% of cases. The intermediate risk group was defined between both groups. Finally, with the FRAX<span class="elsevierStyleSup">®</span> detected cut-off points, the fracture frequencies for each risk group were calculated, with a 95% confidence interval (CI 95%).</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Cost-effectiveness calculation</span><p id="par0070" class="elsevierStylePara elsevierViewall">For the cost-effectiveness and diagnostic precision analyses, the European Guidelines for the Treatment of Osteoporosis were followed.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">9</span></a> These guidelines recommend prescribing a healthy lifestyle when there is low risk, ordering DXA to reevaluate low or high risk with FRAX<span class="elsevierStyleSup">®</span>, BMD when there is moderate risk and, finally, pharmacological treatment in cases of high risk. However, in this study, the alternative of ordering a DXA in high fracture risk cases and reevaluating the risk was also included, as the results from the DXA of all the cases were available. Thus, in the case of low fracture risk, the cost of the actual fractures experienced in the group during follow-up was input. In the case of moderate risk groups, DXA was ordered. Cases with osteoporosis in the DXA and/or that exceeded the FRAX<span class="elsevierStyleSup">®</span> threshold with high-risk BMD were considered high-risk cases. For this group, we input DXA cost, cost of the pharmacological treatments and cost of the actual fractures experienced, while subtracting those that would be potentially prevented with pharmacological treatments (a mean 50% reduction of the cases with fractures in the following 10 years was assumed). In the case of high fracture risk groups, 2 options were analysed: the administration of treatment to all the cases without a DXA and the use of DXA and reevaluation, as in the moderate risk cases. In the first option, the cost of the pharmacological treatments and the cost of the actual fractures experienced were input, while subtracting those that would be potentially prevented by pharmacological treatments (50%); in the second option, the cost of the DXA was added.</p><p id="par0075" class="elsevierStylePara elsevierViewall">The amounts for the direct and indirect costs were taken from costs published in Spain<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">27</span></a> (per unit): DXA 59€, hip fracture €15,536, vertebral fracture €8876, forearm fracture €2064 and humerus fracture €3034. For humerus fractures, data were extrapolated from another official publication (DOG 6079, available at: <a href="http://www.municat.gencat.cat/upload/normativa/acord_gov_14_2012.pdf">http://www.municat.gencat.cat/upload/normativa/acord_gov_14_2012.pdf</a>). To calculate the mean cost of one year of treatment, data were taken from the same ISCIII-MS<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">27</span></a> publication on the most commonly used drugs for the treatment of osteoporosis in Spain in 2010, without considering calcium and/or vitamin D supplements. Based on the medication consumption profile for osteoporosis in Spain, the mean cost is €427/year in 2010. For both models, the cost of 10 years of treatment was input (€4272). The cost-effective and diagnostic precision analyses for standard practice in our area were also carried out (traditional model). The traditional model is based on the request of a DXA for suspected low BMD or risk factors (in this cohort, DXA was performed for all cases) and the indication for pharmacological treatment for cases with a T-score ≤<span class="elsevierStyleHsp" style=""></span>−2.5 standard deviation,<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">20</span></a> following the WHO criteria.<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">3</span></a> In the costs calculation, for these cases we input the cost of the DXA, the cost of pharmacological treatments that would be indicated in case of osteoporosis shown by DXA and the cost of the actual fractures occurred, minus the ones that would be potentially prevented by the treatment (50%). For the diagnostic precision analysis, we also calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the 2 models.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Statistical analysis</span><p id="par0080" class="elsevierStylePara elsevierViewall">The population characteristics population are described with univariate descriptive statistics. The statistical comparison of the variables analysed among cases with or without fractures was done with the Chi-square test for the qualitative variables and the Student <span class="elsevierStyleItalic">t</span> test for the quantitative variables. In cases of significant differences (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05), the 95% CI was calculated. All the statistical tests were performed with a 95% CI and with the use of the SPSS<span class="elsevierStyleSup">®</span> statistical package, version 17 (Statistical Package for the Social Sciences 2008, SPSS Inc., Chicago, IL, USA).</p><p id="par0085" class="elsevierStylePara elsevierViewall">This study followed the STROBE initiative guidelines for epidemiological studies (<a href="http://www.strobe-statement.org/index.php?id=strobe-publications">http://www.strobe-statement.org/index.php?id=strobe-publications</a>).</p></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Results</span><p id="par0090" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> shows the profile of the cohort of 816 women, with a mean age of 56.8<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>8.2 years old, and 15.6% had osteoporosis at any of the 3 points defined in the DXA. During the 10-year follow-up, 76 women (9.3%) experienced an osteoporotic fracture and 49 (6%) a major fracture (15 hip, 4 vertebrae, 13 proximal humerus and 17 forearm fractures). In 8 cases (16%), the women experienced more than one fracture, but these were not contemplated by the different analyses.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0095" class="elsevierStylePara elsevierViewall">Among the risk factors (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>), age, previous fractures, falls in the previous year and a diagnosis of osteoporosis in the baseline DXA were significant. The discriminative capacity of FRAX<span class="elsevierStyleSup">®</span> analysed by the ROC curve for the association between risk measurement and fractures showed that the AUC was 0.736 (95% CI 0.657–0.815) for FRAX<span class="elsevierStyleSup">®</span> without BMD and 0.733 (95% CI 0.652–0.814) for FRAX<span class="elsevierStyleSup">®</span> with BMD of the femoral neck. The AUC of the DXA with osteoporosis values was 0.697 (95% CI 0.614–0.780).</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0100" class="elsevierStylePara elsevierViewall">The predictive capacity analysed through the ratio of ObsFx and ExpFx by the FRAX<span class="elsevierStyleSup">®</span> tool for major fracture (ObsFx/ExpFx) was 1.72 (95% CI 1.27–2.27) for FRAX<span class="elsevierStyleSup">®</span> without BMD and 1.61 (95% CI 1.19–2.12) for FRAX<span class="elsevierStyleSup">®</span> with the BMD of the femoral neck.</p><p id="par0105" class="elsevierStylePara elsevierViewall">The values that defined the risk groups were the following: <5% for low risk, ≥7.5% for high risk and the values between them defined moderate risk (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> and <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>).</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0110" class="elsevierStylePara elsevierViewall">The predictive precision results of the cohort with FRAX<span class="elsevierStyleSup">®</span> cut-off points to identify the risk groups showed: sensitivity 40.8%, specificity 92.3%, PPV 25.3% and NPV 96%. The values of the traditional model were: 40.8, 86, 15.7 and 95.8%, respectively.</p><p id="par0115" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a> shows the cost-effective analysis of the proposed FRAX<span class="elsevierStyleSup">®</span> calibration for identifying risk thresholds compared to the traditional method. With FRAX<span class="elsevierStyleSup">®</span> cut-off points, 143 DXA would be required and 82 pharmacological treatments would be indicated for 20 women with fractures. In turn, the treatment of 29 women would be discontinued. With the traditional model, 816 DXA would be required and 127 pharmacological treatments would be indicated for 20 women with fractures. In turn, the treatment of 29 women would be discontinued. The economic cost of using FRAX<span class="elsevierStyleSup">®</span> with the cut-off points would be €601,852, including DXA, pharmacological treatments and the cost of unprevented fractures. When the traditional model was applied, the cost would be €843,081. Using the cut-off points, the moderate and risk cases (143/816; 17.5%) of the cohort (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a>) were selected for DXA. After having re-evaluated the moderate- and high-risk cases with DXA, 82/816 (10%) of the cohort population were selected for treatment, representing 82/143 (57.3%) of the women who were required to have the DXA performed due to FRAX<span class="elsevierStyleSup">®</span> detected threshold values.</p><elsevierMultimedia ident="tbl0020"></elsevierMultimedia></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Discussion</span><p id="par0120" class="elsevierStylePara elsevierViewall">In this study, we have seen that not all the risk factors included in FRAX<span class="elsevierStyleSup">®</span> have been significant. Age, previous fracture and presence of osteoporosis at baseline DXA were seen to be significant. Excessive alcohol consumption, glucocorticoids, rheumatoid arthritis, low BMI and hip fracture in the parents showed no significant differences. The first 3 are considered weak risk factors in FRAX<span class="elsevierStyleSup">®</span>, and low BMI is considered a strong risk factor<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">13</span></a>; its non-significance could be justified by its scarce representation in the cohort. History of fractures in any of the parents was not significant, in spite of the fact that it is well represented in the cohort and that it constitutes a strong risk factor in FRAX<span class="elsevierStyleSup">®</span>. Having suffered more than one fall in the previous year is also shown to be significant. Falls, however, are not included in the FRAX<span class="elsevierStyleSup">®</span>, tool but they are included in other risk scales.<a class="elsevierStyleCrossRefs" href="#bib0180"><span class="elsevierStyleSup">7,8</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">FRAX<span class="elsevierStyleSup">®</span> demonstrated better discriminative capacity than the traditional DXA-based model, although both models exhibit moderate capacity. This improvement has been previously observed in the Spanish population<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">18,19</span></a> and in other populations.<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">10,28</span></a> The low predictive capacity of FRAX<span class="elsevierStyleSup">®</span> measured globally by the ObsFx/ExpFx ratio is consistent with previous studies.<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">17–19</span></a> Meanwhile, in other cohorts, such as the Canadian, FRAX<span class="elsevierStyleSup">®</span> risk prediction approaches the rate of observed fractures (ratio of 1.12 and 1.09 for major fracture without and with DXA, respectively).<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">10</span></a> This could be due to representation problems in the Spanish cohorts introduced in the FRAX<span class="elsevierStyleSup">®</span> tool.<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">19</span></a></p><p id="par0130" class="elsevierStylePara elsevierViewall">To determine the threshold values, FRAX<span class="elsevierStyleSup">®</span> baseline values were adjusted with the 10-year fracture results, stratified as proposed by the CAROC System.<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">10,26</span></a> Thus, the equivalent results are between the real fracture results obtained in the FRIDEX cohort and the FRAX<span class="elsevierStyleSup">®</span> baseline values in these women. With these threshold values, 82.5% of the women were at low risk, 8.9% at moderate risk and 8.6% at high risk. This means that, with these thresholds, 17.5% of the women in the cohort were identified at moderate to high risk, although it must be considered that these women were referred for BMD determination because they presented some signs of risk.</p><p id="par0135" class="elsevierStylePara elsevierViewall">The economic assessment was based on European guidelines<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">9,13</span></a> that recommend following a healthy lifestyle in low-risk cases, DXA to reevaluate risk with FRAX<span class="elsevierStyleSup">®</span> in moderate-risk cases, and treatment in high-risk cases.<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">9,13</span></a> In this study, it has been observed that the performance of DXA also in the cases at high risk of fracture is the most cost-effective option. Thus, 15 of the 70 cases identified as high risk with the FRAX<span class="elsevierStyleSup">®</span> tool did not have osteoporosis in the DXA and were left outside the range of high risk of fracture. The first cost-effective proposal calibrated by FRAX<span class="elsevierStyleSup">®</span> was carried out by the NOGG with a population from the United Kingdom<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">13</span></a> for primary prevention.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">13</span></a> Other countries have followed the same example and made making proposals in cost-effective terms.<a class="elsevierStyleCrossRefs" href="#bib0215"><span class="elsevierStyleSup">14,15</span></a></p><p id="par0140" class="elsevierStylePara elsevierViewall">The diagnostic precision analysis showed a sensitivity of 40.8% in both methods, but the use of FRAX<span class="elsevierStyleSup">®</span> calibration thresholds improved specificity by more than 6 points and PPV by almost 10 points compared to the traditional diagnosis model based on the T-score ≤−2.5 of the DXA. The low sensitivity of the DXA<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">13</span></a> was already known, and FRAX<span class="elsevierStyleSup">®</span> calibrated thresholds do not improve sensitivity; however, the remaining predictive parameters do. It should be noted that neither of the two systems is recommended for population screening, but they should instead be used for the opportunistic identification of cases with potential risk factors.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">13</span></a></p><p id="par0145" class="elsevierStylePara elsevierViewall">In the cost-benefit analysis, direct and indirect costs of fractures have been included, as well as the costs corresponding to the DXA and the pharmacological treatments that both models would assign according to the fractures and real treatment profile costs in Spain<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">27</span></a> (<a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a>). In the treatment cases, a 50% fracture reduction risk was assumed, as medical literature shows different percentages of relative risk reduction and also variable percentages for the different types of fractures, ranging from 19 to 44% for alendronate.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">13</span></a> In summary, the use of FRAX<span class="elsevierStyleSup">®</span> calibrated thresholds would allow us to avoid more than 82% of DXA and more than 35% of treatments to treat the same number of women with fractures, but at an almost 30% cheaper global cost when compared to standard practice or the traditional model based on DXA. Although in the traditional model all DXA have been counted and this model may seem disadvantaged in the comparison, the real cost of DXA only accounts for 3.2% of the difference between the models. If the DXAs were not included, the difference would change from 28.7 to 25.5% in favour of the use of FRAX<span class="elsevierStyleSup">®</span> calibrated thresholds. The treatments were estimated for 10 years, and if the estimation were for 5 years, the difference would be reduced by 3%. It has been proven that the most cost-effective option is also DXA in the cases of high fracture risk, basically because there are cases with high-risk FRAX<span class="elsevierStyleSup">®</span> values, but with BMD without osteoporosis. Although numerous societies recommend the administration of pharmacological treatment in cases of fractures in postmenopausal women without the need for DXA,<a class="elsevierStyleCrossRefs" href="#bib0210"><span class="elsevierStyleSup">13–15</span></a> some experts specify that this option should be specifically used in cases of hip or vertebrae fracture.<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">10,26</span></a></p><p id="par0150" class="elsevierStylePara elsevierViewall">Among the limitations of the study, it is worth mentioning that the FRIDEX cohort includes women who were referred by their physician for DXA, which could affect the results as it is a potentially higher risk population. However, this analysis excluded women who had initiated anti-osteoporotic treatment during follow-up, so mean age and fracture cases of the cohort were reduced. However, this particularity would not affect the comparative analyses of diagnostic precision and cost-effectiveness between the models.</p><p id="par0155" class="elsevierStylePara elsevierViewall">Another potential limitation may be the non-inclusion in the analysis of the women who died during follow-up, and for whom the study could not be completed. The mortality rate of the 10-year study was 5.8%. There were few cases of previous fractures but, given the low percentage and the difficulty to confirm the fractures with clinical records, they were excluded from the study. This aspect might affect the predictive capacity analysis, but not the comparative cost-effective study.</p><p id="par0160" class="elsevierStylePara elsevierViewall">Although it is the most commonly used system, retrospective case registration of incident fractures is considered less reliable than prospective studies due to the memory effect and possible patient confusion.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">13</span></a> To minimize this effect, all the fracture cases were verified in the clinical records. Thus, 16% of the women initially detected with fractures were excluded from the study, mostly due to lack of records. Only 2 cases of humerus fracture were excluded due to a difference in interpretation between the questionnaire and the records, along with one hip fracture due to a traffic accident. Without this exclusion, the ObsFx/ExpFx ratio would be still higher. Unlike most of the epidemiological studies on osteoporotic fractures that centre on hip fractures<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">29</span></a> and perform extrapolations for the remainder,<a class="elsevierStyleCrossRefs" href="#bib0200"><span class="elsevierStyleSup">11–13</span></a> in this study all the fractures localizations have been identified, even though for the FRAX<span class="elsevierStyleSup">®</span> prediction analysis the major fractures determined by the FRAX<span class="elsevierStyleSup">®</span> tool have been analysed. This type of analyses assume underestimations of non-clinical vertebral fractures, and it is known that up to two-thirds of these fractures are unknown by the patient and the physician.<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">14</span></a> Despite these potential limitations, the data on the discriminative and predictive capacity of FRAX<span class="elsevierStyleSup">®</span> in this study are consistent with previous studies in this and other Spanish cohorts.<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">17–19</span></a> However, clinical judgement shall always prevail in the decision making process, as the results of this study offer excellent specificity but deficient sensitivity. It is likely that an improvement in risk estimation by FRAX<span class="elsevierStyleSup">®</span> (for example, including falls) could increase its PPV.</p><p id="par0165" class="elsevierStylePara elsevierViewall">In conclusion, osteoporotic fractures have a multifactorial pathogenesis, so it is difficult to predict them using only one risk factor, although strongly associated, as happens with osteoporosis values in DXA. Clinical judgement should prevail, and to adjust the risk of fragility fracture, it is more efficient to consider the greater number of fracture risk factors possible among those that demonstrated evidence. The FRIDEX model to calibrate the Spanish FRAX<span class="elsevierStyleSup">®</span> improves the predictive parameters of fracture risk and is more cost-effective than standard clinical practice based on DXA. However, new studies are necessary for the external validation of these FRAX<span class="elsevierStyleSup">®</span> thresholds or cut-off points in other cohorts and in the general population.</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Funding</span><p id="par0170" class="elsevierStylePara elsevierViewall">This project was financed by grants of the <span class="elsevierStyleGrantSponsor" id="gs1">Instituto de Salud Carlos III, Ministry of Science</span> [<span class="elsevierStyleGrantNumber" refid="gs1">PI09/90507</span>], and the <span class="elsevierStyleGrantSponsor" id="gs2">Instituto Universitario de Investigación en Atención Primaria IDIAP Jordi Gol, Institut Català de la Salut, Barcelona, Spain</span>.</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Conflict of interest</span><p id="par0175" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:11 [ 0 => array:3 [ "identificador" => "xres548859" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Background and objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Patients and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec566576" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres548860" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Fundamento y objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Pacientes y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec566575" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Patients and methods" "secciones" => array:6 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Study population" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Determination of bone mass" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Diagnostic reliability tests" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Detection of the best FRAX threshold values for calibration" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Cost-effectiveness calculation" ] 5 => array:2 [ "identificador" => "sec0040" "titulo" => "Statistical analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0045" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0050" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0055" "titulo" => "Funding" ] 9 => array:2 [ "identificador" => "sec0060" "titulo" => "Conflict of interest" ] 10 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2013-07-18" "fechaAceptado" => "2013-11-07" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec566576" "palabras" => array:5 [ 0 => "Osteoporosis" 1 => "Osteoporotic fracture" 2 => "FRAX<span class="elsevierStyleSup">®</span>" 3 => "<span class="elsevierStyleItalic">Dual-energy X-ray absorptiometry</span>" 4 => "Cost-effectiveness" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec566575" "palabras" => array:5 [ 0 => "Osteoporosis" 1 => "Fractura osteoporótica" 2 => "FRAX<span class="elsevierStyleSup">®</span>" 3 => "Absorciometría dual de rayos X" 4 => "Coste-efectividad" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Background and objective</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">To detect FRAX<span class="elsevierStyleSup">®</span> threshold levels that identify groups of the population that are at high/low risk of osteoporotic fracture in the Spanish female population using a cost-effective assessment.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Patients and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">This is a cohort study. Eight hundred and sixteen women 40–90 years old selected from the FRIDEX cohort with densitometry and risk factors for fracture at baseline who received no treatment for osteoporosis during the 10 year follow-up period and were stratified into 3 groups/levels of fracture risk (low <10%, 10–20% intermediate and high >20%) according to the real fracture incidence.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The thresholds of FRAX<span class="elsevierStyleSup">®</span> baseline for major osteoporotic fracture were: low risk <5; intermediate ≥5 to <7.5 and high ≥7.5. The incidence of fracture with these values was: low risk (3.6%; 95% CI 2.2–5.9), intermediate risk (13.7%; 95% CI 7.1–24.2) and high risk (21.4%; 95% CI 12.9–33.2). The most cost-effective option was to refer to <span class="elsevierStyleItalic">dual energy X-ray absorptiometry</span> (DXA-scan) for FRAX<span class="elsevierStyleSup">®</span><span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>5 (Intermediate and high risk) to reclassify by FRAX<span class="elsevierStyleSup">®</span> with DXA-scan at high/low risk. These thresholds select 17.5% of women for DXA-scan and 10% for treatment. With these thresholds of FRAX<span class="elsevierStyleSup">®</span>, compared with the strategy of opportunistic case finding isolated risk factors, would improve the predictive parameters and reduce 82.5% the DXA-scan, 35.4% osteoporosis prescriptions and 28.7% cost to detect the same number of women who suffer fractures.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">The use of FRAX<span class="elsevierStyleSup">®</span> thresholds identified as high/low risk of osteoporotic fracture in this calibration (FRIDEX model) improve predictive parameters in Spanish women and in a more cost-effective than the traditional model based on the T-score<span class="elsevierStyleHsp" style=""></span>≤<span class="elsevierStyleHsp" style=""></span>−2.5 of DXA scan.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Background and objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Patients and 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">Fundamento y objetivo</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Detectar los umbrales de la herramienta FRAX<span class="elsevierStyleSup">®</span> que determinen los grupos de riesgo alto/bajo de fractura osteoporótica en la población femenina española y su valoración coste-efectiva.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Pacientes y métodos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Estudio de cohortes. Ochocientas dieciséis mujeres de 40-90 años de la cohorte FRIDEX con densitometría basal, factores de riesgo de fractura y sin tratamiento para la osteoporosis en los 10 años de seguimiento. Se estratificaron en 3 grupos/niveles de riesgo de fractura principal (bajo<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>10%, intermedio 10-20% y alto<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>20%) según la incidencia real de fractura, y se buscaron los puntos de corte equivalentes de FRAX<span class="elsevierStyleSup">®</span> basal.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Los umbrales de FRAX<span class="elsevierStyleSup">®</span> basal para fractura principal fueron: riesgo bajo<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>5; intermedio<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>5 y <<span class="elsevierStyleHsp" style=""></span>7,5 y alto<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>7,5. La incidencia real de fractura con estos valores fue: riesgo bajo (3,6%; IC 95% 2,2-5,9); intermedio (13,7%; IC 95%7,1-24,2), y alto (21,4%; IC 95% 12,9-33,2). La opción más coste-efectiva fue realizar una <span class="elsevierStyleItalic">dual energy X-ray absorptiometry</span> (DXA, «absorciometría dual de rayos X») para FRAX<span class="elsevierStyleSup">®</span><span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>5 (riesgo intermedio y alto) para reclasificar los casos mediante FRAX<span class="elsevierStyleSup"><span class="elsevierStyleItalic">®</span></span> con DXA en riesgo alto/bajo. Así se seleccionarían 17,5% de las mujeres para DXA y 10% para tratamiento. Con estos umbrales calibrados de FRAX<span class="elsevierStyleSup">®</span>, comparados con la estrategia tradicional basada en la DXA, se mejoran los parámetros predictivos y se reducen las DXA (82,5%), los tratamientos (35,4%) y el coste global (28,7%) para detectar al mismo número de mujeres que tuvieron fracturas.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">La utilización de los umbrales de FRAX<span class="elsevierStyleSup">®</span> identificados como alto/bajo riesgo de fractura osteoporótica en la presente calibración (modelo FRIDEX) mejorarían los parámetros predictivos en mujeres españolas y de una forma más coste-efectiva que el modelo tradicional basado en el T-score ≤ -2,5 de la DXA.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Fundamento y objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Pacientes y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0025">Please cite this article as: Azagra R, Roca G, Martín-Sánchez JC, Casado E, Encabo G, Zwart M, et al. Umbrales de FRAX<span class="elsevierStyleSup">®</span> para identificar personas con alto o bajo riesgo de fractura osteoporótica en población femenina española. Med Clin (Barc). 2015;144:1–8.</p>" ] ] "multimedia" => array:6 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1457 "Ancho" => 2337 "Tamanyo" => 179514 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Flowchart for the selection of the cohort of Spanish women who did not receive treatment during the 10 years of follow-up anti-OP: treatment with osteoporotic medications not supplemented with calcium/vitamin D; OPFx: osteoporotic fracture; Major Fx: major fracture (major osteoporotic in FRAX<span class="elsevierStyleSup">®</span>).</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1583 "Ancho" => 1578 "Tamanyo" => 134269 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Decision-making diagram according to the most cost-effective option in the FRIDEX cohort of Spanish women who did not receive treatment during the 10 years of follow-up DXA: dual energy X-ray absorptiometry.</p>" ] ] 2 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">FN: femoral neck; SD: standard deviation; DXA: <span class="elsevierStyleItalic">dual energy X-ray absorptiometry</span>; FRIDEX: Fracture RIsk factors and bone DEnsitometry type central dual X-ray; BMI: body mass index; TF: total femur; L1–L4: total lumbar spine.</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 " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">Women of the FRIDEX cohort without treatment for osteoporosis</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Women, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">816 (100) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Age in years, mean (SD) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">56.8 (8.24) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">>65 years old, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">147 (18.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Weight in kg (SD) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">68.4 (11.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Height in cm, mean (SD) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">155.4 (5.95) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">BMI in kg/cm<span class="elsevierStyleSup">2</span>, mean (SD) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">28.4 (4.80) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Active smoker, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">102 (12.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Hazardous alcohol consumption, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 (0.74) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Previous fractures, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">166 (20.3) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Hip fracture in parents, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">114 (14.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Glucocorticoids, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">38 (4.66) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Rheumatoid arthritis, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">11 (1.35) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Falls in the previous year, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">199 (24.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Osteoporosis in the DXA (CF, TF or L1–L4), <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">127 (15.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Prescriptions: calcium and/or vitamin D, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">303 (37.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Prescription of medications with bone activity, <span class="elsevierStyleItalic">n</span> (%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 (0) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab886658.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">Profile of the FRIDEX cohort with 816 women with 10-year follow-up.</p>" ] ] 3 => array:7 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">BMD: bone mineral density; FRIDEX: Fracture RIsk factors and bone DEnsitometry type central dual X-ray; CI 95%: confidence interval 95%; BMI: body mass index; ns: not significant; u: units.</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">Women with fracture (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>49) \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">Women without fracture (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>767) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Age in years, mean (SD) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">62.6 (9.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">56.5 (8.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">3.3–9.0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">BMI in kg/cm<span class="elsevierStyleSup">2</span>, mean (SD) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27.9 (4.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">28.4 (4.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.437 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">ns \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">BMI <20<span class="elsevierStyleHsp" style=""></span>kg/cm<span class="elsevierStyleSup">2</span>, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.682 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">ns \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Previous fracture, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">49.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">15.7–45.4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Hip fracture in parents, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.360 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">ns \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Active smoker, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.956 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">ns \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Alcohol ≥3<span class="elsevierStyleHsp" style=""></span>μl/day, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.311 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">ns \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Glucocorticoids, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.722 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">ns \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Rheumatoid arthritis, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.137 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">ns \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Falls in the previous year, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">38.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">23.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.016 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.4–30.6 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">BMD with osteoporosis, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">60.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">30.1–59.2 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Normal BMD, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">32.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">14.7–32.3 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab886656.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Risk factors in the groups of women with fracture and without fracture in the FRIDEX cohort without treatment during the 10 years.</p>" ] ] 4 => array:7 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">BMD: bone mineral density determined by central DXA; DXA: <span class="elsevierStyleItalic">dual energy X-ray absorptiometry</span>; FRAX<span class="elsevierStyleSup">®</span>: <span class="elsevierStyleItalic">Fracture Risk Assessment</span><span class="elsevierStyleSup">®</span>, <span class="elsevierStyleItalic">WHO Technical Report</span>; FRIDEX: Fracture RIsk factors and bone DEnsitometry type central dual X-ray; Fx: fractures; OP: osteoporosis.</p><p id="spar0095" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Source</span>: Leslie et al.<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">26</span></a></p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Fracture risk groups as per the CAROC System<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">26</span></a> criteria \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">Best cut-off points of baseline FRAX<span class="elsevierStyleSup">®</span> identified in the FRIDEX cohort \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Real results of Fx in the FRIDEX cohort after 10-year follow-upMean (SD) \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">Situation when performing the DXA and calculating FRAX<span class="elsevierStyleSup">®</span> with BMD in case of moderate or high risk \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">Cases without major Fx \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">Cases with major Fx \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">Low/high final risk after reevaluation \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">Final result \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Low (<10%): 673 cases \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top"><5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3.6% (2.2–5.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Low risk FRAX<span class="elsevierStyleSup">®</span> without BMD <5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">649 (96.4%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">24 (3.6%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Low \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">673 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Moderate (≥10 and <20%): 73 cases \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">≥5 and <7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13.7% (7.1–24.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">No OP in DXA and FRAX<span class="elsevierStyleSup">®</span> with BMD <7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">42 (91.3%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4 (8.7%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Low \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">46 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">OP in DXA and FRAX<span class="elsevierStyleSup">®</span> with BMD <7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12 (85.7%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2 (14.3%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">High \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">No OP in DXA and FRAX<span class="elsevierStyleSup">®</span> with BMD ≥7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3 (100.0%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 (0.0%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">High \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">3 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">OP in DXA and FRAX<span class="elsevierStyleSup">®</span> with BMD ≥7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">6 (60.0%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4 (40.0%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">High \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">10 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">High (≥20%): 70 cases \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">≥7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">21.4% (12.9–33.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">No OP in DXA and FRAX<span class="elsevierStyleSup">®</span> with BMD <7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">14 (93.3%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 (6.7%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Low \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">15 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">No OP in DXA and FRAX<span class="elsevierStyleSup">®</span> with BMD ≥7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13 (72.2%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">5 (27.8%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">High \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">OP in DXA and FRAX<span class="elsevierStyleSup">®</span> with BMD ≥7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">28 (75.7%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 (24.3%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">High \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">37 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">All cases \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">767 (94.0%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">49 (6.0%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">816 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab886657.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Risk groups, FRAX<span class="elsevierStyleSup">®</span> threshold values that define them, situation during reevaluation with FRAX<span class="elsevierStyleSup">®</span> with bone mineral density, and final result in low/high risk after reevaluation.</p>" ] ] 5 => array:7 [ "identificador" => "tbl0020" "etiqueta" => "Table 4" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:3 [ "leyenda" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">DXA: <span class="elsevierStyleItalic">dual energy X-ray absorptiometry</span>; FRAX<span class="elsevierStyleSup">®</span>: <span class="elsevierStyleItalic">Fracture Risk Assessment</span><span class="elsevierStyleSup">®</span>; FRIDEX: Fracture RIsk factors and bone DEnsitometry type central dual X-ray; Fx: fractures.</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"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">Model based on FRAX<span class="elsevierStyleSup">®</span></th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">Traditional model</th></tr><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">FRIDEX calibration \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">€ \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">Clinical judgement and DXA \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">€ \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Pharmacological treatments \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">350,337 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">127 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">542,595 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">DXA in intermediate<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>high risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">143 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">8523 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">816 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">48,634 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Cases with real Fx that ARE treated/cost in € \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20<a class="elsevierStyleCrossRef" href="#tblfn0005"><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="left" valign="top">100,082 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20<a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">c</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">90,501 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Cases with real Fx that are NOT treated/cost in € \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29<a class="elsevierStyleCrossRef" href="#tblfn0010"><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">142,910 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">d</span></a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">162,072 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Value in € \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">601,852 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">843,082 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Difference in € in favour of FRIDEX calibration \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">−241,950 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Savings percentage in favour of FRIDEX calibration \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">28.7% \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab886655.png" ] ] ] "notaPie" => array:4 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Fractures treated with calibrated FRAX<span class="elsevierStyleSup">®</span> model: hip 11; vertebra 1; humerus 4; forearm 4.</p>" ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "b" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Fractures NOT treated with calibrated FRAX<span class="elsevierStyleSup">®</span> model: hip 4; vertebra 3; humerus 9; forearm 13.</p>" ] 2 => array:3 [ "identificador" => "tblfn0015" "etiqueta" => "c" "nota" => "<p class="elsevierStyleNotepara" id="npar0015">Fractures treated with DXA model: hip 9; vertebra 2; humerus 5; forearm 4.</p>" ] 3 => array:3 [ "identificador" => "tblfn0020" "etiqueta" => "d" "nota" => "<p class="elsevierStyleNotepara" id="npar0020">Fractures NOT treated with DXA model: hip 6; vertebra 2; humerus 8; forearm 13.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Cost-effectiveness parameters 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