array:23 [ "pii" => "S2387020617302723" "issn" => "23870206" "doi" => "10.1016/j.medcle.2017.04.015" "estado" => "S300" "fechaPublicacion" => "2017-05-10" "aid" => "3880" "copyright" => "Elsevier España, S.L.U.. All rights reserved" "copyrightAnyo" => "2016" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Med Clin. 2017;148:387-93" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "Traduccion" => array:1 [ "es" => array:19 [ "pii" => "S0025775316306650" "issn" => "00257753" "doi" => "10.1016/j.medcli.2016.11.025" "estado" => "S300" "fechaPublicacion" => "2017-05-10" "aid" => "3880" "copyright" => "Elsevier España, S.L.U." "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Med Clin. 2017;148:387-93" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:2 [ "total" => 13 "formatos" => array:2 [ "HTML" => 9 "PDF" => 4 ] ] "es" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original</span>" "titulo" => "Concordancia del FRAX México con y sin el valor de la densidad mineral ósea en la evaluación del riesgo de fractura en la práctica clínica diaria" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "387" "paginaFinal" => "393" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Agreement of Mexican FRAX with and without the value of bone mineral density in assessing the risk of fracture in daily clinical practice" ] ] "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" => 962 "Ancho" => 2329 "Tamanyo" => 142431 ] ] "descripcion" => array:1 [ "es" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Izquierda: representa la correlación entre el logaritmo del <span class="elsevierStyleItalic">major fracture risk</span> (MFR, «riesgo de fractura mayor») y la DMO a la altura del cuello femoral. Derecha: representa la correlación entre el logaritmo del <span class="elsevierStyleItalic">hip fracture risk</span> (HFR, «riesgo de fractura de cadera») y la DMO en el cuello femoral.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Gabriel Horta-Baas, Arturo Pérez Bolde-Hernández, Argelia Pérez-Pérez, Imelda Vergara-Sánchez, María del Socorro Romero-Figueroa" "autores" => array:5 [ 0 => array:2 [ "nombre" => "Gabriel" "apellidos" => "Horta-Baas" ] 1 => array:2 [ "nombre" => "Arturo" "apellidos" => "Pérez Bolde-Hernández" ] 2 => array:2 [ "nombre" => "Argelia" "apellidos" => "Pérez-Pérez" ] 3 => array:2 [ "nombre" => "Imelda" "apellidos" => "Vergara-Sánchez" ] 4 => array:2 [ "nombre" => "María del Socorro" "apellidos" => "Romero-Figueroa" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2387020617302723" "doi" => "10.1016/j.medcle.2017.04.015" "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/S2387020617302723?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0025775316306650?idApp=UINPBA00004N" "url" => "/00257753/0000014800000009/v1_201704190127/S0025775316306650/v1_201704190127/es/main.assets" ] ] "itemSiguiente" => array:19 [ "pii" => "S2387020617302735" "issn" => "23870206" "doi" => "10.1016/j.medcle.2017.04.016" "estado" => "S300" "fechaPublicacion" => "2017-05-10" "aid" => "3881" "copyright" => "Elsevier España, S.L.U." "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Med Clin. 2017;148:394-400" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "en" => array:13 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original article</span>" "titulo" => "Thrombocytopenia as a thrombotic risk factor in patients with antiphospholipid antibodies without disease criteria" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "es" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "394" "paginaFinal" => "400" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Trombocitopenia como factor de riesgo trombótico en pacientes con anticuerpos antifosfolipídicos sin criterios de enfermedad" ] ] "contieneResumen" => array:2 [ "en" => true "es" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1480 "Ancho" => 1559 "Tamanyo" => 66932 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Association between number of positive antiphospholipid antibodies and thrombocytopenia. AB: antibodies.</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Rosalia Demetrio Pablo, Pedro Muñoz, Marcos López-Hoyos, Vanesa Calvo, Leyre Riancho, Victor Manuel Martínez-Taboada" "autores" => array:6 [ 0 => array:2 [ "nombre" => "Rosalia" "apellidos" => "Demetrio Pablo" ] 1 => array:2 [ "nombre" => "Pedro" "apellidos" => "Muñoz" ] 2 => array:2 [ "nombre" => "Marcos" "apellidos" => "López-Hoyos" ] 3 => array:2 [ "nombre" => "Vanesa" "apellidos" => "Calvo" ] 4 => array:2 [ "nombre" => "Leyre" "apellidos" => "Riancho" ] 5 => array:2 [ "nombre" => "Victor Manuel" "apellidos" => "Martínez-Taboada" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S0025775316306662" "doi" => "10.1016/j.medcli.2016.11.026" "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/S0025775316306662?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2387020617302735?idApp=UINPBA00004N" "url" => "/23870206/0000014800000009/v2_201706121018/S2387020617302735/v2_201706121018/en/main.assets" ] "en" => array:20 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original article</span>" "titulo" => "Agreement of Mexican FRAX with and without the value of bone mineral density in assessing the risk of fracture in daily clinical practice" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "387" "paginaFinal" => "393" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Gabriel Horta-Baas, Arturo Pérez Bolde-Hernández, Argelia Pérez-Pérez, Imelda Vergara-Sánchez, María del Socorro Romero-Figueroa" "autores" => array:5 [ 0 => array:4 [ "nombre" => "Gabriel" "apellidos" => "Horta-Baas" "email" => array:1 [ 0 => "gabho@hotmail.com" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Arturo" "apellidos" => "Pérez Bolde-Hernández" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 2 => array:3 [ "nombre" => "Argelia" "apellidos" => "Pérez-Pérez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "Imelda" "apellidos" => "Vergara-Sánchez" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 4 => array:3 [ "nombre" => "María del Socorro" "apellidos" => "Romero-Figueroa" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Servicio de Reumatología, Hospital General Regional 220, Instituto Mexicano del Seguro Social, Toluca, Estado de México, Mexico" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Servicio de Pediatría, Neurología Pediátrica, Hospital General Regional 1, Instituto Mexicano del Seguro Social, Mérida, Yucatán, Mexico" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Coordinación de Investigación en Salud, Delegación Estado de México Poniente, Instituto Mexicano del Seguro Social, Toluca, Estado de México, Mexico" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Concordancia del FRAX México con y sin el valor de la densidad mineral ósea en la evaluación del riesgo de fractura en la práctica clínica diaria" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 961 "Ancho" => 2329 "Tamanyo" => 139868 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Left: Correlation between the log of major fracture risk (MFR) and BMD at the femoral neck. Right: Correlation between the log of hip fracture risk (HFR) and BMD in the femoral neck.</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 systemic condition characterized by low bone mass and deterioration of bone microarchitecture that increases its brittleness and fracture risk (FR)<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">1</span></a>; It is asymptomatic until fracture occurs. The ability to assess skeletal strength using dual X-ray absorptiometry (DXA) led the World Health Organization (WHO) to define osteoporosis in terms of bone mineral density (BMD). However, its clinical significance lies in the fractures that occur.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> Fragility fracture is one that occurs without a trauma that justifies the breaking in a previously healthy bone. Due to the increased life expectancy of the population, fragility fractures are a challenge for public health because of the increase in their prevalence and its consequences (associated mortality, high costs and deterioration in quality of life).</p><p id="par0010" class="elsevierStylePara elsevierViewall">There are currently treatments that reduce the likelihood of fracture. Therefore, in order to prevent fractures in a cost-effective manner, high risk subjects should be early detected and treated.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">3</span></a> Prevention strategies were initially focused on DXA, as FR increased exponentially with decreased BMD. However, in epidemiological studies it has been shown that half of the fractures occur in women without osteoporosis (44–67%).<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">4,5</span></a> Also, the use of central DXA for the general population is limited due to its cost, and since it is not portable it has a restricted availability. In addition, it has a low sensitivity and predictive power, which diminishes its usefulness as a screening test.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">6</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">In order to improve its predictive power, prediction algorithms have been developed weighing clinical risk factors (CRFs) with each other to determine the absolute risk of fracture. The use of CRFs adds FR information regardless of BMD, improving sensitivity.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">6</span></a> FRAX is the WHO FR evaluation tool. In its algorithm it combines 11 CRFs, the inclusion of BMD is optional but improves accuracy. The use of CRFs provides a risk gradient ranging from 1.4 to 2.1 depending on age and type of predicted fracture,<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">3,7</span></a> which are comparable to the use of BMD to predict fractures. FRAX has become very relevant in the treatment of osteoporosis and the prevention of fractures due to its accessibility and scientific rigor. FRAX results are considered as one of the criteria for initiating pharmacological treatment.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> Even the National Bone Health Alliance Working Group suggested including in the definition of osteoporosis adults aged over 50 years, with a major fracture probability within 10 years after FRAX above the intervention threshold.<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">8</span></a> However, this approach is not fully accepted.<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">9</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">One of the main advantages of FRAX is the ability to assess FR when DXA is not available.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> In Mexico, the model adjusted to its epidemiology has been used since 2010. Therefore, FRAX without BMD (FRAX-BMI) might be useful in identifying patients at high risk and in reporting treatment decisions in medical practice,<a class="elsevierStyleCrossRefs" href="#bib0225"><span class="elsevierStyleSup">10,11</span></a> since it provides a reasonable measure of FR.<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">12,13</span></a> However, its usefulness is a matter of discussion.<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">3,14</span></a> Different results have been reported in the existing studies, probably due to the differences in the methodology used and the populations studied. Therefore, this study is aimed at assessing the concordance between FRAX-BMI and FRAX (CRFs with BMD) in daily clinical practice.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Material and methods</span><p id="par0025" class="elsevierStylePara elsevierViewall">After authorization by the local ethics and health research committee, a cross-sectional study was conducted in a second-level hospital. We included women aged 40 to 90 years who were referred to the outpatient Rheumatology office from July 2012 to February 2016, their BMD measured through central DXA, regardless of the causes that motivated their request. Inclusion criteria were: no previous treatment with bisphosphonates, calcitonin, hormone replacement therapy, strontium ranelate, parathyroid hormone or raloxifene and who accepted their participation through an informed consent form. The exclusion criteria were: suffering from a metabolic disease other than osteoporosis (osteomalacia, Paget's disease), myeloma or any cancer, chronic kidney failure and previous oophorectomy. Weight and height, CRFs included in FRAX, comorbidities and concomitant treatments were measured to every subject.</p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Assessment of bone mineral density</span><p id="par0030" class="elsevierStylePara elsevierViewall">BMD (g/cm<span class="elsevierStyleSup">2</span>) was measured at the femoral neck and lumbar spine (L2 to L4) using 2 units of DXA: Hologic QDR 4000 and GE Lunar Prodigy Advance. The reference standard to calculate <span class="elsevierStyleItalic">t</span> value was the database of the Third National Health and Nutrition Examination Survey of the United States – NHANES III.<a class="elsevierStyleCrossRef" href="#bib0250"><span class="elsevierStyleSup">15</span></a> Results were categorized as osteoporosis, osteopenia or normal, according to the WHO classification.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Assessment of fracture risk and treatment recommendations</span><p id="par0035" class="elsevierStylePara elsevierViewall">The FRAX model, calibrated to the epidemiology of Mexico was used for the absolute fracture risk assessment. We estimated the individual probability of fracture with and without the inclusion of femoral neck BMD. The result was the probability of presenting a hip fracture (HFR) and a major osteoporotic fracture (MFR): spine, carpal, proximal humerus, and hip, within ten years. Treatment thresholds recommended by the National Osteoporosis Foundation (NOF), HFR ≥3% and/or an MFR ≥20%<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> were used to assess the agreement between the outcome of the FRAX-BMI and treatment recommendations.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Statistical analysis</span><p id="par0040" class="elsevierStylePara elsevierViewall">The results obtained are reported as absolute number of cases and their percentage (n [%]), and the mean with their standard deviation (mean<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>SD). The results of the FR presented a bias to the right. Thus, a transformation of the variables was carried out for the data normalization. For the MFR a logarithmic transformation was performed −log(<span class="elsevierStyleItalic">x</span>); Because the results of the HFR included 0, a log(<span class="elsevierStyleItalic">x</span><span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>1) transformation was performed. For each transformation <span class="elsevierStyleItalic">y</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">f</span>(<span class="elsevierStyleItalic">x</span>), the mean and confidence intervals are presented using the inverse transformation applied to the mean of the transformed values (<span class="elsevierStyleItalic">y</span>)<span class="elsevierStyleSup">−1</span>, and the corresponding confidence intervals. Thus, for the analysis using <span class="elsevierStyleItalic">y</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>log(<span class="elsevierStyleItalic">x</span>), the inverse is <span class="elsevierStyleItalic">x</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>exp(<span class="elsevierStyleItalic">y</span>); for the analysis using <span class="elsevierStyleItalic">y</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>log(<span class="elsevierStyleItalic">x</span><span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>1), the inverse is <span class="elsevierStyleItalic">x</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>exp(<span class="elsevierStyleItalic">y</span>)−1.</p><p id="par0045" class="elsevierStylePara elsevierViewall">Pearson's coefficient was used to evaluate the correlation between FR and BMD at the femoral neck. Concordance was studied by two different supplementary methods: Lin's concordance correlation coefficient (CCC) and the Bland–Altman plot. CCC provides information on how much data are diverted from perfect concordance (accuracy) and precision. A disadvantage of CCC is related to the difficulty in understanding its values and its translation to clinical relevance. Based on the Fleiss classification of the intraclass correlation coefficient (ICC), it was considered as: poor if <0.4, moderate if 0.4–0.75, good if 0.75–0.9, and excellent if >0.9.<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">16,17</span></a> To estimate the degree of dispersion, concordance limits were calculated using the Bland–Altman method. To assess whether the differences affect the resulting treatment recommendations, the degree of agreement was assessed by the kappa test based on the Landis-Koch classification. A logistic regression analysis was performed to evaluate the impact of various explanatory variables on the agreement of the treatment thresholds. Statistical analysis was performed with the Stata-13 and GraphPad Prism-6 softwares for Windows®.</p></span></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Results</span><p id="par0050" class="elsevierStylePara elsevierViewall">We included 431 women with a mean age of 62.6<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>9.8 years. The prevailing age group was 40–64 years (59.6%), body mass index (BMI) was 27.8<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4.2<span class="elsevierStyleHsp" style=""></span>kg/m<span class="elsevierStyleSup">2</span>, in 90.7% of cases BMD was measured with the use of a DXA Lunar equipment, femoral neck BMD was 0.82<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.14; The femoral neck mean <span class="elsevierStyleItalic">t</span>-score was −1.43<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.04, and at the lumbar spine −2.01<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.27. In accordance with the WHO classification, at the femoral neck level 31.6% were classified as having normal BMD, 52% with osteopenia and 16.5% with osteoporosis. At the spine level, 29.9% were normal, 49.7% had osteopenia and 20.4%, osteoporosis. Taking into account the lower <span class="elsevierStyleItalic">t</span>-score in hip and spine, 27.4% were classified as having normal BMD, 50.8% with osteopenia and 21.8% with osteoporosis. The prevalence of CRFs included in the FRAX and the comorbidities of the subjects are shown in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0055" class="elsevierStylePara elsevierViewall">There was a moderate correlation between the probability of major fracture (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0.55, 95% CI −0.62 to −0.48, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001) and hip fracture (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0.54, 95% CI −0.61 to −0.47, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001) obtained with FRAX-BMI and femoral neck BMD (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). Considering the treatment threshold, the femoral neck mean <span class="elsevierStyleItalic">t</span>-score was −1.11<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.84 and −1.17<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.93<span class="elsevierStyleHsp" style=""></span>at the lumbar spine in subjects below the threshold. And −2.75<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.65 at the femoral neck and −2.83<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.81 at the lumbar spine in subjects above the threshold. This shows that the higher the likelihood of fracture, the lower BMD, with main selection of subjects with low BMD.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">The mean fracture probability calculated by FRAX-BMI was 5.3<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.03% (95% CI 4.97–5.66) for the MFR, and for the HFR it was 1.30<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03% (95% CI 1.16–1.38); With the inclusion of BMD, MFR was 5.37<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.28% (95% CI 5.1–5.83), and for HFR it was 1.22<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.03% (95% CI 1.08–1.38). <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> shows the Bland–Altman plot. The mean difference between MFR was −1.02<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.40% with concordance limits of 2 scores in either direction (95% CI −2 to 1.90), and for the HFR it was −0.03<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.51% (95% CI −1.18 to 1.32). The results of the CCC showed high concordance, for MFR it was 0.879 (95% CI 0.857–0.90), and 0.821 (95% CI 0.79–0.851) for HFR. The graphical analysis of the CCC components is shown in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>. In women with osteopenia, concordance was high for MFR (0.877, 95% CI 0.85–0.90) and moderate for HFR (0.748, 95% CI 0.69–0.8). The mean difference between MFR was −1.02<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.3% (95% CI −1.7 to 1.64), and −0.06<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.41% (95% CI −1.85 to 1.1) for HFR.</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0065" class="elsevierStylePara elsevierViewall">The agreement between treatment recommendations was 87%, with a 0.61 kappa index (high concordance). Inclusion of BMD reclassified 32 subjects (9.3%) of 334 patients with FRAX-BMI below the treatment threshold, with a risk above the treatment threshold. And inclusion of BMD reclassified 24 subjects (27.6%) of the 87 subjects at risk above the FRAX-BMI treatment threshold, to a lower risk. The results of the logistic regression analysis to assess the impact of several explanatory variables on the concordance in the treatment thresholds are shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>; The variables independently associated with discordance were age, BMI and number of CRFs. In the subgroup analysis (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>) concordance was high for MFR 40–59 years of age, but only moderate over 60 years of age. This pattern was reversed in HFR. In subjects with obesity, concordance was moderate in HFR compared to high concordance in non-obese patients. However, it did not influence the concordance in MFR. Concordance was high according to the number of CRFs.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Discussion</span><p id="par0070" class="elsevierStylePara elsevierViewall">The purpose of treating osteoporosis is to reduce the number of fractures. However, there is no method to measure bone strength directly in clinical practice. Therefore, treatment is focused on identifying subjects with FR and not just on bone densitometry. However, there is no gold standard evaluation algorithm. Risk classification depends on the combination of BMD measurement and fracture and fall CRFs. FR must be determined by absolute risk prediction, for which there are several algorithms (FRAX, QFracture, Garvan) with a 39–79% sensitivity and a 39–93% specificity.<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">18</span></a></p><p id="par0075" class="elsevierStylePara elsevierViewall">Concordance between treatment decisions with FRAX-BMI and FRAX reported in previous studies range from 76% to 99%. Our results support high concordance and the classification of patients at high risk by FRAX-BMI preferably selected subjects with low BMD. It should be noted that FRAX was not designed to detect osteoporosis, but its CRFs are not totally independent of BMD,<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">19</span></a> which explains why the increased risk is correlated with lower BMD. Studies conducted in Turkey,<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">16</span></a> the United States,<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">20</span></a> Canada<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">21</span></a> and Brazil<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">17</span></a> report that the FRAX scores with and without BMD were comparable, with high concordance for MFR (ICC 0.76–0.86) and moderate-high for HFR (ICC 0.64–0.79). Olmez Sarikaya et al.<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">16</span></a> reported that by excluding subjects with a previous fracture the agreement for HFR improved; Unlike reported in this study, the exclusion of subjects with previous fracture in our sample decreased concordance for MFR (0.78, 95% CI 0.75–0.82) and for HFR (0.78; 95% CI 0.75–0.82).</p><p id="par0080" class="elsevierStylePara elsevierViewall">Among the papers with low agreement on treatment thresholds (kappa<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.33), the study conducted in Greece by Ilias et al.<a class="elsevierStyleCrossRef" href="#bib0285"><span class="elsevierStyleSup">22</span></a> evaluated 88 women with the FRAX model in Italy, which might explain this disagreement. Studies in Spain and Denmark reported an overestimated risk with FRAX-BMI. Gómez-Vaquero et al.<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">23</span></a> demonstrated lower MFR (6.3<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>5.5 vs 5.4<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4.8%) and HFR (2.1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>3.3 vs 1.5<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>2.9%) with the inclusion of BMD in 853 women. In patients with better BMD, this difference was statistically significant (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001). However, the study included 210 subjects with treatment, which might have modified the BMD measured. In another study including 140 women, Egsmose et al.<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">14</span></a> reported an overestimated MFR with FRAX-BMI (28.2<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>11.9 vs 23.8<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>21.4%) and HFR (11.1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>9.5 versus 7.6<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>8.3%). The difference, assessed by the Bland–Altman method, was 3.5% (95% CI −11.6 to 18.6), concluding that risk assessments are not easily interchangeable individually. In contrast, the individual difference in our study was lower (MFR −1.02<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.40% and HFR −0.03<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.51%).</p><p id="par0085" class="elsevierStylePara elsevierViewall">On the contrary, studies in the United Kingdom and South Korea reported that the inclusion of BMD resulted in increased FRAX scores and a higher number of candidates to be treated. Kim et al.<a class="elsevierStyleCrossRef" href="#bib0295"><span class="elsevierStyleSup">24</span></a> assessed 1446 women, with the limitation that the family history of hip fractures and the use of glucocorticoids were not available. MFR (7.3<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4 versus 7.7<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>4.4%, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001) and HFR (2.4<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>2.5 vs 2.6<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>2.9%, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.001) were increased when including BMD. However, the probability of MFR and HFR calculated with FRAX-BMI was lower in subjects <70 years. The degree of agreement was high for MFR (ICC 0.834) and HFR (ICC 0.792). In the United Kingdom the number of women with a probability of HFR above the intervention threshold was lower with the use of FRAX-BMI, moderate with FRAX and the highest number was with BMD alone. However, FRAX identified the population with the highest risk and with the lowest number needing to be treated.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">6</span></a></p><p id="par0090" class="elsevierStylePara elsevierViewall">In a recent review of intervention thresholds recommended in the treatment guidelines, 67% used a MFR probability ≥20% as a therapy threshold, and most also considered a HFR probability ≥3%.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> It is expected that depending on the treatment threshold, different patients will be identified at different risks and the percentage of patients to be treated will differ. However, the degree of agreement between high-risk and low-risk classification based on probability estimates in FRAX with and without BMD shows that discordance rates are low. The agreement percentage reported in the studies assessing HFR threshold ≥3% was 93–94.6%<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">16,17</span></a>; In our sample the agreement percentage was 96%. The agreement percentage in studies with MFR threshold was ≥20% and concordance was 91.5–99.7%.<a class="elsevierStyleCrossRefs" href="#bib0260"><span class="elsevierStyleSup">17,21</span></a> In our study we found a lower percentage agreement (81%). The lowest concordance reported (84%) was when both intervention thresholds were considered, similar to 87% according to our study.</p><p id="par0095" class="elsevierStylePara elsevierViewall">In our study, most cases of disagreement about treatment threshold were in overtreatment (27.6% had a lower risk) and to a lesser extent underestimation of risk (9.3% had a higher risk), similar to as reported in other studies where in most of the disagreement cases FRAX-BMI pharmacological treatment was recommended.<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">16,20</span></a> As in our study, it has been reported that the variables influencing the disagreement are older age<a class="elsevierStyleCrossRefs" href="#bib0260"><span class="elsevierStyleSup">17,20</span></a> and BMI.<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">17</span></a> Contrary to what was reported in 2 other studies,<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">16,17</span></a> a history of previous fracture did not predict disagreement. Hamdy and Kiebzak<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">25</span></a> reported that the disagreement occurred in 2 subgroups: patients with a mean age 71<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>10 years, with a normal <span class="elsevierStyleItalic">t</span>-score and with FRAX-BMI scores that exceeded the treatment threshold (4.4%). And patients with a mean age of 63.2<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>7.5 years, with a high BMI and a low <span class="elsevierStyleItalic">t</span>-score where FRAX-BMI score did not exceed treatment recommendations (6.1%).</p><p id="par0100" class="elsevierStylePara elsevierViewall">The validity of FRAX-BMI depends on its ability to predict fractures. The results of our study and those reported previously evaluate concordance and not validity. However, there is evidence that FRAX provides a validated tool to identify patients most likely to benefit from an intervention. Therefore, it is probably the most recommended method.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">7</span></a> It has 26 validation studies in 9 different countries and is used in the FR assessment in general population in 58 countries.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> FRAX provides a better risk stratification if the BMD is known. In a meta-analysis including BMD increased the area under the curve from 0.74 to 0.79 for predicting hip fracture in females.<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">26</span></a> Fraser et al.<a class="elsevierStyleCrossRef" href="#bib0310"><span class="elsevierStyleSup">27</span></a> observed that FR estimates with FRAX with and without BMD are close to the number of fractures observed in men and women. And in most cases, FRAX underestimated the fracture rate observed by only 1–2%.<a class="elsevierStyleCrossRef" href="#bib0310"><span class="elsevierStyleSup">27</span></a></p><p id="par0105" class="elsevierStylePara elsevierViewall">The application of FRAX in clinical practice requires the definition of intervention thresholds: a risk from which to schedule a treatment or request a densitometry, which are not provided in the FRAX validation study. Most guidelines using a fixed intervention threshold choose an MFR ≥20% justified only following the NOF recommendation.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> However, because mortality and FR varies depending on the country, the adjustment of thresholds has to be country-specific.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> This will depend on the incidence of fractures, morbidity and mortality, the cost of fractures, the gross national product, and the estimation of what can or should be paid for a diagnostic or therapeutic strategy, in order to provide a cost-benefit threshold.</p><p id="par0110" class="elsevierStylePara elsevierViewall">Variability is known in clinical practice of medicine, and osteoporosis is not excluded from this circumstance. Current guidelines do not agree in their recommendations on significant issues such as to whom we should apply for a DXA and whom to treat. In postmenopausal women without a previous fracture, DXA is the simplest option to identify those with osteoporosis. Population screening in women aged ≥65 years is recommended by the NOF, the International Society for Clinical Densitometry and the Spanish Society of Rheumatology,<a class="elsevierStyleCrossRef" href="#bib0315"><span class="elsevierStyleSup">28</span></a> but not by the National Osteoporosis Guideline Group, which recommends the use of FRAX-BMI for screening population.<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">2</span></a> In women below this age, their indication should be evaluated individually. In Mexico, as in other countries, population screening with DXA does not seem feasible, essentially for economic reasons. Limited resources must be compatible with the increasing demands. The timely case detection strategy, based on FRAX-BMI to identify subjects with FR who will benefit from densitometry or treatment in high-risk subjects,<a class="elsevierStyleCrossRefs" href="#bib0320"><span class="elsevierStyleSup">29,30</span></a> would represent a more viable option, and may help rationalize available resources, standardize results and improve clinical decisions in accordance with clinical practice guidelines. Available studies on the Spanish population report a reduction in the number of DXAs ranging from 16 to 40% when using this strategy, depending on the evaluation threshold used.<a class="elsevierStyleCrossRef" href="#bib0315"><span class="elsevierStyleSup">28</span></a> With the assessment and intervention threshold suggested by Azagra et al.,<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">31</span></a> based on the FRIDEX cohort, the overall cost decreased by 19–29% compared to the standard strategy.<a class="elsevierStyleCrossRefs" href="#bib0330"><span class="elsevierStyleSup">31,32</span></a></p><p id="par0115" class="elsevierStylePara elsevierViewall">The advantage of FRAX is that it indicates the probability of fracture in a person in a given period of time. This is easier to explain by doctors and to understand by patients the severity of the disease and, finally, make a shared decision with the patient about the risk-benefit of initiating treatment, or to request a DXA in case of doubt, which is the most useful risk expression in clinical practice. Shared decisions might improve compliance, which is necessary to reduce the risk of vertebral fractures (40–70%), non-vertebral fractures (20–25%) and hip fractures (20–40%).<a class="elsevierStyleCrossRef" href="#bib0340"><span class="elsevierStyleSup">33</span></a></p><p id="par0120" class="elsevierStylePara elsevierViewall">The optimal interval to repeat DXA in the context of screening in subjects without osteoporosis and low FR at baseline is uncertain. In general, intervals 2 to 5 years are recommended.<a class="elsevierStyleCrossRefs" href="#bib0345"><span class="elsevierStyleSup">34,35</span></a> Because FR will change with age or with the development of new CRFs, it is recommended that the interval be individualized according to the CRF assessment (not only by the <span class="elsevierStyleItalic">t</span>-score) and repeat it if we consider that the probability of the DXA results will influence the treatment decision or if rapid changes in BMD are expected.<a class="elsevierStyleCrossRefs" href="#bib0345"><span class="elsevierStyleSup">34,35</span></a> Therefore, FRAX-BMI may be sufficient for this purpose.</p><p id="par0125" class="elsevierStylePara elsevierViewall">Despite these advantages, FRAX has its limitations: it does not weigh the risk in dose-dependent CRFs (e.g. glucocorticoids, number of fractures, smoking and alcoholism), it does not include CRFs of falls, does not consider spine BMD, it is not a tool sensitive to modification. Therefore, it may not estimate FR properly in situations with rapid loss of BMD (cancer, prolonged rest, medication, etc.). For these reasons, clinicians should use their clinical judgment and consider DXA application and FR reassessment with BMD, or use an algorithm that includes falls (Garvan, QFracture) for a better FR estimation.</p><p id="par0130" class="elsevierStylePara elsevierViewall">Finally, the use of FRAX-BMI may be the only possible FR assessment in areas with restricted access to DXA. Our data support the idea that FRAX-BMI is an effective tool “most of the time” and it is recommended compared to not using any tool,<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">18</span></a> which is not necessarily similar to FRAX including BMD but can help in making medical decisions.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Conflict of interests</span><p id="par0135" class="elsevierStylePara elsevierViewall">The authors report no conflict of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:10 [ 0 => array:3 [ "identificador" => "xres852164" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 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" => "xpalclavsec846724" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres852165" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción" ] 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" => "xpalclavsec846723" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Material and methods" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Assessment of bone mineral density" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Assessment of fracture risk and treatment recommendations" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Statistical analysis" ] ] ] 6 => array:2 [ "identificador" => "sec0030" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0035" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0040" "titulo" => "Conflict of interests" ] 9 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2016-08-22" "fechaAceptado" => "2016-11-17" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec846724" "palabras" => array:5 [ 0 => "Osteoporotic fracture" 1 => "Risk assessment" 2 => "Agreement" 3 => "FRAX" 4 => "Osteoporosis" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec846723" "palabras" => array:5 [ 0 => "Fractura osteoporótica" 1 => "Evaluación del riesgo" 2 => "Concordancia" 3 => "FRAX" 4 => "Osteoporosis" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">The use of FRAX without the inclusion of bone mineral density (FRAX-BMI) may be useful in clinical practice to identify patients at high risk of fracture and inform treatment decisions, but its usefulness is debated. The aim of the study is to evaluate the agreement between the risk of fracture calculated by FRAX with or without bone mineral density (BMD).</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Patients and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">A cross-sectional study was conducted with 431 women (40–90 years) without treatment. The concordance of the probability of fracture was assessed by the concordance correlation coefficient (CCC), and by Bland–Altman method. The kappa index was used to evaluate the agreement between treatment indications.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The difference between the risks of a major osteoporosis fracture (MOFR) was 1.02<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.40% (95% CI −2 to 1.90) and −0.03<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.51% (95% CI −1.18 to 1.32) for the hip fracture risk (HFR). Agreement between MOFR and HFR FRAX scores was good (CCC 0.879, 95% CI 0.85–0.90 and CCC 0.821, 95% CI 0.79–0.85, respectively). The correlation between BMD of the femoral neck and fracture risk calculated by FRAX-BMI was a moderate, MOFR (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0.55, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleMonospace">0</span>.001) and HFR (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0.54, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001). The agreement between the recommendations of treatment was 87% (kappa 0.61).</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">The good agreement between the risk of fracture obtained suggests that FRAX-BMI allows us to provide an estimate of risk in most cases.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 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">Introducción</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">El empleo del FRAX sin la inclusión de la densidad mineral ósea (FRAX-BMI) puede ser útil en la práctica clínica para identificar a los pacientes con riesgo elevado de fractura e informar sobre las decisiones de tratamiento, aunque su utilidad es motivo de debate. El objetivo del estudio es evaluar la concordancia entre el FRAX con y sin inclusión de la densidad mineral ósea (DMO).</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Pacientes y métodos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Estudio trasversal que incluyó 431 mujeres entre 40–90 años sin tratamiento previo. La concordancia de la probabilidad de fractura fue evaluada mediante el coeficiente de correlación y concordancia (CCC), y mediante el método de Bland-Altman. Se empleó el índice de kappa para evaluar la concordancia entre las indicaciones de tratamiento.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">La diferencia entre el riesgo de fractura osteoporótica principal (RFP) fue 1,02<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1,40% (IC 95% −2 a 1,90) y de −0,03<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0,51% (IC 95% −1,18 a 1,32) para el riesgo de fractura de cadera (RFC). Los resultados del CCC demostraron una buena concordancia, para el RFP fue de 0,879 (IC 95% 0,85-0,90), y de 0,821 (IC 95% 0,79-0,85) para el RFC. Existió una correlación moderada entre el riesgo de fractura obtenida con el FRAX-BMI y la DMO del cuello femoral, RFM (r<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0,55, p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0,001) y RFC (r<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−0,54, p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0,001). El acuerdo entre las recomendaciones de tratamiento fue del 87% (kappa 0,61).</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">La buena concordancia entre el riesgo de fractura obtenido evidencia que el FRAX-BMI nos permite brindar una estimación del riesgo en la mayoría de los casos.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Introducción" ] 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="npar0015">Please cite this article as: Horta-Baas G, Pérez Bolde-Hernández A, Pérez-Pérez A, Vergara-Sánchez I, Romero-Figueroa MS. Concordancia del FRAX México con y sin el valor de la densidad mineral ósea en la evaluación del riesgo de fractura en la práctica clínica diaria. Med Clin (Barc). 2017;148:387–393.</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" => 961 "Ancho" => 2329 "Tamanyo" => 139868 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Left: Correlation between the log of major fracture risk (MFR) and BMD at the femoral neck. Right: Correlation between the log of hip fracture risk (HFR) and BMD in the femoral neck.</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" => 1020 "Ancho" => 2537 "Tamanyo" => 216892 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Left: Bland–Altman plot of agreement between the probability of major fracture risk (MFR) at 10 years calculated through FRAX-BMI and FRAX. Right: Bland–Altman plot of agreement between the probability of hip fracture at 10 years calculated using the FRAX-BMI and FRAX.</p>" ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1005 "Ancho" => 2711 "Tamanyo" => 180177 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Chart of Lin's concordance correlation coefficient. Left: Precision and accuracy in the logarithm of main fracture risk obtained using FRAX-BMI and FRAX. Right: Precision and accuracy in the logarithm of hip fracture risk obtained using FRAX-BMI and FRAX.</p>" ] ] 3 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "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">Number (percentage) \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleItalic">Age group</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>40–64 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">257 (59.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>≥65 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">174 (40.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleItalic">Risk factors</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Parents with hip fracture \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">63 (14.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Previous fracture \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">47 (10.9) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Rheumatoid arthritis \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">33 (7.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Glucocorticoids \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">23 (5.3) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Currently smoking \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29 (6.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Secondary osteoporosis<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="char" valign="top">74 (17.2) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Alcohol (≥3 units/day) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleItalic">Number of risk factors per subject</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">236 (54.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">137 (31.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">45 (10.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">13 (3) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="2" align="left" valign="top"><span class="elsevierStyleItalic">Comorbidity</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">Hypertension</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">76 (17.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Obesity \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">120 (27.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Diabetes mellitus \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">49 (11.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Osteoarthritis \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">68 (15.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Hypothyroidism \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">29 (6.7) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Systemic lupus erythematosus \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 (2.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1438587.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Two women with insulin-dependent diabetes mellitus, one case with hyperthyroidism, 71 cases with early menopause.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Demographic characteristics of the women studied (No.<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>431).</p>" ] ] 4 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Hosmer and Lemeshow test, <span class="elsevierStyleItalic">χ</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>8.09, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.42.</p><p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Variable <span class="elsevierStyleItalic">Y</span>: agreement with treatment thresholds (0<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>agreement, 1<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>disagreement).</p><p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">In bold, statistically significant values:</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">OR (CI 95%) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleItalic">Age</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.30 (1.22–1.38) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><span class="elsevierStyleBold">¿0.01</span> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleItalic">Body mass index</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.77 (0.70–0.85) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><span class="elsevierStyleBold">¿0.01</span> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleItalic">Previous fracture (0</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">absent, 1</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">present)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.10 (0.40–3.01) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.849 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleItalic">Number of risk factors</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.92 (1.30–6.57) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><span class="elsevierStyleBold">0.009</span> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">7.94 (2.25–28.01) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><span class="elsevierStyleBold">0.001</span> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>≥3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12.2 (1.04–142.78) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><span class="elsevierStyleBold">0.046</span> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleItalic">Diabetes (0</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">absent, 1</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">present)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.10 (0.43–2.81) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.840 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleItalic">Osteoarthritis (0</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">absent, 1</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">present)</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.59 (0.23–1.47) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.259 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1438586.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Odds ratio estimated from multiple logistic regression model to predict concordance between National Osteoporosis Foundation intervention thresholds based on FRAX with and without bone mineral density, age, history of fracture, body mass index, number of risk factors, presence of osteoarthritis and diabetes in 431 women.</p>" ] ] 5 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "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 " align="left" 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">Major fracture risk</th><th class="td" title="table-head " colspan="2" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">Hip fracture risk</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="" 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">CCC (95% CI) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">BA<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">a</span></a> (95% CI) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">CCC (95% CI) \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">BA<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">a</span></a> (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 " colspan="6" align="left" valign="top"><span class="elsevierStyleItalic">Age in decades</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>40–49 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">38 (8.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.9 (0.84–0.96) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−2.41<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.22 (−1.61 to 1.38) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.54 (0.33–0.76) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.002<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.18 (−0.37 to 0.38) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>50–59 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">129 (29.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.86 (0.81–0.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-1.06<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.28 (−1.75 to 1.53) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.53 (0.41–0.64) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.01<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.39 (−0.94 to 0.89) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>60–69 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">154 (35.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.74 (0.67–0.81) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-1.01<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.41 (−2 to 1.96) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.53 (0.42–0.64) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.04<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.58 (−1.34 to 1.57) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>70–79 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">87 (20.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.72 (0.63–0.82) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.38 (−1.8 to 1.89) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.69 (0.58–0.79) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.05<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.6 (−1.38 to 1.66) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>80–90 years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">23 (5.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.4 (0.13–0.67) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.15<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>2.07 (−3.65 to 4.84) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.64 (0.42–0.86) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.15<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.74 (−1.57 to 2.41) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="6" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="6" align="left" valign="top"><span class="elsevierStyleItalic">BMI classification in accordance with WHO</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Normal weight \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">114 (26.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.85 (0.8–0.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.06<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.52 (−2.15 to 2.42) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.84 (0.79–0.89) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.08<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.57 (−1.25 to 1.64) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Overweight \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">196 (45.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.9 (0.87–0.92) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-1.05<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.34 (−1.89 to 1.71) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.81 (0.76–0.86) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.48 (−1.19 to 1.17) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Class I obesity \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">96 (22.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.84 (0.78–0.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-1.06<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.37 (−1.99 to 1.75) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.72 (0.63–0.82) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.03<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.52 (−1.19 to 1.37) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>Class II obesity \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">25 (5.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.87 (0.78–0.96) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-1.07<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.3 (−1.8 to 1.55) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.73 (0.55–0.91) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.06<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.34 (−0.66 to 0.9) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="6" align="left" valign="top"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="6" align="left" valign="top"><span class="elsevierStyleItalic">Number of risk factors</span></td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">236 (54.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.82 (0.78–0.86) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-1.06<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.42 (−2.14 to 1.87) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.76 (0.71–0.81) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-0.006<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.46 (−1.11 to 1.08) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">137 (31.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.86 (0.82–0.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.03<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.38 (−1.82 to 1.94) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.81 (0.76–0.87) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.52 (−1.07 to 1.52) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top"><span class="elsevierStyleHsp" style=""></span>≥2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">58 (13.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.9 (0.86–0.95) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">-1<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>1.36 (−1.85 to 1.83) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.83 (0.75–0.91) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.03<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>0.66 (−1.63 to 1.81) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1438588.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Difference in fracture risk between FRAX-BMI and FRAX<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>standard deviation.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Concordance and differences in fracture risk in accordance with variables predicting disagreement in treatment recommendations in multivariate analysis.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:35 [ 0 => array:3 [ "identificador" => "bib0180" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Screening for osteoporosis: an update for the U. 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Original article
Agreement of Mexican FRAX with and without the value of bone mineral density in assessing the risk of fracture in daily clinical practice
Concordancia del FRAX México con y sin el valor de la densidad mineral ósea en la evaluación del riesgo de fractura en la práctica clínica diaria
Gabriel Horta-Baasa,
, Arturo Pérez Bolde-Hernándeza, Argelia Pérez-Péreza, Imelda Vergara-Sánchezb, María del Socorro Romero-Figueroac
Corresponding author
a Servicio de Reumatología, Hospital General Regional 220, Instituto Mexicano del Seguro Social, Toluca, Estado de México, Mexico
b Servicio de Pediatría, Neurología Pediátrica, Hospital General Regional 1, Instituto Mexicano del Seguro Social, Mérida, Yucatán, Mexico
c Coordinación de Investigación en Salud, Delegación Estado de México Poniente, Instituto Mexicano del Seguro Social, Toluca, Estado de México, Mexico