Original Article
Osteonecrosis in individuals with systemic lupus erythematosus: A predictive model
Osteonecrosis en individuos con lupus eritematoso sistémico: un modelo predictivo
Leído
4508
Vecesse ha leído el artículo
2044
Total PDF
2464
Total HTML
Compartir estadísticas
array:23 [ "pii" => "S1699258X18301141" "issn" => "1699258X" "doi" => "10.1016/j.reuma.2018.05.002" "estado" => "S300" "fechaPublicacion" => "2020-03-01" "aid" => "1231" "copyright" => "Elsevier España, S.L.U. and Sociedad Española de Reumatología y Colegio Mexicano de Reumatología" "copyrightAnyo" => "2018" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Reumatol Clin. 2020;16:161-4" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:2 [ "total" => 450 "formatos" => array:3 [ "EPUB" => 52 "HTML" => 209 "PDF" => 189 ] ] "itemSiguiente" => array:18 [ "pii" => "S1699258X18301189" "issn" => "1699258X" "doi" => "10.1016/j.reuma.2018.05.004" "estado" => "S300" "fechaPublicacion" => "2020-03-01" "aid" => "1235" "documento" => "simple-article" "crossmark" => 1 "subdocumento" => "crp" "cita" => "Reumatol Clin. 2020;16:165-8" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:2 [ "total" => 1141 "formatos" => array:3 [ "EPUB" => 36 "HTML" => 894 "PDF" => 211 ] ] "es" => array:12 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original breve</span>" "titulo" => "Osteogénesis imperfecta. Descripción de 15 casos" "tienePdf" => "es" "tieneTextoCompleto" => "es" "tieneResumen" => array:2 [ 0 => "es" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "165" "paginaFinal" => "168" ] ] "titulosAlternativos" => array:1 [ "en" => array:1 [ "titulo" => "Osteogenesis imperfecta. Report of 15 Cases" ] ] "contieneResumen" => array:2 [ "es" => true "en" => true ] "contieneTextoCompleto" => array:1 [ "es" => true ] "contienePdf" => array:1 [ "es" => true ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "María Díaz López, Juan José Alegre Sancho, Àngels Martínez-Ferrer" "autores" => array:3 [ 0 => array:2 [ "nombre" => "María" "apellidos" => "Díaz López" ] 1 => array:2 [ "nombre" => "Juan José" "apellidos" => "Alegre Sancho" ] 2 => array:2 [ "nombre" => "Àngels" "apellidos" => "Martínez-Ferrer" ] ] ] ] ] "idiomaDefecto" => "es" "Traduccion" => array:1 [ "en" => array:9 [ "pii" => "S2173574319301029" "doi" => "10.1016/j.reumae.2018.05.001" "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/S2173574319301029?idApp=UINPBA00004M" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S1699258X18301189?idApp=UINPBA00004M" "url" => "/1699258X/00000016000002P2/v1_202005111112/S1699258X18301189/v1_202005111112/es/main.assets" ] "itemAnterior" => array:18 [ "pii" => "S1699258X18301153" "issn" => "1699258X" "doi" => "10.1016/j.reuma.2018.06.001" "estado" => "S300" "fechaPublicacion" => "2020-03-01" "aid" => "1232" "copyright" => "Elsevier España, S.L.U. and Sociedad Española de Reumatología y Colegio Mexicano de Reumatología" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Reumatol Clin. 2020;16:156-60" "abierto" => array:3 [ "ES" => true "ES2" => true "LATM" => true ] "gratuito" => true "lecturas" => array:2 [ "total" => 384 "formatos" => array:3 [ "EPUB" => 34 "HTML" => 239 "PDF" => 111 ] ] "en" => array:12 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original</span>" "titulo" => "Prevalence of clinical and radiological osteoarthritis in knee, hip, and hand in an urban adult population of Mexico City" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "es" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "156" "paginaFinal" => "160" ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Prevalencia clínica y radiológica de osteoartritis de rodillas, caderas y manos en una población urbana adulta de la Ciudad de México" ] ] "contieneResumen" => array:2 [ "en" => true "es" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Salvador Israel Macías-Hernández, Erick Rodrigo Zepeda-Borbón, Blanca Isabel Lara-Vázquez, Nuria María Cuevas-Quintero, Juan Daniel Morones-Alba, Eva Cruz-Medina, Tania Inés Nava-Bringas, Antonio Miranda-Duarte" "autores" => array:8 [ 0 => array:2 [ "nombre" => "Salvador Israel" "apellidos" => "Macías-Hernández" ] 1 => array:2 [ "nombre" => "Erick Rodrigo" "apellidos" => "Zepeda-Borbón" ] 2 => array:2 [ "nombre" => "Blanca Isabel" "apellidos" => "Lara-Vázquez" ] 3 => array:2 [ "nombre" => "Nuria María" "apellidos" => "Cuevas-Quintero" ] 4 => array:2 [ "nombre" => "Juan Daniel" "apellidos" => "Morones-Alba" ] 5 => array:2 [ "nombre" => "Eva" "apellidos" => "Cruz-Medina" ] 6 => array:2 [ "nombre" => "Tania Inés" "apellidos" => "Nava-Bringas" ] 7 => array:2 [ "nombre" => "Antonio" "apellidos" => "Miranda-Duarte" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S1699258X18301153?idApp=UINPBA00004M" "url" => "/1699258X/00000016000002P2/v1_202005111112/S1699258X18301153/v1_202005111112/en/main.assets" ] "en" => array:19 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Osteonecrosis in individuals with systemic lupus erythematosus: A predictive model" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "161" "paginaFinal" => "164" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Jennifer Mendoza-Alonzo, José Zayas-Castro, Karina Soto-Sandoval" "autores" => array:3 [ 0 => array:4 [ "nombre" => "Jennifer" "apellidos" => "Mendoza-Alonzo" "email" => array:1 [ 0 => "jennifermend@mail.usf.edu" ] "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" => "José" "apellidos" => "Zayas-Castro" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 2 => array:3 [ "nombre" => "Karina" "apellidos" => "Soto-Sandoval" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "Department of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620, USA" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Departamento de Gobierno y Empresa, Universidad de Los Lagos, Campus Puerto Montt, Chinquihue km 6, Chile" "etiqueta" => "b" "identificador" => "aff0010" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Osteonecrosis en individuos con lupus eritematoso sistémico: un modelo predictivo" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0020" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1154 "Ancho" => 1506 "Tamanyo" => 71445 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Posterior distribution: tobacco use.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Patients with systemic lupus erythematosus (SLE) have a higher incidence of a variety of secondary associated diseases than the general population.<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">1</span></a> These comorbid diseases arise from the SLE itself or because of the use of some medications to treat it.<a class="elsevierStyleCrossRefs" href="#bib0070"><span class="elsevierStyleSup">2–4</span></a> A secondary disease associated with SLE is osteonecrosis (ON), whose prevalence varies widely, from 4% to 40% in patients with lupus.<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">4,5</span></a> ON is considered the main secondary disease that causes morbidity in patients with SLE.<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">5</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Several studies aimed to determine predictive factors of ON in patients with SLE<a class="elsevierStyleCrossRefs" href="#bib0070"><span class="elsevierStyleSup">2,4,6–10</span></a>; however, none have attempted to develop a predictive model, which is the next step after determining the significant variables. A predictive model is a tool that supports the decision-making of the providers to apply proper treatments considering the uniqueness of each patient. The objective of this work is to develop a predictive model to determine if an individual suffering from SLE can also be diagnosed with ON using pharmacological, demographic, and psychoactive factors.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Materials and Methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Data Collection</span><p id="par0015" class="elsevierStylePara elsevierViewall">The literature was reviewed to identify factors that are deemed to be related to the development of ON in SLE patients. Data for the Chilean population was collected through an online survey, which was developed based on findings from the literature, health care providers, survey development experts, and individuals with SLE. The effort resulted in an 89-question instrument distributed in four sections: general information, information about the SLE, healthy lifestyle, and information about the ON. The survey was administered through a confidential online platform across Chile for a period of three weeks during December 2015. Each participant was required to read and sign a form providing consent. The process resulted in 46 de-identified records where 15.22% developed ON and 98.21% were women.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Development and Evaluation of a Predictive Model</span><p id="par0020" class="elsevierStylePara elsevierViewall">The de-identified data were used to create two models using a Bayesian logistic regression approach. The response variable was the occurrence of the first ON (1: individual developed first ON, 0: individual did not develop ON). The explanatory variables analyzed were mean consumption of corticosteroids per day (mg), cumulative consumption of corticosteroids (mg), tobacco use, alcohol consumption, age at first ON, and race (Mapuche—indigenous—origins or not). Models were validated using leave-one-out cross-validation. The first model used a non-informative prior multivariate normal distribution for the parameters’ betas, specifically, βi∼N(0,10,000),   j=0,1,…,6. The second model used a multivariate normal distribution, mixing non-informative and informative prior normal distributions recently available in the literature (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). The priors were selected based on the significance of the variables in the studies (α=0.1): mean consumption of corticosteroids per day, with a <span class="elsevierStyleItalic">P</span>-value equal to .0002; tobacco use, with a <span class="elsevierStyleItalic">P</span>-value equal to .05; and age at first ON, with a <span class="elsevierStyleItalic">P</span>-value equal to .08.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0025" class="elsevierStylePara elsevierViewall">The likelihood contribution of each individual was binomial. The posterior distribution was simulated using Markov chain Monte Carlo (MCMC) and the random walk metropolis (RWM) algorithm implemented in R software. The total of iterations was 10,000,000 with a burn-in of 9,000,000 iterations. The threshold was determined using the complete sample size through the receiver operating characteristic (ROC) curve, maximizing the summation of the specificity and sensitivity. The analysis of significance for the first and second models used 90% and 95% credible intervals (CI), respectively.</p><p id="par0030" class="elsevierStylePara elsevierViewall">The performances of the Bayesian models were compared to the non-parametric random decision forest model for accuracy, sensitivity, and specificity. The optimal input variables were determined using the <span class="elsevierStyleItalic">tuneRF</span> function in R, minimizing out-of-bag (OOB) errors. The number of trees was determined screening from 1 to 1000 trees, plotting the values against the OOB errors. The random decision forest splits were performed using the Gini index.</p></span></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Results</span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Bayesian Logistic Regression Models</span><p id="par0035" class="elsevierStylePara elsevierViewall">Using a non-informative prior distribution and a 95% CI, none of the variables seemed to be significant (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). With a 90% CI, the variables of mean consumption of corticosteroids per day and tobacco use were both significant. Using prior information, the same variables are significant with a 95% CI. These two variables were used to create the Bayesian logistic regression model. <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> shows the ROC curve for the non-informative (smooth line) and informative (dotted line) Bayesian logistic regression models. The threshold for the non-informative prior model was 0.1819, and the model for the informative prior was 0.2187. These values were used to validate the respective models.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0040" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> shows that the sensitivity, specificity, and accuracy were higher for the Bayesian logistic regression models with prior information than for the model with non-informative prior information. The mean of the posterior distributions for the informative prior provides the estimators of the parameters. The estimators, considering the mean and the standard deviation (mean±SD), are as follows: intercept (βˆ0)   −3.300±0.534, mean of corticosteroids per day (βˆ1)   0.048±0.009, and tobacco use (βˆ2)   0.562±0.238.</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Random Decision Forest Model</span><p id="par0045" class="elsevierStylePara elsevierViewall">The random decision forest model used 85 trees and two input variables. The accuracy of the random forest model (0.8478) was higher than the Bayesian logistic regression model with prior information. The sensitivity was the highest (1.0), but the specificity was the lowest (0.0), which means that the model was unable to predict the development of ON (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>). The variable importance plot (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>) displays that the mean corticosteroids per day led to the largest mean decrease in Gini impurity (3.7878).</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Discussion</span><p id="par0050" class="elsevierStylePara elsevierViewall">Various studies have established that patients who receive high doses of corticosteroids are susceptible to developing ON in certain areas of the body.<a class="elsevierStyleCrossRefs" href="#bib0070"><span class="elsevierStyleSup">2,4,6,10,11</span></a> Patients with SLE are administered high doses of corticosteroids in their therapies for long periods, and therefore, they are at risk of developing ON. However, there is uncertainty whether the cumulative doses and the duration of treatment with corticosteroids or the use of large doses of corticosteroids on a daily basis are the contributing factors to development of the disease. Therefore, it is not surprising that a variable related to corticosteroids is significant in the Bayesian models and influences the prediction power in random forest. In addition, it is not unusual that tobacco use was significant in the models because studies have related ON with non-corticosteroid factors such as tobacco use, alcohol consumption, age, gender, and race, among others.<a class="elsevierStyleCrossRefs" href="#bib0090"><span class="elsevierStyleSup">6,7,9</span></a></p><p id="par0055" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRefs" href="#fig0015">Figs. 3 and 4</a> show the comparison of the posterior distributions for the parameters of the Bayesian models with prior and non-prior distribution. <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a> depicts the posterior distribution of the regression coefficient for mean corticosteroids per day, and <a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a> shows the posterior distribution of the regression coefficient for tobacco use. There is a significant reduction in the variance in the models with prior information. The variance of the posterior distribution for mean corticosteroids per day decreased in 91%, and the mean decreased in 20%. The estimators of the variable mean corticosteroids per day for the model with prior distribution and the model without prior distribution are close. This highlights the relevance of this factor. A similar reduction occurred with the posterior distribution for tobacco use: the variance decreased in 95.2%, and the mean decreased in 68.5%.</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">With regard to the best performance model—Bayesian logistic regression model with prior information—the probability of developing ON, θi, is calculated using Eq. <a class="elsevierStyleCrossRef" href="#eq0005">(1)</a>, where <span class="elsevierStyleItalic">i</span> is the individual. Since the coefficient for mean corticosteroids per day and for tobacco use are positive, the probability of developing ON will also increase if any of these variables increase.<elsevierMultimedia ident="eq0005"></elsevierMultimedia></p><p id="par0070" class="elsevierStylePara elsevierViewall">Specifically, if the explanatory variable, mean corticosteroids per day, increases by 1<span class="elsevierStyleHsp" style=""></span>mg, and the variable tobacco use keeps constant, the ratio between the probability that the individual develops ON and the probability that the individual does not develop ON increases by e0.048⋅(≈1.049). Likewise, if an individual consumes tobacco and the other variable is held constant, the ratio increases in e0.048⋅(≈1.754).</p><p id="par0075" class="elsevierStylePara elsevierViewall">The use of the preceding information is one of the main advantages of the Bayesian approach, which is not possible with random forest and other methods. In addition, the estimators of the parameters calculated in this study provide prior information for future works in this matter. Although random forest produces a higher accuracy than Bayesian logistic regression with prior information, it is non-trivial to interpret and analyze, and it seems to present problems when the sample size is small. The Bayesian approach provides better interpretability and inferences. In summary, this work explores the opportunity of better supporting a provider's decision when treating individuals with lupus. The use of this tool along with other outcome metrics, specifically, measurements of disease activity (e.g., SLE diseases activity index – SLEDAI) could further support the providers, since a higher disease activity score appears associated with the incidence of ON in individuals with SLE.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">12</span></a></p><p id="par0080" class="elsevierStylePara elsevierViewall">This study has three main limitations. First is the possibility of bias due to the auto report nature of the data because the data was extracted using a survey rather than clinical records. Second, the type and depth of clinical questions on the survey because the individuals responding are not able to address complicated clinical questions. Third, the sample size, which does not allow for more in-depth training, testing, and validation.</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Conflicts of Interest</span><p id="par0085" class="elsevierStylePara elsevierViewall">The authors declare no conflicts of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:10 [ 0 => array:3 [ "identificador" => "xres1334362" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Method" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1229086" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres1334361" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Método" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1229085" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Materials and Methods" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Data Collection" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Development and Evaluation of a Predictive Model" ] ] ] 6 => array:3 [ "identificador" => "sec0025" "titulo" => "Results" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0030" "titulo" => "Bayesian Logistic Regression Models" ] 1 => array:2 [ "identificador" => "sec0035" "titulo" => "Random Decision Forest Model" ] ] ] 7 => array:2 [ "identificador" => "sec0040" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0045" "titulo" => "Conflicts of Interest" ] 9 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2018-01-05" "fechaAceptado" => "2018-05-03" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1229086" "palabras" => array:3 [ 0 => "Osteonecrosis" 1 => "Systemic lupus erythematosus" 2 => "Bayesian model" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1229085" "palabras" => array:3 [ 0 => "Osteonecrosis" 1 => "Lupus eritematoso sistémico" 2 => "Modelo bayesiano" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">This work attempts to provide a model to predict the development of osteonecrosis (ON) in individuals with systemic lupus erythematosus (SLE) using pharmacological, demographic, and psychoactive factors.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Method</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">A review of the literature was conducted to construct a survey administered across Chile to individuals with SLE during a period of three weeks. This work used a sample size of 46 de-identified data records. Two Bayesian logistic regression models were created, with non-informative prior and informative prior distributions, and a random forest model was done for comparison. All models were cross-validated.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The significant variables used were <span class="elsevierStyleItalic">mean corticosteroids per day</span> (mg) and <span class="elsevierStyleItalic">tobacco use</span>. The random forest model provided good accuracy and sensitivity, but low specificity. Bayesian logistic regression with prior information increased the specificity.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">This work determined that the use of corticosteroids and tobacco are significant variables to predict ON. Using prior information provides good accuracy, specificity, and sensitivity to the prediction. Further studies need to be conducted to validate the model using a testing set.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Method" ] 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">Objetivo</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Este trabajo busca determinar un modelo predictivo de desarrollo de osteonecrosis (ON) en individuos diagnosticados con lupus eritematoso sistémico (LES) utilizando factores farmacológicos, demográficos y psicoactivos.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Método</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Se realizó una revisión bibliográfica para construir una encuesta, la cual fue administrada a individuos con LES a lo largo de Chile durante un periodo de 3 semanas. En este trabajo se utilizó una muestra de 46 registros de datos no identificados. Se desarrollaron 2 modelos de regresión logística bayesiana con información <span class="elsevierStyleItalic">a priori</span> no informativa e informativa, y también se desarrolló un modelo comparativo utilizando bosques aleatorios. Los modelos fueron validados usando validación cruzada.</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Se usaron las variables significativas promedio de corticosteroides por día (mg) y consumo de tabaco. Bosques aleatorios provee una precisión y sensibilidad alta, pero una baja especificidad. La regresión logística bayesiana con información <span class="elsevierStyleItalic">a priori</span> incrementó el valor de la especificidad.</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Este trabajo ha determinado que el uso de corticosteroides y tabaco son variables significativas para predecir ON. Usando información <span class="elsevierStyleItalic">a priori</span> arroja buenos resultados en precisión, especificidad y sensibilidad en la predicción. Se requieren realizar más estudios aumentando el tamaño de la muestra para validar el modelo usando un conjunto de prueba.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Método" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "multimedia" => array:8 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1322 "Ancho" => 1576 "Tamanyo" => 78691 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">ROC curve for non-informative and informative prior for Bayesian logistic regression models.</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" => 1207 "Ancho" => 1592 "Tamanyo" => 101715 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Mean decrease in Gini of random decision forest model.</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" => 1150 "Ancho" => 1509 "Tamanyo" => 73842 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Posterior distribution: mean corticosteroids per day.</p>" ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1154 "Ancho" => 1506 "Tamanyo" => 71445 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Posterior distribution: tobacco use.</p>" ] ] 4 => 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 [ "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">OR: odd ratio; CI: confidence interval.</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="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">OR [95% CI] \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Prior   N(μ,σ2) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Source \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mean consumption of corticoids per day (mg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.05 [1.02, 1.07] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N(log1.05,0.000093) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Gladman (2017) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cumulative consumption of corticoids (mg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">– \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N(0,10,000) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">– \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Tobacco use \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.64 [1.01, 2.65] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N(log1.64,0.0023) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Wang (2016)<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">9</span></a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Alcohol consumption \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">– \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N(0,10,000) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">– \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age at first ON (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.92 [0.84, 1.01] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N(log0.92,0.0023) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Gladman (2017) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Race \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">– \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N(0,10,000) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">– \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2286928.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Prior Information for Bayesian Logistic Regression.</p>" ] ] 5 => 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="spar0080" class="elsevierStyleSimplePara elsevierViewall">CI: credible interval.</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-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Non-informative prior</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Informative prior</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">90% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">90% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">95% CI \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mean consumption of corticoids per day (mg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[0.0004, 0.1129] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−0.0093, 0.1258] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[0.0325, 0.0625] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[0.0296, 0.0654] \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cumulative consumption of corticoids (mg) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−0.0000, 0.0000] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−0.0000, 0.0000] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[0.0000, 0.0000] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[0.0000, 0.0000] \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Tobacco use \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[0.2837, 4.3183] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−0.0828, 4.7780] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[0.1848, 0.9716] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[0.1086, 1.0460] \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Alcohol consumption \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−3.0076, 1.0271] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−0.0034, 1.3970] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−2.3742, 1.0969] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−2.7870, 1.4020] \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age at first ON (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−0.1280, 0.0704] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−0.1498, 0.0892] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−0.1096, 0.0118] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−0.1220, 0.0227] \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Race \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−3.2597, 1.7970] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−3.9850, 2.1930] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−3.2140, 1.6673] \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">[−3.9290, 2.0330] \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2286927.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">90% and 95% Credible Intervals For Non-informative and Informative Prior.</p>" ] ] 6 => 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 [ "leyenda" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">Model 1: Bayesian logistic regression with non-informative prior.</p><p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">Model 2: Random decision forest.</p><p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">Model 3: Bayesian logistic regression with informative prior.</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="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Model 1 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Model 2 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Model 3 \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Accuracy \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.7174 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.8478 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.8261 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sensitivity \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.7949 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.0000 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.8974 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Specificity \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.2857 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0000 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.4286 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2286929.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Accuracy, Sensitivity, and Specificity of the Model.</p>" ] ] 7 => array:6 [ "identificador" => "eq0005" "etiqueta" => "(1)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "θi=exp(β0+β1xi1+β2xi2)1+exp(β0+β1xi1+β2xi2)" "Fichero" => "STRIPIN_si12.jpeg" "Tamanyo" => 3529 "Alto" => 36 "Ancho" => 218 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:12 [ 0 => array:3 [ "identificador" => "bib0065" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Comorbilidad en lupus eritematoso sistémico" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "E. Úcar Angulo" 1 => "N. Rivera García" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "Reumatol Clínica" "fecha" => "2008" "volumen" => "4" "paginaInicial" => "17" "paginaFinal" => "21" "itemHostRev" => array:3 [ "pii" => "S0016510711015410" "estado" => "S300" "issn" => "00165107" ] ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0070" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "High-dose intravenous methylprednisolone therapy associated with osteonecrosis in patients with systemic lupus erythematosus" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "L. Massardo" 1 => "S. Jacobelli" 2 => "M. Leissner" 3 => "M. Gonz" 4 => "L. Villarroel" 5 => "S. Rivero" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1177/096120339200100610" "Revista" => array:6 [ "tituloSerie" => "Lupus" "fecha" => "1992" "volumen" => "1" "paginaInicial" => "401" "paginaFinal" => "405" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/1304409" "web" => "Medline" ] ] ] ] ] ] ] ] 2 => array:3 [ "identificador" => "bib0075" "etiqueta" => "3" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Risk factors for osteonecrosis in systemic lupus erythematosus" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "M.A. Mont" 1 => "C.J. Glueck" 2 => "I.H. Pacheco" 3 => "P. Wang" 4 => "D.S. Hungerford" 5 => "M. Petri" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:7 [ "tituloSerie" => "J Rheumatol" "fecha" => "1997" "volumen" => "24" "paginaInicial" => "654" "paginaFinal" => "662" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/9101497" "web" => "Medline" ] ] "itemHostRev" => array:3 [ "pii" => "S0016510711000216" "estado" => "S300" "issn" => "00165107" ] ] ] ] ] ] ] 3 => array:3 [ "identificador" => "bib0080" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Osteonecrosis in SLE: prevalence, patterns, outcomes and predictors" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:4 [ 0 => "D. Gladman" 1 => "N. Dhillon" 2 => "J. Su" 3 => "M. Urowitz" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1177/0961203317711012" "Revista" => array:6 [ "tituloSerie" => "Lupus" "fecha" => "2018" "volumen" => "27" "paginaInicial" => "76" "paginaFinal" => "81" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/28530464" "web" => "Medline" ] ] ] ] ] ] ] ] 4 => array:3 [ "identificador" => "bib0085" "etiqueta" => "5" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Avascular necrosis of bone in systemic lupus erythematosus" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "R.M. Ghaleb" 1 => "G.M. Omar" 2 => "M.A. Ibrahim" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:2 [ "tituloSerie" => "Egypt Rheumatol" "fecha" => "2011" ] ] ] ] ] ] 5 => array:3 [ "identificador" => "bib0090" "etiqueta" => "6" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Osteonecrosis en Lupus Eritematoso Sistémico" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "LCD" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Libro" => array:3 [ "fecha" => "2005" "paginaInicial" => "79" "paginaFinal" => "83" ] ] ] ] ] ] 6 => array:3 [ "identificador" => "bib0095" "etiqueta" => "7" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "The role of non-corticosteroid related factors in osteonecrosis (ON) in systemic lupus erythematosus: a nested case–control study of inception patients" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:4 [ 0 => "R. Prasad" 1 => "D. Ibanez" 2 => "D. Gladman" 3 => "M. Urowitz" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Lupus" "fecha" => "2007" "volumen" => "16" "paginaInicial" => "157" "paginaFinal" => "162" ] ] ] ] ] ] 7 => array:3 [ "identificador" => "bib0100" "etiqueta" => "8" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Osteonecrosis in systemic lupus erythematosus osteonecrosis en lupus eritematoso sistémico" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "R.P. Gontero" 1 => "M.E. Bedoya" 2 => "E. Benavente" 3 => "S. Graciela Roverano" 4 => "S.O. Paira" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Reum Clin" "fecha" => "2015" "volumen" => "11" "paginaInicial" => "151" "paginaFinal" => "155" ] ] ] ] ] ] 8 => array:3 [ "identificador" => "bib0105" "etiqueta" => "9" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Non-corticosteroid-related risk factors for osteonecrosis in patients with systemic lupus erythematosus: a meta-analysis" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "T. Wang" 1 => "Z. Li" 2 => "X. Li" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:6 [ "tituloSerie" => "Int J Clin Exp Med" "fecha" => "2016" "volumen" => "9" "paginaInicial" => "8085" "paginaFinal" => "8096" "itemHostRev" => array:3 [ "pii" => "S0016510715021380" "estado" => "S300" "issn" => "00165107" ] ] ] ] ] ] ] 9 => array:3 [ "identificador" => "bib0110" "etiqueta" => "10" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Time trend and risk factors of avascular bone necrosis in patients with systemic lupus erythematosus" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "S.M. Tse" 1 => "C.C. Mok" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1177/0961203316676384" "Revista" => array:7 [ "tituloSerie" => "Lupus" "fecha" => "2017" "volumen" => "26" "paginaInicial" => "715" "paginaFinal" => "722" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/27831540" "web" => "Medline" ] ] "itemHostRev" => array:3 [ "pii" => "S0016510712030726" "estado" => "S300" "issn" => "00165107" ] ] ] ] ] ] ] 10 => array:3 [ "identificador" => "bib0115" "etiqueta" => "11" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "A vascular osteonecrosis in patients with SLE: relation to corticosteroid therapy and anticardiolipin antibodies" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:3 [ 0 => "S. Migliaresi" 1 => "U. Picillo" 2 => "L. Ambrosone" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1177/096120339400300108" "Revista" => array:6 [ "tituloSerie" => "Lupus" "fecha" => "1994" "volumen" => "3" "paginaInicial" => "37" "paginaFinal" => "41" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/8025584" "web" => "Medline" ] ] ] ] ] ] ] ] 11 => array:3 [ "identificador" => "bib0120" "etiqueta" => "12" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Systemic lupus erythematosus patients with high disease activity are associated with accelerated incidence of osteonecrosis: a systematic review and meta-analysis" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "K. Zhang" 1 => "Y. Zheng" 2 => "J. Jia" 3 => "J. Ding" 4 => "Z. Wu" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1007/s10067-017-3820-5" "Revista" => array:5 [ "tituloSerie" => "Clin Rheumatol" "fecha" => "2018" "volumen" => "37" "paginaInicial" => "5" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/28948379" "web" => "Medline" ] ] ] ] ] ] ] ] ] ] ] ] ] "idiomaDefecto" => "en" "url" => "/1699258X/00000016000002P2/v1_202005111112/S1699258X18301141/v1_202005111112/en/main.assets" "Apartado" => array:4 [ "identificador" => "17499" "tipo" => "SECCION" "es" => array:2 [ "titulo" => "Originales" "idiomaDefecto" => true ] "idiomaDefecto" => "es" ] "PDF" => "https://static.elsevier.es/multimedia/1699258X/00000016000002P2/v1_202005111112/S1699258X18301141/v1_202005111112/en/main.pdf?idApp=UINPBA00004M&text.app=https://reumatologiaclinica.org/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S1699258X18301141?idApp=UINPBA00004M" ]
| año/Mes | Html | Total | |
|---|---|---|---|
| 2024 Noviembre | 5 | 11 | 16 |
| 2024 Octubre | 44 | 29 | 73 |
| 2024 Septiembre | 58 | 31 | 89 |
| 2024 Agosto | 53 | 42 | 95 |
| 2024 Julio | 46 | 41 | 87 |
| 2024 Junio | 45 | 34 | 79 |
| 2024 Mayo | 44 | 29 | 73 |
| 2024 Abril | 45 | 28 | 73 |
| 2024 Marzo | 33 | 34 | 67 |
| 2024 Febrero | 34 | 32 | 66 |
| 2024 Enero | 25 | 21 | 46 |
| 2023 Diciembre | 28 | 44 | 72 |
| 2023 Noviembre | 46 | 38 | 84 |
| 2023 Octubre | 35 | 47 | 82 |
| 2023 Septiembre | 129 | 41 | 170 |
| 2023 Agosto | 40 | 17 | 57 |
| 2023 Julio | 36 | 25 | 61 |
| 2023 Junio | 32 | 24 | 56 |
| 2023 Mayo | 37 | 24 | 61 |
| 2023 Abril | 28 | 18 | 46 |
| 2023 Marzo | 60 | 34 | 94 |
| 2023 Febrero | 56 | 39 | 95 |
| 2023 Enero | 33 | 20 | 53 |
| 2022 Diciembre | 73 | 39 | 112 |
| 2022 Noviembre | 69 | 36 | 105 |
| 2022 Octubre | 64 | 43 | 107 |
| 2022 Septiembre | 60 | 34 | 94 |
| 2022 Agosto | 44 | 50 | 94 |
| 2022 Julio | 38 | 34 | 72 |
| 2022 Junio | 30 | 34 | 64 |
| 2022 Mayo | 45 | 46 | 91 |
| 2022 Abril | 40 | 55 | 95 |
| 2022 Marzo | 45 | 54 | 99 |
| 2022 Febrero | 32 | 33 | 65 |
| 2022 Enero | 37 | 38 | 75 |
| 2021 Diciembre | 42 | 41 | 83 |
| 2021 Noviembre | 40 | 51 | 91 |
| 2021 Octubre | 57 | 52 | 109 |
| 2021 Septiembre | 33 | 42 | 75 |
| 2021 Agosto | 30 | 41 | 71 |
| 2021 Julio | 20 | 28 | 48 |
| 2021 Junio | 35 | 37 | 72 |
| 2021 Mayo | 60 | 64 | 124 |
| 2021 Abril | 91 | 79 | 170 |
| 2021 Marzo | 51 | 32 | 83 |
| 2021 Febrero | 22 | 25 | 47 |
| 2021 Enero | 34 | 36 | 70 |
| 2020 Diciembre | 35 | 29 | 64 |
| 2020 Noviembre | 46 | 39 | 85 |
| 2020 Octubre | 28 | 21 | 49 |
| 2020 Septiembre | 54 | 31 | 85 |
| 2020 Agosto | 1 | 0 | 1 |
| 2020 Junio | 3 | 6 | 9 |
| 2020 Mayo | 4 | 2 | 6 |
| 2019 Mayo | 60 | 53 | 113 |
| 2019 Abril | 41 | 23 | 64 |
| 2019 Marzo | 27 | 29 | 56 |
| 2019 Febrero | 13 | 21 | 34 |
| 2019 Enero | 30 | 19 | 49 |
| 2018 Diciembre | 38 | 44 | 82 |
Mostrar todo
