dc.creator | Víctor Manuel Treviño Alvarado | |
dc.creator | José Gerardo Tamez Peña | |
dc.date | 2015 | |
dc.date.accessioned | 2018-10-18T20:35:00Z | |
dc.date.available | 2018-10-18T20:35:00Z | |
dc.identifier.issn | 1748670X | |
dc.identifier.doi | 10.1155/2015/794141 | |
dc.identifier.uri | http://hdl.handle.net/11285/630415 | |
dc.description | In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. © 2015 Jorge I. Galván-Tejada et al. | |
dc.language | eng | |
dc.publisher | Hindawi Limited | |
dc.relation | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945377076&doi=10.1155%2f2015%2f794141&partnerID=40&md5=e2e224d70edeb2d5f859afaf88a02c96 | |
dc.relation | Investigadores | |
dc.relation | Estudiantes | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0 | |
dc.source | Computational and Mathematical Methods in Medicine | |
dc.subject | adult | |
dc.subject | Article | |
dc.subject | computer assisted diagnosis | |
dc.subject | computer prediction | |
dc.subject | controlled study | |
dc.subject | data analysis | |
dc.subject | diagnostic accuracy | |
dc.subject | elastic tissue | |
dc.subject | female | |
dc.subject | femur condyle | |
dc.subject | human | |
dc.subject | incidence | |
dc.subject | information processing | |
dc.subject | knee pain | |
dc.subject | male | |
dc.subject | middle aged | |
dc.subject | normal human | |
dc.subject | osteophyte | |
dc.subject | pain assessment | |
dc.subject | physician | |
dc.subject | quantitative analysis | |
dc.subject | quantitative study | |
dc.subject | radiography | |
dc.subject | aged | |
dc.subject | biological model | |
dc.subject | case control study | |
dc.subject | computer simulation | |
dc.subject | diagnostic imaging | |
dc.subject | factual database | |
dc.subject | image enhancement | |
dc.subject | knee | |
dc.subject | knee osteoarthritis | |
dc.subject | longitudinal study | |
dc.subject | multivariate analysis | |
dc.subject | pain | |
dc.subject | pain measurement | |
dc.subject | pathophysiology | |
dc.subject | statistical model | |
dc.subject | statistics and numerical data | |
dc.subject | Aged | |
dc.subject | Case-Control Studies | |
dc.subject | Computer Simulation | |
dc.subject | Databases, Factual | |
dc.subject | Female | |
dc.subject | Humans | |
dc.subject | Knee Joint | |
dc.subject | Linear Models | |
dc.subject | Longitudinal Studies | |
dc.subject | Male | |
dc.subject | Middle Aged | |
dc.subject | Models, Biological | |
dc.subject | Multivariate Analysis | |
dc.subject | Osteoarthritis, Knee | |
dc.subject | Pain | |
dc.subject | Pain Measurement | |
dc.subject | Radiographic Image Enhancement | |
dc.subject.classification | 7 INGENIERÍA Y TECNOLOGÍA | |
dc.title | Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI | |
dc.type | Artículo | |
dc.identifier.volume | 2015 | |
refterms.dateFOA | 2018-10-18T20:35:00Z | |