Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI.
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.
Grupo de Investigación en Bioinformática, Escuela de Medicina, Tecnológico de Monterrey, 64849 Monterrey, NL, Mexico.
Jorge I
JI
Galván-Tejada
Grupo de Investigación en Bioinformática, Escuela de Medicina, Tecnológico de Monterrey, 64849 Monterrey, NL, Mexico.
José M
JM
Celaya-Padilla
Grupo de Investigación en Bioinformática, Escuela de Medicina, Tecnológico de Monterrey, 64849 Monterrey, NL, Mexico ; Departamento de Investigación e Innovación, Escuela de Medicina, Tecnológico de Monterrey, 64710 Monterrey, NL, Mexico.
Victor
V
Treviño
Departamento de Investigación e Innovación, Escuela de Medicina, Tecnológico de Monterrey, 64710 Monterrey, NL, Mexico.
José G
JG
Tamez-Peña
D000368:Aged; D016022:Case-Control Studies; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007719:Knee Joint / Q000000981:diagnostic imaging / Q000503:physiopathology; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D020370:Osteoarthritis, Knee / Q000000981:diagnostic imaging / Q000503:physiopathology*; D010146:Pain / Q000000981:diagnostic imaging / Q000209:etiology* / Q000503:physiopathology; D010147:Pain Measurement / Q000706:statistics & numerical data; D011856:Radiographic Image Enhancement