General Linear Models for Pain Prediction in Knee Osteoarthritis: Data from the Osteoarthritis Initiative

Authors

  • J. I. Galván-Tejada CIIAM, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas http://orcid.org/0000-0002-7555-5655
  • J. G. Arceo-Olague CIIAM, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas
  • H. Luna-García CIIAM, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas
  • H. Gamboa-Rosales CIIAM, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas
  • J. M. Celaya-Padilla CONACyT, Universidad Autónoma de Zacatecas
  • L. A. Zanella-Calzada MACII, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas
  • R. Magallanes-Quintanar CIIAM, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas
  • C. E. Galván-Tejada CIIAM, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas

DOI:

https://doi.org/10.17488/RMIB.39.1.3

Keywords:

linear stochastic models, osteoarthritis, knee pain prediction, cross-sectional studies, radiological biomarkers

Abstract

Knee pain is the most common and disabling symptom in Osteoarthritis (OA). Joint pain is a late manifestation of the OA. In earlier stages of the disease changes in joint structures are shown. Also, formation of bony osteophytes, cartilage degradation, and joint space reduction which are some of the most common, among others. The main goal of this study is to associate radiological features with the joint pain symptom. Univariate and multivariate studies were performed using Bioinformatics tools to determine the relationship of future pain with early radiological evidence of the disease. All data was retrieved from the Osteoarthritis Initiative repository (OAI). A case-control study was done using available data from participants in OAI database. Radiological data was assessed with different OAI radiology groups. We have used quantitative and semi-quantitative scores to measure two different relations between radiological data in three different time points. The goal was to track the appearance and prevalence of pain as a symptom. All predictive models were statistically significant (P ≤ 0,05), obtaining the receiving operating characteristic (ROC) curves with their respective area under the curves (AUC) of 0.6516, 0.6174, and 0.6737 for T-0, T-1 and T-2 in quantitative analysis. For semi-quantitative an AUC of 0.6865, 0.6486,and 0.6406 for T-0, T-1 and T-2. The models obtained in the Bioinformatics study suggest that early joint structure changes can be associated with future joint pain. An image based biomarker that could predict future pain, measured in early OA stages, could become a useful tool to improve the quality of life of people dealing OA.

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Published

2018-01-15

How to Cite

Galván-Tejada, J. I., Arceo-Olague, J. G., Luna-García, H., Gamboa-Rosales, H., Celaya-Padilla, J. M., Zanella-Calzada, L. A., Magallanes-Quintanar, R., & Galván-Tejada, C. E. (2018). General Linear Models for Pain Prediction in Knee Osteoarthritis: Data from the Osteoarthritis Initiative. Revista Mexicana De Ingenieria Biomedica, 39(1), 29–40. https://doi.org/10.17488/RMIB.39.1.3

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Section

Research Articles

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