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      General Linear Models for Pain Prediction in Knee Osteoarthritis: Data from the Osteoarthritis Initiative Translated title: Modelos Lineales Generales para Predecir Dolor en Osteoartritis de Rodilla: Datos de la “Osteoarthritis Initiative”

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          Abstract

          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 AU C 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.

          Translated abstract

          Resumen: El dolor de rodilla es el síntoma más común y limitante de la Osteoartritis (OA), además de presentarse como una manifestación tardía de la enfermedad. Los cambios que ocurren en las estructuras de las articulaciones se presentan en las primeras etapas de la OA. Algunos de los cambios más comunes son la formación de osteofitos óseos, degradación del cartílago, y la reducción del espacio en la articulación, entre otros. El principal objetivo de este estudio es la asociación de características radiológicas con el síntoma de dolor de las articulaciones, para lo que fueron realizados dos estudios: univariado y multivariado, usando herramientas bioinformáticas para determinar la relación de futuro dolor con la evidencia radiológica temprana de la enfermedad. Todos los datos fueron recuperados de la Osteoarthritis Initiative repository (OAI). Este estudio de caso-control se llevó a cabo utilizando los datos disponibles de los participantes de la base de datos de la OAI. Los datos radiológicos fueron evaluados con diferentes grupos de radiología de la OAI. Fueron usadas puntuaciones cuantitativas y semi- cuantitativas para medir las dos diferentes relaciones entre los datos radiológicos en tres diferentes puntos de tiempo. El objetivo fue seguir la trayectoria de la aparición y prevalencia del dolor como síntoma. Todos los modelos predictivos fueron estadísticamente significativos (P ≤ 0,05). Para el análisis cuantitativo se calcularon las áreas bajo la curva (AUC): 0.6516, 0.6174, y 0.6737 para T-0, T-1 y T-2, y para el análisis semi-cuantitativo se calcularon las AU C: 0.6865, 0.6486, y 0.6406 para T-0, T-1 y T-2. Los modelos obtenidos en el estudio bioinformático sugieren que los cambios tempranos en la estructura de las articulaciones pueden estar asociados con el futuro dolor de rodilla. Un biomarcador basado en imágenes que pueda predecir el futuro dolor, medido en las primeras etapas de OA, podría convertirse en una herramienta útil para mejorar la calidad de vida de la gente que padece OA.

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          Rank-based inverse normal transformations are increasingly used, but are they merited?

          Many complex traits studied in genetics have markedly non-normal distributions. This often implies that the assumption of normally distributed residuals has been violated. Recently, inverse normal transformations (INTs) have gained popularity among genetics researchers and are implemented as an option in several software packages. Despite this increasing use, we are unaware of extensive simulations or mathematical proofs showing that INTs have desirable statistical properties in the context of genetic studies. We show that INTs do not necessarily maintain proper Type 1 error control and can also reduce statistical power in some circumstances. Many alternatives to INTs exist. Therefore, we contend that there is a lack of justification for performing parametric statistical procedures on INTs with the exceptions of simple designs with moderate to large sample sizes, which makes permutation testing computationally infeasible and where maximum likelihood testing is used. Rigorous research evaluating the utility of INTs seems warranted.
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            GALGO: an R package for multivariate variable selection using genetic algorithms.

            The development of statistical models linking the molecular state of a cell to its physiology is one of the most important tasks in the analysis of Functional Genomics data. Because of the large number of variables measured a comprehensive evaluation of variable subsets cannot be performed with available computational resources. It follows that an efficient variable selection strategy is required. However, although software packages for performing univariate variable selection are available, a comprehensive software environment to develop and evaluate multivariate statistical models using a multivariate variable selection strategy is still needed. In order to address this issue, we developed GALGO, an R package based on a genetic algorithm variable selection strategy, primarily designed to develop statistical models from large-scale datasets.
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              The factors associated with pain severity in patients with knee osteoarthritis vary according to the radiographic disease severity: a cross-sectional study.

              Knee osteoarthritis (OA) pain is suggested to be associated with inflammation and detrimental mechanical loading across the joint. In this cross-sectional study, we simultaneously examined the inflammation and alignment of the lower limb and examined how the pain components varied depending on the disease progression.
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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                rmib
                Revista mexicana de ingeniería biomédica
                Rev. mex. ing. bioméd
                Sociedad Mexicana de Ingeniería Biomédica (México, Distrito Federal, Mexico )
                0188-9532
                2395-9126
                April 2018
                : 39
                : 1
                : 29-40
                Affiliations
                [2] orgnameUniversidad Autónoma de Zacatecas orgdiv1CONACyT Mexico
                [3] orgnameUniversidad Autónoma de Zacatecas orgdiv1Unidad Académica de Ingeniería Eléctrica orgdiv2MACII Mexico
                [1] orgnameUniversidad Autónoma de Zacatecas orgdiv1Unidad Académica de Ingeniería Eléctrica orgdiv2CIIAM Mexico
                Article
                S0188-95322018000100029
                10.17488/rmib.39.1.3
                dbf03f80-762e-44b4-ab70-8cbc82805e2e

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 06 July 2017
                : 09 November 2017
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 34, Pages: 12
                Product

                SciELO Mexico


                radiological biomarkers,cross-sectional studies,knee pain prediction,osteoarthritis,linear stochastic models,predicción de dolor, estudios transversales, modelos estocásticos, biomarcadores radiológicos,dolor de rodilla,osteoartritis,modelo lineal

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