scholarly journals Risk factors can classify individuals who develop accelerated knee osteoarthritis: Data from the osteoarthritis initiative

2017 ◽  
Author(s):  
Jeffrey B. Driban ◽  
Timothy E. McAlindon ◽  
Mamta Amin ◽  
Lori L. Price ◽  
Charles B. Eaton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Knee Osteoarthritis ◽  
Osteoarthritis Initiative

scholarly journals Risk factors and characteristics of adults who develop end-stage knee osteoarthritis: Data from the Osteoarthritis Initiative

2016 ◽  
Vol 24 ◽  
pp. S204-S205
Author(s):  
J.B. Driban ◽  
L.L. Price ◽  
C.B. Eaton ◽  
J. Lynch ◽  
M. Nevitt ◽  
...  
Keyword(s):  
Risk Factors ◽  
Knee Osteoarthritis ◽  
Osteoarthritis Initiative ◽  
End Stage

scholarly journals Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients

2020 ◽  
Vol 10 (19) ◽  
pp. 6797
Author(s):  
Christos Kokkotis ◽  
Serafeim Moustakidis ◽  
Giannis Giakas ◽  
Dimitrios Tsaopoulos
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Knee Osteoarthritis ◽  
Prediction Models ◽  
Majority Vote ◽  
Vote Scheme ◽  
Multidisciplinary Data ◽  
Osteoarthritis Initiative ◽  
Reported Data ◽  
And Function

Knee Osteoarthritis (KOA) is a multifactorial disease that causes low quality of life, poor psychology and resignation from life. Furthermore, KOA is a big data problem in terms of data complexity, heterogeneity and size as it has been commonly considered in the literature with most of the reported studies being limited in the amount of information they can adequately process. The aim of this paper is: (i) To provide a robust feature selection (FS) approach that could identify important risk factors which contribute to the prediction of KOA and (ii) to develop machine learning (ML) prediction models for KOA. The current study considers multidisciplinary data from the osteoarthritis initiative (OAI) database, the available features of which come from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams’ data. The novelty of the proposed FS methodology lies on the combination of different well-known approaches including filter, wrapper and embedded techniques, whereas feature ranking is decided on the basis of a majority vote scheme to avoid bias. The validation of the selected factors was performed in data subgroups employing seven well-known classifiers in five different approaches. A 74.07% classification accuracy was achieved by SVM on the group of the first fifty-five selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to classification errors and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of KOA progression.

scholarly journals Meniscal Root Tears and Extrusion Are Significantly Associated with the Development of Accelerated Knee Osteoarthritis: Data from the Osteoarthritis Initiative

Cartilage ◽  
2020 ◽  
pp. 194760352093452
Author(s):  
Sarah C. Foreman ◽  
Yao Liu ◽  
Michael C. Nevitt ◽  
Jan Neumann ◽  
Gabby B. Joseph ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Risk Factors ◽  
Magnetic Resonance ◽  
Knee Osteoarthritis ◽  
Meniscal Repair ◽  
Normal Rate ◽  
Resonance Imaging ◽  
Meniscal Extrusion ◽  
Meniscal Root ◽  
Osteoarthritis Initiative

Objective To identify joint structural risk factors, measured using quantitative compositional and semiquantitative magnetic resonance imaging (MRI) scoring, associated with the development of accelerated knee osteoarthritis (AKOA) compared with a more normal rate of knee osteoarthritis (OA) development. Design From the Osteoarthritis Initiative we selected knees with no radiographic OA (Kellgren-Lawrence grade [KL] 0/1) that developed advanced-stage OA (KL 3/4; AKOA) within a 4-year timeframe and a comparison group with a more normal rate of OA development (KL 0/1 to KL 2 in 4 years). MRIs at the beginning of the 4-year timeframe were assessed for cartilage T2 values and structural abnormalities using a modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Associations of MRI findings with AKOA versus normal OA were assessed using multivariable logistic regression models. Results A total of 106 AKOA and 168 subjects with normal OA development were included. Mean cartilage T2 values were not significantly associated with AKOA (odds ratio [OR] 1.06; 95% confidence interval [CI] 0.82-1.36). Risk factors for AKOA development included higher meniscus maximum scores (OR 1.37; 95% CI 1.11-1.68), presence of meniscal extrusion (OR 6.30; 95% CI 2.57-15.49), presence of root tears (OR 4.64; 95% CI 1.61-13.34), and higher medial tibia cartilage lesion scores (OR 1.96; 95% CI 1.19-3.24). Conclusions We identified meniscal damage, especially meniscal extrusion and meniscal root tears as risk factors for AKOA development. These findings contribute to identifying subjects at risk of AKOA at an early stage when preventative measures targeting modifiable risk factors such as meniscal repair surgery could still be effective.

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