Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

Abstract

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.

Image Registration Measures and Chronic Osteoarthritis Knee Pain Prediction: Data from the Osteoarthritis Initiative

“…After evaluating the three multivariate models, we can say that there is a close association between the measures obtained automatically and chronic pain presented in the three time points observed. The predictive power of the models presented in Figure 5 is very similar but superior to the models obtained by radiological information measured by expert radiologists [41] . Multivariate models have acceptable performance in their predictability based on the AUC, and then only the model for T1 has a higher performance univariate model, and is very similar.…”

A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women

“…Therefore, we assume, as common practice for clinical studies, that the published AUCs are equivalent to our AUC-Full values. In 24 is mentioned that a "10-fold cross-validation The ROC curves generated by the best performing models using five knee OA outcome measures. The AUC values refer to the AUC-Full.…”

Assessment of Fatigue Using Wearable Sensors: A Pilot Study

“…Research in this area is most advanced in creating digital measures of function, for example monitoring changes in mobility [7] or cognition [8, 9]. Similar methods are now also being applied to establish proof-of-principle to create objective, orthogonal measures to augment PROs which measure perceived changes in symptom relief and aspects of HRQoL, including mobility [10], pain [11, 12], stress [13, 14], and mood [15]. Most work so far has focused on domains where feature sets are derived from a single mode of sensor data, for example inertial sensors for mobility or galvanic skin response for stress; however, most HRQoL domains are extremely multifactorial and therefore require multimodal data.…”