Longitudinal Change in Knee Cartilage Thickness and Function in Subjects with and without MRI-Diagnosed Cartilage Damage

Development and evaluation of nomograms for predicting osteoarthritis progression based on MRI cartilage parameters: data from the FNIH OA biomarkers Consortium

BACKGROUND

Osteoarthritis (OA) is a leading cause of disability worldwide. However, the existing methods for evaluating OA patients do not provide enough comprehensive information to make reliable predictions of OA progression. This retrospective study aimed to develop prediction nomograms based on MRI cartilage that can predict disease progression of OA.

METHODS

A total of 600 subjects with mild-to-moderate osteoarthritis from the Foundation for National Institute of Health (FNIH) project of osteoarthritis initiative (OAI). The MRI cartilage parameters of the knee at baseline were measured, and the changes in cartilage parameters at 12- and 24-month follow-up were calculated. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to extract the valuable characteristic parameters at different time points including cartilage thickness, cartilage volume, subchondral bone exposure area and uniform cartilage thickness in different sub regions of the knee, and the MRI cartilage parameters score0, scoreΔ12, and scoreΔ24 at baseline, 12 months, and 24 months were constructed. ScoreΔ12, and scoreΔ24 represent changes between 12 M vs. baseline, and 24 M vs. baseline, respectively. Logistic regression analysis was used to construct the nomogram0, nomogramΔ12, and nomogramΔ24, including MRI-based score and risk factors. The area under curve (AUC) was used to evaluate the differentiation of nomograms in disease progression and subgroup analysis. The calibration curve and Hosmer-Lemeshow (H-L) test were used to verify the calibration of the nomograms. Clinical usefulness of each prediction nomogram was verified by decision curve analysis (DCA). The nomograms with predictive efficacy were analyzed by secondary analysis. Internal verification was assessed using bootstrapping validation.

RESULTS

Each nomogram included cartilage score, KL grade, WOMAC pain score, WOMAC disability score, and minimum joint space width. The AUC of nomogram0, nomogramΔ12, and nomogramΔ24 in predicing the progression of radiology and pain were 0.69, 0.64, and 0.71, respectively. All three nomograms had good calibration. Analysis by DCA showed that the clinical effectiveness of nomogramΔ24 was higher than others. Secondary analysis showed that nomogram0 and nomogramΔ24 were more capable of predicting OA radiologic progression than pain progression.

CONCLUSION

Nomograms based on MRI cartilage change were useful for predicting the progression of mild to moderate OA.

Linking physical activity with clinical, functional, and structural outcomes: an evidence map using the Osteoarthritis Initiative

Physical activity is consistently recommended across clinical practice guidelines for managing knee osteoarthritis, yet prescription rates are low. Evidence mapping uses a systematic approach to visually illustrate and summarize published evidence, highlight gaps in the literature, and formulate research questions. The purpose of this study was to review and summarize evidence published from the Osteoarthritis Initiative (OAI) linking physical activity with clinical, functional, and structural knee osteoarthritis outcomes. Electronic databases were searched until June 2021. Studies from the OAI reporting subjective (Physical Activity Scale for the Elderly, PASE) or objective (accelerometry) physical activity data were included. Scatter plots were created to represent each outcome group (clinical, functional, structural) and physical activity measure (PASE, accelerometry) to map the evidence by the directional effect (positive, interaction, negative, or no effect) associated with physical activity. Forty-two articles were included in this review. Physical activity was quantified using PASE (n = 21), accelerometry (n = 20), or both (n = 1). Studies reported consistently positive physical activity effects on clinical (n = 22) and functional (n = 20) outcomes, with few exceptions. Structural (n = 15) outcomes were largely reported as interaction effects by physical activity intensity or sex, or as no significant effect. A network of interconnected outcomes emerged, with clinical and functional outcomes often reported together, and structural outcomes reported individually. This study provides an overview of current evidence linking physical activity to multiple interrelated knee osteoarthritis outcomes using an OAI-driven model. These evidence maps can be used as a framework to guide future investigations of the effects of physical activity on knee osteoarthritis.

Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis

Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59-5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82-18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.