Can We Predict Severity of Osteoarthritis of Knees and Compartmental Involvement Based on a Set of Predefined Clinical Questions in Patients of Knee Pain?

Background: We investigated whether the severity of Osteoarthritis (OA) knees can be predicted based on a set of predefined clinical questions (PCQs) about activities of daily living (ADL). We studied the association of demographic factors and advanced radiographic OA (KL 3 and 4) and the relationship between various physical activities and radiographic involvement of knee joint compartments based on PCQs.

Materials And Methods: Demographic data, radiographic grading of knee OA and PCQs score, were obtained prospectively. Patients' responses to PCQs were marked as scores-that were predefined and graded according to the severity of knee pain. Radiographic knee OA grades were dichotomized and patients were classified as either negative (KL grade 1, 2) or positive (KL grade 3, 4). Multivariate logistic regression was performed to obtain the adjusted odds for total PCQs score in relation with positive radiographic OA considering confounders like age, gender and BMI in the model. Log odds score (LOS) were obtained and ROC analysis was performed on scores to obtain the cut-off value for the screening of knee OA in patients of knee pain.

Results: Age and BMI were significantly negatively correlated with PCQs score ( = - 0.473;  < 0.0001 and  = - 0.136;  = 0.046). PCQs scores were significantly lower in females ( = 0.031). Total PCQs score had corresponding OR of 0.901 ( = 0.002) towards knee OA after adjusting for age, gender and BMI. Multivariate model-based LOS resulted in a cut-off of 1.315, which had a sensitivity of 85.5%, specificity of 66.7% and PPV of 92.7%.

Conclusion: Severity of knee OA can be predicted based on PCQs. PCQs can predict severity of knee OA and patellofemoral or medial tibiofemoral compartment without radiographs. LOS based on demographics and total PCQs score can be developed as a screening tool for advanced knee OA.