Background/Objectives: Patient-level predictors of treatment response after genicular artery embolization (GAE) for knee osteoarthritis (OA) are poorly understood. We evaluated clinical, serum, and imaging biomarkers for their ability to predict achievement of the minimally clinically important difference (MCID) for WOMAC pain and stiffness subscales following GAE. Methods: Data from a prospective single-arm clinical trial of 25 patients who underwent GAE for symptomatic knee OA was retrospectively analyzed. Candidate predictors included sex, age, BMI, contralateral Kellgren–Lawrence (KL) scores, and baseline values for serum IL-1Ra, serum VEGF, and total bone marrow edema scores on MRI using the MOAKS methodology. The primary outcomes were the frequency of achieving the MCID in WOMAC pain and WOMAC stiffness at 1, 3, and 12 months, modeled as an ordinal outcome (0–3). Ordinal logistic regression models were constructed. Variance inflation factors (VIFs) were assessed to detect multicollinearity, and leave-one-out cross-validation was performed to evaluate model robustness. Results: All candidate predictors were successfully incorporated into regression models, with no evidence of multicollinearity by VIF analysis. Lower contralateral KL scores (OR: 0.087 0.012–0.618, p = 0.0146) and higher BMI (OR: 1.383 1.001–1.910, p = 0.049) were significantly associated with achievement of the MCID for WOMAC pain, although significance for BMI was borderline. Lower baseline serum IL-1Ra levels (OR: 0.122 0.018–0.816, p = 0.030) were significantly associated with achievement of the MCID for WOMAC stiffness. The remaining clinical, serum, and imaging biomarkers were not significantly associated with MCID achievement. Conclusions: In this exploratory analysis, specific baseline clinical and serum factors were associated with achievement of clinically meaningful improvements in pain and stiffness. Analysis of larger cohorts will help clarify ideal demographic-, biomarker- and imaging-based patient selection strategies that can improve prediction of treatment response and guide clinical decision-making in GAE for knee OA.
Mabud et al. (Sat,) studied this question.