ROLE OF THE WOMAC SCORES IN PREOPERATIVE DECISION-MAKING AND ANALYSIS OF KNEE REPLACEMENT FOR KNEE OSTEOARTHRITIS PATIENTS

Abstract

Objective: Knee replacement (KR) is a widely accepted procedure for end-stage knee arthritis. Patients’ subjective perception of knee joint symptoms is an important factor in their decision to undergo knee replacement. This paper mainly studies the correlation between knee joint symptoms and knee replacement, aimed to establish the corresponding preoperative decision-making model. Methods: 122 knee arthritis patients were included from the knee arthritis database FNIH OAI, of which 61 had knee arthroplasty and 61 without knee arthroplasty. First, WOMAC score was used to quantitatively evaluate the knee joint symptoms of patients; then the correlation between clinical risk factors, single WOMAC score and knee replacement was analyzed. Then K-means cluster analysis was used to divide the multidimensional WOMAC score into different groups of knee arthritis symptoms’ severity. Finally, based on the retained clinical risk factors and WOMAC cluster variables, the clinical models, WOMAC scoring model and clinical +WOMAC scoring model were constructed, respectively, and compared. Results: Age and BMI were significant risk factors for knee replacement ([Formula: see text]), which could be used to construct the clinical model. There was no significant correlation between any single WOMAC score and knee replacement ([Formula: see text]). The cluster variable of WOMAC score obtained by cluster analysis was significantly correlated with knee replacement ([Formula: see text]). Based on the above risk factors, we established the Age+BMI, Cluster and Age+BMI+Cluster models, respectively. The comparison results showed that the Age+BMI+Cluster model ([Formula: see text]) showed the highest predictive value for knee replacement, and the corresponding nomogram also showed good predictive consistency; the performance of Cluster model ([Formula: see text]) was the second. The pure clinical risk factor model Age+BMI showed the worst predictive performance ([Formula: see text]). Conclusion: This paper analyzed the correlation between patients’ subjective perception of knee arthritis symptoms and the final knee replacement, and constructed a new biomarker based on the WOMAC score. After analysis and modeling, this marker could be used to predict knee replacement. The constructed Age+BMI+Cluster nomogram could be used for personalized assessment of the risk of knee replacement.