Can individual functional improvements be predicted in osteoarthritic patients after total knee arthroplasty?

PURPOSE

Total knee arthroplasty (TKA) is an effective treatment for advanced osteoarthritis, and achieving optimal outcomes can be challenging due to various influencing factors. Previous research has focused on identifying factors that affect postoperative functional outcomes. However, there is a paucity of studies predicting individual postoperative improvement following TKA. Therefore, a quantitative prediction model for individual patient outcomes is necessary.

MATERIALS AND METHODS

Demographic data, radiologic variables, intraoperative variables, and physical examination findings were collected from 976 patients undergoing TKA. Preoperative and 1-year postoperative Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were assessed, and multivariate regression analysis was conducted to identify significant factors influencing one-year WOMAC scores and changes in WOMAC scores. A predictive model was developed on the basis of the findings.

RESULTS

The predictive accuracy of the model for 1-year WOMAC scores was poor (all adjusted R < 0.08), whereas the model for changes in WOMAC scores demonstrated strong predictability (all adjusted R > 0.75). Preoperative WOMAC scores, sex, and postoperative knee range of motion significantly affected all pain, stiffness, and physical function aspects of the WOMAC scores (all P < 0.05). Age, cerebrovascular disease, and patellar resurfacing were associated with changes in physical function (all P < 0.05).

CONCLUSIONS

The developed quantitative model demonstrated high accuracy in predicting changes in WOMAC scores after TKA. The identified factors influencing postoperative improvement in WOMAC scores can assist in optimizing patient outcomes after TKA.