Kim Sung Eun, Ro Du Hyun, Lee Myung Chul, Han Hyuk-Soo
Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul, South Korea.
Department of Orthopaedic Surgery, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 110-744, South Korea.
Knee Surg Relat Res. 2024 Oct 14;36(1):31. doi: 10.1186/s43019-024-00238-1.
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.
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.
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).
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.
全膝关节置换术（TKA）是治疗晚期骨关节炎的有效方法，但由于各种影响因素，实现最佳治疗效果具有挑战性。以往的研究主要集中在确定影响术后功能结果的因素。然而，预测TKA术后个体改善情况的研究较少。因此，需要一个针对个体患者结果的定量预测模型。
收集了976例行TKA患者的人口统计学数据、放射学变量、术中变量和体格检查结果。评估术前和术后1年的西安大略和麦克马斯特大学骨关节炎指数（WOMAC）评分，并进行多因素回归分析，以确定影响1年WOMAC评分和WOMAC评分变化的显著因素。根据研究结果建立了一个预测模型。
该模型对1年WOMAC评分的预测准确性较差（所有调整后的R<0.08），而对WOMAC评分变化的模型显示出较强的预测能力（所有调整后的R>0.75）。术前WOMAC评分、性别和术后膝关节活动范围显著影响WOMAC评分的所有疼痛、僵硬和身体功能方面（所有P<0.05）。年龄、脑血管疾病和髌骨表面置换与身体功能变化相关（所有P<0.05）。
所建立的定量模型在预测TKA术后WOMAC评分变化方面具有较高的准确性。所确定的影响WOMAC评分术后改善的因素有助于优化TKA术后患者的治疗效果。
