Shan Sen, Shi Qingpeng, Zhang Hengyuan
The Second School of Clinical Medicine, Binzhou Medical University, Yantai, Shandong, China.
Department of Bone and Joint Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, China.
Front Med (Lausanne). 2024 Aug 29;11:1427768. doi: 10.3389/fmed.2024.1427768. eCollection 2024.
Total Knee Arthroplasty (TKA) is a widely performed procedure that significantly benefits patients with severe knee degeneration. However, the recovery outcomes post-surgery can vary significantly among patients. Identifying the factors influencing these outcomes is crucial for improving patient care and satisfaction.
In this retrospective study, we analyzed 362 TKA cases performed between January 1, 2018, and July 1, 2022. Multivariate logistic regression was employed to identify key predictors of recovery within the first year after surgery.
The analysis revealed that Body Mass Index (BMI), age-adjusted Charlson Comorbidity Index (aCCI), sleep quality, Bone Mineral Density (BMD), and analgesic efficacy were significant predictors of poor recovery ( < 0.05). These predictors were used to develop a clinical prediction model, which demonstrated strong predictive ability with an Area Under the Receiver Operating Characteristic (AUC) curve of 0.802. The model was internally validated.
The findings suggest that personalized postoperative care and tailored rehabilitation programs based on these predictors could enhance recovery outcomes and increase patient satisfaction following TKA.
全膝关节置换术（TKA）是一种广泛开展的手术，能让重度膝关节退变患者显著受益。然而，术后恢复结果在患者之间可能有很大差异。确定影响这些结果的因素对于改善患者护理和满意度至关重要。
在这项回顾性研究中，我们分析了2018年1月1日至2022年7月1日期间进行的362例TKA病例。采用多因素逻辑回归来确定术后第一年恢复情况的关键预测因素。
分析显示，体重指数（BMI）、年龄校正的查尔森合并症指数（aCCI）、睡眠质量、骨密度（BMD）和镇痛效果是恢复不佳的显著预测因素（<0.05）。这些预测因素被用于建立一个临床预测模型，该模型在受试者工作特征（ROC）曲线下面积为0.802，显示出很强的预测能力。该模型进行了内部验证。
研究结果表明，基于这些预测因素的个性化术后护理和量身定制的康复计划可以改善TKA后的恢复结果并提高患者满意度。
