Riddle Daniel L, Dumenci Levent
Virginia Commonwealth University, Richmond.
Temple University, Philadelphia, Pennsylvania.
Arthritis Rheumatol. 2024 Jul;76(7):1036-1046. doi: 10.1002/art.42819. Epub 2024 Mar 13.
Poor outcome after knee arthroplasty (KA), a common major surgery worldwide, reportedly occurs in approximately 20% of patients. These patients demonstrate minimal improvement, at least moderate knee pain, and difficulty performing many routine daily activities. The purposes of our study were to comprehensively determine poor outcome risk after KA and to identify predictors of poor outcome.
Data from 565 participants with KA in the Osteoarthritis Initiative and the Multicenter Osteoarthritis studies were used. Previously validated latent class analyses (LCAs) of good versus poor outcome trajectories of Western Ontario and McMaster Universities Arthritis Index (WOMAC) Pain and Disability were generated to describe minimal improvement and poor final outcome. The modified Escobar RAND appropriateness system was used to generate classifications of appropriate, inconclusive, and rarely appropriate. Multivariable prediction models included LCA-based good versus poor outcome, modified Escobar classifications, and evidence-driven preoperative prognostic variables.
Modified Escobar appropriateness classifications were nonsignificant predictors of WOMAC Pain good versus poor outcomes, indicating the methods provide independent outcome estimates. For WOMAC Pain and WOMAC Disability, approximately 34% and 45% of participants, respectively, had a high probability of either minimal improvement via "rarely appropriate" classifications or poor outcome via LCA. In multivariable prediction models, greater contralateral knee pain consistently predicted poor outcome (eg, odds ratio 1.21, 95% confidence interval 1.10-1.33).
Appropriateness criteria and LCA estimates provided combined poor outcome estimates that were approximately double the commonly reported poor outcome of 20%. Rates of poor outcome could be reduced if clinicians screened patients using appropriateness criteria and LCA predictors before surgery to optimize outcome.
膝关节置换术（KA）是全球常见的大型手术，据报道约20%的患者术后效果不佳。这些患者改善甚微，至少有中度膝关节疼痛，且难以进行许多日常常规活动。我们研究的目的是全面确定KA术后效果不佳的风险，并识别效果不佳的预测因素。
使用来自骨关节炎倡议组织和多中心骨关节炎研究中565例KA患者的数据。对西安大略和麦克马斯特大学骨关节炎指数（WOMAC）疼痛与残疾的良好与不佳结局轨迹进行了先前验证的潜在类别分析（LCA），以描述改善甚微和最终结局不佳的情况。采用改良的埃斯科瓦尔兰德适宜性系统进行适宜、不确定和极少适宜的分类。多变量预测模型包括基于LCA的良好与不佳结局、改良的埃斯科瓦尔分类以及证据驱动的术前预后变量。
改良的埃斯科瓦尔适宜性分类不是WOMAC疼痛良好与不佳结局的显著预测因素，这表明这些方法提供了独立结局估计。对于WOMAC疼痛和WOMAC残疾，分别约34%和45%的参与者通过“极少适宜”分类有极小改善或通过LCA有不佳结局的高概率。在多变量预测模型中，对侧膝关节疼痛加剧始终预测结局不佳（例如，优势比1.21，95%置信区间1.10 - 1.33）。
适宜性标准和LCA估计提供的综合不佳结局估计约为通常报道的20%不佳结局的两倍。如果临床医生在手术前使用适宜性标准和LCA预测因素筛查患者以优化结局，不佳结局发生率可能会降低。
