Growth Mixture Modeling of Patient-reported Outcomes After Total Knee Arthroplasty: No Recovery Trajectory Shows Postoperative Decline or Stagnation.

IF 2.1 Q2 ORTHOPEDICS
Kareem Omran, Colleen Wixted, Daniel Waren, Joshua C Rozell, Ran Schwarzkopf
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引用次数: 0

Abstract

Background: Recovery after total knee arthroplasty (TKA) shows considerable variability in both pain relief and functional improvement. The Knee Injury and Osteoarthritis Outcome Score (KOOS-JR) is a widely used measure for evaluating these outcomes. This study aimed to identify distinct latent recovery trajectories, which represent underlying, unobserved patterns of postoperative recovery inferred from KOOS-JR scores, and to explore patient characteristics associated with these trajectories.

Methods: This retrospective cohort study analyzed patients who underwent primary TKA for osteoarthritis at a tertiary academic center from January 2020 to March 2023. Inclusion criteria required patients to have completed a preoperative KOOS-JR questionnaire and at least two postoperative follow-ups at 1, 3, 6, or 12 months. Exclusion criteria included bilateral or revision procedures. Collected characteristics included age, sex, Body Mass Index, American Society of Anesthesiologists physical status classification, race, smoking status, procedure type, anesthesia type, length of hospital stay, and discharge disposition. Growth mixture modeling was used to model recovery trajectories, with associations evaluated using the "three-step approach." Model fit was assessed using the Akaike and Bayesian Information Criteria, Vuong-Lo-Mendell-Rubin likelihood ratio, posterior probabilities, and entropy values.

Results: Of 700 eligible patients, growth mixture modeling identified two recovery trajectories: 95.4% of patients (trajectory 1 [T1]) demonstrated steady improvement, while 4.6% (trajectory 2 [T2]) began with lower KOOS-JR scores (mean 9.7 vs. 47.9 for T1) but recovered to near T1 levels by 1 month. Trajectory 2 patients were markedly younger (mean 64 vs. 67 years), had higher Body Mass Index (36 vs. 31), included more Black or African American individuals (38% vs. 20%), and were more frequently discharged to rehabilitation facilities (16% vs. 3.3%; all P < 0.05). Each additional year of age reduced the likelihood of following T2 by 4% (odds ratio = 0.96, 95% confidence interval, 0.92 to 0.99; P = 0.016), while discharge to rehabilitation increased the likelihood 6-fold (odds ratio = 6.22, 95% confidence interval, 1.89 to 17.8; P = 0.001).

Conclusion: This study identified two distinct recovery trajectories after TKA, with notably no trajectory emerging showing decline or stagnation from preoperative levels. Despite lower baseline scores, patients in T2 achieved substantial recovery, suggesting TKA provides meaningful improvement even for those with substantially compromised function. The findings also highlight the need to explore whether rehabilitation discharge directly influences the observed postoperative gains.

Abstract Image

Abstract Image

全膝关节置换术后患者报告结果的生长混合模型:没有恢复轨迹显示术后下降或停滞。
背景:全膝关节置换术(TKA)后的恢复在疼痛缓解和功能改善方面表现出相当大的可变性。膝关节损伤和骨关节炎预后评分(KOOS-JR)是一种广泛使用的评估这些结果的方法。本研究旨在确定不同的潜在恢复轨迹,这些轨迹代表了从KOOS-JR评分推断的潜在的、未观察到的术后恢复模式,并探讨与这些轨迹相关的患者特征。方法:本回顾性队列研究分析了2020年1月至2023年3月在某三级学术中心接受骨关节炎原发性TKA治疗的患者。纳入标准要求患者完成术前KOOS-JR问卷调查,并在术后1、3、6或12个月至少进行两次随访。排除标准包括双边或修订程序。收集的特征包括年龄、性别、体重指数、美国麻醉医师协会身体状况分类、种族、吸烟状况、手术类型、麻醉类型、住院时间和出院处置。生长混合模型用于模拟恢复轨迹,并使用“三步法”评估关联。模型拟合使用赤池和贝叶斯信息标准、Vuong-Lo-Mendell-Rubin似然比、后验概率和熵值进行评估。结果:在700名符合条件的患者中,生长混合模型确定了两种恢复轨迹:95.4%的患者(轨迹1 [T1])表现出稳定的改善,而4.6%的患者(轨迹2 [T2])以较低的KOOS-JR评分开始(平均9.7比47.9 T1),但在1个月后恢复到接近T1水平。轨迹2患者明显更年轻(平均64岁vs. 67岁),身体质量指数更高(36比31),包括更多的黑人或非裔美国人(38%比20%),更频繁地出院到康复机构(16%比3.3%;P < 0.05)。每增加一岁,发生T2的可能性降低4%(优势比= 0.96,95%可信区间,0.92 ~ 0.99;P = 0.016),而出院康复的可能性增加6倍(优势比为6.22,95%可信区间为1.89 ~ 17.8;P = 0.001)。结论:本研究确定了TKA后两种不同的恢复轨迹,值得注意的是,没有出现显示术前水平下降或停滞的轨迹。尽管基线评分较低,但T2患者获得了实质性的恢复,这表明TKA即使对功能严重受损的患者也提供了有意义的改善。研究结果还强调需要探讨康复出院是否直接影响观察到的术后收益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.60
自引率
6.70%
发文量
282
审稿时长
8 weeks
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