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与重要变化的金标准对照物相比,基线四分位数分层的最小临床重要差异估计值的表现优于个体最小临床重要差异估计值。

Performance of baseline quartile-stratified minimal clinically important difference estimates was superior to individual minimal clinically important difference estimates when compared with a gold standard comparator of important change.

作者信息

Riddle Daniel L, Dumenci Levent

机构信息

Virginia Commonwealth University, Richmond, VA, United States.

Temple University, Philadelphia, PA, United States.

出版信息

Pain. 2025 Jun 1;166(6):1450-1456. doi: 10.1097/j.pain.0000000000003492. Epub 2025 Jan 9.

Abstract

A variety of minimal clinically important difference (MCID) estimates are available to distinguish subgroups with differing outcomes. When a true gold standard is absent, latent class growth curve analysis (LCGC) has been proposed as a suitable alternative for important change. Our purpose was to evaluate the performance of individual and baseline quartile-stratified MCIDs. The current study included data from 346 persons with baseline and 12-month postoperative outcome data from KASTPain, a no-effect randomized clinical trial conducted on persons with knee arthroplasty and pain catastrophizing. Subgroup trajectories from LCGC were used as a gold standard comparator. Minimal clinically important difference-specific trajectories of recovery were calculated for the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain, Disability and EuroQol-5 Dimension Visual Analogue Scale of self-reported health. The latent Kappa (K l ) chance-corrected agreement between MCIDs and LCGCs were estimated to indicate which MCID method was best at detecting important change. For all 3 outcomes, the average latent class probabilities ranged from 0.90 to 0.99, justifying the use of LCGCs as a gold standard. The K l for LCGC and individual MCIDs ranged from 0.21 (95% CI = 0.13, 0.28) to 0.52 (95% CI = 0.41, 0.66). Baseline quartile-stratified K l for WOMAC Pain and Disability were 0.85 (95% CI = 0.78, 0.92) and 0.74 (95% CI = 0.68, 0.83), respectively. Classification errors in individual MCID estimates most likely result from ceiling effects. Minimal clinically important differences calculated for each baseline quartile are superior to individually calculated MCIDs and should be used when latent class methods are not available. Use of individual MCIDs likely contribute substantial error and are discouraged for clinical application.

摘要

有多种最小临床重要差异(MCID)估计值可用于区分具有不同结果的亚组。当缺乏真正的金标准时,潜在类别增长曲线分析(LCGC)已被提议作为重要变化的合适替代方法。我们的目的是评估个体和基线四分位数分层MCID的性能。当前研究纳入了346名患者的数据,这些患者有来自KASTPain的基线数据和术后12个月的结果数据,KASTPain是一项针对膝关节置换术患者和疼痛灾难化患者进行的无效随机临床试验。来自LCGC的亚组轨迹被用作金标准对照。针对西安大略和麦克马斯特大学骨关节炎指数(WOMAC)疼痛、残疾以及自我报告健康的欧洲五维视觉模拟量表,计算了特定于最小临床重要差异的恢复轨迹。估计MCID与LCGC之间的潜在Kappa(K l )机会校正一致性,以表明哪种MCID方法在检测重要变化方面最佳。对于所有3个结果,平均潜在类别概率范围为0.90至0.99,证明使用LCGC作为金标准是合理的。LCGC与个体MCID的K l 范围为0.21(95% CI = 0.13, 0.28)至0.52(95% CI = 0.41, 0.66)。WOMAC疼痛和残疾的基线四分位数分层K l 分别为0.85(9...

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