当与重要变化的金标准比较时,基线四分位分层最小临床重要差异估计的性能优于个体最小临床重要差异估计。

IF 5.9 1区 医学 Q1 ANESTHESIOLOGY
Daniel L Riddle, Levent Dumenci
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引用次数: 0

摘要

摘要:各种最小临床重要差异(MCID)估计可用于区分具有不同结果的亚组。当没有真正的黄金标准时,潜在类别增长曲线分析(LCGC)被提议作为重要变化的合适替代方案。我们的目的是评估个体和基线四分位分层MCIDs的表现。目前的研究包括346名患者的基线数据和术后12个月的预后数据,这些数据来自KASTPain,这是一项针对膝关节置换术和疼痛突变患者的无效应随机临床试验。LCGC的亚组轨迹被用作金标准比较物。对西安大略省和麦克马斯特大学骨关节炎指数(WOMAC)疼痛、残疾和自我报告健康的EuroQol-5维度视觉模拟量表计算最小临床重要差异特异性恢复轨迹。估计MCIDs和lcgc之间的潜在Kappa (Kl)机会校正一致性,以表明哪种MCID方法最适合检测重要变化。对于所有3个结果,平均潜在分类概率范围为0.90至0.99,证明lcgc作为金标准的使用是合理的。LCGC和个体mcid的Kl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分层Kl分别为0.85 (95% CI = 0.78, 0.92)和0.74 (95% CI = 0.68, 0.83)。单个MCID估计的分类错误最有可能是天花板效应造成的。每个基线四分位数计算的最小临床重要差异优于单独计算的mcid,当潜在分类方法不可用时应使用。单个MCIDs的使用可能造成严重的误差,不鼓励临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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 (Kl) 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 Kl 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 Kl 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.

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来源期刊
PAIN®
PAIN® 医学-临床神经学
CiteScore
12.50
自引率
8.10%
发文量
242
审稿时长
9 months
期刊介绍: PAIN® is the official publication of the International Association for the Study of Pain and publishes original research on the nature,mechanisms and treatment of pain.PAIN® provides a forum for the dissemination of research in the basic and clinical sciences of multidisciplinary interest.
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