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监测骨关节炎的进展与治疗。

Monitoring osteoarthritis progression and therapy.

作者信息

Dougados Maxime

机构信息

Université René Descarte and Hôpital Cochin, Paris, France.

出版信息

Osteoarthritis Cartilage. 2004;12 Suppl A:S55-60. doi: 10.1016/j.joca.2003.09.009.

Abstract

Monitoring the progression of osteoarthritis (OA) and the effects of therapy during clinical trials requires valid and reliable outcome measurements. This paper discusses the selection of outcome measures in Phase III clinical trials of OA, the importance of presenting these variables in terms of the response of individual patients, and the combination of information from several outcome measures into a composite index. Four domains-pain, physical function, patient global assessment, and joint imaging (for studies >;1 year in duration)-have been identified as core outcome measures for Phase III clinical trials of OA. Within the symptom severity domains, several measurement instruments may be considered, including visual analog scales (VAS), the Western Ontario McMasters Universities Osteoarthritis (WOMAC) Index, the Lequesne Functional Severity Index, and the Arthritis Impact Measurement Scales (AIMS). Imaging techniques consist of radiography, magnetic resonance imaging (MRI), or other techniques. Although evaluation of these variables is often based on the average improvement in the study population as a whole, evaluation in terms of individual patients is more relevant. Therefore, continuous data collected from individuals (e.g., pain VAS 0-100mm) require conversion to a dichotomous variable (e.g., improvement yes/no) so that the percentage of responders can be determined. Continuous data from individual patients' data may be converted to a dichotomous variable based on global assessments, statistical modeling, or predictive capacities. Such methods suggest that a change in joint space width of >/=0.5mm may be considered clinically relevant. A composite index combining symptomatic and structural efficacy variables, adverse events, costs, and the requirement for surgery into a single variable would be useful. The requirement for total hip arthroplasty has been evaluated as a composite outcome measure for trials of hip OA and found to be valid, simple, and clinically pertinent.

摘要

在临床试验期间监测骨关节炎(OA)的进展以及治疗效果需要有效且可靠的结果测量方法。本文讨论了OA III期临床试验中结果测量指标的选择、根据个体患者的反应呈现这些变量的重要性,以及将来自多个结果测量指标的信息整合为一个综合指数的方法。四个领域——疼痛、身体功能、患者整体评估和关节成像(针对持续时间>1年的研究)——已被确定为OA III期临床试验的核心结果测量指标。在症状严重程度领域内,可以考虑几种测量工具,包括视觉模拟量表(VAS)、西安大略和麦克马斯特大学骨关节炎指数(WOMAC)、勒凯斯内功能严重程度指数以及关节炎影响测量量表(AIMS)。成像技术包括X线摄影、磁共振成像(MRI)或其他技术。尽管对这些变量的评估通常基于整个研究人群的平均改善情况,但从个体患者角度进行评估更为相关。因此,从个体收集的连续数据(例如,疼痛VAS 0 - 100mm)需要转换为二分变量(例如,改善是/否),以便能够确定有反应者的百分比。个体患者数据中的连续数据可以基于整体评估、统计建模或预测能力转换为二分变量。此类方法表明,关节间隙宽度变化≥0.5mm可能被认为具有临床相关性。将症状和结构疗效变量、不良事件、成本以及手术需求整合为一个单一变量的综合指数会很有用。全髋关节置换术的需求已被评估为髋OA试验的综合结果测量指标,并且被发现是有效、简单且具有临床相关性的。

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