2022 年骨关节炎年度回顾:流行病学与治疗。

Osteoarthritis year in review 2022: Epidemiology & therapy.

机构信息

STARS Education and Research Alliance, Surgical Treatment and Rehabilitation Service (STARS), The University of Queensland and Metro North Health, Queensland, Australia.

The University of Queensland, UQ Library, Herston 4006, Queensland, Australia.

出版信息

Osteoarthritis Cartilage. 2023 Jul;31(7):876-883. doi: 10.1016/j.joca.2023.03.008. Epub 2023 Mar 23.

Abstract

This 'Year in Review' provides a synopsis of key research themes and individual studies from the clinical osteoarthritis (OA) field, focused on epidemiology and therapy. The electronic database search for the review was adapted from the 2021 year in review search, to increase search specificity for relevant study designs, and was conducted in Medline, Embase and medRxiv (31st March 2021 to 4th March 2022). Following screening for eligibility, studies were grouped according to their key research design, including reviews, cohorts and randomised trials. 11 key themes emerged, including the importance of several comorbidities in predicting OA incidence and prevalence, surgical approaches that can reduce the risk of post-traumatic OA, the heterogenous but nevertheless relatively stable nature of OA subgroup trajectories, the paucity of robust studies particularly of surgery for OA and the very modest benefit of many therapies under evaluation in trials. A particular interest of the authors was to consider whether new studies are helping determine how to better ensure the right patient with OA is matched to the right treatment at the right time. There are several new studies developing improved predictive models through big data analytics and machine learning which show promise, need validation, and may support new approaches to stratified care.

摘要

这篇“年度回顾”概述了临床骨关节炎(OA)领域的关键研究主题和个别研究,重点关注流行病学和治疗。本综述的电子数据库检索改编自 2021 年的检索,以提高相关研究设计的检索特异性,并在 Medline、Embase 和 medRxiv(2021 年 3 月 31 日至 2022 年 3 月 4 日)中进行。经过资格筛选后,根据关键研究设计对研究进行分组,包括综述、队列研究和随机试验。出现了 11 个关键主题,包括几种合并症在预测 OA 发病率和患病率方面的重要性、可以降低创伤后 OA 风险的手术方法、OA 亚组轨迹的异质性但仍然相对稳定的性质、缺乏针对 OA 手术的稳健研究以及在试验中评估的许多治疗方法的获益非常有限。作者特别关注的是,新的研究是否有助于确定如何更好地确保 OA 患者在正确的时间获得正确的治疗。有几项新的研究通过大数据分析和机器学习开发了改进的预测模型,这些模型显示出前景,需要验证,并可能支持分层护理的新方法。

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