预测银屑病关节炎患者对生物和靶向合成改变病情抗风湿药物治疗反应的因素--系统回顾和荟萃分析。

IF 2.9 3区 医学 Q2 RHEUMATOLOGY
Tabea Künzler, Manuel Bamert, Haiko Sprott
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引用次数: 0

摘要

银屑病关节炎(PsA)患者的治疗反应差异很大,而且往往不尽人意。因此,必须进行个体化治疗选择,以尽量减少长期并发症。本研究旨在确定可预测银屑病关节炎患者对生物和靶向合成改善病情抗风湿药物(bDMARDs 和 tsDMARDs)治疗反应的因素,并利用人工智能(AI)概述这些因素的潜在应用。研究人员筛选了五个电子数据库,以确定相关研究。对至少有四项研究调查过的因素进行了随机效应荟萃分析。最后,共纳入了 37 项研究,共计 17,042 名患者。这些研究中最常调查的预测因素包括性别、年龄、C反应蛋白(CRP)、健康评估问卷(HAQ)、体重指数(BMI)和病程。荟萃分析表明,男性(几率比(OR)= 2.188,95% 置信区间(CI)= 1.912-2.503)和较高的基线 CRP(1.537,1.111-2.125)与更大的治疗反应相关。年龄较大(0.982,0.975-0.99)、基线 HAQ 评分较高(0.483,0.336-0.696)、基线 DAPSA 评分较高(0.789,0.663-0.938)和基线关节压痛计数(TJC)较高(0.97,0.945-0.996)与治疗反应呈负相关。其他因素没有统计学意义,但在复杂的人工智能测试中可能具有临床重要性。还需要进一步研究来验证这些发现,并找出可指导 PsA 患者个性化治疗决策的新因素,特别是在开发人工智能应用时。根据最新的医学发展,已经提出了基于监督学习算法的决策支持工具,作为这些预测因素的临床应用。关键信息--鉴于银屑病关节炎(PsA)患者的治疗反应往往不尽人意且难以预测,因此治疗选择必须高度个体化。- 为了确定 PsA 患者对生物制剂和靶向合成改善病情抗风湿药物治疗反应的最可靠预测指标,我们进行了一项系统性文献综述。- 本文讨论了将这些预测指标整合到人工智能工具中用于常规临床实践的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Factors predicting treatment response to biological and targeted synthetic disease-modifying antirheumatic drugs in psoriatic arthritis - a systematic review and meta-analysis.

The therapeutic response of patients with psoriatic arthritis (PsA) varies greatly and is often unsatisfactory. Accordingly, it is essential to individualise treatment selection to minimise long-term complications. This study aimed to identify factors that might predict treatment response to biological and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs) in patients with PsA and to outline their potential application using artificial intelligence (AI). Five electronic databases were screened to identify relevant studies. A random-effects meta-analysis was performed for factors that were investigated in at least four studies. Finally, 37 studies with a total of 17,042 patients were included. The most frequently investigated predictors in these studies were sex, age, C-reactive protein (CRP), the Health Assessment Questionnaire (HAQ), BMI, and disease duration. The meta-analysis revealed that male sex (odds ratio (OR) = 2.188, 95% confidence interval (CI) = 1.912-2.503) and higher baseline CRP (1.537, 1.111-2.125) were associated with greater treatment response. Older age (0.982, 0.975-0.99), higher baseline HAQ score (0.483, 0.336-0.696), higher baseline DAPSA score (0.789, 0.663-0.938), and higher baseline tender joint count (TJC) (0.97, 0.945-0.996) were negatively correlated with the response to therapy. The other factors were not statistically significant but might be of clinical importance in the context of a complex AI test battery. Further studies are needed to validate these findings and identify novel factors that could guide personalised treatment decisions for PsA patients, in particular in developing AI applications. In accordance with the latest medical developments, decision-support tools based on supervised learning algorithms have been proposed as a clinical application of these predictors. Key messages • Given the often unsatisfactory and unpredictable therapeutic response in patients with Psoriatic Arthritis (PsA), treatment selection must be highly individualized. • A systematic literature review was conducted to identify the most reliable predictors of treatment response to biologic and targeted synthetic disease-modifying antirheumatic drugs in PsA patients. • The potential integration of these predictors into AI tools for routine clinical practice is discussed.

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来源期刊
Clinical Rheumatology
Clinical Rheumatology 医学-风湿病学
CiteScore
6.90
自引率
2.90%
发文量
441
审稿时长
3 months
期刊介绍: Clinical Rheumatology is an international English-language journal devoted to publishing original clinical investigation and research in the general field of rheumatology with accent on clinical aspects at postgraduate level. The journal succeeds Acta Rheumatologica Belgica, originally founded in 1945 as the official journal of the Belgian Rheumatology Society. Clinical Rheumatology aims to cover all modern trends in clinical and experimental research as well as the management and evaluation of diagnostic and treatment procedures connected with the inflammatory, immunologic, metabolic, genetic and degenerative soft and hard connective tissue diseases.
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