Medical Faculty of the University of Zurich, CH-8006, Zurich, Switzerland.
Retail Value Stream, Galenica AG, Untermattweg 8, CH-3027, Bern, Switzerland.
Clin Rheumatol. 2024 Dec;43(12):3723-3746. doi: 10.1007/s10067-024-07193-y. Epub 2024 Oct 28.
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.
患有银屑病关节炎(PsA)的患者的治疗反应差异很大,且往往不尽人意。因此,对治疗方法进行个体化选择以减少长期并发症至关重要。本研究旨在确定可能预测接受生物制剂和靶向合成改善病情抗风湿药物(bDMARDs 和 tsDMARDs)治疗的 PsA 患者的治疗反应的因素,并探讨使用人工智能(AI)对其进行潜在应用。我们对五个电子数据库进行了筛选,以确定相关研究。对至少有四项研究调查的因素进行了随机效应荟萃分析。最终纳入了 37 项研究,共纳入了 17042 名患者。这些研究中最常研究的预测因素包括性别、年龄、C 反应蛋白(CRP)、健康评估问卷(HAQ)、BMI 和疾病持续时间。荟萃分析显示,男性(比值比(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)与治疗反应更好相关。年龄较大(0.982,0.975-0.99)、较高的基线 HAQ 评分(0.483,0.336-0.696)、较高的基线 DAPSA 评分(0.789,0.663-0.938)和较高的基线压痛关节计数(TJC)(0.97,0.945-0.996)与治疗反应呈负相关。其他因素虽然没有统计学意义,但在复杂的 AI 测试组合的背景下可能具有临床意义。需要进一步的研究来验证这些发现,并确定可能指导 PsA 患者个体化治疗决策的新因素,特别是在开发 AI 应用方面。根据最新的医学进展,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不可预测,因此必须高度个体化选择治疗方法。
进行了系统的文献回顾,以确定生物制剂和靶向合成改善病情抗风湿药物治疗 PsA 患者的治疗反应的最可靠预测因素。
讨论了将这些预测因素整合到人工智能工具中以用于常规临床实践。