背景:类风湿性关节炎(RA)的早期诊断和治疗对于预防关节损伤和提高患者预后至关重要。由于非特异性和可变的临床体征和症状,早期诊断RA具有挑战性。我们的研究旨在确定手部超声(US)对RA发展最具预测性的特征,并评估机器学习模型在诊断临床前RA中的表现。
方法:我们进行了一项前瞻性队列研究,研究对象为326名成人,他们经历了少于12个月的手关节痛且没有临床关节炎。我们在基线时通过手US对参与者进行了临床评估,并随访了24个月。根据ACR/EULAR标准定义RA的临床进展。回归建模和机器学习方法用于分析预测US特征。
结果:在326名参与者中(45.10±11.37岁/83%为女性),123例(37.7%)在随访期间发展为临床RA。在基线,84.6%的进展者患有美国滑膜炎,而16.3%的非进展者这样做(p<0.0001)。只有5.7%的进展者具有阳性PD。多因素分析显示桡骨滑膜厚度(OR=39.8),PIP/MCP滑膜炎(OR=68和39),US和腕部积液(OR=12.56)显着增加了患RA的几率。ML证实了这些美国特征,以及射频和反CCP水平,作为RA最重要的预测因子。
结论:HandUS可以识别临床前滑膜炎并确定RA风险。桡骨滑膜厚度,PIP/MCP滑膜炎,手腕积液,RF和抗CCP水平与RA发展相关。
BACKGROUND: The early diagnosis and treatment of rheumatoid
arthritis (RA) are essential to prevent joint damage and enhance patient outcomes. Diagnosing RA in its early stages is challenging due to the nonspecific and variable clinical signs and symptoms. Our study aimed to identify the most predictive features of hand ultrasound (US) for RA development and assess the performance of machine learning models in diagnosing preclinical RA.
METHODS: We conducted a prospective cohort study with 326 adults who had experienced hand joint pain for less than 12 months and no clinical
arthritis. We assessed the participants clinically and via hand US at baseline and followed them for 24 months. Clinical progression to RA was defined according to the ACR/EULAR criteria. Regression modeling and machine learning approaches were used to analyze the predictive US features.
RESULTS: Of the 326 participants (45.10 ± 11.37 years/83% female), 123 (37.7%) developed clinical RA during follow-up. At baseline, 84.6% of the progressors had US synovitis, whereas 16.3% of the non-progressors did (p < 0.0001). Only 5.7% of the progressors had positive PD. Multivariate analysis revealed that the radiocarpal synovial thickness (OR = 39.8), PIP/MCP synovitis (OR = 68 and 39), and wrist effusion (OR = 12.56) on US significantly increased the odds of developing RA. ML confirmed these US features, along with the RF and anti-CCP levels, as the most important predictors of RA.
CONCLUSIONS: Hand US can identify preclinical synovitis and determine the RA risk. The radiocarpal synovial thickness, PIP/MCP synovitis, wrist effusion, and RF and anti-CCP levels are associated with RA development.