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与ANCA相关的血管炎中的眼部表现:来自中国医疗中心的综合分析

Shulin Liu¹, Mei Xu¹, Xinyu Zhao²

¹Department of Ophthalmology, Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Clinical rheumatology

|July 13, 2024

概括

眼部并发症影响了超过20%的ANCA相关血管炎 (AAV) 患者,在多重膜炎 (GPA) 的粒状瘤炎 (Granulomatosis) 中的比例更高. 机器学习模型可以预测眼睛参与AAV,帮助早期干预.

科学领域:

眼科医生眼科眼科
类风湿病学类风湿病学
免疫学免疫学免疫学

背景情况:

与ANCA相关的血管炎 (AAV) 涵盖了几种系统性自身免疫性疾病,包括带有多炎的粒状瘤 (GPA),带有多炎的异性粒状瘤 (EGPA) 和微观多炎 (MPA).
眼部表现是AAV的已知的并发症,影响患者的发病率和视觉预后.
了解眼睛干扰的频谱和预测因素对于及时诊断和管理至关重要.

研究的目的:

研究患有GPA,EGPA和MPA的患者眼部表现的患病率和类型.
确定眼部并发症与实验室参数或系统性疾病特征之间的关联.
使用机器学习开发用于AAV的眼睛和视网膜参与的预测模型.

主要方法:

来自两个中国医疗中心的533名AAV患者的回顾性分析 (2016年1月 - 2023年11月).
数据收集包括诊断,头骨和眼部表现以及实验室参数.
统计分析涉及单变量/多变量逻辑回归和机器学习模型进行预测.

主要成果:

在20.64%的AAV患者中存在眼部并发症,与MPA (7.37%) 相比,GPA (36.67%) 和EGPA (18.18%) 的比率更高.
在GPA中,硬化症和逆轨道质/乳囊炎很常见;视网膜感染发生在9.09%的EGPA病例中.
机器学习识别了乙素百分比,hs-CRP和T4 / T8比率作为视网膜干扰的预测因素,以及WBC,APTT,IgA和PR3-ANCA用于一般眼睛干扰.

结论:

眼部表现在所有AAV亚型中都是常见的,需要进行警的眼科评估.
机器学习模型在预测眼睛和视网膜干扰方面表现有前途,使早期检测和干预成为可能.
综合风湿病学和眼科的多学科方法对于优化AAV患者的治疗结果至关重要.

关键词:

与ANCA相关的血管炎带有多炎的埃索诺菲尔颗粒瘤症.带有多阳性炎的粒状瘤.微观的多阳性炎.眼睛的表现眼睛的表现

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