| 抗CCP抗体及RF在类风湿关节炎患者心血管疾病中的价值及预测模型的构建 |
| 投稿时间：2024-09-25 修订日期：2024-10-17 点此下载全文 |
| 引用本文：黄霏雨,封利欣,孙静,韩家荣,朱丽娟,杨磊.抗CCP抗体及RF在类风湿关节炎患者心血管疾病中的价值及预测模型的构建[J].医学研究杂志,2025,54(3):108-113 |
| DOI：
10.11969/j.issn.1673-548X.2025.03.020 |
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| 基金项目:河南省医学科技攻关计划联合共建项目(LHGJ20210774) |
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| 中文摘要:目的 基于抗环瓜氨酸肽抗体(抗CCP抗体)及类风湿因子(rheumatoid factor, RF)等构建列线图预测模型,用于预测类风湿关节炎(rheumatoid arthritis, RA)患者合并心血管疾病(cardiovascular diseases, CVD)的概率。方法 选取2018年1月～2024年2月郑州大学附属郑州中心医院收治的437例RA患者作为研究对象,搜集患者的临床资料,结合随访结果将其分为RA+CVD组(n=88)和RA组(n=349)。通过单因素以及多因素Logistic回归分析筛选RA患者发生CVD的危险因素,据此构建RA患者发生CVD的预测模型,并进行内部验证。结果 多因素Logistic回归分析结果显示,年龄、病程、尿酸、C反应蛋白、是否有高血压及糖尿病、抗CCP抗体、RF是RA患者合并CVD的独立危险因素(P＜0.05)。对所构建的模型进行内部验证。其训练集和验证集的曲线下面积(area under the area, AUC)分别为0.891(95% CI:0.851～0.930)和0.867(95% CI:0.790～0.944)。结论 本研究所构建的预测模型预测及区分能力较好,对RA患者是否合并CVD具有较高的预测价值。 |
| 中文关键词:类风湿关节炎 抗环瓜氨酸肽抗体 类风湿因子 心血管疾病风险预测 预测模型 |
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| Value of Anti-CCP Antibody and RF in Cardiovascular Disease in Patients with Rheumatoid Arthritis and the Construction of Prediction Model. |
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| Abstract:Objective To construct a nomogram prediction model based on anti-cyclic citrullinated peptide antibody (anti-CCP antibody) and rheumatoid factor (RF) to predict the probability of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA). Methods A total of 437 RA patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2018 to February 2024 were selected as the study objects, the clinical data of the patients were collected, and they were divided into RA+CVD group (n=88) and RA group (n=349) according to the follow-up results. The risk factors of CVD in RA patients were screened by univariate and multivariate Logistic regression analysis, and the prediction model of CVD in RA patients was constructed and internally validated. Results Multivariate Logistic regression analysis showed that age, course of disease, uric acid, C-reactive protein, hypertension and diabetes, anti-CCP antibody and RF were independent risk factors for CVD in RA patients (P < 0.05). The constructed model was internally validated. The area under the area (AUC) of the training set and the validation set were 0.891 (95% CI:0.851-0.930) and 0.867 (95% CI:0.790-0.944). Conclusion The prediction model constructed in this paper has good predictive and discrimination ability, and has a high predictive value for whether RA patients are complicated with CVD. |
| keywords:Rheumatoid arthritis Anti-CCP antibody Rheumatoid factor Cardiovascular disease risk prediction Predictor model |
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