Lu Yiwei, Zhao Xu, He Xinyi, Li Menglan, Xie Qingqing, Shuai Shiquan
Department of Rheumatology and Immunology, Nanchong Central Hospital (Nanchong Clinical Medical Research Center)-The Second Clinical Medical College of North Sichuan Medical College, 637000 Nanchong, Sichuan, China.
Key Laboratory of Inflammation and Immunity Nanchong, Nanchong Central Hospital (Nanchong Clinical Medical Research Center)-The Second Clinical Medical College of North Sichuan Medical College, 637000 Nanchong, Sichuan, China.
Rev Cardiovasc Med. 2025 Sep 28;26(9):38668. doi: 10.31083/RCM38668. eCollection 2025 Sep.
To develop a predictive model for cardiac valve calcification (CVC) in rheumatoid arthritis (RA) patients using a novel nomogram approach.
We analyzed data from patients diagnosed with RA at the Department of Rheumatology and Immunology, Nanchong Central Hospital, between January 1, 2020, and October 31, 2023. Data were gathered on patient demographics, disease characteristics, laboratory tests, and imaging findings. Patients were randomly divided into a training set (n = 210) and a validation set (n = 140), in a ratio of 6:4, respectively. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify risk predictors. Meanwhile, both single-factor and multi-factor logistic regression analyses were conducted to ascertain the risk factors associated with cardiac valve calcification. A predictive model was constructed using R software and validated through Bootstrap techniques. The performance of the model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA).
A total of 350 RA patients were included in the study, of whom 67 (19.1%) were diagnosed with CVC. Multivariate analysis identified several significant risk factors for CVC, including hypertension (odds ratio (OR) = 15.496, 95% confidence interval (CI): 4.373-54.916; < 0.01), age (OR = 1.118, 95% CI: 1.003-1.246; = 0.043), disease duration (OR = 1.238, 95% CI: 1.073-1.427; = 0.003), and elevated erythrocyte sedimentation rate (ESR) (OR = 1.026, 95% CI: 1.006-1.047; = 0.012). The predictive model demonstrated excellent discriminatory performance, with an AUC of 0.9474 (95% CI: 0.9044-0.9903) in the training set. The model also showed strong internal validity (C-index = 0.947) and maintained robust performance in external validation (AUC = 0.9390; 95% CI: 0.8880-0.9893). Calibration analysis further confirmed the predictive accuracy and reliability of the model.
The developed model can effectively identify RA patients at high risk for CVC. This tool provides a scientific basis for clinical decision-making and has significant potential for enhancing patient management and outcomes.
采用一种新型列线图方法为类风湿关节炎（RA）患者的心脏瓣膜钙化（CVC）建立预测模型。
我们分析了2020年1月1日至2023年10月31日期间在南充市中心医院风湿免疫科诊断为RA的患者的数据。收集了患者的人口统计学资料、疾病特征、实验室检查和影像学检查结果。患者按6:4的比例随机分为训练集（n = 210）和验证集（n = 140）。采用最小绝对收缩和选择算子（LASSO）回归来识别风险预测因子。同时，进行单因素和多因素逻辑回归分析以确定与心脏瓣膜钙化相关的危险因素。使用R软件构建预测模型，并通过Bootstrap技术进行验证。使用受试者工作特征（ROC）曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）来评估模型的性能。
本研究共纳入350例RA患者，其中67例（19.1%）被诊断为CVC。多因素分析确定了CVC的几个重要危险因素，包括高血压（优势比（OR）= 15.496，95%置信区间（CI）：4.373 - 54.916；P < 0.01）、年龄（OR = 1.118，95% CI：1.003 - 1.246；P = 0.043）、病程（OR = 1.238，95% CI：1.073 - 1.427；P = 0.003）和红细胞沉降率（ESR）升高（OR = 1.026，95% CI：1.006 - 1.047；P = 0.012）。预测模型显示出优异的区分性能，训练集中的AUC为0.9474（95% CI：0.9044 - 0.9903）。该模型还显示出较强的内部效度（C指数 = 0.947），并在外部验证中保持了稳健的性能（AUC = 0.9390；95% CI：0.8880 - 0.9893）。校准分析进一步证实了模型的预测准确性和可靠性。
所开发的模型能够有效识别CVC高风险的RA患者。该工具为临床决策提供了科学依据，在改善患者管理和治疗结果方面具有巨大潜力。
