Li Ruoyan, Xie Yizhou, Lin Kexin, Fan Xiaohong
Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine and Chengdu University of Traditional Chinese Medicine Chengdu 610075, Sichuan, China.
Am J Transl Res. 2025 May 15;17(5):3345-3356. doi: 10.62347/VUDA3928. eCollection 2025.
To develop a predictive model for infection risk in elderly rheumatoid arthritis (RA) patients using laboratory markers, particularly erythrocyte sedimentation rate (ESR), albumin (ALB), and C-reactive protein (CRP).
This retrospective study included 452 elderly RA patients admitted to the Hospital of Chengdu University of Traditional Chinese Medicine between January 2021 and June 2024. The patients were randomly divided into a training group (271 patients) and a validation group (181 patients) at a 6:4 ratio. Key clinical and laboratory data were collected, including ESR, ALB, CRP, and others. Lasso regression and multivariable logistic regression were employed to identify infection-related factors. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
The study identified ALB<33.75 g/L, CRP≥32.75 mg/L, and ESR≥51.50 mm/h as independent risk factors for infection in elderly RA patients. A predictive model incorporating these three markers demonstrated high diagnostic accuracy, with an AUC of 0.909 in the training group and 0.880 in the validation group. DCA further confirmed the clinical utility of the model.
This study successfully developed a predictive model combining ESR, ALB, and CRP to assess infection risk in elderly RA patients. This model has significant potential to enhance early infection detection and support clinical decision-making, offering a valuable tool for managing this vulnerable population.
利用实验室指标,特别是红细胞沉降率(ESR)、白蛋白(ALB)和C反应蛋白(CRP),建立老年类风湿关节炎(RA)患者感染风险的预测模型。
这项回顾性研究纳入了2021年1月至2024年6月期间入住成都中医药大学附属医院的452例老年RA患者。患者按6:4的比例随机分为训练组(271例患者)和验证组(181例患者)。收集关键的临床和实验室数据,包括ESR、ALB、CRP等。采用Lasso回归和多变量逻辑回归来确定感染相关因素。使用受试者工作特征(ROC)曲线、校准曲线和决策曲线分析(DCA)评估模型性能。
该研究确定ALB<33.75 g/L、CRP≥32.75 mg/L和ESR≥51.50 mm/h为老年RA患者感染的独立危险因素。包含这三个指标的预测模型显示出较高的诊断准确性,训练组的AUC为0.909,验证组为0.880。DCA进一步证实了该模型的临床实用性。
本研究成功建立了一个结合ESR、ALB和CRP的预测模型,以评估老年RA患者的感染风险。该模型在提高早期感染检测和支持临床决策方面具有巨大潜力,为管理这一脆弱人群提供了一个有价值的工具。