| 蒋玮,张利锋,桂斌捷,等.基于血清标志物评估类风湿关节炎疾病活动度的可视化风险模型价值分析.骨科,2026,17(5): 445-451. |
| 基于血清标志物评估类风湿关节炎疾病活动度的可视化风险模型价值分析 |
| Value analysis of a visualized risk model for evaluating rheumatoid arthritis disease activity based on serum biomarkers |
| 投稿时间：2026-03-11 |
| DOI：10.3969/j.issn.1674-8573.2026.05.007 |
| 中文关键词: 类风湿关节炎 疾病活动度 25羟维生素D C反应蛋白与前白蛋白比值 红细胞沉降率 列线图 预测模型 |
| 英文关键词: Rheumatoid arthritis Disease activity 25-hydroxyvitamin D C-reactive protein-to-prealbumin ratio Erythrocyte sedimentation rate Nomogram Risk prediction model |
| 基金项目:合肥综合性国家科学中心大健康研究院健康大数据与群体医学研究所项目（JKS2023001） |
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| 中文摘要: |
| 目的 分析血清25羟维生素D[25(OH)D]、C反应蛋白与前白蛋白比值（CPR）、红细胞沉降率（ESR），以及压痛关节计数与肿胀关节计数在评估类风湿关节炎（RA）病情活动度中的应用价值，并建立与验证可视化风险预测模型。方法 选取2023年1月至2025年7月我院收治的190例RA患者作为研究对象开展回顾性分析。基于28个关节疾病活动度评分（DAS28）结果，将受试者分为病情缓解组（72例）与病情活动组（118例）。收集两组基线资料并检测血清25(OH)D、CPR及ESR水平；通过多因素Logistic回归模型筛选影响RA疾病活动度的独立风险因素；利用受试者工作特征（ROC）曲线评价各指标单独及联合使用的预测效能。基于筛选出的独立风险因素构建可视化风险模型，以ROC曲线下面积（AUC）评估模型区分度，并采用Bootstrap重抽样法完成内部验证。结果 病情缓解组的血清25(OH)D水平高于病情活动组，压痛关节计数、肿胀关节计数、血清CPR、ESR水平低于病情活动组，差异有统计学意义（P<0.05）。Logistic回归分析显示，25(OH)D水平降低、CPR升高、ESR升高、压痛关节计数增加、肿胀关节计数增加均为RA疾病活动期的独立危险因素（P<0.05）。ROC曲线分析表明，上述五项指标联合检测的预测效能最佳，AUC达0.979，敏感性（96.61%）优于单一指标检测。Hosmer-Lemeshow检验显示，列线图模型预测RA疾病活动度的概率与实际概率差异无统计学意义（χ2=3.096，P=0.281），实际概率和预测概率基本一致；校准曲线和理想曲线较为接近，模型校准度良好。ROC结果显示，风险预测模型预测患者RA疾病活动度的AUC为0.969，特异性为88.89%，敏感性为90.68%。结论 血清CPR、ESR、25(OH)D、压痛关节计数、肿胀关节计数是RA疾病活动度的独立影响因素，基于以上因素构建的可视化风险模型具有良好的区分度和校准度，可作为临床个体化评估RA疾病活动度的参考工具，但其在不同人群中的普适性尚需多中心外部验证予以确认。 |
| 英文摘要: |
| Objective To investigate the value of serum 25-hydroxyvitamin D [25(OH)D], the C-reactive protein-to-prealbumin ratio (CPR), erythrocyte sedimentation rate (ESR), tender joint count, and swollen joint count in assessing disease activity in rheumatoid arthritis (RA), and to develop and validate a visualized risk prediction model. Methods A retrospective analysis was performed on 190 RA patients admitted to our hospital from January 2023 to July 2025. Based on the 28-joint disease activity score (DAS28), the patients were divided into a remission group (72 cases) and an active disease group (118 cases). Baseline data were collected, and serum 25(OH)D, CPR and ESR levels were measured. Independent risk factors for RA disease activity were identified using multivariate Logistic regression. The predictive performance of each indicator alone and in combination were evaluated using receiver operating characteristic (ROC) curves. A visualized risk model was constructed based on the selected independent risk factors. Model discrimination was assessed by the area under the ROC curve (AUC), and internal validation was performed using Bootstrap resampling. Results The serum 25(OH)D level was higher in the remission group than that in the active disease group, whereas tender joint count, swollen joint count, CPR, and ESR were lower; all differences were statistically significant (P<0.05). Logistic regression analysis showed that decreased 25(OH)D, increased CPR, increased ESR, increased tender joint count, and increased swollen joint count were all independent risk factors for RA disease activity (P<0.05). ROC curve analysis showed that the combined use of these five indicators yielded the best predictive performance, with an AUC of 0.979 and a sensitivity of 96.61%, which was superior to that of any single indicator. The Hosmer-Lemeshow test indicated no significant difference between the predicted and observed probabilities of RA disease activity in the Nomogram model (χ2=3.096, P=0.281), suggesting good calibration. The calibration curve was close to the ideal curve. The risk prediction model achieved an AUC of 0.969, with a specificity of 88.89% and a sensitivity of 90.68%. Conclusion Serum CPR, ESR, 25(OH)D, tender joint count, and swollen joint count are independent factors associated with RA disease activity. The visualized risk model constructed from these factors demonstrates good discrimination and calibration, and may serve as a useful tool for individualized clinical assessment of RA disease activity. However, its generalizability across different populations requires confirmation through multicenter external validation. |
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