检验医学 ›› 2026, Vol. 41 ›› Issue (3): 239-244.DOI: 10.3969/j.issn.1673-8640.2026.03.005

• 论著 • 上一篇    下一篇

基于多指标构建自身免疫性疾病患者血栓发生预测模型及其临床应用评价

郭郑斌1, 胡莉娜1, 张睿1, 陈洁1, 张静2, 但刚1()   

  1. 1. 中国人民解放军西部战区总医院检验科四川 成都 610083
    2. 成都中医药大学2021级四川 成都 610075
  • 收稿日期:2024-11-07 修回日期:2025-06-09 出版日期:2026-03-30 发布日期:2026-04-14
  • 通讯作者: 但刚
  • 作者简介:但 刚,E-mail:dangang333@163.com
    郭郑斌,女,1992年生,学士,检验技师,主要从事临床检验工作。
  • 基金资助:
    四川省科技厅重点研发项目(2021YJ0191)

Construction of a prediction model for thrombosis in patients with autoimmune diseases based on multiple indicators and its clinical application

GUO Zhengbin1, HU Lina1, ZHANG Rui1, CHEN Jie1, ZHANG Jing2, DAN Gang1()   

  1. 1. Department of Clinical Laboratorythe General Hospital of Western Theater Command of People's Liberation ArmyChengdu 610083,Sichuan, China
    2. Grade 2021Chengdu University of Traditional Chinese MedicineChengdu 610075,Sichuan, China
  • Received:2024-11-07 Revised:2025-06-09 Online:2026-03-30 Published:2026-04-14
  • Contact: DAN Gang

摘要:

目的 分析自身免疫性疾病患者发生血栓的影响因素,构建列线图模型,并评估模型的临床应用价值。 方法 选取2021年1月—2023年12月中国人民解放军西部战区总医院自身免疫性疾病患者250例,其中抗磷脂综合征(APS)17例、系统性红斑狼疮(SLE)233例。将所有患者按7∶3的比例随机分为训练集(175例)和验证集(75例)。收集所有患者的临床资料和实验室检测结果。对所有患者随访3年,根据有无发生血栓事件分为血栓组(40例)和无血栓组(210例)。采用LASSO回归分析筛选变量,并构建血栓发生风险列线图模型。采用受试者工作特征(ROC)曲线、校准曲线和决策曲线评估列线图模型的临床价值。 结果 训练集中,血栓组>50岁所占比例、合并高血压所占比例、狼疮抗凝物(LA)阳性率和活化部分凝血活酶时间(APTT)均显著高于非血栓组(P<0.05)。采用LASSO回归分析筛选出5个影响因素(年龄、合并高血压、合并狼疮肾炎、APTT、LA),并构建列线图模型。在训练集和验证集中,列线图模型判断APS患者和SLE患者发生血栓的曲线下面积(AUC)分别为0.79、0.81;预测APS患者和SLE患者发生血栓的概率与实际概率均相近(Hosmer-Lemeshow χ2值分别为8.615、12.192,P>0.05)。在0.1~0.7阈值概率范围内,列线图模型具有良好的净收益率,净收益率最大为0.18。 结论 基于年龄、合并高血压、合并狼疮肾炎、APTT、LA建立的预测APS患者和SLE患者血栓发生的列线图模型具有较高的临床价值,可在APS患者和SLE患者血栓事件的预测、识别中发挥重要作用。

关键词: 抗磷脂抗体, 狼疮抗凝物, 抗磷脂综合征, 系统性红斑狼疮, 自身免疫性疾病, 列线图模型, 血栓

Abstract:

Objective To analyze the influencing factors of thrombosis in patients with autoimmune diseases and construct a nomogram model,and to evaluate its clinical application value. Methods A total of 250 patients with autoimmune diseases from the General Hospital of Western Theater Command of People's Liberation Army from January 2021 to December 2023 were enrolled,including 17 cases of antiphospholipid syndrome(APS) and 233 cases of systemic lupus erythematosus(SLE). All the patients were randomly classified into a training set and a validation set at a ratio of 7︰3. The clinical data and laboratory determination results were collected. All the patients were followed up for 3 years and were classified into thrombosis group(40 cases) and non-thrombosis group(210 cases) based on the occurrence of thrombosis. LASSO regression analysis was used to screen variables and construct nomogram model. The clinical value of the nomogram model was evaluated by receiver operating characteristic(ROC) curves,calibration curves and decision curves. Results In the training set,the proportion of patients >50 years old,the proportion of patients with hypertension,the positive rate of lupus anticoagulant(LA) and activated partial thromboplastin time(APTT)in thrombosis group were higher than those in non-thrombosis group(P<0.05). Five influencing factors(age,hypertension,lupus nephritis,APTT,LA)were screened out by the LASSO regression model,and a nomogram model was constructed. In the training set and validation set,the areas under curves(AUC) of the nomogram model for predicting thrombosis in APS and SLE patients were 0.79 and 0.81,respectively. The predicted probability of thrombosis in APS and SLE patients was close to the actual probability(Hosmer-Lemeshow χ2 values were 8.615 and 12.192,P>0.05). Within the threshold probability range of 0.1-0.7,the nomogram model had a good net benefit,with the maximum net benefit being 0.18. Conclusions The nomogram model constructed based on age,hypertension,lupus nephritis,APTT and LA for predicting thrombosis in APS and SLE patients has high clinical value and can play a role in the prediction and identification of thrombotic events in APS and SLE patients.

Key words: Antiphospholipid antibody, Lupus anticoagulant, Antiphospholipid syndrome, Systemic lupus erythematosus, Autoimmune disease, Nomogram model, Thrombosis

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