2025 年第 12 期 第 20 卷

原发性血小板增多症患者血栓形成的危险因素分析及列线图预测模型构建

The risk factors analysis of thrombosis in patients with essential thrombocythemia and construction of a nomogram prediction model

作者：郑茂凤1李芋锦2牛继聪3孙妍4吕妍3刘为易3王明镜3陈杨2胡晓梅3,4

英文作者：Zheng Maofeng1 Li Yujin2 Niu Jicong3 Sun Yan4 Lyu Yan3 Liu Weiyi3 Wang Mingjing3 Chen Yang2 Hu Xiaomei34

单位：1北京中医药大学研究生院，北京100029；2云南省第一人民医院血液科，昆明650032；3中国中医科学院西苑医院血液科，北京100091；4中国中医科学院西苑医院苏州医院血液科，苏州215009

英文单位：1Graduate School of Beijing University of Chinese Medicine Beijing 100029 China; 2Department of Hematology the First People′s Hospital of Yunnan Province Kunming 650032 China; 3Department of Hematology Xiyuan Hospital China Academy of Chinese Medical Sciences Beijing 100091 China; 4Department of Hematology Suzhou Hospital of Xiyuan Hospital China Academy of Chinese Medical Sciences Suzhou 215009 China

关键词：原发性血小板增多症；血栓形成；危险因素；中性粒细胞；列线图

英文关键词：Essentialthrombocythemia;Thrombosis;Riskfactors;Neutrophils;Nomogram

• 摘要：

• 目的 探讨原发性血小板增多症（ET）患者血栓形成的危险因素，并构建可视化风险预测模型。方法 回顾性分析2022年1月至2024年12月中国中医科学院西苑医院收治的156例ET患者临床资料，根据是否发生血栓分为血栓组（44例）和无血栓组（112例）。采用单因素和多因素Logistic回归方法分析血栓形成的危险因素，并据此构建风险预测模型。结果 血栓组年龄≥60岁、合并高血压病及高脂血症、存在JAK2突变的比例、白细胞计数、中性粒细胞计数（NEUT）、中性粒细胞与淋巴细胞比值均显著高于无血栓组（均P<0.05）。多因素Logistic回归分析提示确定年龄≥60岁（比值比=3.08，95%置信区间：1.20～7.92）、脾大（比值比=3.04，95%置信区间：1.06～8.71）、高脂血症（比值比=3.06，95%置信区间：1.20～7.80）及高NEUT（比值比=1.35，95%置信区间：1.20～1.52）为ET患者发生血栓的独立危险因素（均P<0.05）。基于多因素 Logistic 回归分析结果构建预测ET患者血栓形成风险的列线图模型，受试者工作特征曲线下面积为0.922（95%置信区间：0.834～0.971），特异度为81.3%，敏感度为93.2%，约登指数为0.744。Hosmer-Lemeshow拟合优度检验χ2=6.32，P=0.612，校准曲线显示模型预测概率与实际观测频率具有良好的一致性。结论 NEUT升高、年龄≥60岁、高脂血症及脾大是ET患者血栓形成的独立危险因素，基于这些因素构建的列线图模型预测能力良好，可作为ET患者血栓风险分层与临床决策的重要工具。

• Objective To explore the risk factors of thrombosis in patients with essential thrombocythemia (ET) and to construct a visual risk prediction model. Methods The clinical data of 156 ET patients admitted to Xiyuan Hospital, China Academy of Chinese Medical Sciences from January 2022 to December 2024 were retrospectively analyzed. According to the presence or absence of thrombosis, the patients were divided into thrombosis group (44 cases) and non-thrombosis group (112 cases). Univariate and multivariate Logistic regression methods were used to analyze the risk factors of thrombosis, and a risk prediction model was constructed. Results The proportion of age ≥60 years, hypertension and hyperlipidemia, JAK2 mutation, white blood cell count, neutrophil count (NEUT), and neutrophil-to-lymphocyte ratio in the thrombosis group were significantly higher than those in the non-thrombosis group (all P<0.05). Multivariate Logistic regression analysis showed that age ≥60 years (odds ratio =3.08, 95% confidence interval: 1.20-7.92), splenomegaly (odds ratio =3.04, 95% confidence interval: 1.06-8.71), hyperlipidemia (odds ratio =3.06, 95% confidence interval: 1.20-7.80) and high NEUT (odds ratio =1.35, 95% confidence interval: 1.20-1.52) were independent risk factors for thrombosis in ET patients (all P<0.05). Based on the results of multivariate Logistic regression analysis, a nomogram model was constructed to predict the risk of thrombosis in ET patients. The area under the receiver operating characteristic curve was 0.922 (95% confidence interval: 0.834-0.971), the specificity was 81.3%, the sensitivity was 93.2%, and the Youden index was 0.744. The Hosmer-Lemeshow goodness of fit test χ2=6.32, P=0.612. The calibration curve showed that the predicted probability of the model was in good agreement with the observed frequency. Conclusion Elevated NEUT, age ≥60 years, hyperlipidemia and splenomegaly are independent risk factors for thrombosis in ET patients. The nomogram model based on these factors has a good predictive ability and can be used as an important tool for thrombosis risk stratification and clinical decision-making in ET patients.