实用老年医学 ›› 2026, Vol. 40 ›› Issue (10): 1012-1017.doi: 10.3969/j.issn.1003-9198.2026.10.007

• 临床研究 • 上一篇    下一篇

老年肺癌患者静脉血栓栓塞风险预测模型构建与验证

陈玲丽, 白兆石, 韦强   

  1. 210029 江苏省南京市,江苏省肿瘤医院/江苏省肿瘤防治研究所/南京医科大学附属肿瘤医院重症医学科(陈玲丽);药学部(白兆石);超声医学科(韦强)
  • 收稿日期:2026-04-23 发布日期:2026-10-23
  • 通讯作者: 韦强,Email:13770630278@163.com
  • 基金资助:
    江苏省卫生健康委科研项目(H2023002)

Development and validation of a risk prediction model for venous thromboembolism in elderly patients with lung cancer

CHEN Lingli, BAI Zhaoshi, WEI Qiang   

  1. Department of Critical Care Medicine(CHEN Lingli); Department of Pharmacy(BAI Zhaoshi); Department of Ultrasound Medicine(WEI Qiang), Jiangsu Cancer Hospital/Jiangsu Institute of Cancer Research/The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210029, China
  • Received:2026-04-23 Published:2026-10-23
  • Contact: WEI Qiang, Email:13770630278@163.com

摘要: 目的 分析老年肺癌患者静脉血栓栓塞(VTE)形成的危险因素,构建列线图预测模型并验证。方法 回顾性收集2020—2024年江苏省肿瘤医院收治的300例老年肺癌患者临床资料作为建模集(按7∶3随机分为训练集和内部验证集),另选取2025年1—12月收治的90例老年肺癌患者作为时序验证队列。根据是否发生VTE将建模集分为VTE组(n=128)和非VTE组(n=172)。采用多因素logistic回归分析筛选老年肺癌患者VTE的独立危险因素,构建列线图预测模型,并通过ROC曲线、校准曲线及决策曲线分析(DCA)评估模型的性能。结果 多因素logistic回归分析结果显示,临床分期Ⅲ~Ⅳ期、高D-二聚体水平、高纤维蛋白原水平、化疗史、中心静脉置管及长期卧床是老年肺癌患者发生VTE的独立危险因素。基于上述6项因素构建列线图模型,内部验证显示模型区分度、校准度及临床净获益良好。时序队列验证ROC曲线下面积为0.905(95%CI:0.841~0.970),提示模型在不同时间段具有稳定判别能力。结论 该列线图模型具有良好的预测效能和临床实用性,可为老年肺癌患者的VTE风险分层管理及个体化干预提供科学依据。
   

关键词: 静脉血栓栓塞, 老年人, 肺癌, 预测模型, 列线图

Abstract: Objective To analyze the risk factors for venous thromboembolism(VTE) in elderly patients with lung cancer, and to construct and validate a nomogram prediction model. Methods A total of 300 elderly patients with lung cancer admitted to Jiangsu Cancer Hospital from January 2020 to December 2024 were retrospectively collected as the modeling cohort. The cohort was randomly divided into a training set and an internal validation set at a 7∶3 ratio. In addition, 90 elderly patients with lung cancer treated from January to December 2025 were selected as the temporal validation set. According to the occurrence of VTE, patients in the modeling cohort were divided into a VTE group(n=128) and a non-VTE group(n=172). Multivariate logistic regression analysis was used to identify the independent risk factors for VTE in elderly patients with lung cancer, and a nomogram was constructed. Model performance was assessed using receiver operating characteristic(ROC) curves, calibration curves, and decision curve analysis(DCA). Results Multivariate logistic regression analysis showed that clinical stage Ⅲ-Ⅳ, elevated D-dimer(D-D) and fibrinogen(FIB) levels, prior chemotherapy, central venous catheterization(CVC), and prolonged bed rest were independent risk factors for VTE in elderly patients with lung cancer. The nomogram was established and demonstrated good discrimination, calibration, and net clinical benefit in internal validation. The area under the ROC curve was 0.905(95%CI: 0.841-0.970) in the temporal set indicating stable discriminative ability across time. Conclusions The nomogram constructed in this study demonstrated robust predictive performance and clinical utility which can support risk-stratified management and individualized intervention for VTE in elderly patients with lung cancer.
   

Key words: venous thromboembolism, aged, lung cancer, prediction model, nomogram

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