中华护理杂志 ›› 2020, Vol. 55 ›› Issue (1): 68-73.DOI: 10.3761/j.issn.0254-1769.2020.01.011

• 专科实践与研究 • 上一篇    下一篇

中老年患者院内发生静脉血栓栓塞症风险预测模型的研究

李海燕,李蓉,植艳茹,王金萍,张玲娟,金志超,钱火红,王筱慧   

  1. 200433 上海市 海军军医大学附属长海医院血管外科(李海燕,李蓉,植艳茹,王金萍),护理处(张玲娟,钱火红),质量管理科(王筱慧);海军军医大学军队统计学教研室(金志超)
  • 收稿日期:2019-07-13 出版日期:2020-01-15 发布日期:2020-01-17
  • 作者简介:李海燕:女,本科(硕士在读),副主任护师,护士长,E-mail: <email>18900163@qq.com</email>
  • 基金资助:
    2018年海军军医大学护理学高峰学科青年培育工程拔尖类项目(18QPBJ11);上海市护理学会2019年度学会级立项课题(2019MS-B18);海军军医大学第一附属医院234学科攀峰计划(2019YXK040)

Study on risk prediction model of in-hospital venous thromboembolism in middle-aged and elderly patients

LI Haiyan,LI Rong,ZHI Yanru,WANG Jinping,ZHANG Lingjuan,JIN Zhichao,QIAN Huohong,WANG Xiaohui   

  • Received:2019-07-13 Online:2020-01-15 Published:2020-01-17

摘要:

目的 了解中老年患者院内发生静脉血栓栓塞症的危险因素,并在此基础上构建风险预测模型。方法 选择2015年1月—2018年12月在某三级甲等综合性医院所有住院期间发生静脉血栓栓塞症(venous thromboembolism,VTE)的55例中老年患者纳入VTE组,按照1:2的比例选择同年度入住同一科室、同一主要诊断、年龄相近、性别相同且住院期间没有发生VTE的患者108例纳入非VTE组,对两组的一般资料、Caprini血栓风险评估模型分值、合并疾病、相关实验室检查指标、可能的危险因素、用药情况等进行病例回顾研究,筛选出危险因素并利用Logistic回归进行风险预测模型的构建,并选择2013年1月—2014年12月同一医院住院期间发生VTE和非VTE患者共54例对模型进行验证。结果 Logistic回归分析显示,呼吸道感染/呼吸衰竭、肝肾疾病、饮酒史、D-二聚体浓度和Caprini血栓风险等级是中老年患者发生VTE的危险因素。结论 本研究构建的VTE风险预测模型可以对中老年患者院内发生VTE起到一定的辅助预测作用,该模型尚需由多中心大样本试验进一步验证和完善。

关键词: 中年人, 老年人, 静脉血栓栓塞症, 静脉血栓形成, 危险因素, 预测模型, 护理

Abstract:

Objective This study was to investigate the risk factors of venous thromboembolism (VTE) in middle-aged and elderly patients in hospital,and to establish a risk prediction model. Methods From January 2015 to December 2018,55 middle-aged and elderly patients who had VTE in a tertiary general hospital were received as VTE group. 108 patients with the same major diagnosis,age,sex and no VTE occurred in the same department in the same year were selected as non-VTE group. The general data of patients in two groups,the score of Caprini thrombosis risk assessment model,complications,related laboratory examination indicators,various possible risk factors and drug usage were retrospectively studied. The risk factors were screened out and the risk prediction model was constructed by logistic regression. A total of 54 cases of VTE and non-VTE patients in the same hospital from January 2013 to December 2014 were used to validate the model. Results Multivariate regression analysis showed that respiratory tract infection or respiratory failure,liver and kidney disease,drinking history,D-dimer and Caprini thrombosis risk grade were risk factors for VTE in middle-aged and elderly patients in hospital. The model constructed by the above risk factors had good predictive ability. Conclusion The thromboembolism risk prediction model can play an auxiliary role in predicting the occurrence of VTE in middle-aged and elderly patients in hospital. This model needs to be further validated and improved under the condition of multi-center and large samples.

Key words: Middle-aged People, the Aged, Venous Thromboembolism, Venous Thrombosis, Risk Factors, Prediction Model, Nursing Care