作者：刘雅鑫，蒋运兰，刘芯君，邱婷婷

单位：

1.成都中医药大学护理学院2.成都中医药大学附属医院血管外科

Units：

1.School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China 2.Department of Vascular Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, China

关键词：

卒中; 深静脉血栓形成; 风险预测模型; Meta分析;

Keywords：

Stroke; Deep venous thrombosis; Risk prediction model; Meta-analysis

CLC：

DOI：

10.12114/j.issn.1008-5971.2023.00.144

Funds：

国家科技部重点研发计划项目（2020YFC20031004）

摘要：

目的 系统评价脑卒中患者下肢深静脉血栓形成（DVT）的预测因子。方法 计算机检索PubMed、Embase、Web of Science、Cochrane Library、中国生物医学文献数据库、维普网、万方数据知识服务平台和中国知网公开发表的脑卒中患者下肢DVT风险预测模型构建的研究，检索时限为建库至2022-11-06。提取纳入文献的资料，采用预测模型研究偏倚风险与适用性评估工具（PROBAST）评价文献质量。采用RevMan 5.4软件进行Meta分析。结果本研究纳入13篇文献，共纳入11 517例患者，脑卒中患者下肢DVT发生率为8.43%～56.47%。13篇文献中，共构建14个脑卒中患者下肢DVT风险预测模型，AUC为0.570～0.912，其中10个风险预测模型的预测价值较好（AUC≥0.7）;4个风险预测模型采用Hosmer-Lemeshow拟合优度检验进行校准；2个风险预测模型进行外部验证，9个风险预测模型进行内部验证；14个风险预测模型包含3～6个预测因子。预测因子可以分为基本因素、疾病因素、治疗因素、实验室检查指标4类，Meta分析结果显示，年龄[OR=1.06,95%CI(1.01,1.10)]、血浆D-二聚体水平[OR=1.11,95%CI(1.07,1.14)]、心房颤动[OR=3.69,95%CI(1.45,9.39)]、意识障碍[OR=3.56,95%CI(1.20,10.61)]、下肢瘫痪[OR=2.60,95%CI(1.85,3.66)]、美国国立卫生研究院卒中量表（NIHSS）评分[OR=1.13,95%CI(1.10,1.16)]是脑卒中患者下肢DVT的预测因子（P<0.05）；性别[OR=1.36,95%CI(0.79,2.34)]不是脑卒中患者下肢DVT的预测因子（P>0.05）。结论 年龄、血浆D-二聚体水平、心房颤动、意识障碍、下肢瘫痪、NIHSS评分是脑卒中患者下肢DVT的有效预测因子。脑卒中患者下肢DVT风险预测模型研究处于发展阶段，未来可进一步在随访时间、模型校准与验证方面提高风险预测模型的质量。

Abstract：

Objective To systematically evaluate the predictors model of lower extremity deep venous thrombosis (DVT) in stroke patients. Methods Databases including the PubMed, Embase, Web of Science, Cochrane Library, CBM, VIP, Wanfang Data and CNKI were retrieved to search for studies on the construction of risk prediction model of lower extremity DVT in stroke patients from inception to November 6, 2022. The data of the included literature were extracted, the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to evaluate the quality of the studies. RevMan 5.4 was used for meta-analysis. Results A total of 13 articles were included, and involving 11 517 patients. The incidence of lower extremity DVT in stroke patients was 8.43%-56.47%. A total of 14 risk prediction models of lower extremity DVT in stroke patients were constructed in 13 articles, AUC was from 0.570 to 0.912. Among them, 10 risk prediction models had good predictive value (AUC ≥ 0.7) . Four risk prediction models were calibrated by Hosmer-Lemeshow goodness-of-fit test; 2 risk prediction models had external verification, and 9 risk prediction models had internal verification. The 14 risk prediction models include 3-6 prediction factors. The predictive factors can be divided into four categories: basic factors, disease factors, treatment factors, and laboratory test indicators. Metaanalysis results showed that age [OR=1.06, 95%CI (1.01, 1.10) ] , plasma D-dimer level [OR=1.11, 95%CI (1.07, 1.14) ] , atrial fibrillation [OR=3.69, 95%CI (1.45, 9.39) ] , disturbance of consciousness [OR=3.56, 95%CI (1.20, 10.61) ] , lower limb paralysis [OR=2.60, 95%CI (1.85, 3.66) ] , National Institute of Health Stroke Scale (NIHSS) score [OR=1.13, 95%CI (1.10, 1.16) ] were predictive factors of lower extremity DVT in stroke patients (P < 0.05) ; gender [OR=1.36, 95%CI (0.79, 2.34) ] was not predictive factors of lower extremity DVT in stroke patients (P > 0.05) . Conclusion Age, plasma D-dimer level, atrial fibrillation, disturbance of consciousness, lower limb paralysis, NIHSS score are effective predictors of lower extremity DVT in stroke patients. The research on risk prediction model of lower extremity DVT in stroke patients is in the development stage, and the quality of risk prediction models can be further improved in terms of follow-up time, model calibration and validation in the future.

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