State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
School of Nursing, Peking Union Medical College, Beijing, China.
Thorac Cancer. 2024 Feb;15(4):277-285. doi: 10.1111/1759-7714.15219. Epub 2024 Jan 17.
Venous thromboembolism (VTE) increases the risk of death or adverse outcomes in patients with lung cancer. Therefore, early identification and treatment of high-risk groups of VTE have been the research focus. In this systematic review, the risk assessment tools of VTE in patients with lung cancer were systematically analyzed and evaluated to provide a reference for VTE management.
Relevant studies were retrieved from major English databases (The Cochrane Library, Embase, Web of Science, PubMed, Scopus, Medline) and Chinese databases (China National Knowledge Infrastructure [CNKI] and WanFang Data) until July 2023 and extracted by two researchers. This systematic review was registered at PROSPERO (no. CRD42023409748).
Finally, two prospective cohort studies and four retrospective cohort studies were included from 2019. There was a high risk of bias in all included studies according to the Prediction Model Risk of Bias Assessment tool (PROBAST). In the included studies, Cox and logistic regression were used to construct models. The area under the receiver operating characteristic curve (AUC) of the model ranged from 0.670 to 0.904, and the number of predictors ranged from 4 to 11. The D-dimer index was included in five studies, but significant differences existed in optimal cutoff values from 0.0005 mg/L to 2.06 mg/L. Then, three studies validated the model externally, two studies only validated the model internally, and only one study validated the model using a combination of internal and external validation.
VTE risk prediction models for patients with lung cancer have received attention for no more than 5 years. The included model shows a good predictive effect and may help identify the risk population of VTE at an early stage. In the future, it is necessary to improve data modeling and statistical analysis methods, develop predictive models with good performance and low risk of bias, and focus on external validation and recalibration of models.
静脉血栓栓塞症（VTE）会增加肺癌患者死亡或不良结局的风险。因此，早期识别和治疗 VTE 的高危人群一直是研究的重点。在这项系统评价中，我们系统地分析和评估了肺癌患者 VTE 的风险评估工具，为 VTE 管理提供参考。
从主要的英文数据库（The Cochrane Library、Embase、Web of Science、PubMed、Scopus、Medline）和中文数据库（中国知网[CNKI]和万方数据）中检索到相关研究，并由两位研究人员进行提取，该系统评价在 PROSPERO 注册（编号：CRD42023409748）。
最终纳入了 2019 年的两项前瞻性队列研究和四项回顾性队列研究。根据预测模型风险偏倚评估工具（PROBAST），所有纳入研究均存在较高的偏倚风险。纳入的研究中，使用 Cox 和逻辑回归构建模型。模型的受试者工作特征曲线下面积（AUC）范围为 0.670 至 0.904，预测因子数量范围为 4 至 11。五项研究中均包含 D-二聚体指数，但最佳截断值差异显著，范围为 0.0005mg/L 至 2.06mg/L。然后，有三项研究对外验证模型，两项研究仅对内验证模型，仅有一项研究采用内部和外部验证相结合的方法验证模型。
肺癌患者 VTE 风险预测模型的研究时间不超过 5 年。纳入的模型具有较好的预测效果，可能有助于早期识别 VTE 的高危人群。未来需要改进数据建模和统计分析方法，开发性能良好且低偏倚风险的预测模型，并重点关注模型的外部验证和再校准。
