Research progress on risk assessment models of venous thromboembolism for cancer patients
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摘要: 静脉血栓栓塞（venous thromboembolism，VTE）已经成为肿瘤患者最常见的并发症，且成为除肿瘤患者外第二大死因。肿瘤本身是一种存在血栓和出血双风险并存的状态，对患者能够进行有效风险评估筛查的模型显得尤为关键。只有能在对肿瘤患者进行精准的风险分层，低风险患者能够不进行血栓预防或仅进行机械预防降低治疗成本和出血风险，高风险患者预防血栓中获益。本文对目前肿瘤并发相关VTE的情况、当前VTE风险评估模型的对比、风险模型的建立及未来发展方向进行探讨，旨在提高对相关VTE风险评估模型的的认识，并对风险模型的建立和发展提出理论支持。Abstract: Venous thromboembolism (VTE) has become the most common complication in tumor patients and is the second leading cause of death in these patients. The presence of a tumor increases the risks of thrombosis and bleeding. Thus, it is important to have a model capable of effective risk assessment and screening for cancer patients. Cancer patients can be categorized based on risk levels. For low-risk patients, the cost of medical treatment and the risk of bleeding caused by prevention of thrombosis or the sole use of mechanical prevention are less. High-risk patients can benefit from prevention measures. This review presents a comparison between the current VTE risk assessment models and discusses the current situation of cancer-associated VTE, establishment of risk models, and future directions. The review aims to improve the understanding of cancer-associated VTE risk assessment models and provide guidance for the establishment and development of risk models.
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Key words:
- neoplasms /
- deep venous thrombosis /
- risk assessment models
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表 1 常用风险评估模型特点
风险评估模型 模型内容 优点 缺点 Khorana模型[12-13] 肿瘤部位；血小板计数；白细胞计数；血红蛋白计数或应用红细胞生长因子；BMI 目前评价癌症患者合并VTE风险评估的最常用模型之一；在大量研究中均得到验证；表格简单易行，适合门诊初筛患者，也同样适用于住院患者；对患者预后有预测意义 风险预测能力较Caprini、Autar、Padua模型差；所使用的BMI指数适用于西方女性，而我国女性BMI指数较小；验证结果不尽相同，在不同癌种预测能力相差较多 Vienna模型[14-15] 在Khorana模型基础上增加sP选择素和D-2聚体 在Khorana模型基础上增加2个指标，明显提高阴性预测值；预测能力较Khorana模型升高，且对风险分级有帮助 sP选择素通常不作为常用检验，且分析较难获取；验证研究较少 PROTECHT模型[16-17] 在Khorana模型基础上增加顺铂/卡铂为基础的化疗或吉西他滨 针对于肿瘤患者评估VTE风险；适用于目前使用化疗药物的肿瘤患者 辨别能力不佳；应用较少 CATS nomogram[14] 肿瘤部位；D-2聚体 提取了K模型与V模型最强因素；简单易行，能快速预测风险，适合门诊患者 涵盖指标较少，容错率低 Caprini模型[18] 包括年龄、肥胖、异常妊娠、下肢水肿等 曾被大规模的回顾性试验研究验证；简答易用，敏感性较高；比较适用于外科患者，也适用于内科患者；分级细致，对患者预后也有预测意义 未使用严谨的统计学方法进一步完善；若想进行针对性预防和治疗需更加细化指标；特异性较差 Tic-ONCO模型[19] 纳入了临床特征（BMI≥25 kg/m2；家族史；肿瘤位置）及显示与肿瘤患者发生相关的几种基因突变类型（rs2232698；rs6025；rs5985；rs4524） 将遗传因素考虑入模型，明显增加预测能力；无需跟踪指标变化 基因检测无法在常规实验室中完成，耗时较长；检测价格昂贵，依从性差 
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