Department of Pharmacy, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
Clin Appl Thromb Hemost. 2023 Jan-Dec;29:10760296231196859. doi: 10.1177/10760296231196859.
Metastatic colorectal cancer (mCRC) patients are predisposed to venous thromboembolism (VTE). This study aimed to (1) evaluate the efficacy of 4 existing cancer-specific VTE models in predicting VTE incidence among hospitalized mCRC patients, and (2) examine the influence of incorporating mCRC molecular subtypes into these models. We conducted an evaluation of 4 cancer-specific VTE models, including Khorana, Vienna CATS, Protecht, and CONKO in a dataset involving 1392 mCRC patients. To evaluate the predictive performance, we utilized receiver operating characteristic (ROC) curves for both the original models and the modified models that incorporated microsatellite instability status or // mutations. Moreover, we computed the net reclassification improvement (NRI) to quantify the enhancements made to the modified VTE risk models. All models demonstrated a moderate area under the ROC curve (ROC-AUC) when predicting the occurrence of VTE: Khorana (0.550), Vienna CATS (0.671), Protecht (0.652), and CONKO (0.578). The incorporation of and mutations significantly improved the ROC-AUC of all 4 existing models (modified Khorana: 0.796, modified Vienna CATS: 0.832, modified Protecht: 0.834, and modified CONKO: 0.809). After dichotomizing the risk using a threshold of 3 points and comparing them with the original models, NRI values for the 4 modified models were 0.97, 0.95, 1.11, and 0.98, respectively. All 4 cancer-specific VTE models exhibit moderate performance when identifying mCRC patients at high risk of VTE. Incorporating and mutations may enhance the prediction of VTE in hospitalized mCRC patients.
转移性结直肠癌(mCRC)患者易发生静脉血栓栓塞症(VTE)。本研究旨在:(1)评估 4 种现有的癌症特异性 VTE 模型在预测住院 mCRC 患者 VTE 发生率方面的有效性;(2)探讨将 mCRC 分子亚型纳入这些模型的影响。我们在一个包含 1392 例 mCRC 患者的数据集里评估了 4 种癌症特异性 VTE 模型,包括 Khorana、Vienna CATS、Protecht 和 CONKO。为了评估预测性能,我们分别使用原始模型和纳入微卫星不稳定性状态或 // 突变的改良模型的受试者工作特征(ROC)曲线。此外,我们计算了净重新分类改善(NRI),以量化改良 VTE 风险模型的改进程度。所有模型在预测 VTE 发生方面的 ROC 曲线下面积(ROC-AUC)均为中度:Khorana(0.550)、Vienna CATS(0.671)、Protecht(0.652)和 CONKO(0.578)。纳入 // 突变可显著提高所有 4 个现有模型的 ROC-AUC(改良 Khorana:0.796,改良 Vienna CATS:0.832,改良 Protecht:0.834,改良 CONKO:0.809)。使用 3 分作为风险截断值将风险分为两类,并与原始模型进行比较,4 个改良模型的 NRI 值分别为 0.97、0.95、1.11 和 0.98。这 4 种癌症特异性 VTE 模型在识别有发生 VTE 高风险的 mCRC 患者方面表现出中等效能。纳入 // 突变可能会提高对住院 mCRC 患者 VTE 的预测能力。