Zhang Lu, Fang Yue, Xing Jianghao, Cheng Hao, Sun Xiaonan, Yuan Zhichao, Xu Yidan, Hao Jiqing
Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, People's Republic of China.
Department of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, People's Republic of China.
J Inflamm Res. 2022 Aug 15;15:4649-4661. doi: 10.2147/JIR.S376601. eCollection 2022.
This study aimed to analyze the association between venous thromboembolism (VTE) and inflammatory markers like systemic immune-inflammation index (SII) and prognosis nutritional index (PNI), and to evaluate their efficacy for the diagnosis of VTE in patients with gastrointestinal malignancies.
A total of 1326 patients with the initial diagnosis of gastrointestinal cancer in the First Affiliated Hospital of Anhui Medical University (AHMU) were enrolled in the training cohort. Univariate and multivariate analysis was used to pinpoint independent predictors of VTE, which were eventually visualized as the nomogram models. The Akaike Information Criterion (AIC) was used to screen the best model. The receiver operating characteristic curve (ROC) and the clinical decision curve analysis (DCA) were utilized to evaluate the models' predictive performance in the training queue and another external sample of 250 patients at the Second Affiliated Hospital of AHMU.
A total of 476 patients were complicated with VTE in the training cohort. Multifactorial analysis of clinical characteristics and inflammatory markers showed that PNI, SII, age, tumor location, and therapy were independent risk factors of VTE, visualized as model A. Another model B was constructed by adding coagulation markers to the previous analysis. Model B was the best prediction model with the minimum AIC value, followed by model A with an AUC of 0.806 (95% CI 0.7820.830) which was similar to model B's 0.832 (95% CI 0.8100.855) but significantly higher than the currently widely used Khorana score's 0.592 (95% CI 0.5620.621) and the CATS score's 0.682 (95% CI 0.6530.712). The external verification yielded similar findings, with the AUC being 0.792 (95% CI 0.7340.851), 0.834 (95% CI 0.7780.890), 0.655 (95% CI 0.5820.729), and 0.774 (95% CI 0.6990.849) respectively. The DCA curves demonstrated that new models had excellent usefulness in screening patients with a high VTE risk.
The SII and PNI were simple and viable inflammatory markers associated with VTE, and the nomogram based on them and clinical features had a meaningful clinical utility for VTE in patients with gastrointestinal malignancies.
本研究旨在分析静脉血栓栓塞症(VTE)与全身免疫炎症指数(SII)和预后营养指数(PNI)等炎症标志物之间的关联,并评估它们在胃肠道恶性肿瘤患者中诊断VTE的效能。
安徽医科大学第一附属医院初次诊断为胃肠道癌的1326例患者纳入训练队列。采用单因素和多因素分析确定VTE的独立预测因素,并最终将其可视化为列线图模型。使用赤池信息准则(AIC)筛选最佳模型。采用受试者工作特征曲线(ROC)和临床决策曲线分析(DCA)评估模型在训练队列以及安徽医科大学第二附属医院250例患者的另一个外部样本中的预测性能。
训练队列中共有476例患者并发VTE。对临床特征和炎症标志物进行多因素分析显示,PNI、SII、年龄、肿瘤部位和治疗是VTE的独立危险因素,可视化为模型A。在先前分析基础上加入凝血标志物构建了另一个模型B。模型B是具有最小AIC值的最佳预测模型,其次是模型A,其AUC为0.806(95%CI 0.7820.830),与模型B的0.832(95%CI 0.8100.855)相似,但显著高于目前广泛使用的Khorana评分的0.592(95%CI 0.5620.621)和CATS评分的0.682(95%CI 0.6530.712)。外部验证得出了类似的结果,AUC分别为0.792(95%CI 0.7340.851)、0.834(95%CI 0.7780.890)、0.655(95%CI 0.5820.729)和0.774(95%CI 0.6990.849)。DCA曲线表明新模型在筛查VTE高风险患者方面具有出色的实用性。
SII和PNI是与VTE相关的简单且可行的炎症标志物,基于它们和临床特征的列线图对胃肠道恶性肿瘤患者的VTE具有有意义的临床应用价值。