Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, China.
Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China.
Cancer Med. 2024 Aug;13(16):e70115. doi: 10.1002/cam4.70115.
Venous thromboembolism (VTE) poses a significant threat to lung cancer patients, particularly those receiving treatment with immune checkpoint inhibitors (ICIs). We aimed to develop and validate a nomogram model for predicting the occurrence of VTE in lung cancer patients undergoing ICI therapy.
The data for this retrospective cohort study was collected from cancer patients admitted to Chongqing University Cancer Hospital for ICI treatment between 2019 and 2022. The research data is divided into training and validation sets using a 7:3 ratio. Univariate and multivariate analyses were employed to identify risk factors for VTE. Based on these analyses, along with clinical expertise, a nomogram model was crafted. The model's predictive accuracy was assessed through receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, clinical impact curve, and other relevant metrics.
The initial univariate analysis pinpointed 13 potential risk factors for VTE. The subsequent stepwise multivariate regression analysis identified age, Karnofsky performance status, chemotherapy, targeted, platelet count, lactate dehydrogenase, monoamine oxidase, D-dimer, fibrinogen, and white blood cell count as significant predictors of VTE. These 10 variables were the foundation for a predictive model, illustrated by a clear and intuitive nomogram. The model's discriminative ability was demonstrated by the ROC curve, which showed an area under the curve of 0.815 (95% CI 0.772-0.858) for the training set, and 0.753 (95% CI 0.672-0.835) for the validation set. The model's accuracy was further supported by Brier scores of 0.068 and 0.080 for the training and validation sets, respectively, indicating a strong correlation with actual outcomes.
We have successfully established and validated a nomogram model for predicting VTE risk in lung cancer patients treated with ICIs.
静脉血栓栓塞症（VTE）对肺癌患者构成重大威胁，尤其是接受免疫检查点抑制剂（ICI）治疗的患者。本研究旨在建立和验证预测接受ICI 治疗的肺癌患者 VTE 发生风险的列线图模型。
本回顾性队列研究的数据来自 2019 年至 2022 年在重庆大学附属肿瘤医院接受 ICI 治疗的癌症患者。研究数据采用 7:3 的比例分为训练集和验证集。采用单因素和多因素分析确定 VTE 的风险因素。在此基础上，结合临床专业知识，构建了列线图模型。通过受试者工作特征（ROC）曲线、校准曲线、决策曲线分析、临床影响曲线和其他相关指标评估模型的预测准确性。
初步单因素分析确定了 13 个 VTE 的潜在风险因素。随后的逐步多因素回归分析确定年龄、卡氏功能状态、化疗、靶向治疗、血小板计数、乳酸脱氢酶、单胺氧化酶、D-二聚体、纤维蛋白原和白细胞计数是 VTE 的显著预测因子。这些 10 个变量构成了预测模型的基础，以清晰直观的列线图形式呈现。ROC 曲线显示，该模型在训练集的曲线下面积为 0.815（95%CI 0.772-0.858），在验证集的曲线下面积为 0.753（95%CI 0.672-0.835），证明了其具有良好的区分能力。训练集和验证集的 Brier 评分分别为 0.068 和 0.080，进一步支持了模型的准确性，表明与实际结果具有较强的相关性。
我们成功建立并验证了预测接受 ICI 治疗的肺癌患者 VTE 风险的列线图模型。
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