Development and validation of a nomogram model for predicting venous thromboembolism risk in lung cancer patients treated with immune checkpoint inhibitors: A cohort study in China

IF 2.9 2区医学 Q2 ONCOLOGY Cancer Medicine Pub Date : 2024-08-20 DOI:10.1002/cam4.70115

Guanzhong Liang, Zuhai Hu, Qianjie Xu, Guixue Wang, Ying Wang, Xiaosheng Li, Wei Zhang, Haike Lei

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Abstract

Objective

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.

Methods

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.

Results

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.

Conclusion

We have successfully established and validated a nomogram model for predicting VTE risk in lung cancer patients treated with ICIs.

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用于预测接受免疫检查点抑制剂治疗的肺癌患者静脉血栓栓塞风险的提名图模型的开发与验证：中国队列研究。

目的：静脉血栓栓塞症（VTE）对肺癌患者，尤其是接受免疫检查点抑制剂（ICIs）治疗的患者构成重大威胁。我们旨在开发并验证一种提名图模型，用于预测接受 ICI 治疗的肺癌患者 VTE 的发生率：这项回顾性队列研究的数据来自2019年至2022年期间重庆大学附属肿瘤医院收治的接受ICI治疗的癌症患者。研究数据按 7:3 的比例分为训练集和验证集。采用单变量和多变量分析来确定 VTE 的风险因素。在这些分析的基础上，结合临床专业知识，建立了一个提名图模型。该模型的预测准确性通过接收者操作特征曲线（ROC）、校准曲线、决策曲线分析、临床影响曲线和其他相关指标进行评估：结果：最初的单变量分析确定了 13 个潜在的 VTE 危险因素。随后的逐步多变量回归分析发现，年龄、卡诺夫斯基表现状态、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 的重要预测因素。这 10 个变量是建立预测模型的基础，并通过清晰直观的提名图加以说明。该模型的判别能力通过 ROC 曲线得到证明，训练集的曲线下面积为 0.815（95% CI 0.772-0.858），验证集的曲线下面积为 0.753（95% CI 0.672-0.835）。训练集和验证集的布赖尔评分分别为 0.068 和 0.080，进一步证明了模型的准确性，表明模型与实际结果密切相关：我们成功地建立并验证了一个预测肺癌患者接受 ICIs 治疗后 VTE 风险的提名图模型。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.