基于炎症负荷指数预测肺癌患者抗肿瘤治疗后心脏损伤的在线动态图。

IF 3.2 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Yumin Wang, Chunyan Huan, Huijuan Pu, Guodong Wang, Yan Liu, Xiuli Zhang, Chengyang Li, Jie Liu, Wanling Wu, Defeng Pan
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引用次数: 0

摘要

导言:心脏毒性已成为癌症患者,特别是肺癌患者关注的主要问题,因为抗肿瘤治疗可显著影响患者的生存和生活质量。本研究旨在建立并验证基于炎症负担指数(inflammatory Burden Index, IBI)的动态图,预测肺癌患者抗肿瘤治疗后一年内心脏损伤的风险。方法:这项单中心回顾性研究纳入了2018年7月至2023年1月期间接受心肌酶检测的1386例肺癌患者。IBI计算公式为:IBI = (CRP (mg/dL) ×中性粒细胞(/μL)) /淋巴细胞(/μL)。使用SPSS 22.0和R 4.4.1进行统计分析,包括机器学习算法和多变量逻辑分析,确定了心脏损伤的独立预测因素。通过内部验证、ROC曲线和决策曲线分析(DCA),开发并验证了在线动态模态图。结果:1386例患者平均年龄为61.98±9.22岁。重要的独立预测因素包括年龄、BMI、高血压、免疫治疗、d -二聚体、LDH、NSE、CKMB和IBI。训练集和验证集的AUC-ROC值分别为0.85和0.86,显示出较强的判别能力。校正曲线拟合良好,DCA具有较高的临床应用价值。结论:基于临床和炎症标志物的在线动态图可以预测肺癌患者抗肿瘤治疗后的心脏损伤。该模型具有较强的判别能力和潜在的临床应用价值,可为肿瘤医师设计个性化临床治疗方案提供重要信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A new online dynamic nomogram based on the inflammation burden index to predict cardiac injury after antitumor therapy in lung cancer patients.

A new online dynamic nomogram based on the inflammation burden index to predict cardiac injury after antitumor therapy in lung cancer patients.

A new online dynamic nomogram based on the inflammation burden index to predict cardiac injury after antitumor therapy in lung cancer patients.

A new online dynamic nomogram based on the inflammation burden index to predict cardiac injury after antitumor therapy in lung cancer patients.

Introduction: Cardiotoxicity has become a major concern in cancer patients, especially those with lung cancer, as anti-tumor therapies can significantly affect patient survival and quality of life. This study aims to develop and validate a dynamic nomogram based on the Inflammation Burden Index (IBI) to predict the risk of cardiac injury within one year after anti-tumor treatment in lung cancer patients.

Methods: This single-center, retrospective study included 1386 lung cancer patients who underwent myocardial enzyme testing between July 2018 and January 2023. The IBI was calculated as: IBI = (CRP (mg/dL) × Neutrophils (/μL)) / Lymphocytes (/μL). Statistical analysis using SPSS 22.0 and R 4.4.1, including machine learning algorithms and multivariate logistic analysis, identified independent predictors of cardiac injury. An online dynamic nomogram was developed and validated using internal validation, ROC curves, and decision curve analysis (DCA).

Results: The average age of the 1386 patients was 61.98 ± 9.22 years. Significant independent predictors included age, BMI, hypertension, immunotherapy, D-dimer, LDH, NSE, CKMB, and IBI. The nomogram showed strong discriminative ability with AUC-ROC values of 0.85 for the training set and 0.86 for the validation set. Calibration curves confirmed good fit, and DCA showed high clinical utility.

Conclusion: An online dynamic nomogram based on clinical and inflammatory markers was developed to predict cardiac injury in lung cancer patients following anti-tumor therapy. The model shows strong discriminative ability and potential clinical value, which can provide vital information for oncologists when designing customized clinical treatments.

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来源期刊
Cardio-oncology
Cardio-oncology Medicine-Cardiology and Cardiovascular Medicine
CiteScore
5.00
自引率
3.00%
发文量
17
审稿时长
7 weeks
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