预测食管癌症放疗后心脏死亡的列线图的开发和验证

Cancer Innovation Pub Date : 2023-09-01 DOI:10.1002/cai2.89
Xinfang Lv, Xue Wu, Kai Liu, Xinke Zhao, Chenliang Pan, Jing Zhao, Juan Chang, Huan Guo, Xiang Gao, Xiaodong Zhi, Chunzhen Ren, Qilin Chen, Hugang Jiang, Chunling Wang, Ying-Dong Li
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引用次数: 0

摘要

背景癌症食管癌放疗后,患者常死于心脏原因。早期发现这些患者的心脏死亡风险对于改善临床决策和预后至关重要。因此,我们对食管癌症放射治疗后心脏死亡的风险进行了建模。方法回顾性分析2000年至2018年SEER数据库中37599例癌症放疗病例。所选病例被随机分配到模型开发组(n = 26320)和模型验证组(n = 11279),比例为7:3。我们通过最小绝对收缩和选择算子回归分析(LASSO)确定了最常见的与心脏死亡相关的风险因素。模型开发和验证的终点是5年和10年生存率。通过决策曲线分析(DCA)和一致性指数(C指数)评估模型的净临床效益。通过创建受试者工作特性曲线(ROC)并计算曲线下面积(AUC)来进一步评估模型的性能。对死亡概率进行Kaplan-Meier(K-M)生存分析。根据死亡概率阈值对患者进行分类。使用K-M曲线显示两组患者的5年和10年生存率。结果心脏性死亡的主要危险因素是年龄、手术、诊断年份、手术和放疗的顺序、化疗和一些肿瘤,这些因素用于创建列线图。开发组和验证组的列线图C指数分别为0.708和0.679。DCA显示列线图在预测5年和10年心脏死亡风险方面具有良好的净临床效益。该模型对5年和10年心脏死亡率(AUC:0.833和0.854)以及开发和验证队列(AUC=0.76和0.813)表现出中等的预测能力。结论我们的列线图可以帮助临床医生在早期检测心脏死亡风险的基础上,对癌症食管放疗患者做出临床决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development and validation of a nomogram to predict cardiac death after radiotherapy for esophageal cancer

Development and validation of a nomogram to predict cardiac death after radiotherapy for esophageal cancer

Background

Patients frequently die from cardiac causes after radiotherapy for esophageal cancer. Early detection of cardiac death risk in these patients is crucial to improve clinical decision-making and prognosis. Thus, we modeled the risk of cardiac death after irradiation for esophageal cancer.

Methods

A retrospective analysis of 37,599 esophageal cancer cases treated with radiotherapy in the SEER database between 2000 and 2018 was performed. The selected cases were randomly assigned to the model development group (n = 26,320) and model validation group (n = 11,279) at a ratio of 7:3. We identified the risk factors most commonly associated with cardiac death by least absolute shrinkage and selection operator regression analysis (LASSO). The endpoints for model development and validation were 5- and 10-year survival rates. The net clinical benefit of the models was evaluated by decision curve analysis (DCA) and concordance index (C-index). The performance of the models was further assessed by creating a receiver operating characteristic curve (ROC) and calculating the area under the curve (AUC). Kaplan-Meier (K-M) survival analysis was performed on the probability of death. Patients were classified according to death probability thresholds. Five- and ten-year survival rates for the two groups were shown using K-M curves.

Results

The major risk factors for cardiac death were age, surgery, year of diagnosis, sequence of surgery and radiotherapy, chemotherapy and a number of tumors, which were used to create the nomogram. The C-indexes of the nomograms were 0.708 and 0.679 for the development and validation groups, respectively. DCA showed the good net clinical benefit of nomograms in predicting 5- and 10-year risk of cardiac death. The model exhibited moderate predictive power for 5- and 10-year cardiac mortality (AUC: 0.833 and 0.854, respectively), and for the development and validation cohorts (AUC: 0.76 and 0.813, respectively).

Conclusions

Our nomogram may assist clinicians in making clinical decisions about patients undergoing radiotherapy for esophageal cancer based on early detection of cardiac death risk.

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