Department of Geriatrics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou City, Gansu Province, China.
School of Integrative Medicine, Gansu University of Chinese Medicine, Lanzhou City, Gansu Province, China.
Cardiovasc Toxicol. 2023 Dec;23(11-12):377-387. doi: 10.1007/s12012-023-09807-4. Epub 2023 Oct 7.
The primary cause of mortality in esophageal cancer survivors is cardiac death. Early identification of cardiac mortality risk during chemotherapy for esophageal cancer is crucial for improving the prognosis. We developed and validated a nomogram model to identify patients with high cardiac mortality risk after chemotherapy for esophageal cancer for early screening and clinical decision-making. We randomly allocated 37,994 patients with chemotherapy-treated esophageal cancer into two groups using a 7:3 split ratio: model training (n = 26,598) and validation (n = 11,396). 5- and 10-year survival rates were used as endpoints for model training and validation. Decision curve analysis and the consistency index (C-index) were used to evaluate the model's net clinical advantage. Model performance was evaluated using receiver operating characteristic curves and computing the area under the curve (AUC). Kaplan-Meier survival analysis based on the prognostic index was performed. Patient risk was stratified according to the death probability. Age, surgery, sex, and year were most closely related to cardiac death and used to plot the nomograms. The C-index for the training and validation datasets were 0.669 and 0.698, respectively, indicating the nomogram's net clinical advantage in predicting cardiac death risk at 5 and 10 years. The 5- and 10-year AUCs were 0.753 and 0.772 for the training dataset and 0.778 and 0.789 for the validation dataset, respectively. The accuracy of the model in predicting cardiac death risk was moderate. This nomogram can identify patients at risk of cardiac death after chemotherapy for esophageal cancer at an early stage.
食管癌幸存者的主要死亡原因是心脏死亡。在食管癌化疗期间早期识别心脏死亡风险对于改善预后至关重要。我们开发并验证了一个列线图模型,以识别接受食管癌化疗后具有高心脏死亡风险的患者,以便进行早期筛查和临床决策。我们使用 7:3 的分割比例将 37994 名接受化疗治疗的食管癌患者随机分配到两个组:模型训练组(n=26598)和验证组(n=11396)。5 年和 10 年生存率被用作模型训练和验证的终点。决策曲线分析和一致性指数(C 指数)用于评估模型的净临床优势。使用接收者操作特征曲线和计算曲线下面积(AUC)来评估模型性能。基于预后指数进行 Kaplan-Meier 生存分析。根据死亡概率对患者风险进行分层。年龄、手术、性别和年份与心脏死亡最密切相关,用于绘制列线图。训练数据集和验证数据集的 C 指数分别为 0.669 和 0.698,表明该列线图在预测 5 年和 10 年心脏死亡风险方面具有净临床优势。训练数据集的 5 年和 10 年 AUC 分别为 0.753 和 0.772,验证数据集的 AUC 分别为 0.778 和 0.789。该模型预测心脏死亡风险的准确性为中等。该列线图可以在早期识别接受食管癌化疗后心脏死亡风险较高的患者。