Early Death Incidence and Prediction in Stage IV Breast Cancer
Early Death Incidence and Prediction in Stage IV Breast Cancer
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IV 期乳腺癌的早期死亡发生率和预测
DOI：
10.12659/msm.924858
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发表时间：
2020-08-11
影响因子：
3.1
通讯作者：
Zhang, Chao
中科院分区：
文献类型：
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作者：
Zhao, Yumei;Xu, Guijun;Zhang, Chao
研究背景：癌症患者的早期死亡是一个全球性的问题。我们的目的是确定IV期乳腺癌早期死亡的危险因素。根据风险因素生成用于早期死亡评估的预测列线图。材料/方法基于监测、流行病学和最终结果（SEER）数据库，选择诊断为IV期乳腺癌的患者。采用Logistic回归模型分析早期死亡（生存时间≤1年）的危险因素。构建预测列线图并进行内部验证。结果建设队列中共有5998例（32.6%）乳腺癌患者被诊断为早期死亡。年龄大于50岁、未婚、黑人、无保险、三阴性、II级和III级、肿瘤大小>5 cm、肺、肝和脑转移是总早期死亡的危险因素，而Luminal B亚型、N1期和手术干预与较低的早期死亡风险相关。至于癌症特异性和非癌症特异性早期死亡，两组之间的几个因素并不一致。构建了全因、癌症特异性和非癌症特异性早期死亡的列线图。校准曲线显示出令人满意的一致性。ROC曲线下面积（AUC）分别为78.3%（95% CI：77.7-78.9%）、75.8%（75.1-76.4%）和72.3%（71.6-72.9%）。在验证队列中，共有689例（19.3%）患者被诊断为早期死亡，校准曲线显示出令人满意的一致性。全因、癌症特异性和非癌症特异性早期死亡预测的AUC分别为74.0%（95% CI：72.5-75.4%）、73.5%（72.0-74.9%）和68.6%（67.0-70.1%）。结论诺模图可以预测早期死亡，具有良好的校正和区分度。该预测模型可为识别IV期乳腺癌患者中早期死亡高危病例提供参考，并对指导个体化治疗起到辅助作用。
Background The early death of patients is a global cancer issue. We aimed to identify the risk factors for early death in stage IV breast cancer. Predictive nomograms for early death evaluation were generated based on the risk factors. Material/Methods Based on the Surveillance, Epidemiology, and End Results (SEER) database, patients diagnosed with IV breast cancer were selected. The risk factors for early death (survival time ≤1 year) were identified using logistic regression model analysis. Predictive nomograms were constructed and internal validation was performed. Results A total of 5998 (32.6%) breast cancer patients were diagnosed as early death in the construction cohort. Age older than 50 years, unmarried status, black race, uninsured status, triple-negative type, grade (II and III), tumor size >5 cm, and metastasis to lung, liver, and brain were risk factors for total early death, while Luminal B subtype, N1 stage, and surgical interventions were associated with lower risk of early death. As for cancer-specific and non-cancer-specific early death, several factors were not consistent between the 2 groups. Nomograms for all-cause, cancer-specific, and non-cancer-specific early death were constructed. The calibration curve showed satisfactory agreement. The areas under the ROC curve (AUC) were 78.3% (95% CI: 77.7–78.9%), 75.8% (75.1–76.4%), and 72.3% (71.6–72.9%), respectively. In the validation cohort, a total of 689 (19.3%) patients were diagnosed as early death and the calibration curve showed satisfactory agreement. The AUCs of the all-cause, cancer-specific, and non-cancer-specific early death prediction were 74.0% (95% CI: 72.5–75.4%), 73.5% (72.0–74.9%), and 68.6% (67.0–70.1%), respectively. Conclusions Nomograms were generated to predict early death, with good calibration and discrimination. The predictive model can provide a reference for identifying cases with high risk of early death among stage IV breast cancer patients and play an auxiliary role in guiding individual treatment.