Yang Wenwei, Fang Yuting, Niu Yaru, Sun Yongkun
Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China.
Front Oncol. 2022 Aug 22;12:972639. doi: 10.3389/fonc.2022.972639. eCollection 2022.
The mean age of gastric cancer (GC) patients has increased due to the aging society. Elderly GC patients with poor physical status tend to develop complications during the treatment courses, which cause early death. This study aimed to identify risk factors and establish nomograms for predicting total early death and cancer-specific early death in elderly GC patients.
Data for elderly GC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to a training cohort and a validation cohort. The univariate logistic regression model and backward stepwise logistic regression model were used to identify independent risk factors for early death. Nomograms were constructed to predict the overall risk of early death and their performance was validated by receiver operating characteristic (ROC) curve, calibration curve, decision curve analyses (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) in both training and validation cohorts.
Among the 3102 enrolled patients, 1114 patients died within three months from the first diagnosis and 956 of them died due to cancer-specific causes. Non-Asian or Pacific Islander (API) race, non-cardia/fundus or lesser/greater curvature, higher AJCC stage, no surgery and no chemotherapy were all related to a high risk of both all-cause early death and cancer-specific early death. Higher T stage and N0 stage were only positively related to total early mortality, while liver metastasis was only positively related to cancer-specific early mortality. Based on these identified factors, two nomograms were developed for predicting the risk of all-cause and cancer-specific early death, which showed good performance with the AUC of the nomograms were 0.775 and 0.766, respectively. The calibration curves, DCAs, NRI, and IDI also confirmed the value of these nomograms.
These nomogram models were considered a practical tool to identify the early death of elderly GC patients and help provide a more individualized treatment strategy.
由于社会老龄化，胃癌（GC）患者的平均年龄有所增加。身体状况较差的老年GC患者在治疗过程中容易出现并发症，导致早期死亡。本研究旨在确定老年GC患者全因早期死亡和癌症特异性早期死亡的危险因素，并建立预测模型。
从监测、流行病学和最终结果（SEER）数据库中提取老年GC患者的数据。这些患者被随机分配到训练队列和验证队列。采用单因素逻辑回归模型和向后逐步逻辑回归模型确定早期死亡的独立危险因素。构建预测早期死亡总体风险的列线图，并通过受试者工作特征（ROC）曲线、校准曲线、决策曲线分析（DCA）、综合判别改善（IDI）和净重新分类改善（NRI）在训练队列和验证队列中验证其性能。
在3102名入选患者中，1114名患者在首次诊断后的三个月内死亡，其中956名死于癌症特异性原因。非亚裔或太平洋岛民（API）种族、非贲门/胃底或胃小弯/胃大弯、较高的美国癌症联合委员会（AJCC）分期、未进行手术和未进行化疗均与全因早期死亡和癌症特异性早期死亡的高风险相关。较高的T分期和N0分期仅与全因早期死亡率呈正相关，而肝转移仅与癌症特异性早期死亡率呈正相关。基于这些确定的因素，开发了两个列线图来预测全因和癌症特异性早期死亡的风险，其列线图的曲线下面积（AUC）分别为0.775和0.766，表现良好。校准曲线、DCA、NRI和IDI也证实了这些列线图的价值。
这些列线图模型被认为是识别老年GC患者早期死亡的实用工具，并有助于提供更个体化的治疗策略。
