Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China.
Department of Urology, The First Affiliated Hospital of Wenzhou Medical University , Wenzhou, China.
Expert Rev Gastroenterol Hepatol. 2020 Aug;14(8):757-764. doi: 10.1080/17474124.2020.1784726. Epub 2020 Jun 30.
The aim of this study was to construct a nomogram to predict the survival of patients with metastatic Siewert Type II adenocarcinomas of the esophagogastric junction (AEG).
Patients were identified using the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was performed to assess the prognostic factors. A nomogram comprising independent prognostic factors was established and evaluated using C-indexes, calibration curves, and decision curve analyses.
In total 1616 eligible patients were enrolled. Race, age, bone metastasis, liver metastasis, lung metastasis, other metastasis sites, and distant lymph nodes metastasis were independent prognostic factors and were integrated to construct the nomogram. The nomogram had a C-index of 0.590 (95% CI: 0.569-0.611) in the training cohort and 0.569 (95% CI: 0.532-0.606) in the validation cohort. The calibration plots for the probabilities of 6-month and 1-year overall survival demonstrated there was an optimum between nomogram prediction and actual observation.
We developed and validated a nomogram to predict individual prognosis for patients with metastatic Siewert Type II AEG, and the risk stratification system based on the nomogram could effectively stratify the patients into two risk subgroups, which can help clinicians accurately predict mortality risk and recommend personalized treatment modalities.
本研究旨在构建一个列线图，以预测食管胃结合部（AEG）Ⅱ型 Siewert 型腺癌转移患者的生存情况。
使用监测、流行病学和最终结果（SEER）数据库来识别患者。使用 Cox 回归分析来评估预后因素。基于独立的预后因素，建立并评估了一个列线图，通过 C 指数、校准曲线和决策曲线分析进行评估。
共纳入 1616 名符合条件的患者。种族、年龄、骨转移、肝转移、肺转移、其他转移部位和远处淋巴结转移是独立的预后因素，并被整合到列线图中。该列线图在训练队列中的 C 指数为 0.590（95%CI：0.569-0.611），在验证队列中的 C 指数为 0.569（95%CI：0.532-0.606）。6 个月和 1 年总生存率的校准图表明，列线图预测与实际观察之间存在最佳契合度。
我们开发并验证了一个用于预测转移性 Siewert Ⅱ型 AEG 患者个体预后的列线图，基于该列线图的风险分层系统可以有效地将患者分为两个风险亚组，这有助于临床医生准确预测死亡率风险，并推荐个性化的治疗方式。
