Qie Shuai, Shi Hongyun, Wang Fang, Liu Fangyu, Gu Jinling, Liu Xiaohui, Li Yanhong, Sun Xiaoyue
Department of Radiation Oncology, Affiliated Hospital of Hebei University, Baoding, China.
Department of Radiation Oncology, Baoding First Central Hospital, Baoding, China.
Front Oncol. 2022 Oct 19;12:1008326. doi: 10.3389/fonc.2022.1008326. eCollection 2022.
The purpose of this study was to analyze the clinical characteristics and prognosis of EPEC and to construct a prediction model based on the SEER database.
All EPECs from the SEER database were retrospectively analyzed. A comprehensive and practical nomogram that predicts the overall survival (OS) of EPEC was constructed. Univariate and multivariate Cox regression analysis was performed to explore the clinical factors influencing the prognosis of EPEC, and finally, the 1 -, 3 - and 5-year OS were predicted by establishing the nomogram. The discriminant and predictive ability of the nomogram was evaluated by consistency index (C-index), calibration plot, area under the curve (AUC), and receiver operating characteristic (ROC) curve. Decision curve analysis (DCA) was used to evaluate the clinical value of the nomogram.
A total of 3478 patients diagnosed with EPEC were extracted from the SEER database, and the data were randomly divided into the training group (n=2436) and the validation group (n=1402). T stage, N stage, M stage, surgery, chemotherapy, radiotherapy, age, grade, and tumor size were independent risk factors for 1 -, 3 - and 5-year OS of EPEC (P< 0.05), and these factors were used to construct the nomogram prediction mode. The C-index of the validation and training cohorts was 0.718 and 0.739, respectively, which were higher than those of the TNM stage system. The AUC values of the nomogram used to predict 1-, 2-, and 3-year OS were 0.751, 0.744, and 0.786 in the validation cohorts (0.761, 0.777, 0.787 in the training cohorts), respectively. The calibration curve of 1-, 2-, and 3-year OS showed that the prediction of the nomogram was in good agreement with the actual observation. The nomogram exhibited higher clinical utility after evaluation with the 1-, 2-, and 3-year DCA compared with the AJCC stage system.
This study shows that the nomogram prediction model for EPEC based on the SEER database has high accuracy and its prediction performance is significantly better than the TNM staging system, which can accurately and individually predict the OS of patients and help clinicians to formulate more accurate and personalized treatment plans.
本研究旨在分析肠致病性大肠埃希菌（EPEC）的临床特征和预后，并基于监测、流行病学与最终结果（SEER）数据库构建预测模型。
对SEER数据库中的所有EPEC病例进行回顾性分析。构建一个全面且实用的列线图，用于预测EPEC的总生存期（OS）。进行单因素和多因素Cox回归分析，以探索影响EPEC预后的临床因素，最后通过建立列线图预测1年、3年和5年总生存期。通过一致性指数（C指数）、校准图、曲线下面积（AUC）和受试者工作特征（ROC）曲线评估列线图的判别能力和预测能力。采用决策曲线分析（DCA）评估列线图的临床价值。
从SEER数据库中提取了3478例诊断为EPEC的患者，数据随机分为训练组（n = 2436）和验证组（n = 1402）。T分期、N分期、M分期、手术、化疗、放疗、年龄、分级和肿瘤大小是EPEC患者1年、3年和5年总生存期的独立危险因素（P < 0.05），利用这些因素构建列线图预测模型。验证组和训练组的C指数分别为0.718和0.739，均高于TNM分期系统。在验证组中，用于预测1年、2年和3年总生存期的列线图的AUC值分别为0.751、0.744和0.786（训练组中分别为0.761、0.777、0.787）。1年、2年和3年总生存期的校准曲线显示，列线图的预测与实际观察结果吻合良好。与美国癌症联合委员会（AJCC）分期系统相比，通过1年、2年和3年DCA评估，列线图具有更高的临床实用性。
本研究表明，基于SEER数据库的EPEC列线图预测模型具有较高的准确性，其预测性能显著优于TNM分期系统，能够准确且个体化地预测患者的总生存期，有助于临床医生制定更准确、个性化的治疗方案。
