Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China.
National Engineering Research Center for Medical Big Data Application Technology, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
Front Endocrinol (Lausanne). 2023 Feb 27;14:1133554. doi: 10.3389/fendo.2023.1133554. eCollection 2023.
Colon adenocarcinoma (COAD) is a highly heterogeneous disease, which makes its prognostic prediction challenging. The purpose of this study was to investigate the clinical epidemiological characteristics, prognostic factors, and survival outcomes of patients with COAD in order to establish and validate a predictive clinical model (nomogram) for these patients.
Using the SEER (Surveillance, Epidemiology, and End Results) database, we identified patients diagnosed with COAD between 1983 and 2015. Disease-specific survival (DSS) and overall survival (OS) were assessed using the log-rank test and Kaplan-Meier approach. Univariate and multivariate analyses were performed using Cox regression, which identified the independent prognostic factors for OS and DSS. The nomograms constructed to predict OS were based on these independent prognostic factors. The predictive ability of the nomograms was assessed using receiver operating characteristic (ROC) curves and calibration plots, while accuracy was assessed using decision curve analysis (DCA). Clinical utility was evaluated with a clinical impact curve (CIC).
A total of 104,933 patients were identified to have COAD, including 31,479 women and 73,454 men. The follow-up study duration ranged from 22 to 88 months, with an average of 46 months. Multivariate Cox regression analysis revealed that age, gender, race, site_recode_ICD, grade, CS_tumor_size, CS_extension, and metastasis were independent prognostic factors. Nomograms were constructed to predict the probability of 1-, 3-, and 5-year OS and DSS. The concordance index (C-index) and calibration plots showed that the established nomograms had robust predictive ability. The clinical decision chart (from the DCA) and the clinical impact chart (from the CIC) showed good predictive accuracy and clinical utility.
In this study, a nomogram model for predicting the individualized survival probability of patients with COAD was constructed and validated. The nomograms of patients with COAD were accurate for predicting the 1-, 3-, and 5-year DSS. This study has great significance for clinical treatments. It also provides guidance for further prospective follow-up studies.
结肠腺癌(COAD)是一种高度异质性疾病,这使得其预后预测具有挑战性。本研究旨在探讨 COAD 患者的临床流行病学特征、预后因素和生存结局,以建立和验证预测这些患者的临床模型(列线图)。
使用 SEER(监测、流行病学和最终结果)数据库,我们确定了 1983 年至 2015 年间诊断为 COAD 的患者。使用对数秩检验和 Kaplan-Meier 方法评估疾病特异性生存(DSS)和总生存(OS)。使用 Cox 回归进行单因素和多因素分析,确定 OS 和 DSS 的独立预后因素。基于这些独立的预后因素,构建预测 OS 的列线图。使用受试者工作特征(ROC)曲线和校准图评估列线图的预测能力,使用决策曲线分析(DCA)评估准确性,使用临床影响曲线(CIC)评估临床实用性。
共确定了 104933 例 COAD 患者,其中女性 31479 例,男性 73454 例。随访研究时间范围为 22 至 88 个月,平均为 46 个月。多因素 Cox 回归分析显示,年龄、性别、种族、部位_ICD 编码、分级、CS 肿瘤大小、CS 扩散程度和转移是独立的预后因素。构建了预测 1、3 和 5 年 OS 和 DSS 的列线图。一致性指数(C 指数)和校准图显示,所建立的列线图具有稳健的预测能力。来自 DCA 的临床决策图(from the DCA)和来自 CIC 的临床影响图(from the CIC)显示了良好的预测准确性和临床实用性。
本研究构建并验证了预测 COAD 患者个体化生存概率的列线图模型。COAD 患者的列线图在预测 1、3 和 5 年 DSS 方面具有较高的准确性。本研究对临床治疗具有重要意义,也为进一步的前瞻性随访研究提供了指导。