基于SEER数据库的转移性结肠癌患者早期死亡预测列线图模型构建

目的构建预测转移性结肠癌(mCC)患者早期死亡的列线图模型。方法从SEER数据库中选择6669例符合条件的mCC患者。根据多因素Logistic回归中的危险因素构建列线图。通过C-index、校准曲线和临床决策曲线分析(DCA)评估列线图的预测性能。结果原发肿瘤位置、肿瘤分化、T分期、M分期、骨转移、脑转移、CEA、肿瘤大小、年龄和婚姻状态是mCC患者早期死亡的独立影响因素。基于这些变量构建列线图,C-index和校准曲线显示模型具有很好的预测能力,DCA曲线显示列线图可以使患者有较好的临床获益。结论该列线图具有良好的预测能力,能够帮助医生识别可能早期死亡的高危mCC患者,有助于制定个性化治疗策略。

Nomogram for Predicting Early Death in Patients with Metastatic Colon Cancer Based on SEER Database

Objective To construct a Nomogram model that can accurately predict early death of metastatic colon cancer (mCC). Methods A total of 6 669 patients from the SEER database were identified using inclusion and exclusion criteria. Multivariate logistic regression was used to identify risk factors for early mortality and to construct a Nomogram. The predictive performance of the Nomogram was evaluated by C-index, calibration curve, and decision curve analysis (DCA). Results Primary tumor location, differentiation grade, T stage, M stage, bone metastases, brain metastases, CEA, tumor size, age and marital status were independent factors for early death in patients with mCC. A Nomogram was constructed based on these variables. The C-index and the calibration curve of the Nomogram showed the good predictive ability of the nomogram. DCA showed that the Nomogram had a superior clinical net benefit in predicting early death compared with TNM stage. Conclusion The developed Nomogram has good predictive ability and can help guide clinicians to identify patients with high-risk mCC for individualized diagnosis and treatment.