Department of Thoracic Surgery, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou, 310005, China.
Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Clinical Medical School of, Yangzhou University, Yangzhou, 225001, China.
World J Surg Oncol. 2023 May 22;21(1):155. doi: 10.1186/s12957-023-03020-x.
To investigate the predictive merit of combined preoperative nutritional condition and systemic inflammation on the prognosis of patients receiving esophagectomy, with the assessment of model construction to extract a multidisciplinary phantom having clinical relevance and suitability.
The software of R 4.1.2 was utilized to acquire the survival optimal truncation value and the confusion matrix of survival for the continuity variables. SPSS Statistics 26 was employed to analyze the correlation of parameters, where including t-test, ANOVA and the nonparametric rank sum test shall. Pearson chi-square test was used for categorical variables. The survival curve was retrieved by Kaplan-Meier method. Univariate analysis of overall survival (OS) was performed through log-rank test. Cox analysis was for survival analyze. The performance of the prediction phantom through the area under curve (AUC) of receiver operating characteristic curve (ROC), decision curve analysis (DCA), nomogram and clinical impact curve (CIC) was plotted by R.
The AUC value of albumin-globulin score and skeletal muscle index (CAS) is markedly superior. Patients with diminished AGS and greater SMI were associated with improved overall survival (OS) and recurrence-free survival (RFS) (P < 0.01). The CAS composite evaluation model was calibrated with better accuracy and predictive performance. The DCA and CIC indicated a relatively higher net revenue for the prediction model.
The prediction model including the CAS score has excellent accuracy, a high net revenue, and favorable prediction function.
研究术前营养状况和全身炎症联合对接受食管切除术患者预后的预测价值,并评估模型构建以提取具有临床相关性和适用性的多学科虚拟模型。
使用 R 4.1.2 软件获取生存最佳截断值和连续性变量的生存混淆矩阵。使用 SPSS Statistics 26 分析参数的相关性,包括 t 检验、方差分析和非参数秩和检验。使用 Pearson 卡方检验进行分类变量分析。使用 Kaplan-Meier 方法获取生存曲线。通过对数秩检验对总生存期(OS)进行单因素分析。Cox 分析用于生存分析。通过 R 绘制受试者工作特征曲线(ROC)、决策曲线分析(DCA)、列线图和临床影响曲线(CIC)来评估预测模型的性能。
白蛋白-球蛋白评分和骨骼肌指数(CAS)的 AUC 值明显更高。白蛋白-球蛋白评分降低和骨骼肌指数增加的患者具有更好的总生存期(OS)和无复发生存期(RFS)(P < 0.01)。CAS 综合评价模型具有更好的准确性和预测性能。DCA 和 CIC 表明预测模型具有相对较高的净收益。
包括 CAS 评分的预测模型具有良好的准确性、高净收益和良好的预测功能。