Liu Xiao-Yu, Kang Bing, Lv Quan, Wang Zi-Wei
Department of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Clinical Nutrition, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Nutr. 2024 Aug 16;11:1446660. doi: 10.3389/fnut.2024.1446660. eCollection 2024.
The aim of this study was to develop a validated nomogram to predict the risk of postoperative complications in colorectal cancer (CRC) patients by analyzing the factors that contribute to these complications.
We retrospectively collected clinical information on patients who underwent CRC surgery at a single clinical center from January 2021 to December 2021. Univariate and multivariate logistic regression analysis to identify independent risk factors for postoperative complications and to develop a predictive model. A receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC) to assess the predicted probability. Calibration curve was drawn to compare the predicted probability of the nomogram with the actual probability, and decision curve analysis (DCA) was employed to evaluate the clinical utility of the nomogram.
A total of 190 CRC patients were included in this study. We retrospectively collected baseline information, clinical information, surgical information, and nutrition-related indicators for all patients. Through multivariate logistic regression analysis, preoperative albumin ( = 0.041, OR = 0.906, 95% CI = 0.824-0.996), surgical time ( = 0.009, OR = 1.006, 95% CI = 1.001-1.010), waistline ( = 0.049, OR = 1.011, 95% CI = 1.002-1.020) and phase angle (PA) ( = 0.022, OR = 0.615, 95% CI = 0.405-0.933) were identified as independent risk factors for postoperative complications in CRC, and a nomogram prediction model was established using the above four variables. The AUC of 0.706 for the ROC plot and the high agreement between predicted and actual probabilities in the calibration curves suggested that the prediction model has good predictive power. The DCA also confirmed the good clinical performance of the nomogram.
This study developed a nomogram to predict the risk of postoperative complications in CRC patients, providing surgeons with a reliable reference to personalized patient management in the perioperative period and preoperative nutritional interventions.
本研究旨在通过分析导致结直肠癌（CRC）患者术后并发症的因素，开发一种经过验证的列线图，以预测其术后并发症风险。
我们回顾性收集了2021年1月至2021年12月在单一临床中心接受CRC手术患者的临床信息。采用单因素和多因素逻辑回归分析来确定术后并发症的独立危险因素，并建立预测模型。使用受试者工作特征（ROC）曲线计算曲线下面积（AUC）以评估预测概率。绘制校准曲线以比较列线图的预测概率与实际概率，并采用决策曲线分析（DCA）评估列线图的临床实用性。
本研究共纳入190例CRC患者。我们回顾性收集了所有患者的基线信息、临床信息、手术信息和营养相关指标。通过多因素逻辑回归分析，术前白蛋白（=0.041，OR=0.906，95%CI=0.824-0.996）、手术时间（=0.009，OR=1.006，95%CI=1.001-1.010）、腰围（=0.049，OR=1.011，95%CI=1.002-1.020）和相位角（PA）（=0.022，OR=0.615，95%CI=0.405-0.933）被确定为CRC患者术后并发症的独立危险因素，并使用上述四个变量建立了列线图预测模型。ROC曲线的AUC为0.706以及校准曲线中预测概率与实际概率之间的高度一致性表明该预测模型具有良好的预测能力。DCA也证实了列线图具有良好的临床性能。
本研究开发了一种列线图来预测CRC患者术后并发症风险，为外科医生在围手术期进行个性化患者管理和术前营养干预提供了可靠的参考。
