Chen Jianqing, Xu Jinxin, He Jianbing, Hu Chao, Yan Chun, Wu Zhaohui, Li Zhe, Duan Hongbing, Ke Sunkui
Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, Xiamen, China.
Department of Thoracic Surgery, Fuqing City Hospital, Fuqing, China.
Front Surg. 2023 Jan 26;9:1079821. doi: 10.3389/fsurg.2022.1079821. eCollection 2022.
The present study aims to identify factors related to anastomotic leakage before esophagectomy and to construct a prediction model.
A retrospective analysis of 285 patients who underwent minimally invasive esophagectomy (MIE). An absolute shrinkage and selection operator was applied to screen the variables, and predictive models were developed using binary logistic regression.
A total of 28 variables were collected in this study. LASSO regression analysis, combined with previous literature and clinical experience, finally screened out four variables, including aortic calcification, heart disease, BMI, and FEV1. A binary logistic regression was conducted on the four predictors, and a prediction model was established. The prediction model showed good discrimination and calibration, with a C-statistic of 0.67 (95% CI, 0.593-0.743), a calibration curve fitting a 45° slope, and a Brier score of 0.179. The DCA demonstrated that the prediction nomogram was clinically useful. In the internal validation, the C-statistic still reaches 0.66, and the calibration curve has a good effect.
When patients have aortic calcification, heart disease, obesity, and a low FEV1, the risk of anastomotic leakage is higher, and relevant surgical techniques can be used to prevent it. Therefore, the clinical prediction model is a practical tool to guide surgeons in the primary prevention of anastomotic leakage.
本研究旨在确定食管癌切除术前与吻合口漏相关的因素，并构建预测模型。
对285例行微创食管癌切除术（MIE）的患者进行回顾性分析。应用绝对收缩与选择算子筛选变量，并使用二元逻辑回归建立预测模型。
本研究共收集了28个变量。通过LASSO回归分析，结合既往文献和临床经验，最终筛选出4个变量，包括主动脉钙化、心脏病、BMI和第1秒用力呼气容积（FEV1）。对这4个预测因子进行二元逻辑回归分析，建立了预测模型。该预测模型具有良好的区分度和校准度，C统计量为0.67（95%CI，0.593 - 0.743），校准曲线拟合45°斜率，Brier评分为0.179。决策曲线分析（DCA）表明预测列线图具有临床实用性。在内部验证中，C统计量仍达到0.66，校准曲线效果良好。
当患者存在主动脉钙化、心脏病、肥胖以及FEV1降低时，吻合口漏的风险较高，可采用相关手术技术进行预防。因此，该临床预测模型是指导外科医生对吻合口漏进行一级预防的实用工具。
