Development Of A Clinical Prediction Model For Anastomotic Leakage In Patients Before Minimally Invasive Mckeown Esophagectomy

Objective: Esophageal cancer is a common malignant tumor of digestive tract,surgical resection is still the main measure for the treatment of esophageal cancer.However,radical resection of oesophageal cancer is a complex and highly invasive operation,the incidence of postoperative complications is high.Anastomotic leakage is the important postoperative complication associated with esophagectomy.The present study aimed to identify factors related to anastomotic leakage before esophagectomy and to contruct a prediction model,it will help physicians screen patients who have a high risk of postoperative anastomotic leakage,take preventative measures in advance,then,primary prevention may be achieved.Methods: A retrospective analysis was conducted from January 2015 to Janury 2020,clinical data of 285 patients with primary esophageal cancer diagnosed at the Thoracic Surgery of our hospital and underwent minimally invasive esophagectomy,which reconstruction of the gastric tube through esophageal bed pathway and mechanical anastomosis was performed in the neck.Lymph node dissection was based on total three-field lymphadenectomy.The general characteristics of patients,the results of clinical examination and intraoperative index were collected.Last absolute shrinkage and selection operator was applied to screen the variables and predictive models were developed using binary logistic regression.C-statistic,the calibration plot,and brier score were used for assessing the discrimination and calibration of the model.We used the bootstrap resampling method to evaluate the reproducibility of the model development process.Results: 1.The intraoperative indicators include operators,operative times,estimated blood loss were analyzed using a non-parametric test and Chi-square analysis.The results show that there was no significant difference between the anastomotic leakage group and no anastomotic leakage group in postoperative esophageal cancer patients.2.A total of 28 variables were collect in this study,including patients’ general characteristics,tumor conditions,blood tests,clinical examination.Lasso regression analysis,combined with previous literature and clinical experience,finally screened out four variables,including aortic calcification,heart disease,BMI and FEV1.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% Cl:0.593-0.743).A calibration curve fitting a 45° slope,and a Brier score of 0.179.In the internal validation,the C-statistic still reach 0.66,and the calibration curve has a good effect.Conclusions: 1.We established a clinical prediction model to preoperatively predict the risk of postoperative anastomotic leakage in patients with esophageal cancer.2.When patients have aortic calcification,heart disease,obesity and low FEV1,the risk of anastomotic leakage is higher,and relevant surgical techniques can be used to prevent anastomotic leakage.Therefore,the clinical prediction model in this study is a practical tool to guide surgeons in the primary prevention of anastomotic leakage in clinical practice.