Construction and validation of a risk-prediction model for anastomotic leakage after radical gastrectomy: A cohort study in China

Abstract

ObjectivesAnastomotic leakage (AL) stands out as a prevalent and severe complication following gastric cancer surgery. It frequently precipitates additional serious complications, significantly influencing the overall survival time of patients. This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer. MethodsThis study included a derivation cohort and validation cohort. The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, from January 1, 2015 to December 31, 2020. An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions. Based on the questionnaire, inpatient data were collected to form a model-derivation cohort. This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events, and a logistic regression model with stepwise regression was developed. A 5-fold cross-validation ensured model reliability. The validation cohort included patients from August 1, 2021 to December 31, 2021 at the same hospital. Using the same imputation method, we organized the validation-queue data. We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue. We compared the predictions with the actual occurrence, and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration curve. ResultsThe derivation cohort included 1377 patients, and the validation cohort included 131 patients. The independent predictors of AL after radical gastrectomy included age ≥65 y, preoperative albumin <35 g/L, resection extent, operative time ≥240 min, and intraoperative blood loss ≥90 mL. The predictive model exhibited a solid AUROC of 0.750 (95% CI: 0.694–0.806; p < 0.001) with a Brier score of 0.049. The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052. External validation showed an AUROC of 0.723 (95% CI: 0.564–0.882; p = 0.006) and a Brier score of 0.055, confirming reliability in different clinical settings. ConclusionsWe successfully developed a risk-prediction model for AL following radical gastrectomy. This tool will aid healthcare professionals in anticipating AL, potentially reducing unnecessary interventions.