Nomograms for postoperative complications in congenital biliary dilatation: a retrospective cohort study

Yu Zhou¹Xintao Zhang²Xue Ren¹Dong Sun¹Jian Wang¹Aiwu Li^1*

• ¹Qilu Hospital, Shandong University, Jinan, China
• ²Children's Hospital of Nanjing Medical University, Nanjing, China

Objective: Postoperative complications after surgery for congenital biliary dilatation (CBD) can be life-threatening and often necessitate redo surgery. We aimed to predict postoperative complications in patients with CBD using machine learning (ML) algorithms. Study design: Data from pediatric patients with CBD who were surgically treated at our hospital between July 2014 and July 2023 waswere retrospectively analyzed. Multiple logistic regression and lasso regression were used to screen risk factors. Predictive models were developed using seven ML algorithms and the better-performing model was selected. Results: A total of 211 patients were included in the final analysis. Among Of these, 31 patients experienced complications (cholangitis: fourteen 14 patients; pancreatitis: twenty-one21 patients). Risk factors for complications identified by variable screening were preoperative perforation, Todani classification type IV-A (type 4A), days of removal of drainage (removal drainage), and serum amylase. Predictors of postoperative cholangitis were preoperative perforation, preoperative cholangitis, type 4A, removal drainage, anemia, level of serum albumin, and serum amylase. Preoperative pPerforation, cholangitis, serum gamma-glutamyl transferase , and amylase were 设置了格式: 字体颜色: 自动设置 predictors of postoperative pancreatitis. Finally, logistic regression was selected to develop the clinical prediction model for postoperative complications, cholangitis, and pancreatitis. Conclusions: We developed nomograms to predict postoperative complications, cholangitis, and pancreatitis after surgery for CBD using ML.