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

## TL;DR

This study uses machine learning to develop nomograms predicting postoperative complications in patients with congenital biliary dilatation.

## Contribution

The novel contribution is the development of clinical prediction models using machine learning for postoperative complications in congenital biliary dilatation.

## Key findings

- 31 out of 211 patients experienced postoperative complications, including cholangitis and pancreatitis.
- Risk factors included preoperative perforation, Todani type IV-A, and serum amylase levels.
- Logistic regression was selected as the best model for predicting complications.

## Abstract

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.

Data from pediatric patients with CBD who were surgically treated at our hospital between July 2014 and July 2023 was 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.

A total of 211 patients were included in the final analysis. Among these, 31 patients experienced complications (cholangitis: 14 patients; pancreatitis: 21 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 amylase. Preoperative perforation, cholangitis, serum gamma-glutamyl transferase and amylase were predictors of postoperative pancreatitis. Finally, logistic regression was selected to develop the clinical prediction model for postoperative complications, cholangitis, and pancreatitis.

We developed nomograms to predict postoperative complications, cholangitis, and pancreatitis after surgery for CBD using ML.