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Nomograms for postoperative complications in congenital biliary dilatation: a retrospective cohort study
Yu Zhou1, Xintao Zhang2, Xue Ren1
1Department of Pediatric Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Insights
Machine learning models can predict postoperative complications, including cholangitis and pancreatitis, after congenital biliary dilatation (CBD) surgery. This aids in identifying at-risk patients and improving surgical outcomes.
Area of Science:
- Pediatric Surgery
- Surgical Outcomes
- Machine Learning in Medicine
Background:
- Postoperative complications following congenital biliary dilatation (CBD) surgery are severe and may require repeat operations.
- Accurate prediction of these complications is crucial for patient management.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting postoperative complications after CBD surgery.
- To identify key risk factors associated with complications such as cholangitis and pancreatitis.
Main Methods:
- Retrospective analysis of pediatric patients with CBD who underwent surgery.
- Utilized logistic regression and lasso regression for risk factor screening.
- Developed and compared seven ML algorithms to select the best predictive model.
Main Results:
- 211 patients were analyzed; 31 experienced complications (cholangitis, pancreatitis).
- Identified risk factors: preoperative perforation, Todani type IV-A, delayed drainage removal, and elevated serum amylase.
- Logistic regression model selected for predicting complications, cholangitis, and pancreatitis.
Conclusions:
- Developed clinical prediction models and nomograms using ML to forecast postoperative complications.
- These tools can aid in risk stratification and management of patients undergoing CBD surgery.
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 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.
Results:
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.
Conclusions:
We developed nomograms to predict postoperative complications, cholangitis, and pancreatitis after surgery for CBD using ML.
