NCT06626724

A Retrospective Cohort Study on Predicting Delayed Graft Function in Liver Transplant Patients with Hepatocellular Carcinoma: a Nomogram and Machine Learning Approaches.

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 131 (actual)
• Sponsor: Jian You · Academic / Other
• Sex: All
• Age: 18 Days – 75 Days
• Healthy volunteers: Not accepted

Summary

Background: This study aimed to develop a predictive model for delayed graft function (DGF) in liver transplant patients with hepatocellular carcinoma (HCC) based on preoperative biochemical indicators, using both logistic regression and XGBoost machine learning algorithms. Methods: A retrospective cohort study was conducted, including 131 liver transplant patients from January 2020 to April 2022. Preoperative biochemical markers and hematological parameters were analyzed. Logistic regression and XGBoost models were constructed to predict DGF, and their performance was evaluated using the area under the ROC curve (AUC). Shapley Additive Explanations (SHAP) analysis was employed to interpret the feature contributions.

Conditions

• Hepatocellular Carcinoma (HCC)
• DGF

Interventions

Type	Name	Description
PROCEDURE	Liver transplantation	Liver transplantation for patients with hepatocellular carcinoma.

Timeline

• Start date: 2020-01-01
• Primary completion: 2024-04-30
• Completion: 2024-04-30
• First posted: 2024-10-04
• Last updated: 2024-10-04

Locations

1 site across 1 country: China