NCT07078578

A Predictive Model for Postoperative Delirium in Kidney Transplant Patients

Construction of a Machine Learning Prediction Model for Postoperative Delirium in Kidney Transplant Patients Based on Clinical Data

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 4,800 (actual)
• Sponsor: Hua Zheng · Academic / Other
• Sex: All
• Age: 16 Years
• Healthy volunteers: Not accepted

Summary

This study aims to develop and prospectively validate a machine learning-based prediction model for postoperative delirium in kidney transplant recipients, using perioperative clinical data. Delirium is a common and serious postoperative complication that significantly increases morbidity, mortality, and healthcare costs. By analyzing electronic medical records from kidney transplant patients, including preoperative, intraoperative, and postoperative variables, the study seeks to identify high-risk patients and key predictors. Six machine learning models, including XGBoost, LGBM, GBC, LR, ANN, and SVM, will be constructed and evaluated, with a soft voting ensemble classifier used to optimize prediction performance. The goal is to improve early recognition and clinical management of postoperative delirium in kidney transplant patients.

Conditions

• Kidney Transplantation
• Delirium - Postoperative
• Cognitive

Timeline

• Start date: 2016-01-01
• Primary completion: 2024-12-01
• Completion: 2025-02-01
• First posted: 2025-07-22
• Last updated: 2025-07-22