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A Predictive Model for Postoperative Delirium in Kidney Transplant Patients

NCT07078578 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 4800

Last updated 2025-07-22

No results posted yet for this study

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

Sponsors & Collaborators

  • Hua Zheng

    lead OTHER

Eligibility

Min Age
16 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2016-01-01
Primary Completion
2024-12-01
Completion
2025-02-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07078578 on ClinicalTrials.gov