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.
Study Type
OBSERVATIONAL
Enrollment
4,800
Incidence of Postoperative Delirium Within 7 Days After Kidney Transplantation
Postoperative delirium will be identified within 7 days of surgery through automated extraction and structured analysis of electronic medical record text fields, including progress notes, nursing records, and medication orders for sedatives or anxiolytics. Delirium will be categorized by onset time, severity, treatment, and recovery status.
Time frame: 7 days after surgery
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