BMC geriatrics · 2025
Establishment of a postoperative delirium risk prediction model for elderly hip fracture patients based on machine learning algorithms.
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Xing Yayun, Wang Yuetong, Huang Yuejiao, Lin Fanguo, Zhang Yuye
Аннотация
BACKGROUND: Although no definitive treatment exists, 30-40% of postoperative delirium cases are preventable through early risk identification and intervention. Therefore our aim was to develop and evaluate a postoperative delirium risk prediction model for elderly hip fracture patients using machine learning algorithms.
METHODS: A retrospective analysis was conducted on the clinical data of elderly hip fracture patients who underwent surgical treatment at our hospital from June 2022 to December 2024. Postoperative delirium occurrence was set as the outcome variable. Six machine learning algorithms, including logistic regression, random forest, decision tree, CatBoost, balanced bagging classifier and XGBoost, were used to build the risk prediction models for postoperative delirium. The model-building process included data preprocessing, feature selection, and hyperparameter tuning through grid search, with 10-fold cross-validation for internal validation. The model performance was evaluated using AUC, accuracy, precision, sensitivity, specificity, and F1 score, and the optimal model was selected.
RESULTS: A total of 801 hip fracture patients were included in the model construction, among which 211 patients developed delirium, with an incidence rate of 26.3%. Among the six models, the CatBoost model demonstrated the best overall performance, with AUC values of 0.976 for the test set and 0.871 for the validation set. It also maintained high sensitivity (0.846) and specificity (0.686). Based on the CatBoost model, the risk factors for postoperative delirium in elderly hip fracture patients were identified as preoperative, intraoperative, and postoperative oxygen saturation, preoperative blood glucose levels, preoperative fasting time, preoperative albumin levels, nutritional status, VAS, and preoperative serum creatinine levels.
CONCLUSION: The postoperative delirium risk prediction model for elderly hip fracture patients, developed using machine learning, demonstrates good performance and can provide precise, individualized predictions.
METHODS: A retrospective analysis was conducted on the clinical data of elderly hip fracture patients who underwent surgical treatment at our hospital from June 2022 to December 2024. Postoperative delirium occurrence was set as the outcome variable. Six machine learning algorithms, including logistic regression, random forest, decision tree, CatBoost, balanced bagging classifier and XGBoost, were used to build the risk prediction models for postoperative delirium. The model-building process included data preprocessing, feature selection, and hyperparameter tuning through grid search, with 10-fold cross-validation for internal validation. The model performance was evaluated using AUC, accuracy, precision, sensitivity, specificity, and F1 score, and the optimal model was selected.
RESULTS: A total of 801 hip fracture patients were included in the model construction, among which 211 patients developed delirium, with an incidence rate of 26.3%. Among the six models, the CatBoost model demonstrated the best overall performance, with AUC values of 0.976 for the test set and 0.871 for the validation set. It also maintained high sensitivity (0.846) and specificity (0.686). Based on the CatBoost model, the risk factors for postoperative delirium in elderly hip fracture patients were identified as preoperative, intraoperative, and postoperative oxygen saturation, preoperative blood glucose levels, preoperative fasting time, preoperative albumin levels, nutritional status, VAS, and preoperative serum creatinine levels.
CONCLUSION: The postoperative delirium risk prediction model for elderly hip fracture patients, developed using machine learning, demonstrates good performance and can provide precise, individualized predictions.