Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms
Abstract Background With the aging demographic on the rise, we’re seeing a spike in the occurrence of postoperative delirium (POD). Our research aims to delve into the connection between plasma bilirubin levels and postoperative delirium, with the goal of crafting ten machine learning (ML) models ca...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | BMC Anesthesiology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12871-025-03259-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849761409028587520 |
|---|---|
| author | Shuhui Hua Chuan Li Yuanlong Wang YiZhi Liang Shanling Xu Jian Kong Hongyan Gong Rui Dong Yanan Lin Xu Lin Yanlin Bi Bin Wang |
| author_facet | Shuhui Hua Chuan Li Yuanlong Wang YiZhi Liang Shanling Xu Jian Kong Hongyan Gong Rui Dong Yanan Lin Xu Lin Yanlin Bi Bin Wang |
| author_sort | Shuhui Hua |
| collection | DOAJ |
| description | Abstract Background With the aging demographic on the rise, we’re seeing a spike in the occurrence of postoperative delirium (POD). Our research aims to delve into the connection between plasma bilirubin levels and postoperative delirium, with the goal of crafting ten machine learning (ML) models capable of predicting POD instances. Methods This study enrolled 621 elderly patients after knee/hip surgery. We used the Confusion Assessment Method (CAM) to assess whether participants had POD. Univariate binary logistic regression analysis and restricted cubic spline (RCS) analysis were used to evaluate the association between plasma total bilirubin and POD. This study further investigated whether cerebrospinal fluid plays some role in the relationship between bilirubin and POD using mediated causal analysis. Subsequently, we employed ten machine learning algorithms to train and develop the predictive models: Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Model (GBM), Neural Network (NN), Random Forest (RF), Xgboost, K-Nearest Neighbors (KNN), AdaBoost, LightGBM, and CatBoost. The performance of the models was evaluated by the area under the receiver operating characteristic curve (AUROC), Brier score, accuracy, sensitivity, specificity, precision, F1 score, calibration curve, decision curve, clinical impact curve, and confusion matrix. In addition, the model was interpreted through Shapley additive interpretation (SHAP) analysis to clarify the importance of bilirubin in the model and its decision-making basis. Results Univariate binary logistic regression analysis revealed that plasma total bilirubin was associated with POD. Furthermore, the RCS analysis illustrated there was no nonlinear relationship between total bilirubin and POD. Mediation analysis indicted that T-tau mediated the effect of total bilirubin on POD. Total bilirubin and other features(age, educational level, BMI, history of diabetes, ASA, albumin, Aβ42, T-tau and P-tau) were used to construct ML models. Compared with other ML algorithms, NN showed better performance, with an AUC of 0.973 (95% CI (0.959–0.987)) in the test set. In addition, the SHAP method determines that age and education are the main determinants that affect the prediction of ML models. Conclusion Plasma total bilirubin was identified as a preoperative risk factor for postoperative delirium (POD). Among ten ML models, the Neural Network (NN) incorporating total bilirubin showed the best predictive performance for POD. Trial registration Clinical Registration No. ChiCTR2000033439. Registration data:2020.06.01. |
| format | Article |
| id | doaj-art-de78bb46b7094e419d2b3f1f7b5e4968 |
| institution | DOAJ |
| issn | 1471-2253 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Anesthesiology |
| spelling | doaj-art-de78bb46b7094e419d2b3f1f7b5e49682025-08-20T03:06:02ZengBMCBMC Anesthesiology1471-22532025-07-0125111510.1186/s12871-025-03259-9Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithmsShuhui Hua0Chuan Li1Yuanlong Wang2YiZhi Liang3Shanling Xu4Jian Kong5Hongyan Gong6Rui Dong7Yanan Lin8Xu Lin9Yanlin Bi10Bin Wang11Department of Anesthesiology, Qingdao Municipal HospitalDepartment of Anesthesiology, Qingdao Municipal HospitalThe Second School of Clinical Medicine, Binzhou Medical UniversityThe Second School of Clinical Medicine, Binzhou Medical UniversityDepartment of Anesthesiology, Shandong Second Medical UniversityDepartment of Anesthesiology, Shandong Second Medical UniversityDepartment of Anesthesiology, Qingdao Municipal HospitalDepartment of Anesthesiology, Qingdao Municipal HospitalDepartment of Anesthesiology, Qingdao Municipal HospitalDepartment of Anesthesiology, Qingdao Municipal HospitalDepartment of Anesthesiology, Qingdao Municipal HospitalDepartment of Anesthesiology, Qingdao Municipal HospitalAbstract Background With the aging demographic on the rise, we’re seeing a spike in the occurrence of postoperative delirium (POD). Our research aims to delve into the connection between plasma bilirubin levels and postoperative delirium, with the goal of crafting ten machine learning (ML) models capable of predicting POD instances. Methods This study enrolled 621 elderly patients after knee/hip surgery. We used the Confusion Assessment Method (CAM) to assess whether participants had POD. Univariate binary logistic regression analysis and restricted cubic spline (RCS) analysis were used to evaluate the association between plasma total bilirubin and POD. This study further investigated whether cerebrospinal fluid plays some role in the relationship between bilirubin and POD using mediated causal analysis. Subsequently, we employed ten machine learning algorithms to train and develop the predictive models: Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Model (GBM), Neural Network (NN), Random Forest (RF), Xgboost, K-Nearest Neighbors (KNN), AdaBoost, LightGBM, and CatBoost. The performance of the models was evaluated by the area under the receiver operating characteristic curve (AUROC), Brier score, accuracy, sensitivity, specificity, precision, F1 score, calibration curve, decision curve, clinical impact curve, and confusion matrix. In addition, the model was interpreted through Shapley additive interpretation (SHAP) analysis to clarify the importance of bilirubin in the model and its decision-making basis. Results Univariate binary logistic regression analysis revealed that plasma total bilirubin was associated with POD. Furthermore, the RCS analysis illustrated there was no nonlinear relationship between total bilirubin and POD. Mediation analysis indicted that T-tau mediated the effect of total bilirubin on POD. Total bilirubin and other features(age, educational level, BMI, history of diabetes, ASA, albumin, Aβ42, T-tau and P-tau) were used to construct ML models. Compared with other ML algorithms, NN showed better performance, with an AUC of 0.973 (95% CI (0.959–0.987)) in the test set. In addition, the SHAP method determines that age and education are the main determinants that affect the prediction of ML models. Conclusion Plasma total bilirubin was identified as a preoperative risk factor for postoperative delirium (POD). Among ten ML models, the Neural Network (NN) incorporating total bilirubin showed the best predictive performance for POD. Trial registration Clinical Registration No. ChiCTR2000033439. Registration data:2020.06.01.https://doi.org/10.1186/s12871-025-03259-9Machine learningPostoperative deliriumCerebrospinal fluidSurgery |
| spellingShingle | Shuhui Hua Chuan Li Yuanlong Wang YiZhi Liang Shanling Xu Jian Kong Hongyan Gong Rui Dong Yanan Lin Xu Lin Yanlin Bi Bin Wang Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms BMC Anesthesiology Machine learning Postoperative delirium Cerebrospinal fluid Surgery |
| title | Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms |
| title_full | Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms |
| title_fullStr | Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms |
| title_full_unstemmed | Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms |
| title_short | Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms |
| title_sort | establishment of predictive models for postoperative delirium in elderly patients after knee hip surgery based on total bilirubin concentration machine learning algorithms |
| topic | Machine learning Postoperative delirium Cerebrospinal fluid Surgery |
| url | https://doi.org/10.1186/s12871-025-03259-9 |
| work_keys_str_mv | AT shuhuihua establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT chuanli establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT yuanlongwang establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT yizhiliang establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT shanlingxu establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT jiankong establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT hongyangong establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT ruidong establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT yananlin establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT xulin establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT yanlinbi establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms AT binwang establishmentofpredictivemodelsforpostoperativedeliriuminelderlypatientsafterkneehipsurgerybasedontotalbilirubinconcentrationmachinelearningalgorithms |