An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery
Abstract Aim The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery. Methods Data of 2785 cases that underwent hip fracture surgery from April 2016 to May 2022 were collected,...
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BMC
2024-12-01
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| Series: | BMC Anesthesiology |
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| Online Access: | https://doi.org/10.1186/s12871-024-02832-y |
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| author | Yongchang Zhou Suo Wang Zhikun Wu Weixing Chen Dong Yang Chaojin Chen Gaofeng Zhao Qingxiong Hong |
| author_facet | Yongchang Zhou Suo Wang Zhikun Wu Weixing Chen Dong Yang Chaojin Chen Gaofeng Zhao Qingxiong Hong |
| author_sort | Yongchang Zhou |
| collection | DOAJ |
| description | Abstract Aim The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery. Methods Data of 2785 cases that underwent hip fracture surgery from April 2016 to May 2022 were collected, covering demographics, medical history and comorbidities, type of surgery and preoperative laboratory results. The primary outcome was the intraoperative RBC transfusion. The predicting performance of six algorithms were respectively evaluated with the area under the receiver operating characteristic (AUROC). The SHapley Additive exPlanations (SHAP) package was applied to interpret the Random Forest (RF) model. Data from 122 patients at The Third Affiliated Hospital of Sun Yat-sen University were collected for external validation. Results 1417 patients (50.88%) were diagnosed with preoperative anemia (POA) and 209 patients (7.5%) received intraoperative RBC transfusion. Longer estimated duration of surgery, POA, older age, hypoproteinemia, and surgery of internal fixation were revealed as the top 5 important variables contributing to intraoperative RBC transfusion. Among the six ML models, the RF model performed the best, which achieved the highest AUC (0.887, CI 0.838 to 0.926) in the internal validation set. Further, it achieved a comparable AUC of 0.834(0.75, 0.911) in the external validation set. Conclusion Our study firstly demonstrated that the RF model with 10 common variables might predict intraoperative RBC transfusion in hip fracture patients. |
| format | Article |
| id | doaj-art-861f940b706d4ad59deeac8a06e7431d |
| institution | DOAJ |
| issn | 1471-2253 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Anesthesiology |
| spelling | doaj-art-861f940b706d4ad59deeac8a06e7431d2025-08-20T02:40:15ZengBMCBMC Anesthesiology1471-22532024-12-0124111210.1186/s12871-024-02832-yAn explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgeryYongchang Zhou0Suo Wang1Zhikun Wu2Weixing Chen3Dong Yang4Chaojin Chen5Gaofeng Zhao6Qingxiong Hong7Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese MedicineGuangzhou University of Chinese MedicineDepartment of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese MedicineDepartment of Data Science, Guangzhou AID Cloud TechnologyDepartment of Data Science, Guangzhou AID Cloud TechnologyDepartment of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese MedicineDepartment of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese MedicineAbstract Aim The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery. Methods Data of 2785 cases that underwent hip fracture surgery from April 2016 to May 2022 were collected, covering demographics, medical history and comorbidities, type of surgery and preoperative laboratory results. The primary outcome was the intraoperative RBC transfusion. The predicting performance of six algorithms were respectively evaluated with the area under the receiver operating characteristic (AUROC). The SHapley Additive exPlanations (SHAP) package was applied to interpret the Random Forest (RF) model. Data from 122 patients at The Third Affiliated Hospital of Sun Yat-sen University were collected for external validation. Results 1417 patients (50.88%) were diagnosed with preoperative anemia (POA) and 209 patients (7.5%) received intraoperative RBC transfusion. Longer estimated duration of surgery, POA, older age, hypoproteinemia, and surgery of internal fixation were revealed as the top 5 important variables contributing to intraoperative RBC transfusion. Among the six ML models, the RF model performed the best, which achieved the highest AUC (0.887, CI 0.838 to 0.926) in the internal validation set. Further, it achieved a comparable AUC of 0.834(0.75, 0.911) in the external validation set. Conclusion Our study firstly demonstrated that the RF model with 10 common variables might predict intraoperative RBC transfusion in hip fracture patients.https://doi.org/10.1186/s12871-024-02832-yHip fractureRed blood cell transfusionMachine learningPredictive modelSHapley Additive exPlanations |
| spellingShingle | Yongchang Zhou Suo Wang Zhikun Wu Weixing Chen Dong Yang Chaojin Chen Gaofeng Zhao Qingxiong Hong An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery BMC Anesthesiology Hip fracture Red blood cell transfusion Machine learning Predictive model SHapley Additive exPlanations |
| title | An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery |
| title_full | An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery |
| title_fullStr | An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery |
| title_full_unstemmed | An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery |
| title_short | An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery |
| title_sort | explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery |
| topic | Hip fracture Red blood cell transfusion Machine learning Predictive model SHapley Additive exPlanations |
| url | https://doi.org/10.1186/s12871-024-02832-y |
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