An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery.

Autor: Zhou Y; Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510030, Guangdong, China., Wang S; Guangzhou University of Chinese Medicine, Guangzhou, 510030, Guangdong, China., Wu Z; Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510030, Guangdong, China., Chen W; Department of Data Science, Guangzhou AID Cloud Technology, Guangzhou, 510663, China., Yang D; Department of Data Science, Guangzhou AID Cloud Technology, Guangzhou, 510663, China., Chen C; Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, Guangdong, China., Zhao G; Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510030, Guangdong, China., Hong Q; Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510030, Guangdong, China. hongqingxiong@gzucm.edu.cn.
Jazyk: angličtina
Zdroj: BMC anesthesiology [BMC Anesthesiol] 2024 Dec 19; Vol. 24 (1), pp. 467. Date of Electronic Publication: 2024 Dec 19.
DOI: 10.1186/s12871-024-02832-y
Abstrakt: 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.
Competing Interests: Declarations. Ethics approval and consent to participate: The present study was a retrospective study, which did not interfere with hip fracture surgeries in any way. All of the clinical judgments were made by clinicians for medical reasons. No written consent was required in view of the purely observational nature of the study. No identifiable data of the patients were recorded during the whole study. The study was approved by the ethics committee of Guangdong Provincial Hospital of Chinese Medicine ((No. ZE2023-201–01). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz: