iScience · 2024
Development and validation of machine learning models for intraoperative blood transfusion prediction in severe lumbar disc herniation.
СтатьяНазвание и аннотация: Перевод в очереди
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Liu Qiang, Chen An-Tian, Li Runmin, Yan Liang, Quan Xubin, Liu Xiaozhu, Zhang Yang, Xiang Tianyu, Zhang Yingang, Chen Anfa, Jiang Hao, Hou Xuewen, Xu Qizhong, He Weiheng, Chen Liang, Zhou Xin, Zhang Qiang, Huang Wei, Luan Haopeng, Song Xinghua, Yu Xiaolin, Xi Xiangdong, Wang Kai, Wu Shi-Nan, Liu Wencai, Zhang Yusi, Zheng Jialiang, Ding Haizhen, Xu Chan, Yin Chengliang, Hu Zhaohui, Qiu Baicheng, Li Wenle
Аннотация
Lumbar disc herniation (LDH) is a common cause of lower back pain and sciatica, and posterior lumbar interbody fusion (PLIF) is always employed. This multicenter retrospective study investigates predicting intraoperative blood transfusion for LDH patients undergoing PLIF in China. The research includes 6,241 patients from 22 medical centers and employs 8 feature selection methods and 10 machine learning models, including an integrated stacking model. The optimal predictive model was selected based on the receiver operating characteristic area under the curve, clinical applicability, and computational efficiency. Among the evaluated combinations, the simulated annealing support vector machine recursive + stacking model achieved the highest performance with an area under the curve of 0.884, supported by robust calibration and decision curve analyses. A publicly accessible web calculator was developed to assist clinicians in decision-making. This work significantly enhances intraoperative transfusion predictions, providing valuable tools for improving patient management.