Development and validation of machine learning models for intraoperative blood transfusion prediction in severe lumbar disc herniation

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.

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Publication Details

Journal
iScience
Published
2024-10-04
DOI
https://doi.org/10.1016/j.isci.2024.111106
Citations
2
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.37
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article

Development and validation of machine learning models for intraoperative blood transfusion prediction in severe lumbar disc herniation

Yusi Zhang, Yingang Zhang, Runmin Li, Qiang Liu et al.
2 citations
iScience
Medical Imaging and Analysis
0.37
article

Development and validation of machine learning models for intraoperative blood transfusion prediction in severe lumbar disc herniation

Yusi Zhang, Yingang Zhang, Runmin Li, Qiang Liu, An‐Tian Chen, Haopeng Luan, Jialiang Zheng, Chan Xu, Xinghua Song, Chengliang Yin, Wenle Li, Tianyu Xiang, Shi‐Nan Wu, Qiang Zhang, Xin Zhou, Wencai Liu, Xiaozhu Liu, Xubin Quan, Weiheng He, Anfa Chen, Xiangdong Xi, Liang Yan, Xiaolin Yu, Wei Huang, Kai Wang, Qizhong Xu, Xuewen Hou, Baicheng Qiu, Zhaohui Hu, Haizhen Ding, Hao Jiang, Liang Chen, Yang Zhang
article en
2 citations

Abstract

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.

iScienceVol. 27(11)
Macau University of Science and Technology (MO), Guiyang Medical University (CN), Xinjiang Medical University (CN), Sun Yat-sen University (CN), Guangxi Medical University (CN), Guangdong Medical College (CN), Shanxi Medical University (CN), Xuzhou Medical College (CN), Second Military Medical University (CN), Shanghai Jiao Tong University (CN), Capital Medical University (CN), Xiamen University (CN), Dalian Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Sixth Affiliated Hospital of Sun Yat-sen University (CN), Second Affiliated Hospital of Chongqing Medical University (CN), Xi'an Honghui Hospital (CN), Hubei Provincial Hospital of Traditional Chinese Medicine (CN), Integrated Chinese Medicine (China) (HK), Affiliated Hospital of Guizhou Medical University (CN), First Affiliated Hospital of Xi'an Jiaotong University (CN), Fourth Affiliated Hospital of Guangxi Medical University (CN), Shanghai Sixth People's Hospital (CN), Xian Yang Central Hospital (CN), Sixth Affiliated Hospital of Xinjiang Medical University (CN), Beijing Shijitan Hospital (CN), Shanxi Academy of Medical Sciences (CN), First Hospital of Xi'an (CN), Shenzhen Second People's Hospital (CN), The Fourth People's Hospital of Ningxia Hui Autonomous Region (CN), The University of Texas at Austin (US), Xi'an Jiaotong University (CN), Chongqing Medical University (CN)
Good health and well-being
Openalex Percentile: Top 43%
Medical Imaging and Analysis
0.37
DOIOpen Access PDFOpenAlex
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