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

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2024-11-15 DOI:10.1016/j.isci.2024.111106

Qiang Liu , An-Tian Chen , Runmin Li , Liang Yan , Xubin Quan , Xiaozhu Liu , Yang Zhang , Tianyu Xiang , Yingang Zhang , Anfa Chen , Hao Jiang , Xuewen Hou , Qizhong Xu , Weiheng He , Liang Chen , Xin Zhou , Qiang Zhang , Wei Huang , Haopeng Luan , Xinghua Song , Wenle Li

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引用次数: 0

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.

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用于严重腰椎间盘突出症术中输血预测的机器学习模型的开发与验证

腰椎间盘突出症（LDH）是导致下背痛和坐骨神经痛的常见原因，通常采用后路腰椎椎间融合术（PLIF）治疗。这项多中心回顾性研究调查了中国接受 PLIF 的 LDH 患者术中输血的预测情况。该研究包括来自 22 个医疗中心的 6241 名患者，采用了 8 种特征选择方法和 10 种机器学习模型，其中包括一个集成堆叠模型。根据曲线下接收者操作特征面积、临床适用性和计算效率选择了最佳预测模型。在评估的组合中，模拟退火支持向量机递归+堆叠模型的性能最高，曲线下面积为 0.884，并得到稳健校准和决策曲线分析的支持。我们还开发了一个可公开访问的网络计算器，以协助临床医生做出决策。这项工作大大提高了术中输血预测能力，为改善患者管理提供了宝贵的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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