文献类型：期刊文献

中文题名：机器学习在腰椎间盘突出症患者预后预测模型中应用价值的系统评价

英文题名：A systematic review of application value of machine learning to prognostic prediction models for patients with lumbar disc herniation

作者：王志鹏[1];张晓刚[1];张宏伟[1];赵希云[1];李元贞[1];郭成龙[1];秦大平[1,2];任真[3]

第一作者：王志鹏

机构：[1]甘肃中医药大学附属医院骨科,甘肃省兰州市730020;[2]甘肃中医药大学中医临床学院,甘肃省兰州市730020;[3]甘肃中医药大学医学信息工程学院,甘肃省兰州市730020

第一机构：甘肃中医药大学第二附属医院

年份：2026

卷号：30

期号：3

起止页码：740

中文期刊名：中国组织工程研究

外文期刊名：Chinese Journal of Tissue Engineering Research

收录：;北大核心:【北大核心2023】；

基金：张晓刚全国名老中医药专家传承工作室建设项目(国中医药人教函[2022]75号),项目负责人:郭成龙;甘肃省自然科学基金项目(24JRRA1037),项目负责人:王志鹏;甘肃省青年人才个人项目(2025QNGR72),项目负责人:王志鹏;兰州市人才创新创业项目(2022-3-25),项目负责人:李元贞;兰州市青年科技计划项目(2023-2-47),项目负责人:王志鹏。

语种：中文

中文关键词：机器学习;腰椎间盘突出症;预后;预测模型;偏倚风险;系统评价

外文关键词：machine learning;lumbar disc herniation;prognosis;prediction model;risk of bias;systematic review

摘要：目的:基于机器学习的不同算法,开展腰椎间盘突出症的预测模型研究已成为目前精准化医学发展的趋势和热点。但目前使用机器学习进行腰椎间盘突出症预后预测模型的报告质量和方法学质量的证据有限。通过全面的文献检索,全面整合分析基于机器学习开发和验证腰椎间盘突出症预后预测模型的既往研究报告质量和偏倚风险,以探索机器学习算法在预测腰椎间盘突出症预后方面的性能。方法:计算机检索中国知网、万方数据库、维普数据库、中国生物医学文献服务系统、PubMed、Web of Science、Embase和The Cochrane Library数据库,搜集关于机器学习用于开发(和/或验证)腰椎间盘突出症预后预测模型的相关研究,检索时限为各数据库建立至2023-12-31。由2名研究者独立筛选文献、提取资料并评估纳入研究的偏倚风险。通过多变量预测模型透明报告(TRIPOD)声明和预测模型偏倚风险评估工具(PROBAST)来评估纳入研究的报告质量和偏倚风险。对于评价的结果使用描述性统计和可视化图表进行分析。结果:①共纳入23项研究,每项研究的TRIPOD遵循度在11%-87%之间,中位遵循度为54%;标题、治疗措施的详细说明、预测因素的盲法、缺失数据的处理方法、危险分层的细节、研究对象纳入的具体流程、模型解释以及模型性能的报告质量大多较差,TRIPOD遵循率在4%-35%之间;②所有纳入的研究中,61%具有高偏倚风险,39%具有不明确的整体偏倚风险;3项研究主要使用曲线下面积、准确度、敏感度及特异性指标评估模型性能;20个模型报道了模型的曲线下面积,范围为0.561-0.999;3个模型报道了模型的准确率,范围为82.07%-89.65%;③在所有纳入的研究中,统计分析领域最常被评估为高偏倚风险,主要是由于有效样本数量较小、根据单变量分析选择预测因素和缺乏研究中模型的校准度、区分度评估所致。结论:结果表明,在腰椎间盘突出症的预后模型开发及验证分析中,机器学习能取得良好的预测能力;常用的算法有回归算法、支持向量机、决策树、随机森林、人工神经网络、朴素贝叶斯等算法,合理的算法结合临床实践可以提高腰椎间盘突出症预后预测的准确性;但基于机器学习的预后预测模型的报告和方法学质量较差,不同模型间的预测性能差异较大,研究模型的普适性和外推性不明确,迫切需要改进此类研究的设计、实施和报告;对于模型开发研究,建模前需全面考虑与疾病预后相关的各类预测因素,建模时严格遵循PROBAST工具的相关标准开展研究,以推动机器学习在腰椎间盘突出症预测模型临床实践中的应用。
OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11%to 87%,with a median compliance of 54%.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4%to 35%.(2)Of all included studies,61%had a high risk of bias and 39%had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07%to 89.65%.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.