机器学习预测肱骨近端骨折钢板内固定后继发性螺钉切出的风险  

Machine learning prediction of the risk of secondary screw perforation after plate internal fixation for proximal humerus fractures

作　　者：徐大星[1,2] 涂泽松[2,3] 纪木强[2] 许伟鹏[3] 牛维 Xu Daxing;Tu Zesong;Ji Muqiang;Xu Weipeng;Niu Wei(Applicants with Equivalent Academic Qualifications for Doctoral Degrees,Guangzhou University of Chinese Medicine,Guangzhou 510006,Guangdong Province,China;Department of Orthopedics,Sanshui Hospital of Foshan Hospital of Traditional Chinese Medicine,Foshan 528100,Guangdong Province,China;Department of Orthopedics,Foshan Hospital of Traditional Chinese Medicine,Foshan 528000,Guangdong Province,China;Department of Articular Surgery,Guangdong Provincial Hospital of Chinese Medicine,Guangzhou 510120,Guangdong Province,China)

机构地区：[1]广州中医药大学,广东省广州市510006 [2]佛山市中医院三水医院骨科,广东省佛山市528100 [3]佛山市中医院骨科,广东省佛山市528000 [4]广东省中医院关节外科,广东省广州市510120

出　　处：《中国组织工程研究》2025年第15期3179-3187,共9页Chinese Journal of Tissue Engineering Research

基　　金：广东省医学科学技术研究基金(B2023493),项目负责人:徐大星;佛山市“十四五”高水平医学重点专科建设项目、佛山市“十四五”中医重点专科建设项目、三水区“十四五”医学重点专科建设项目(202KJS09,佛山市中医院三水医院中医骨伤科),项目负责人:涂泽松。

摘　　要：背景:继发性螺钉切出关节面是肱骨近端骨折锁定钢板内固定术后的主要并发症之一,切出的螺钉会磨损关节盂和引起肩峰撞击,影响肩关节功能。因此,准确的风险预测有积极的临床意义。目的:通过机器学习方法筛选肱骨近端骨折钢板内固定后继发性螺钉切出的风险因素,开发并验证风险预测模型,便于临床医生早期甄别并干预高风险患者。方法:收集2013年6月至2022年6月接受锁定钢板内固定治疗的214例肱骨近端骨折患者的临床资料作为训练组建立模型,将同一时间段另一医院收治的同类患者61例纳入外部验证组。按照患者术后是否出现继发性螺钉切出,分为螺钉切出组和螺钉维持组。训练组利用随机森林、支持向量机、逻辑回归3种机器学习算法构建预测模型;采用递归特征消除法、10折交叉验证重抽样作为变量的筛选方法,并将3种模型准确度最高时纳入变量的交集作为与螺钉切出高度相关的可靠风险变量。通过R语言软件构建动态预测模型,以网页计算器形式展示,并对模型进行内、外部验证。模型内部检验采用Bootstrap法重抽样1000次,使用受试者工作特征曲线下面积、校准曲线、临床决策曲线评价模型的区分度、校准能力及临床应用价值。通过Youden指数确定预测模型的最佳风险分界值,据此将外部验证组患者分为高、低风险组,根据模型风险预测能力的准确度来评价其稳定性和外延性。结果与结论:①机器学习算法筛选出继发性螺钉切出高度相关的4个风险变量,分别为肱骨近端内侧柱皮质支撑、三角肌结节指数、骨折类型及术后复位情况;②构建的风险预测模型表现出良好的区分度和准确度[曲线下面积=0.874,95%置信区间(0.827,0.922)],校准曲线显示模型预测风险和实际发生风险有较好的一致性;③临床决策曲线提示风险阈值概率在0.1-0.75范围内时,模型具�BACKGROUND:Secondary screw perforation of the articular surface is one of the major complications after locking plate internal fixation of proximal humerus fracture,and cut-out screws can damage shoulder function by abrading the glenoid and causing impingement of the acromion.Therefore,accurate risk prediction has positive clinical significance.OBJECTIVE:To screen risk factors for secondary screw perforation after proximal humerus fracture plating by machine learning methods,and to develop and validate a risk prediction model that facilitates clinicians to identify and intervene in high-risk patients at an early stage.METHODS:Clinical data of 214 patients with proximal humerus fractures who underwent locking plate internal fixation from June 2013 to June 2022 were collected as a training group to establish the model,and 61 similar patients from another hospital in the same period were included in the external validation group.The patients were divided into secondary screw perforation and screw maintenance groups according to whether they developed secondary screw perforation after surgery.The training group used three machine learning algorithms,namely,random forest,support vector machine,and logistic regression,to construct the prediction model.The recursive feature elimination method was used,and 10-fold cross-validation resampling was used as the screening method for the variables,and the intersection of the variables that were included when the accuracy of the three models was the highest was taken as the highly correlated with the secondary screw perforation reliable risk variables.The dynamic predictive model was constructed by R language software and presented as a web calculator,and the model was internally and externally validated.The internal test of the model was conducted by the Bootstrap method with 1000 resamples,and the area under the receiver operating characteristic curve,the calibration curve,and the clinical decision curve were used to evaluate the differentiation,calibration ability,and clinic

关 键 词：肱骨近端骨折 继发性螺钉切出 机器学习 影响因素 风险预测模型 

分 类 号：R459.9[医药卫生—治疗学] R318[医药卫生—临床医学] R683.41