摘要
目的:分析乳腺癌病人术前衰弱的影响因素并构建术前衰弱风险预测模型。方法:选取2024年8月—2025年1月在山东省滨州市某2所三级甲等医院乳腺外科拟行手术治疗的住院病人583例,按照7∶3的比例随机分为训练集408例和验证集175例,基于LASSO回归和Boruta算法共同筛选变量,利用支持向量机、决策树、轻量梯度提升和极端梯度提升4种机器学习算法分别构建预测模型,采用准确度、精确度、敏感度、特异度、F1值和受试者工作特征(ROC)曲线下面积(AUC)比较各模型性能。基于Shaply加性解释(SHAP)算法对最优模型进行解释。结果:乳腺癌病人术前衰弱的发生率为26.93%。与支持向量机、决策树、轻量梯度提升机模型比较,极端梯度提升模型性能最优,其AUC为0.909,准确度为0.829,精确度为0.646,敏感度为0.857,特异度为0.818,F1值为0.737。SHAP条形图显示,排名前5位的影响因素为年龄、血红蛋白、白蛋白、中性粒细胞百分比和合并症。结论:极端梯度提升模型预测乳腺癌病人术前衰弱风险性能最佳,可为临床工作者有效评估和科学管理乳腺癌术前衰弱病人提供依据。
Objective:To analyze the influencing factors of preoperative frailty in breast cancer patients and to develop a risk prediction model.Methods:A total of 583 inpatients scheduled for surgical treatment in the breast surgery departments of two Grade A tertiary hospitals in Binzhou city,Shandong province,were selected between August 2024 and January 2025.They were randomly divided into a training set(408 cases)and a validation set(175 cases)in a 7∶3 ratio.Variables were screened using both Lasso regression and the Boruta algorithm.Four machine learning algorithms-support vector machine,decision tree,light gradient boosting machine,and extreme gradient boosting-were employed to develop prediction models.Model performance was compared based on accuracy,precision,sensitivity,specificity,F1-score,and the area under the receiver operating characteristic(ROC)curve(AUC).And the optimal model was interpreted using the SHAP method.Results:The incidence of preoperative frailty in breast cancer patients was 26.93%.Compared to the support vector machine,decision tree,and light gradient boosting machine models,the extreme gradient boosting model demonstrated the best performance,with an AUC of 0.909,accuracy of 0.829,precision of 0.646,sensitivity of 0.857,specificity of 0.818,and an F1-score of 0.737.The SHAP bar plot identified the top five influencing factors as age,hemoglobin,albumin,neutrophil percentage,and comorbidities.Conclusions:The extreme gradient boosting model exhibits the best predictive performance and can serve as a reliable tool for healthcare providers to effectively assess and scientifically manage preoperative frailty in breast cancer patients.
作者
张晴
张英
董建丽
于文龙
熊银环
赵佳月
许红梅
ZHANG Qing;ZHANG Ying;DONG Jianli;YU Wenlong;XIONG Yinhuan;ZHAO Jiayue;XU Hongmei(School of Nursing,Binzhou Medical University(School of Gerontology),Shandong 256600 China;Binzhou People′s Hospital;Affiliated Hospital of Binzhou Medical University)
出处
《护理研究》
北大核心
2026年第1期60-70,共11页
Chinese Nursing Research
基金
山东省自然科学基金面上项目,编号:ZR2023MH378。
关键词
乳腺癌
衰弱
术前
机器学习
预测模型
影响因素
合并症
breast cancer
frailty
preoperative
machine learning
prediction model
influencing factors
comorbidities
