摘要
目的综合评估分析上消化道出血患者的输血结局。方法采用输血(科)管理系统和医院信息系统(HIS),回顾性收集了浙江省人民医院及其分院2018年6月至2021年6月收治的230例上消化道出血患者的临床数据,筛选得到101例,并根据输血结局分为输血组(n=56)和未输血组(n=45),男性68名、女性33名。分别建立基于AIMS65评分的单因素模型和Logistic多因素回归模型,以及采用随机森林、支持向量机和人工神经网络等机器学习方法建立的多因素输血模型,比较各个模型的敏感度、特异度、准确率和受试者工作特征曲线ROC。结果基于AIMS65评分单因素模型,最佳阈值1.5,该模型的灵敏度为0.446,特异度为0.822,AUC=0.67,准确率ACC=0.614,KAPPA=0.256,F1=0.655。Logistics回归模型最佳临界概率0.459,该模型的灵敏度为0.929,特异度为0.889,AUC面积为0.96,准确率ACC=0.911,KAPPA=0.819,F1=0.899。随机森林模型最佳临界概率0.458,预测灵敏度为0.964,特异度为0.956,AUC面积为0.99,准确率ACC=0.960,KAPPA=0.920,F1=0.956。支持向量机模型最佳临界概率为0.474,预测灵敏度为0.875,特异度为0.933,AUC面积为0.94,准确率ACC=0.901,KAPPA=0.801,F1分数=0.894。人工神经网络模型,最佳临界概率0.797,预测灵敏度为0.804,特异度为0.956,AUC面积为0.96,准确率ACC=0.871,KAPPA=0.745,F1分数=0.869。十折交叉验证也进一步确认了结果。结论分析发现综合患者临床各项检验指标,建立Logistic回归模型和多种机器学习模型,对预测患者输血具有借鉴价值,表明机器学习算法在预测输血方面具备应用前景。
Objective To comprehensively evaluate and analyze the transfusion outcomes of patients with acute upper gastrointestinal bleeding(UGIB).Methods The transfusion management system and hospital information system(HIS)were used to retrospectively collect clinical data of 230 patients with UGIB admitted to Zhejiang Provincial People′s Hospital and its branches from June 2018 to June 2021.101 cases were screened and categorized into transfusion group(n=56)and non-transfusion group(n=45)based on transfusion outcomes.The cohort comprised 68 males and 33 females.A univariate model based on the AIMS65 score,a logistic multiple regression model,and multivariate transfusion models using machine learning methods(including Random Forest,Support Vector Machine,and Artificial Neural Network)were established.The sensitivity,specificity,accuracy,and receiver operating characteristic(ROC)curves of each model were compared.Results For the univariate model based on the AIMS65 scoring,the optimal threshold was 1.5.This model demonstrated a sensitivity of 0.446,a specificity of 0.822,an AUC of 0.67,an accuracy(ACC)of 0.614,a Kappa value of 0.256,and an F1-score of 0.655.For logistics regression model(optimal critical probability:0.459),the sensitivity was 0.929,specificity was 0.889,AUC was 0.96,ACC was 0.911,Kappa was 0.819,and F1-score was 0.899.For the Random Forest model(optimal critical probability:0.458),the sensitivity was 0.964,specificity was 0.956,AUC was 0.99,ACC was 0.960,Kappa was 0.920,and F1-score was 0.956.For the Support Vector Machine model(optimal critical probability:0.474),the sensitivity was 0.875,specificity was 0.933,AUC was 0.94,ACC was 0.901,Kappa was 0.801,and F1-score was 0.894.For the Artificial Neural Network model(optimal critical probability:0.797),the sensitivity was 0.804,specificity was 0.956,AUC was 0.96,ACC was 0.871,Kappa was 0.745,and F1-score was 0.869.Ten-fold cross validation also confirmed the reliability of the results.Conclusion Based on integrated various clinical test indicators of patients,we could establish logistic regression model and multiple machine learning models.These models hold significant value for predicting the need for blood transfusion in patients,indicating a promising application prospect for machine learning algorithms in transfusion prediction.
作者
杜垚强
章碧琴
徐怡琳
陈秉宇
胡卫国
DU Yaoqiang;ZHANG Biqin;XU Yilin;CHEN Bingyu;HU Weiguo(Laboratory Medicine Center,Department of Transfusion Medicine,Zhejiang Provincial People’s Hospital(Affiliated People’s Hospital),Hangzhou Medical College,Hangzhou 310014,China;Cancer Center,Department of Hematology,Zhejiang Provincial People’s Hospital(Affiliated People’s Hospital),Hangzhou Medical College,Hangzhou 310014,China;Department of Laboratory Medicine,The First Affiliated Hospital of Ningbo University,Ningbo 315010,China;Zaozhuang Blood Center,Zaozhuang,277102,China)
出处
《中国输血杂志》
2025年第11期1488-1494,共7页
Chinese Journal of Blood Transfusion
基金
浙江省医药卫生科技计划项目(2024KY020)
浙江省卫生高层次人才计划(2023年度浙江省医坛新秀)。
关键词
上消化道出血
AIMS65
逻辑回归
机器学习
多因素输血模型
upper gastrointestinal bleeding
AIMS65
logistic regression
machine learning
multivariate transfusion models
