机器学习预测急性上消化道出血患者干预及再出血的风险价值

The value of predicting intervention and rebleeding risk for patients with acute upper gastrointestinal bleeding based on machine learning

目的:探讨机器学习(ML)对急性上消化道出血(AUGIB)患者输血干预及再出血的预测价值。方法:回顾性分析2020年7月至2023年10月云南省第三人民医院收治的512例AUGIB患者的临床资料。采用极端梯度提升树(XGBoost)进行变量重要度分析,将筛选得到的重要度排名前10项的因素作为模型中的变量;使用logistic回归、XGBoost、随机森林、支持向量机(SVM)及K近邻算法(KNN)进行分类预测并对比,选取最佳模型并采用SHAP图对ML筛选出的特征进行可解释性分析;并用最佳模型与临床常用AUGIB评分系统进行比较,评估临床价值。结果:XGBoost算法模型中输血干预危险因素得分前10项分别为血红蛋白、国际标准化比值(INR)、白蛋白、收缩压、尿素、麻醉风险评分、脉搏、肌酐、年龄、是否休克。利用以上重要特征进行建模,XGBoost预测AUGIB患者输血干预效果最好,得分最高,即能够尽可能找出更多发生消化道出血进行输血干预的患者,且优于临床常用格拉斯哥—布拉奇福德出血评分(GBS)、AIMS65、ABC及T评分系统。通过XGBoost算法模型中再出血患者重要特征得分前10项为年龄、肌酐、INR、血红蛋白、麻醉风险评分、白蛋白、收缩压、尿素、肝硬化、性别。利用得分排前10的危险因素进行建模,XGBoost预测AUGIB患者再出血的效果最佳,且优于以上4种评分系统。结论:在预测AUGIB患者输血干预及再出血的价值中,ML模型优于GBS、AIMS65、ABC及T评分系统;XGBoost模型算法更佳,具有更好的有效性。

Objective:To investigate the value of machine learning(ML)in predicting blood transfusion intervention and rebleeding in patients with acute upper gastrointestinal bleeding(AUGIB).Methods:A retrospective analysis was conducted on the clinical data of 512 AUGIB patients who were admitted to the Third People’s Hospital of Yunnan Province from July 2020 to October 2023.Variable importance analysis was performed using eXtreme gradient boosting(XGBoost),and the top 10 factors in importance ranking were selected as variables in the model.Classification predictions were carried out and compared using logistic regression,XGBoost,random forest,support vector machine(SVM),and K-nearest neighbors algorithms(KNN).The best model was chosen,and interpretable analysis of the features selected by ML was performed using SHAP plots.The clinical value was assessed by comparing the best model with the commonly used AUGIB scoring systems.Results:The XGBoost algorithm model identified the top 10 risk factors for transfusion intervention as hemoglobin,international normalized ratio(INR),albumin,systolic blood pressure,urea,anesthesia risk score,pulse,creatinine,age,and presence of shock.Using these important features for modeling,the XGBoost algorithm provided the best predictive per formance for transfusion intervention in AUGIB patients and it achieved the highest score,indicating its superior ability to identify patients at risk for gastrointestinal bleeding who required transfusion intervention,and outperforming the common clinical Glasgow Blatchford(GBS),AIMS65,ABC,and T scoring systems.According to the XGBoost algorithm model,the top 10 important features scores for patients with rebleeding were age,creatinine,INR,hemoglobin,anesthesia risk score,albumin,systolic blood pressure,urea,liver cirrhosis,and gender.Modeling with these top 10 risk factors,the XGBoost algorithm also showed the best predictive performance for re-bleeding in AUGIB patients,surpassing the aforementioned four scoring systems.Conclusion:In predicting the value of transfusion intervention and rebleeding in AUGIB patients,ML model is superior to GBS,AIMS65,ABC and T scoring systems.The XGBoost model algorithm is superior,with better effectiveness.