急性A型主动脉夹层患者术后死亡风险的预测模型

Prediction model for the risk of postoperative death in patients with acute type A aortic dissection

目的采用不同的机器学习算法,构建并筛选预测急性A型主动脉夹层患者术后30天内死亡风险的最佳预测模型。方法纳入2015年至2022年间行手术治疗的急性A型主动脉夹层患者521例,收集其围手术期资料并进行筛选后保留329例。分别通过Lasso回归和主成分分析确定两组不同的预测变量后,使用逻辑回归和支持向量机、随机森林、梯度提升、超级学习算法建立预测术后30天内死亡风险的预测模型,并使用ROC曲线、敏感度值和特异度值等指标对各个模型进行比较。结果所有模型的ROC曲线下面积(AUC)0.791~0.959,使用Lasso回归确定预测变量,并通过超级学习算法建立的模型预测效果最佳,AUC 0.959。结论在对急性A型主动脉夹层术后30内死亡的预测中,超级学习算法优于其他算法。

Objective Using different machine learning methods to construct and screen the best prediction model for predicting the risk of death within 30 days after surgery in patients with acute type A aortic dissection.Methods Five hundred and twenty-one patients with acute type A aortic dissection who underwent surgery between 2015 and 2022 were included,after collecting their perioperative date and screening them,329 patients were retained.two different groups of predictor variables were generated by using Lasso regression and principal component analysis,after that,logistic regression,support vector machine algorithm,random forest algorithm,gradient boosting algorithm,and super learning algorithm were used to develop prediction models for the risk of death within 30 days after surgery.Finally,we compare the models and select the best one.Results The AUC values for all models rangrd from 0.791-0.959.The model using Lasso regression to determine the predictor variables and built by the super learning algorithm had the best prediction with an AUC value of 0.959.Conclusion The super learning algorithm better than other algorithms in predicting death within 30 days after acute type A aortic dissection.