基于LASSO-logistic回归的老年瓣膜病患者术后院内死亡风险预测模型

A postoperative in-hospital mortality risk model for elderly patients undergoing cardiac valvular surgery based on LASSO-logistic regression

目的探索影响心脏瓣膜术后院内死亡的危险因素,建立老年心脏瓣膜术后院内全因死亡的风险预测模型,为心脏瓣膜术后患者死亡风险评估提供新思路。方法连续纳入2016—2018年中国心血管外科注册登记研究数据库中接受心脏瓣膜手术的≥65岁患者,其中2016年1月—2018年6月患者纳入训练队列,2018年7—12月患者纳入测试队列,分析老年患者心脏瓣膜术后死亡的风险因素,采用LASSO-logistic回归构建预测模型,并与传统的EuroSCOREⅡ评分进行对比。结果共纳入7163例患者,其中男3939例、女3224例,平均年龄(69.8±4.5)岁。训练队列5774例,测试队列1389例。290例(4.0%)患者术后死亡。通过LASSO回归变量筛选及logistic回归分析,最终纳入预测模型的危险因素包括年龄、术前左室射血分数、合并冠状动脉旁路移植手术、肌酐清除率、既往心脏手术史、体外循环时间、纽约心脏协会分级。LASSO-logistic回归模型在训练队列[受试者工作特征曲线下面积(area under the curve,AUC)=0.785,0.627]及测试队列(AUC=0.739,0.642)中均具有较好的区分度及校准度,优于传统的EuroSCOREⅡ评分。结论老年患者心脏瓣膜术后死亡率较高,LASSOlogistic回归预测模型可以较好地预测老年患者瓣膜术后死亡的发生率。

Objective To evaluate the risk factors for postoperative in-hospital mortality in elderly patients receiving cardiac valvular surgery,and develop a new prediction models using the least absolute shrinkage and selection operator(LASSO)-logistic regression.Methods The patients≥65 years who underwent cardiac valvular surgery from 2016 to 2018 were collected from the Chinese Cardiac Surgery Registry(CCSR).The patients who received the surgery from January 2016 to June 2018 were allocated to a training set,and the patients who received the surgery from July to December 2018 were allocated to a testing set.The risk factors for postoperative mortality were analyzed and a LASSOlogistic regression prediction model was developed and compared with the EuroSCOREⅡ.Results A total of 7163 patients were collected in this study,including 3939 males and 3224 females,with a mean age of 69.8±4.5 years.There were 5774 patients in the training set and 1389 patients in the testing set.Overall,the in-hospital mortality was 4.0%(290/7163).The final LASSO-logistic regression model included 7 risk factors:age,preoperative left ventricular ejection fraction,combined coronary artery bypass grafting,creatinine clearance rate,cardiopulmonary bypass time,New York Heart Association cardiac classification.LASSO-logistic regression had a satisfying discrimination and calibration in both training[area under the curve(AUC)=0.785,0.627]and testing cohorts(AUC=0.739,0.642),which was superior to EuroSCOREⅡ.Conclusion The mortality rate for elderly patients undergoing cardiac valvular surgery is relatively high.LASSO-logistic regression model can predict the risk of in-hospital mortality in elderly patients receiving cardiac valvular surgery.