南京医科大学学报（自然科学版）2026，Vol.46Issue(2)：173-180,187,9.DOI:10.7655/NYDXBNSN251184
基于机器学习的心脏术后衰弱预测模型的构建与验证
Development and validation of a machine learning-based nomogram model for predicting frailty after cardiac surgery
摘要
Abstract
Objective:To develop and externally validate a patient-level prediction model for postoperative frailty in adults undergoing cardiac surgery.Methods:Our study included patients who underwent cardiac surgery at the First Affiliated Hospital of Nanjing Medical University between January and December 2023.Frailty was assessed one month postoperatively using the FRAIL scale,and patients were categorized into frailty and non-frailty groups.Three machine learning algorithms,least absolute shrinkage and selection operator(LASSO),random forest(RF),and extreme gradient boosting(XGBoost),were employed to identify common predictors.A nomogram prediction model was subsequently constructed using Logistic regression.The model's discriminative ability was evaluated using the receiver operating characteristic(ROC)curve and the area under the curve(AUC).Calibration curves assessed consistency,and decision curve analysis(DCA)evaluated clinical utility.The model was validated internally and externally.Results:A total of 301 patients were included.Among them,235 patients were divided into a training set(n=165)and an internal validation set(n=70)at a ratio of 7∶3,while the remaining 66 patients served as temporal validation.Based on the machine learning results,four common predictors were identified:age,preoperative left ventricular ejection fraction(LVEF),preoperative albumin level,and preoperative left ventricular end-diastolic dimension(LVDd).These were used to construct the nomogram.The model demonstrated excellent discriminative ability in the training set(AUC=0.846,95%CI:0.763-0.928),internal validation set(AUC=0.821,95%CI:0.701-0.940),and temporal validation set(AUC=0.846,95%CI:0.740-0.951).The calibration curve indicated high consistency between predicted and observed risks.Decision curve analysis further confirmed its good clinical practicality.Conclusion:The nomogram prediction model for post-cardiac surgery frailty,based on patient age,preoperative albumin level,LVEF,and LVDd,exhibits good predictive performance and clinical applicability,facilitating the early identification of high-risk patients.关键词
机器学习/预测模型/心脏手术/衰弱/列线图Key words
machine learning/prediction model/cardiac surgery/frailty/nomogram分类
医药卫生引用本文复制引用
杨玥,葛愿,李明辉,耿丹丹..基于机器学习的心脏术后衰弱预测模型的构建与验证[J].南京医科大学学报（自然科学版）,2026,46(2):173-180,187,9.基金项目
国家自然科学基金(82200340) （82200340）
中国社会福利基金会护爱基金(HLCXKT-20230180) （HLCXKT-20230180）
