术后患者脑卒中发生风险预测模型的构建与验证OA

Aim To construct and validate a predictive nomogram model for the risk of postoperative stroke.Methods We retrospectively enrolled 358 surgical patients who developed stroke between July 2017 and August 2023 at the Affiliated Hospital of Jining Medical University,and matched them 1∶2 with 714 contemporaneous surgical patients without postoperative stroke.Using R software,the cohort was randomly split into a training set(n=751)and a valida-tion set(n=321)in a7∶3 ratio.Significant variables in univariate Logistic regression were included in Lasso regression to determine independent predictors,which were then used to build a nomogram model for postoperative stroke risk.Dis-crimination and calibration were assessed with ROC curves and calibration plots,respectively;decision-curve analysis was used to evaluate clinical utility and net benefit.Results Except for age and total bilirubin,baseline clinical data were comparable between the training and validation sets(P>0.05).Lasso regression revealed history of cerebral infarction,preoperative VTE score,serum albumin and plasma fibrinogen as independent predictors of postoperative stroke.A nomo-gram model incorporating these factors was constructed.The area under the ROC curve was 0.651 in the training set and 0.683 in the validation set.Calibration plots demonstrated high agreement between predicted and observed probabilities.Decision-curve analysis indicated favorable net benefit within a risk threshold probability range of 0～0.61.Conclusion History of cerebral infarction,high risk of preoperative VTE score,decreased albumin,and increased plasma fibrinogen are related to the risk of postoperative stroke.The constructed nomogram model can effectively predict the prog-nosis of patients.