基于多模态CT影像学参数构建急性脑梗死预后评估的Nomogram模型分析OACSTPCD

Objective:To investigate the multimodal CT imaging parameters and clinical risk factors affecting the prognosis of acute cerebral infarction(ACI),and establish a Nomogram model based on this analysis.Methods:A total of 155 ACI patients treated in our hospital from June 2020 to August 2022 were retrospectively selected.All patients were followed up for 1 year to evaluate their prognosis.155 patients were divided into modeling set(n=109)and validation set(n=46)according to a ratio of 7∶3.Patients in the modeling set were divided into good prognosis group(n=74)and bad prognosis group(n=35).The results of computer tomography angiography(CTA),computer tomography perfusion(CTP)and clinical data of the patient were collected.The corresponding CT imaging risk parameters and clinical risk factors were obtained through univariate analysis and multivariate Logistic regression analysis,and the corresponding prediction model was constructed based on the regression analysis.The combined prediction model was constructed according to the CT imaging risk parameters and clinical risk factors,and the corresponding Nomogram map was drawn using R language software.Receiver operating characteristic(ROC)curve and calibration curve were used to test the prediction efficiency.Clinical decision curve was used to evaluate the model's prediction benefits.Three models were constructed using validation set data,and ROC,calibration curve and decision curve were drawn.The prediction efficiency of the model is verified externally.Results:Multivariate Logistic regression analysis of the combined model showed that smoking history,time to onset of treatment,interleukin(IL)-6,CTA results,cerebral blood volume(CBV),cerebral blood flow(CBF),mean time to passage(mean)transit time(MTT)and time to peak(TTP)of contrast agent were significantly associated with adverse prognosis of acute cerebral infarction,and the difference was statistically significant(P＜0.05).The area under curve(AUC)of the combined prediction model in the modeling set was 0.970,and the sensitivity and specificity corresponding to the optimal cutoff value of 0.480 were 0.875 and 0.946,respectively,which were significantly higher than the CT imaging parameter model and clinical factor model.The calibration curve results showed that mean absolute error(MAE)was 0.049,and there was little difference between the predicted probability of the combined model and the actual probability,which had good clinical practicability.The clinical decision curve shows that the combined model has better clinical benefits.Conclusion:This Nomogram model based on multi-modal CT imaging parameters and clinical data can accurately predict the prognosis of patients with acute cerebral infarction,and provide references for the formulation of rehabilitation prognosis plan and allocation of medical resources.