Establishment Of A Predictive Model For Poor Prognosis Of Incomplete Revascularization In Patients With Coronary Artery Disease And Multivessel Disease

Objectives:To establish a predictive model for poor prognosis after incomplete revascularization（ICR）in patients with multivessel coronary artery disease（MVD）.Methods:Clinical data of 757 patients with MVD and ICR after percutaneous coronary intervention（PCI）at the Affiliated Hospital of Chengde Medical University from January 1^st,2020 to August 31^st,2021 were retrospectively collected.According to inclusion criteria,including 757 subjects,and dividing them into validation set（20%;n=151）and training set（80%;n=606）according to the discharge time sequence.Clinical data were collected and followed up to record the occurrence of major adverse cardiovascular events（MACEs）within 1 year.The Stata15.0,SPSS26.0,and RStudio（3.5）statistical software were used to examine all of the data.The least absolute shrinkage and selection operator（LASSO）regression method was used to screen variables,and multivariate logistic regression was used to establish a predictive model.An independent cohort was used to validate the model.The area under the receiver operating characteristic（AUROC）was used to verify and evaluate the discriminative ability of the model;the calibration curve was drawn,and decision curve analysis（DCA）was performed to evaluate the calibration degree,clinical net benefit,and practicability of the model.Results:1.Univariate Logistic analysis showed that the proportion of total oc clusion（TO）（36.5%versus 27.8%,P=0.03）,unconjugated bilirubin（UCB）[8.18（5.20,11.94）versus 9.39（6.82,12.76）,P=0.03]and age[58（53,65）vers us 61（54,67）,P=0.02]in training set were statistically significant.2.Publishing a predictive model to predict the occurrence of MACE s after ICR in patients with MVD,the predict factors including:sex（OR=1.894,95%CI:1.155-3.103,P=0.011）,age（OR=0.978,95%CI:0.956-1.000,P=0.047）,TO（OR=1.504,95%CI:0.953-2.375,P=0.080）,tortuosity（OR=1.540,95%CI:0.847-2.801,P=0.157）,low-density lipoprotein cholesterol（LDL-C）（OR=1.025,95%CI:0.868-1.212,P=0.768）,uric acid（OR=1.002,95%C I:1.000-1.005,P=0.022）,UCB（OR=0.965,95%CI:0.928-1.003,P=0.068）,fasti ng blood glucose（FBG）（OR=1.014,95%CI:0.959-1.073,P=0.617）.3.The AUROC for predicting the risk of MACEs after ICR in patients with MVD was 0.628（95%CI=0.551-0.684）in the training set and0.745（95%CI=0.630-0.860）in the validation set.The unreliability index U test result of calibration in the validation cohort was-0.022（P=0.993）,and the maximum excursion and average excursion were 0.080 and 0.034,respectively.The chi-square statistic according to the Hosmer-Lemeshow test was 6.27（P=0.792）.DCA curve shows that this model is clinically applicable when the predictive probability value of MACEs is between 0.07 and 0.68,the model predicts the risk of MACEs after ICR in patients with MVD is reasonable,and patients can obtain the greatest clinical benefit.Conclusions:We established a predictive model for poor prognosis after ICR in patients with MVD,the predict factors including:sex,age,TO,tortuosity,low-density lipoprotein cholesterol,uric acid,UCB,fasting blood glucose.The AUROC of the model proves that the predictive ability of the predictive model is good,the Hosmer-Lemeshow test result proves that the calibration ability of the model is good,and the DCA curve indicates that the clinical net benefit of the model is good,indicating that it can be used as an effective tool for the early prediction of poor prognosis after ICR in patients with MVD.