Predictive Value Of Age,creatinine,and Ejection Fraction(ACEF) Score And Derived-scores In Coronary Artery Disease Patients Undergoing Percutaneous Coronary Intervention

Background:The risk of major adverse cardiovascular and cerebrovascular events（MACCE）in patients with coronary artery disease（CAD）after percutaneous coronary intervention（PCI）remains high.At present,there is no risk model to predict the long-term risk of MACCE in patients with CAD.Establishing a more accurate and easy-to-use risk score model has always been the goal of researchers and clinicians.Recently,age,creatinine,and ejection fraction（ACEF）score has received extensive attention because it is easy to calculate and has good prediction ability.However,the research of ACEF score in Asian patients is insufficient and the conclusions are inconsistent.In addition,some ACEF derived-scores have emerged in recent years,the predictive value of ACEF derived-scores for MACCE remains to be explored.Objective:To explore the predictive value of ACEF score and its derived-scores,including ACEF_CG score,ACEF_CKD-EPIscore and ACEFⅡscore,in patients undergoing PCI.Methods:The subjects of this study were patients with CAD underwent PCI in the Department of Cardiology of Chengdu Third People’s Hospital from July 2018 to December2019.The patients were followed up for 24 months after discharge,and the outcome was the occurrence of MACCE during follow-up,which included all-cause mortality,non-fatal myocardial infarction,unexpected target vessel revascularization and non-fatal stroke.According to the occurrence of MACCE,the patients were divided into the MACCE group and the non-MACCE group,and patients’baseline characteristics were compared between groups.The predictive value of ACEF score,ACEF_CG score,ACEF_CKD-EPIscore and ACEFⅡscore in predicting MACCE during follow-up in CAD patients was analyzed by receiver operating characteristic（ROC）curve.The optimal cut-off values of ACEF score,ACEF_CGscore,ACEF_CKD-EPIscore and ACEFⅡscore for predicting the occurrence of MACCE were calculated according to the ROC curve.According to the optimal cut-off value of ACEF,the patients were divided into two groups:high ACEF group and non-high ACEF group.According to the optimal cut-off value of ACEF_CG,the patients were divided into two groups:high ACEF_CG group and non-high ACEF_CG group.According to the optimal cut-off value of ACEF_CKD-EPI,the patients were divided into two groups:high ACEF_CKD-EPI group and non-high ACEF_CKD-EPI group.According to the optimal cut-off value of ACEFⅡ,the patients were divided into two groups:high ACEFⅡgroup and non-high ACEFⅡgroup.The incidence of MACCE between high ACEF group and non-high ACEF group,high ACEF_CG group and non-high ACEF_CG group,high ACEF_CKD-EPI group and non-high ACEF_CKD-EPI group,high ACEFⅡgroup and non-high ACEFⅡgroup were compared,respectively.The cumulative incidence of MACCE during follow-up was expressed by Kaplan Meier curve and compared between the groups using the Log-rank test.In order to assess whether ACEF score,ACEF_CG score,ACEF_CKD-EPIscore or ACEFⅡscore were associated with a worse long-term prognosis,univariate and multivariate Cox regression analyses were applied.The subgroup analysis was carried out to explore the predictive value of ACEF score and derived-scores in elderly patients with CAD.Results:The study included 1006 patients with CAD.Patients were followed up for 24 months,982 patients completed the follow-up and 24（2.4%）patients were lost.During follow-up,47（4.8%）,18（1.8%）,59（6.9%）and 27（2.8%）cases of all-cause death,non-fatal myocardial infarction,unexpected target vessel revascularization and non-fatal stroke occurred,respectively.The incidence of MACCE was 13.1%.1.Comparison of clinical baseline characteristicsCompared with non-MACCE group,patients in MACCE group were more likely to be older,had a higher prevalence of ACS diagnosis,hypertension and diabetes,longer hospital stay,higher hospitalization costs,cardiogenic shock,elevated high sensitivity troponin T,brain natriuretic peptide,serum creatinine,urea,cystatin C,glycemic,homocysteine,fibrinogen,and platelet count,and higher ACEF score,ACEF_CG score,ACEF_CKD-EPIscore and ACEFⅡscore（P<0.05）,while the concentration of albumin,RBC,hemoglobin,and HCT showed an opposite trend（P<0.05）.2.ROC curve analysis results of the four scores（1）The ROC curve analysis showed that the optimal critical value of ACEF to predict MACCE was 1.29（the sensitivity was 61.2%,the specificity was 66.7%）,and the area under the ROC curve was 0.671（95%CI:0.622-0.719,P<0.001）.（2）The ROC curve analysis showed that the optimal critical value of ACEF_CG to predict MACCE was 1.35（the sensitivity was 62.8%,the specificity was 66.7%）,and the area under the ROC curve was 0.670（95%CI:0.621-0.719,P<0.001）.（3）The ROC curve analysis showed that the optimal critical value of ACEF_CKD-EPI to predict MACCE was 1.35（the sensitivity was 58.1%,the specificity was 70.8%）,and the area under the ROC curve was 0.674（HR 3.054,95%CI:2.105-4.431,P<0.001）.（4）The ROC curve analysis showed that the optimal critical value of ACEFⅡto predict MACCE was 1.35（the sensitivity was 69.8%,the specificity was 56.4%）,and the area under the ROC curve was 0.625（95%CI:0.576-0.674,P<0.001）.（5）Compared with ACEF,ACEF_CG and ACEF_CKD-EPI had no significant difference in the predictive value of MACCE（P>0.05）;while,ACEFⅡfailed to improve the predictive value of MACCE in patients with CAD（P=0.008）.3.Comparison of the cumulative incidence of MACCE（1）Patients in high ACEF group had a significantly higher cumulative incidence of MACCE than patients in non-high ACEF group（HR 2.915,95%CI:2.031-4.184,P<0.001）.（2）Patients in high ACEF_CG group had a significantly higher cumulative incidence of MACCE than patients in non-high ACEF_CG group（HR 3.102,95%CI:2.161-4.451,P<0.001）.（3）Patients in high ACEF_CKD-EPI group had a significantly higher cumulative incidence of MACCE than patients in non-high ACEF_CKD-EPI group（HR 3.054,95%CI:2.105-4.431,P<0.001）.（4）Patients in high ACEFⅡgroup had a significantly higher cumulative incidence of MACCE than patients in non-high ACEFⅡgroup（HR 2.738,95%CI:1.936-3.871,P<0.001）.4.The result of multivariate Cox regression analysis of MACCE for CAD patientsThe multivariate Cox regression analysis for CAD patients showed that the independent predictors of MACCE were age,cardiogenic shock,BNP,platelet count and multivascular disease（P<0.05）.Based on the above multivariate Cox regression analysis,ACEF score,ACEF_CG score,ACEF_CKD-EPIscore and ACEFⅡscore were added,respectively.The results showed that ACEF≥1.29（HR 2.494,95%CI:1.657-3.754,P<0.001）,ACEF_CG≥1.35（HR 2.592,95%CI:1.719-3.908,P<0.001）,ACEF_CKD-EPI≥1.35（HR 1.975,95%CI:1.264-3.087,P=0.003）,ACEFⅡ≥1.35（HR 2.525,95%CI:1.634-3.901,P<0.001）were still significant after analyzing confounding factors by multivariate Cox regression model.5.Subgroup analysis resultsIn elderly patients with CAD,ACEF≥1.29（HR 1.834,95%CI:1.092-3.081,P=0.022）,ACEF_CG≥1.35（HR 2.042,95%CI:1.189-3.506,P=0.010）,ACEF_CKD-EPI≥1.35（HR 1.846,95%CI:1.112-3.064,P=0.018）and ACEFⅡ≥1.35（HR 2.587,95%CI:1.146-4.557,P=0.001）were independent predictors of MACCE.Conclusions:ACEF score,ACEF_CG score,ACEF_CKD-EPIscore and ACEFⅡscore had quiet predictive value for MACCE in CAD patients undergoing PCI,and were independent predictors of MACCE.Compared with ACEF,ACEF_CG and ACEF_CKD-EPI had no significant difference in the predictive value of MACCE,while the predictive value of ACEFⅡwas lower than that of ACEF score.In elderly patients with CAD,the four scores also had a certain predictive value of MACCE.ACEF score and its derived-scores can be used to predict long-term MACCE in CAD patients.