Development And Validation Of Predictive Nomogram For Significant Coronary Artery Stenosis In Suspected NSTE-ACS Patients With Low-to-intermediate Risk

BackgroundPatients with non-ST-segment coronary artery syndrome(NSTE-ACS)have significant heterogeneity in their coronary arteries.A better assessment of significant coronary artery stenosis(SCAS)in low-to-intermediate risk NSTE-ACS patients would help identify who might benefit from invasive coronary angiography(ICA).At present,the assessment of coronary artery disease in patients with medium-low risk of NSTE-ACS is insufficient,and there is a lack of simple and convenient noninvasive assessment methods.AimsIn this study,a prediction model of significant coronary stenosis based on multiple clinical variables was constructed and confirmed for patients suspected of low or moderate risk of NSTE-ACS to evaluate the prior probability of invasive coronary examination,screen patients with significant coronary stenosis,and exclude those who are not necessary for invasive examination.MethodsA total of 469 patients with suspected NSTE-ACS evaluated as low-to-intermediate risk were retrospectively screened from Zhujiang Hospital between January 2019 and January 2022,all of whom underwent initial invasive coronary angiography during hospitalization.Subjects admitted before May 1,2021 were set as the development dataset(n=331),and subjects admitted on or after May 1,2021 were served as the validation dataset(n=138).Firstly,the minimum absolute contraction and selection operator(LASSO)regression method was used to reduce the variables initially.Then the indexes with the most predictive value were selected based on the clinical significance and the significance of multivariate regression of indexes to fit the logistic regression model.Then a nomogram was drawn based on the standardized regression coefficients of the predictors,and the model will be verified and evaluated.Bootstrap self-sampling was used for internal validation of the model(500 samples).The data of 138 subjects admitted to the hospital after May 1,2021 were used as the validation group.The area under the receiver operating characteristic curve(AUC)was respectively used to evaluate the model’s differentiation.Hosmer-Lemeshow test and calibration curve were used to evaluate the consistency between the predicted risk and the actual risk,and the net benefit of the model was evaluated according to the decision curve.Finally,the optimal prediction probability truncation value of the prediction model was determined by The Youden index method.ResultsSeven key predictors were screened including Smoker,Diabetes,Heart rate,cardiac troponin T,N-terminal pro-B-type natriuretic peptide,high-density lipoprotein cholesterol,and left atrial diameter.Models with AUC of 0.833(95%CI:0.791-0.875)and 0.797(95%CI:0.715-0.867)in the development and validation group,respectively,the calibration degree of prediction was good,and the decision curve indicates the value of clinical application.ConclusionsThe prediction model of significant coronary stenosis for medium-low risk patients with NSTE-ACS constructed in this study has certain diagnostic and predictive values.The nomogram contains seven simple features The easily accessible clinical indicators,which are convenient and easy to use,may be used as a preclinical evaluation tool for ICA examination of medium-low risk patients with NSTE-ACS,providing valuable help for screening medium-low risk patients with severe coronary artery disease.