Development and validation of a nomogram to predict the risk of in-hospital MACE for emergence NSTE-ACS: A retrospective multicenter study based on the Chinese population.

Abstract

Our study aims to develop and validate an effective in-hospital major adverse cardiovascular events(MACE) prediction model for patients with emergency Non-ST elevation acute coronary syndrome(NSTE-ACS). We retrospectively collected NSTE-ACS patients in three tertiary hospitals in Chongqing. In-hospital MACE was the predicted outcome. Patients from one hospital were divided into training set and internal validation set according to the ratio of 7:3. Besides, 662 patients from two other tertiary hospitals were for external validation. Patient information including demographics, laboratory tests results and disease course records were for comprehensive analysis. Finally, LASSO were used to identify the predictors and develop the model. This model was subsequently visualized as a nomogram, followed by both internal and external validations.The receiver operating characteristic curve, calibration curve and clinical decision curve analysis were used to assess the model's discrimination, calibration and clinical applicability, respectively. A total of 3,308 patients were included, 375 of whom developed in-hospital MACE. The LR model demonstrated that length of stay, neutrophils, myoglobin, NYHA, CCI, NT-proBNP, LVEF and respiratory failure were risk factors for in-hospital MACE in emergence NSTE-ACS patients. In the training set, the AUC was 0.860 (95%CI:0.831-0.889). In external validation,the AUC was 0.855(95%CI:0.808-0.902), and both the calibration curve and DCA in validation set also revealed stable predictive accuracy and clinical validity.Additionally,it is available to calculate the MACE risk online via the web page (https://cocozhou99.shinyapps.io/DynNomapp/). The prediction model we constructed has good predictive performance and can help healthcare professionals accurately assess the risk of in-hospital MACE in emergence NSTE-ACS patients.