Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation
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RESEARCH ARTICLE
Open Access
Establishment and validation of a risk model for prediction of in‑hospital mortality in patients with acute ST‑elevation myocardial infarction after primary PCI Nan Gao1, Xiaoyong Qi2* , Yi Dang2, Yingxiao Li2, Gang Wang3, Xiao Liu2, Ning Zhu1 and Jinguo Fu3
Abstract Background: Currently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI). Methods: This was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results: Totally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of β-blocker, ACEI/ARB, symptom-todoor time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic = 0.987; model calibration: P = 0.722; validation set: C-statistic = 0.984, model calibration: P = 0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981–0.994, P = 0.003) and 0.990 (95% CI: 0.987–0.998, P = 0.007), respectively. DCA shows that the nomogram can achieve good net benefit. Conclusions: A novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI. Keywords: Predictive value of tests, Nomogram, ST-elevated myocardial infarction, Percutaneous coronary intervention, Hospital mortality
*Correspondence: [email protected] 2 Department of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei, China Full list of author information is available at the end of the article
Background Acute myocardial infarction (AMI) is a leading cause of death worldwide [1–3]. In the United States, the incidence of AMI was 208 per 100,000 person-years in 2008 [4]. In patients with symptoms of myocardial ischemia, ST-elevation myocardial infarction (STEMI) is defined by the combination of persistent ST-segment elevation
© The Author(s) 2020. Open Access This article is licensed
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