Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

被引:22
|
作者
Gao, Nan [1 ]
Qi, Xiaoyong [2 ]
Dang, Yi [2 ]
Li, Yingxiao [2 ]
Wang, Gang [3 ]
Liu, Xiao [2 ]
Zhu, Ning [1 ]
Fu, Jinguo [3 ]
机构
[1] Hebei Med Univ, Dept Internal Med, Shijiazhuang, Hebei, Peoples R China
[2] Hebei Gen Hosp, Dept Cardiol, Shijiazhuang, Hebei, Peoples R China
[3] Cangzhou Cent Hosp, Dept Cardiol, Cangzhou, Hebei, Peoples R China
关键词
Predictive value of tests; Nomogram; ST-elevated myocardial infarction; Percutaneous coronary intervention; Hospital mortality; PERCUTANEOUS CORONARY INTERVENTION; TO-BALLOON TIME; SEGMENT ELEVATION; HARMONIZING OUTCOMES; DIABETES-MELLITUS; STEMI-PATIENTS; REVASCULARIZATION; SELECTION; THROMBUS; ASPIRATION;
D O I
10.1186/s12872-020-01804-7
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BackgroundCurrently, 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).MethodsThis 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.ResultsTotally, 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 beta -blocker, ACEI/ARB, symptom-to-door 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.ConclusionsA 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.
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页数:10
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