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BMC Cardiovascular Disorders
Published in: BMC Cardiovascular Disorders 1/2020

Open Access 01-12-2020 | Myocardial Infarction | Research article

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

Authors: Nan Gao, Xiaoyong Qi, Yi Dang, Yingxiao Li, Gang Wang, Xiao Liu, Ning Zhu, Jinguo Fu

Published in: BMC Cardiovascular Disorders | Issue 1/2020

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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-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.

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.
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Metadata
Title
Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI
Authors
Nan Gao
Xiaoyong Qi
Yi Dang
Yingxiao Li
Gang Wang
Xiao Liu
Ning Zhu
Jinguo Fu
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Cardiovascular Disorders / Issue 1/2020
Electronic ISSN: 1471-2261
DOI
https://doi.org/10.1186/s12872-020-01804-7

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