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. 2023 Dec 18;23(1):619.
doi: 10.1186/s12872-023-03665-2.

Establishment and validation of a prediction nomogram for heart failure risk in patients with acute myocardial infarction during hospitalization

Shengyue Chen  1 , Xinling Pan  2 , Jiahang Mo  3 , Bin Wang  4
Affiliations

Establishment and validation of a prediction nomogram for heart failure risk in patients with acute myocardial infarction during hospitalization

Shengyue Chen et al. BMC Cardiovasc Disord. .

Abstract

Background: Acute myocardial infarction (AMI) with consequent heart failure is one of the leading causes of death in humans. The aim of this study was to develop a prediction model to identify heart failure risk in patients with AMI during hospitalization.

Methods: The data on hospitalized patients with AMI were retrospectively collected and divided randomly into modeling and validation groups at a ratio of 7:3. In the modeling group, the independent risk factors for heart failure during hospitalization were obtained to establish a logistic prediction model, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical value. Machine learning models with stacking method were also constructed and compared to logistic model.

Results: A total of 1875 patients with AMI were enrolled in this study, with a heart failure rate of 5.1% during hospitalization. The independent risk factors for heart failure were age, heart rate, systolic blood pressure, troponin T, left ventricular ejection fraction and pro-brain natriuretic peptide levels. The area under the curve (AUC) of the model in modeling group and validation group were 0.829 and 0.846, respectively. The calibration curve showed high prediction accuracy and the DCA curve showed good clinical value. The AUC value of the ensemble model by the stacking method in the validation group were 0.821, comparable to logistic prediction model.

Conclusions: This model, combining laboratory and clinical factors, has good efficacy in predicting heart failure during hospitalization in AMI patients.

Keywords: Acute myocardial infarction; Heart failure; Influencing factors; Nomogram; Prediction model.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Nomogram graph displaying the contributions of involved variables to the risk of heart failure among patients with AMI during hospitalization
Fig. 2
Fig. 2
Evaluation of the prediction ability of the established model in the modeling group. A Discrimination power indicated by ROC curve; B Prediction accuracy indicated by calibration chart; C Net clinical benefit indicated by DCA
Fig. 3
Fig. 3
Evaluation of the prediction ability of the established model in the validation group. A Discrimination power indicated by ROC curve; B Prediction accuracy indicated by calibration chart; C Net clinical benefit indicated by DCA
Fig. 4
Fig. 4
The machine learning models by the ensemble method. A ROC curves; B Calibration curves
See this image and copyright information in PMC

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