Development and Validation of a Prognostic Model to Predict Mortality in Patients With Heart Failure With Mildly Reduced Ejection Fraction After Acute Myocardial Infarction
Zhi Can Liu, Ling Ling Zhang, Li Peng, Jian Ping Zeng, Ming Yan Jiang

TL;DR
This study created a model to predict mortality risk in heart failure patients after heart attacks, using factors like age and kidney function.
Contribution
A novel prognostic model was developed and validated for mortality prediction in HFmrEF patients following AMI.
Findings
Six predictors (age, stroke history, NYHA classification, hemoglobin, eGFR, PPCI) were identified for mortality risk.
The model showed good predictive performance with C-indexes of 0.795 in training and 0.741 in validation cohorts.
AUC metrics indicated strong survival prediction across multiple time points in the training cohort.
Abstract
Accurately assessing mortality risk in patients with heart failure with mildly reduced ejection fraction (HFmrEF) after acute myocardial infarction (AMI) remains challenging. This study developed and validated a mortality risk predictive model for such patients. In this single-center retrospective study of 873 hospitalized patients with HFmrEF after AMI, 611 patients were included in the training cohort and 262 in the validation cohort. The primary outcome was all-cause mortality over an average 33-month follow-up. Least absolute shrinkage and selection operator (LASSO) regression identified predictive variables for post-discharge mortality, with model performance assessed via receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Six mortality risk predictors were identified: age, stroke history, New York Heart Association (NYHA) classification,…
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Taxonomy
TopicsCardiovascular Function and Risk Factors · Heart Failure Treatment and Management · Acute Myocardial Infarction Research
