Key result
Four-variable nomogram predicts in-hospital HFpEF after AMI with ~0.75 validation AUC.
Why the study?
The study aimed to establish a clinical prediction model to evaluate the risk of developing HFpEF during hospitalization in patients with acute myocardial infarction.
Does a clinical prediction model incorporating age, heart rate, neutrophil-to-lymphocyte ratio, and total bilirubin accurately predict the risk of developing in-hospital HFpEF in patients hospitalized with acute myocardial infarction and LVEF ≥50%?
Cohort (n=266)
Yes
Does a clinical prediction model incorporating age, heart rate, neutrophil-to-lymphocyte ratio, and total bilirubin accurately predict the risk of developing in-hospital HFpEF in patients hospitalized with acute myocardial infarction and LVEF ≥50%?
A four-variable clinical nomogram using age, heart rate, neutrophil-to-lymphocyte ratio, and total bilirubin provides a simple, non-invasive tool for early risk stratification of in-hospital HFpEF development among patients hospitalized with acute myocardial infarction.
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May aid post-AMI HFpEF risk stratification; leaves open prospective validation before clinical adoption.
This study aimed to establish a clinical prediction model for evaluating the risk of heart failure (HF) with preserved ejection fraction (HFpEF) occurrence in patients with acute myocardial infarction (AMI). We selected AMI patients with left ventricular ejection fraction ≥ 50% without a history of HF from the MIMIC-III database (training cohort) and those from The First Affiliated Hospital of Jinan University (validation cohort). Eligibility criteria for identifying patients who develop HFpEF were based on ICD-9 diagnosis codes in the training cohort and the 2021 European Society of Cardiology HF Guidelines in the external validation cohort. The LASSO-multivariate logistic regression analysis was performed to identify the predictive factors for establishing the nomogram prediction model. Then, this model was evaluated by discrimination, calibration, clinical effectiveness, and internal validation. Finally, using the validation cohort, the model was externally validated and updated. 201 patients were included in the training cohort and 65 in the validation cohort. Older age, higher heart rate, higher neutrophil-to-lymphocyte ratio, and lower total bilirubin were identified as independent predictors. The area under the receiver operator characteristic curve (AUC) of the model and the bootstrap validation were 0.823 and 0.804, respectively. Both the prediction model’s and the bootstrap validation’s calibration curve were in good agreement with the standard reference line, and the Hosmer-Lemeshow test showed P = 0.183. The decision curve analysis showed that using this model could produce a net benefit. In external validation, the AUC was 0.749, with a calibration slope of 0.833, a calibration intercept of 3.746, and a Brier score of 0.572. After being updated, the external calibration performance has improved significantly (slope 1, intercept 0, Brier score 0.185). Our prediction model provides an additional perspective into the early identification of high-risk patients for HFpEF occurrence following AMI.
Liu et al. (2026) conducted a cohort in Acute myocardial infarction with preserved left ventricular ejection fraction (≥50%) and no prior heart failure (n=266). LASSO-logistic regression nomogram prediction model (incorporating age, heart rate, neutrophil-to-lymphocyte ratio, and total bilirubin) was evaluated on Occurrence of heart failure with preserved ejection fraction (HFpEF) during hospitalization following acute myocardial infarction (AUC 0.749, 95% CI 0.630-0.868). A nomogram combining age, heart rate, NLR, and total bilirubin predicted in-hospital HFpEF after AMI with an AUC of 0.823 in training and 0.749 (95% CI 0.630-0.868) in external validation.
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