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Updated: Jan 9, 2026

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

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Development of a Nomogram to Classify In-Hospital Atrial Fibrillation Among Patients Hospitalized With Acute

Geng Yang¹, Long Feng¹, Yilin Pan¹

¹Department of Emergency, Beijing Anzhen Hospital, Capital Medical University, 100029 Beijing, China.

Reviews in Cardiovascular Medicine

|December 8, 2025

Summary

This study developed a nomogram model to predict atrial fibrillation (AF) in patients with acute myocardial infarction (AMI). The model, using easily obtainable clinical variables, demonstrated good predictive performance for personalized risk assessment.

Keywords:

acute myocardial infarction atrial fibrillation prediction model risk factors

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Area of Science:

Cardiology
Medical Informatics
Clinical Prediction Models

Background:

Limited research exists on prognostic predictors for acute myocardial infarction (AMI) complicated by atrial fibrillation (AF).
A need for effective prognosis models for AMI-AF patients is evident.

Purpose of the Study:

To develop and validate a predictive model for atrial fibrillation (AF) in patients experiencing acute myocardial infarction (AMI).
To identify independent risk factors for AF in AMI patients.

Main Methods:

Retrospective analysis of 126 AMI patients with AF and 1719 AMI patients without AF.
Multivariate logistic regression and Receiver Operating Characteristic (ROC) curve analysis were used to identify predictors and evaluate model performance.
A nomogram model was constructed using R software, with calibration plots and Decision Curve Analysis (DCA) for validation.

Main Results:

Older age and longer hospitalization were identified as independent risk factors for AF in AMI patients.
The nomogram model achieved an Area Under the Curve (AUC) of 0.833, with 81.7% sensitivity and 72.6% specificity.
The model showed clinical utility for risk thresholds above 0.06.

Conclusions:

The developed multivariable prediction model, a nomogram, exhibits strong predictive capabilities for AF in AMI patients.
The variables utilized in the nomogram are readily accessible in clinical settings.
This model offers valuable insights for individualized AF risk prediction in AMI patients.