# Construction and validation of a predictive model for new-onset atrial fibrillation in patients with acute myocardial infarction following emergency percutaneous coronary intervention based on novel inflammatory markers

## TL;DR

This study develops a predictive model using inflammatory markers to identify patients at risk of developing atrial fibrillation after heart attack treatments.

## Contribution

The study introduces a novel nomogram incorporating the systemic inflammatory response index (SIRI) for predicting new-onset atrial fibrillation after PCI in AMI patients.

## Key findings

- SIRI was the most accurate inflammatory biomarker for predicting new-onset atrial fibrillation (AUC: 0.813).
- A nomogram incorporating SIRI showed strong predictive accuracy (C-index: 0.868 in training and 0.859 in validation).
- Calibration plots and decision curve analysis confirmed the model's clinical utility and accuracy.

## Abstract

Inflammatory biomarkers are established predictors of outcomes in cardiovascular diseases, yet their accuracy in predicting new-onset atrial fibrillation (NOAF) after emergency percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) remains uncertain. This study aimed to evaluate the predictive value of emerging inflammatory indices for NOAF following PCI in AMI patients and to develop a clinically applicable nomogram.

A retrospective analysis was performed on 509 AMI patients who had no prior history of atrial fibrillation. Univariate and multivariate logistic regression analyses were utilized to determine significant preoperative inflammatory biomarkers and other clinical risk factors associated with NOAF. A predictive nomogram was subsequently created based on these identified factors. The discriminative ability, accuracy of calibration, and clinical utility of the nomogram were evaluated through receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Among the 509 patients studied, 94 (18.5%) experienced NOAF during hospitalization. Multivariate logistic regression analyses revealed that advanced age, increased left atrial diameter (LAD), higher Killip class, log-transformed NT-proBNP and increased systemic inflammatory response index (SIRI) independently predicted NOAF risk among AMI patients after PCI. ROC analyses comparing several novel inflammatory indicators, neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and SIRI—demonstrated that SIRI exhibited the highest predictive accuracy for NOAF occurrence (AUC: 0.813, 95% CI: 0.739–0.887). Additionally, a nomogram including SIRI was constructed to predict the risk of in-hospital NOAF in AMI patients. The C-index, equivalent to the area under the ROC curve (AUC), was 0.868 (95% CI: 0.821–0.916) in the training cohort and 0.859 (95% CI: 0.793–0.925) in the validation cohort, indicating good discrimination. Calibration plots confirmed good agreement between predicted probabilities and observed outcomes, and decision curve analysis verified substantial clinical benefit.

SIRI was identified as the most effective inflammatory biomarker for predicting NOAF in AMI patients following PCI. The constructed nomogram, which incorporates inflammatory indicators, allows rapid and personalized assessment, enabling clinicians to better identify and manage AMI patients at increased risk for NOAF.