The Association Between S100A12 Protein and C-Reactive Protein with Malignant Ventricular Arrhythmias Following Acute

Lei Song1, Ying-Min Lu1, Jin-Chun Zhang1

  • 1Department of Cardiology, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, 202150, People's Republic of China.

PubMed

Insights

S100A12 protein and C-reactive protein (CRP) are independent risk factors for malignant ventricular arrhythmias (MVA) in elderly patients after acute myocardial infarction (AMI). Their combined use offers higher diagnostic accuracy for MVA prediction.

Area of Science:

  • Cardiology
  • Biomarkers
  • Geriatric Medicine

Background:

  • Acute myocardial infarction (AMI) poses significant risks for elderly patients, including life-threatening malignant ventricular arrhythmias (MVA).
  • Identifying reliable biomarkers for MVA prediction post-AMI is crucial for timely intervention in the elderly population.

Purpose of the Study:

  • To investigate the association of S100A12 protein and C-reactive protein (CRP) with the onset of MVA after AMI in elderly individuals.
  • To evaluate the diagnostic and predictive value of S100A12 and CRP, both individually and in combination, for MVA in this demographic.

Main Methods:

  • A cohort of 159 elderly AMI patients was studied.
  • Serum levels of S100A12 (ELISA) and CRP (biochemical analyzer) were measured.
  • Multivariate logistic regression and ROC curve analysis were employed to identify risk factors and assess predictive accuracy.

Main Results:

  • S100A12 protein and CRP were identified as independent risk factors for MVA post-AMI in the elderly (p < 0.05).
  • The area under the ROC curve (AUC) for S100A12 was 0.7147, for CRP was 0.7356, and for the combined diagnosis was 0.8350 (p < 0.05).

Conclusions:

  • S100A12 protein and CRP are significant independent risk factors for MVA following AMI in elderly patients.
  • The combined assessment of S100A12 protein and CRP demonstrates superior diagnostic sensitivity and specificity for MVA prediction.
Abstract