Non-stenotic fibro-calcific aortic valve as a predictor of myocardial infarction recurrence

Abstract

Abstract Aims Patients with acute myocardial infarction (AMI) are at increased risk of recurrent cardiovascular events. Non-stenotic aortic valve fibro-calcific remodelling (called aortic valve sclerosis; AVSc), reflecting systemic damage, may serve as a new marker of risk. This study aims to stratify subgroups of AMI patients with specific probabilities of recurrent AMI and to evaluate the importance of AVSc in this setting. Methods and results Consecutive AMI patients (n = 2530) were admitted at Centro Cardiologico Monzino (2010–19) and followed up for 5 years. Patients were divided into study (n = 1070) and test (n = 966) cohorts. Topological data analysis (TDA) was used to stratify patient subgroups, while Kaplan–Meier and Cox regression analyses were used to evaluate the significance of baseline characteristics. Topological data analysis identified 11 subgroups of AMI patients with specific baseline characteristics. Two subgroups showed the highest rate of re-infarction after 5 years from the indexed AMI with a combined hazard ratio (HR) of 3.8 [95% confidence interval (CI): 2.7–5.4] compared with the other subgroups. This was confirmed in the test cohort (HR = 3.1; 95% CI: 2.2–4.3). These two subgroups were mostly men, with hypertension and dyslipidaemia, who exhibit a higher prevalence of AVSc, higher levels of high-sensitive C-reactive protein and creatinine. In the year-by-year analysis, AVSc, adjusted for all confounders, showed an independent association with the increased risk of re-infarction (odds ratio of ∼2 at all time points), in both the study and the test cohorts (all P < 0.01). Conclusion AVSc is a crucial variable for identifying AMI patients at high risk of recurrent AMI and its presence should be considered when assessing the management of AMI patients. The inclusion of AVSc in risk stratification models may improve the accuracy of predicting the likelihood of recurrent AMI, leading to more personalized treatment decisions.