Comparative Prognostic Value of Phenotypic and Chronological Age for Post-STEMI Mortality and Cardiovascular Events
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background The global population aged ≥ 65 years is increasing and is expected to outnumber those under 18 by 2080. Aging is a key risk factor for cardiovascular mortality, and many acute coronary syndrome cases occur in individuals > 75 years. Chronological age may not fully reflect physiological aging; thus, biological age metrics such as phenotypic age (PhenoAge) may offer superior prognostic insight. Objectives To evaluate the prognostic value of PhenoAge and phenotypic age acceleration(PhenoAccel) in patients with STEMI, and compare their predictive capacity to chronological age. Methods This retrospective, single-center study included 358 STEMI patients treated between 2021 and 2024. PhenoAge was calculated using a validated model incorporating nine clinical biomarkers. Primary outcomes included in-hospital mortality, all-cause mortality, recurrent myocardial infarction, and heart failure hospitalization over a median follow-up of 28 months. Associations between age metrics and outcomes were assessed using multivariable logistic and Cox regression models. Kaplan-Meier and log-rank tests were used for survival analysis. Results Median chronological age was 59 years; PhenoAge, 71.4 years; and PhenoAccel, 12.9 years. In-hospital mortality was 5.3%, all-cause mortality 13.4%, recurrent MI 7.3%, and heart failure hospitalization 11%. PhenoAge (OR: 1.057; p = 0.005) and PhenoAccel (OR: 1.086; p = 0.002) independently predicted in-hospital mortality. Both metrics also predicted long-term mortality, whereas chronological age was only marginally significant (p = 0.07). Chronological age best predicted heart failure hospitalization. Conclusions PhenoAge and PhenoAccel are independent predictors of short- and long-term mortality in STEMI patients. Their incorporation into clinical practice may improve risk stratification and support personalized treatment strategies.