Early recognition and risk stratification of cardiac rupture after acute myocardial infarction: from clinical red flags to emerging biomarkers

Cardiac rupture (CR) remains among the most lethal mechanical complications of acute myocardial infarction (AMI), with in-hospital mortality exceeding 50%. Although primary percutaneous coronary intervention has reduced CR incidence to 1%-2%, mortality has not declined proportionally because the abrupt onset of rupture limits the opportunity for intervention. The early post-infarction period-particularly the first 72 h for free wall rupture and extending through day 7 for ventricular septal and papillary muscle rupture-represents the critical window for detection. This mini review synthesizes clinical warning signs, point-of-care imaging findings, and circulating biomarker trajectories that may herald impending CR, organized by temporal window and rupture subtype. We evaluate emerging biomarkers reflecting inflammatory, extracellular-matrix-degradation, and myocardial-stress pathways, as well as artificial intelligence-assisted prediction tools. Building upon the prediction model synthesis by Mao et al. within this Research Topic, we propose a four-step risk stratification algorithm integrating baseline clinical predictors, dynamic three-track surveillance (clinical, imaging, and biomarker), red-flag-triggered risk re-classification, and subtype-specific diagnostic pathways. This framework is designed to bridge the translational gap between statistical risk estimation and actionable bedside recognition during the critical post-AMI period.