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Toward Precision in Myocardial Injury Management: A Critical Synthesis and the C.A.L.I.B.R.A.T.E. Framework for
Chukwuemeka Nwokocha1, Javier Palacios2, Tolunigba Kolawole1
1Basic Medical Sciences, Faculty of Medical Sciences, University of the West Indies Mona, Jamaica.
Abstract:
Cardiovascular diseases remain the leading cause of global mortality, necessitating precise strategies for diagnosing and managing myocardial injury. This review critically synthesizes the current biomarker landscape and proposes an integrative framework to address the significant translational gaps between biomarker science and clinical practice. We conducted a comprehensive critical review, analyzing established and emerging biomarkers, including cardiac troponins, natriuretic peptides, and inflammatory and fibrotic markers, within the context of biological confounders, etiology-specific challenges, and clinical implementation barriers. Through thematic synthesis, we developed the novel C.A.L.I.B.R.A.T.E. framework to structure biomarker integration. While high-sensitivity assays have improved detection, they reveal chronic elevations in conditions like renal dysfunction, aging, and heart failure, reducing specificity and complicating acute diagnosis. Emerging biomarkers (eg, sST2, galectin-3) offer prognostic insight but lack therapeutic guidance and specificity. Etiology-specific puzzles (eg, myocarditis, COVID-19, MINOCA) highlight the limitations of isolated biomarker interpretation. The C.A.L.I.B.R.A.T.E. framework addresses these gaps by integrating Clinical context, Assay characteristics, Likelihood, Injury mechanism, Biomarker profiles, Rule-out/in thresholds, Adjunctive tests, Time kinetics, and Etiologies & comorbidities. The future of myocardial injury management depends on shifting from isolated biomarker measurement to integrated, algorithm-driven interpretation. The C.A.L.I.B.R.A.T.E. framework provides a structured pathway for personalized diagnostic reasoning, bridging the translational chasm and advancing precision cardiology through context-dependent, multi-modal data synthesis.
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