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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.
Diagnosing myocardial injury requires integrating biomarkers with clinical context. The novel C.A.L.I.B.R.A.T.E. framework enhances precision cardiology by synthesizing multi-modal data for better patient outcomes.
Area of Science:
- Cardiology
- Biomarker Science
- Translational Medicine
Background:
- Cardiovascular diseases are a leading cause of mortality, demanding improved myocardial injury diagnosis and management.
- Current biomarker strategies face translational gaps between research and clinical application.
- Established and emerging biomarkers have limitations in specificity and clinical utility.
Purpose of the Study:
- To critically review the current landscape of myocardial injury biomarkers.
- To propose an integrative framework for biomarker interpretation in clinical practice.
- To address translational gaps in biomarker science for precision cardiology.
Main Methods:
- Comprehensive critical review of established and emerging cardiac biomarkers.
- Thematic synthesis to develop the C.A.L.I.B.R.A.T.E. framework.
- Analysis of biological confounders, etiology-specific challenges, and implementation barriers.
Main Results:
- High-sensitivity assays improve detection but reduce specificity due to chronic elevations.
- Emerging biomarkers offer prognostic value but lack therapeutic guidance and specificity.
- Etiology-specific conditions complicate isolated biomarker interpretation, highlighting the need for integrated approaches.
Conclusions:
- The C.A.L.I.B.R.A.T.E. framework integrates clinical context, assay characteristics, and patient factors for structured diagnostic reasoning.
- Future myocardial injury management requires a shift from isolated measurements to algorithm-driven, integrated interpretation.
- This framework advances precision cardiology by enabling context-dependent, multi-modal data synthesis for personalized diagnosis.
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