Methodological evaluation and clinical interpretation of hs-cTnI and hs-cTnT variations: a reappraisal
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Aldo Clerico
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Martina Zaninotto
Abstract
Recent debates have focused on the impact of analytical imprecision in high-sensitivity cardiac troponin (hs-cTnI and hs-cTnT) assays on the diagnosis of non-ST-elevation myocardial infarction (NSTEMI), based on the absolute delta thresholds for the rapid rule-in/rule-out algorithms in emergency department (ED) according to the 2020 ESC guidelines. Consensus guidelines by the AACC/IFCC and the Fourth Universal Definition of Myocardial Infarction emphasize precision at the 99th percentile upper reference limit and the clinical significance of biomarker changes. Evidence from the Italian Study Group on Cardiac Biomarkers demonstrates that hs-cTn assays generally achieve ≤10 % CV at concentrations relevant to diagnostic thresholds, and recommends a Reference Change Value (RCV) >30 % to define clinically meaningful variation. Conversely, van Schrojenstein Lantman et al. aimed to define the assay performance specifications (APS) required to reliably detect the recommended absolute delta thresholds and the consequences of failure to meet these APS. The aim of this opinion paper is to highlight the methodological and conceptual differences between these two approaches. More specifically, while APS are influenced by both assay characteristics and clinical context, the RCV approach is based on the intra-individual biological variation that remains consistently low (individual index ≈0.3), supporting the interpretation of circulating troponins as markers of physiological cardiomyocyte turnover. Ultimately, diagnosis of acute myocardial infarction requires integration of biomarker changes with clinical evidence of ischemia, ECG, and imaging findings. Emerging artificial intelligence–based models that combine hs-cTn with clinical variables further improve diagnostic accuracy, underscoring the importance of contextual interpretation and paving the way toward personalized medicine in the management of suspected NSTE-ACS.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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